OFDM Underwater Acoustic Communication …m...OFDM Underwater Acoustic Communication System Implementation on FPGA by Diego Andres Cuji Dutan Master of Science in Electrical and Computer

OFDM Underwater Acoustic Communication System Implementation

on FPGA

A Thesis Presented

by

Diego Andres Cuji Dutan

to

The Department of Electrical and Computer Engineering

in partial fulfillment of the requirements

for the degree of

Master of Science

in

Electrical and Computer Engineering

Northeastern University

Boston, Massachusetts

June 2019

To my family.

i

Contents

List of Figures iv

List of Tables vi

List of Acronyms vii

Acknowledgments ix

Abstract of the Thesis x

1 Introduction 11.1 Motivation and contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 State of the Art 42.1 Underwater Acoustic Communication Channel Models . . . . . . . . . . . . . . . 52.2 Range, bandwidth, and system requirements . . . . . . . . . . . . . . . . . . . . . 62.3 System Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.3.1 Non-Coherent Modulation . . . . . . . . . . . . . . . . . . . . . . . . . . 72.3.2 Coherent Modulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.3.3 Multi-carrier Modulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.3.4 Spatial Modulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.4 Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3 Underwater Acoustic Channel Modeling 113.1 Path loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113.2 Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123.3 Signal-to-Noise ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133.4 Multipath . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143.5 The Doppler Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

4 Orthogonal Frequency Division Multiplexing and System Design 184.1 Orthogonal Frequency Division Multiplexing (OFDM) Data Transmission and Re-

ception . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

ii

4.2 Differentially coherent detection . . . . . . . . . . . . . . . . . . . . . . . . . . . 224.2.1 Method based on multiple fast Fourier transform (FFT) demodulation . . . 23

4.3 Doppler compensation for mobile communications . . . . . . . . . . . . . . . . . 244.3.1 Beta (β) algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

4.4 Transmitter design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264.5 Receiver design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

5 Underwater Platform Overview 295.1 Hardware Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

5.1.1 Microzed Board . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295.2 Software Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335.3 Hardware Description Language (HDL) Design . . . . . . . . . . . . . . . . . . . 34

5.3.1 Simulink Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365.3.2 Block Diagram Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

6 Results 416.1 Underwater Acoustic (UWA) Modem . . . . . . . . . . . . . . . . . . . . . . . . 41

6.1.1 Hardware components . . . . . . . . . . . . . . . . . . . . . . . . . . . . 426.1.2 Prototype . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

6.2 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456.2.1 System parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456.2.2 Experimental Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

7 Conclusion 51

Bibliography 53

iii

List of Figures

3.1 Absorption Coefficient. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123.2 Empirical model of the noise power spectral density for UWA channel [40]. . . . . 133.3 Frequency-distance dependence part of the Signal-to-Noise Ratio (SNR). The spread-

ing factor is k = 1.5 for the path-loss. The noise Power Spectral Density (PSD) iscalculated for moderate shipping activity (s = 0.5) and no wind (vω = 0 m/s) [36]. 14

3.4 Geometry of the channel. [36]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153.5 Channel response functions [36]. . . . . . . . . . . . . . . . . . . . . . . . . . . . 163.6 Motion-induced Doppler effects in time and frequency domains [36]. . . . . . . . . 17

4.1 OFDM transmitter scheme. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194.2 OFDM receiver scheme. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214.3 Partial Fast Fourier Transform (FFT) demodulation scheme [3]. . . . . . . . . . . . 244.4 Motion-induced Doppler shift is not uniform in a wideband system [15]. . . . . . . 254.5 Illustration of the structure of a sample Cyclic Prefix (CP)-OFDM frame. . . . . . 274.6 Block diagram of the CP-OFDM transmitter. . . . . . . . . . . . . . . . . . . . . . 274.7 Block diagram of the CP-OFDM receiver. . . . . . . . . . . . . . . . . . . . . . . 28

5.1 Microzed board [48]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305.2 AXI4-Stream Signal Diagram [51]. . . . . . . . . . . . . . . . . . . . . . . . . . . 335.3 High-Level SW Tool Workflow for Zynq PL and PS. . . . . . . . . . . . . . . . . 345.4 OFDM digital implementation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355.5 OFDM transmitter and receiver models. . . . . . . . . . . . . . . . . . . . . . . . 375.6 System Parameters. A Matlab function, that includes the underwater system pa-

rameters, such as number of carriers, number of blocks, sampling frequency, etc,generates a .mat file called ”ofdm SystemParameters” and this file is further calledinside the Simulink models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

5.7 OFDM Transmitter and Receiver Platform. . . . . . . . . . . . . . . . . . . . . . 39

6.1 Hydrophone TC4013 frequency response [53]. . . . . . . . . . . . . . . . . . . . . 436.2 Hardware block diagrams. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446.3 Tank and pool dimensions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456.4 Channel profile based on preamble correlation measured in (a) tank and (b) pool. . 466.5 Geometry of the experiment in (a) tank and (b) pool. . . . . . . . . . . . . . . . . 47

iv

6.6 The estimated Cumulative Distribution Function (CDF)s of the Mean Squared Error(MSE) per block and MSE per frame measured in tank (top) and pool (bottom)setups. The experiments, both in tank and pool, consist of 50 frame transmissionsand each frame is composed of 8 OFDM blocks. The CDFs in (a) and (c) reflectthe recorded MSE on each OFDM block, while the CDFs in (b) and (d) reflect theaverage MSE per frame on each receiving point during the experiment. . . . . . . . 49

6.7 Average block error rate (Block Error Rate (BLER)) and bit error rate (Bit ErrorRate (BER)) measured in tank (top) and pool (bottom) setups. The results in (a) and(c) reflect the average BLER for 50 frame transmissions recorded on each position,in tank (A, B, C, and D) and in pool (A, B, and C). The plots (b) and (d) indicate theBER calculated on each OFDM frame transmission. . . . . . . . . . . . . . . . . . 50

v

List of Tables

2.1 Available bandwidth for UWA communication ranges [7]. . . . . . . . . . . . . . . 62.2 Underwater communications applications and corresponding required BER [8]. . . 72.3 Evolution of non-coherent modulation techniques [8]. . . . . . . . . . . . . . . . . 82.4 Evolution of coherent modulation techniques [8]. . . . . . . . . . . . . . . . . . . 82.5 Evolution of multi-carrier modulation techniques [8]. . . . . . . . . . . . . . . . . 9

5.1 Software tools and corresponding versions. . . . . . . . . . . . . . . . . . . . . . 34

6.1 Hardware components of UWA prototype. . . . . . . . . . . . . . . . . . . . . . . 416.2 Experiment Signal Parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 466.3 MSE performance for 90% of the OFDM blocks and frames recorded in tank. . . . 486.4 MSE performance for 90% of the OFDM blocks and frames recorded in pool. . . . 48

vi

List of Acronyms

ADC Analog-to-Digital Converter.

ARM Advanced RISC Machine.

AXI Advanced eXtensible Interface.

BER Bit Error Rate.

BLER Block Error Rate.

BPSK Binary Phase Shift Keying.

CDF Cumulative Distribution Function.

CP Cyclic Prefix.

DAC Digital-to-Analog Converter.

DFT Discrete Fourier Transform.

DMA Direct Memory Access.

DSP Digital Signal Processing.

FFT Fast Fourier Transform.

FIFO First In First Out.

FPGA Field Programmable Gate Array.

FSK Frequency Shift Keying.

HDL Hardware Description Language.

ICI Inter-carrier Interference.

IDFT Inverse Discrete Fourier Transform.

IFFT Inverse Fast Fourier Transform.

IP Intellectual Property.

vii

ISI Inter-symbol Interference.

MSE Mean Squared Error.

MIMO Multiple-Input Multiple-Output.

OFDM Orthogonal Frequency Division Multiplexing.

PL Programmable Logic.

PS Processing System.

PSD Power Spectral Density.

PSK Phase Shift Keying.

QAM Quadrature Amplitude Modulation.

QPSK Quadrature Phase Shift Keying.

SDK Software Development Kit.

SISO Single Input Single Output.

SNR Signal-to-Noise Ratio.

SoC System on a Chip.

UWA Underwater Acoustic.

viii

Acknowledgments

My sincere gratitude to my advisor Prof. Milica Stojanovic for her support, encouragement,and patience throughout this thesis. She has been crucial during my Master’s study program to definethe direction of my research. I feel grateful with Prof. Miriam Leeser who has guided me to improvemy research work.

Also, I would like to thank my colleagues in the lab. In special to Amir Tadayon for sharinghis vast knowledge in the field of signal processing. My humble recognition to Emrecan Demirorsand Jiacheng Shi who were always ready to support and help.

My eternal gratitude to my beloved parents and siblings. They have been part of my growthas a human and as a professional. Mayte has been an important person during this study experienceabroad. I will always remember her with love.

ix

Abstract of the Thesis

OFDM Underwater Acoustic Communication System Implementation on

FPGA

by

Diego Andres Cuji Dutan

Master of Science in Electrical and Computer Engineering

Northeastern University, June 2019

Dr. Milica Stojanovic, Advisor

This thesis focuses on the design and implementation of a highly flexible underwater acous-tic (UWA) communications prototype that can be used for rapid testing and research purposes. Theprototype contains software and hardware targeted to the Programmable Logic (PL) and ProcessingSystem (PS) sections of the Xilinx Zynq System on a Chip (SoC) device, respectively. The systemmodel is developed by exploiting the Simulink software tool, which offers both model simulationand hardware description language (HDL) code generation. The HDL code is deployed onto theembedded Field Programmable Gate Array (FPGA) of the Zynq SoC.

We propose a system model based on orthogonal frequency division multiplexing (OFDM)with cyclic prefix and differentially coherent detection. OFDM is a robust multi-carrier modulationtechnique used to combat the frequency-selectivity and multipath effects of the UWA channels. Theinnate OFDM virtue of having narrowband signal on each carrier, motivates the straightforwardimplementation of differentially coherent detection. This technique avoids estimating the channelcoefficients by taking advantage of the existing coherence between consecutive OFDM carriers, andthus, the overall system complexity is reduced significantly.

Finally, the prototype is evaluated for short range point-to-point communication linksestablished in tank and pool setups. We report on the system performance in terms of data detectionmean squared error and bit error rate from a system that uses the 120− 130 kHz band.

x

Chapter 1

Introduction

There is a growing interest and emerging need for UWA communication platforms which

allow easy flexibility to adapt to different scenarios according to the changing environmental condi-

tions, and high data rate transmission. Moreover, in existing commercial modems, its proprietary

software-hardware feature restricts to make changes on the signal processing algorithms running

on the physical layer. Thus, testing novel modulation techniques or adding new functionality to the

existing ones are not feasible.

In this thesis we develop a highly flexible underwater acoustic communications platform

that can be used for rapid testing and research purposes. The UWA platform contains software and

hardware targeted to the PL and PS sections of the Zynq SoC device, respectively. The system

model provides a unified simulation and rapid prototyping environment by exploiting Simulink tools.

Simulation code developed in Simulink is converted to HDL code that further runs on the PL part of

the Zynq SoC.

The system is designed to use OFDM as a robust multi-carrier modulation technique

to combat the frequency-selectivity of the UWA channel. The appeal of OFDM relies on the

computational efficiency of FFT-based processing and the fact that it easily scales to different system

bandwidths. Similarly, the OFDM advantage of having narrowband signal on each carrier, motivates

an straightforward implementation of differentially coherent detection.

Differentially coherent detection is a low complexity alternative that avoids estimating

the channel coefficients. When there is perfect channel knowledge, differential detection has a

theoretical 3dB penalty in performance compared to coherent detection techniques over Rayleigh

fading channels. However, inaccurate channel estimation can lead to degrade the performance of

systems based on coherent detection, and thus differentially coherent detection reports superior

1

CHAPTER 1. INTRODUCTION

performance.

The developed UWA platform prototype has been tested and evaluated in both indoor

(water tank) and outdoor (sea water pool) environments. We present the system performance results

in terms of data detection mean squared error from a system designed to use the 120-130 kHz band.

1.1 Motivation and contribution

OFDM has been extensively adopted in wireless radio communications and similarly in

UWA communications for several years. There is evidence that all the real-time OFDM modems

have implemented coherent detection techniques [1]. However, we capitalize on the design and

implementation of UWA prototype based on both OFDM and differentially coherent detection.

The allure of differentially coherent detection in combination with OFDM was first in-

troduced in [2], and their performance was demonstrated using synthetic data. Furthermore, an

expanded study developed in [3] reports the system performance applied on recorded data from

the MACE’10 experiment. This technique relies on partial-FFT demodulation to mitigate the Inter-

carrier Interference (ICI) distortions along with differentially coherent detection to maintain the

system complexity at minimum. Moreover, the authors in [4] present a novel Doppler compensation

method for OFDM systems. This approach is built upon the FFT demodulation and differentially

coherent detection. Thus, all these facts motivate us to develop an FPGA-based UWA platform which

incorporates differentially coherent detection as a low-computational technique.

The contribution of this thesis are:

• Design and implementation of an OFDM scheme that integrates differential encoding and

differentially coherent detection in frequency domain.

• Development of the system Simulink model which is easy scalable for different system

requirements.

• Design, implementation, and testing of UWA platform prototype. This platform contains:

1) Microzed board which is built around a Zynq 7000 SoC. The HDL code generated from

Simulink is targeted to the PL section and the software part designed in Xilinx Software

Development Kit (SDK) is targeted to the PS section of the Zynq SoC device. 2) The Digital-to-

Analog Converter (DAC) and Analog-to-Digital Converter (ADC) boards are used to generate

the OFDM signal and sample the received signal, respectively. These boards are directly

2

CHAPTER 1. INTRODUCTION

connected to the Microzed board through a parallel bus. 3) Teledyne 4013 omnidirectional

transducers. 4) Amplifier and pre-amplifier.

1.2 Thesis Organization

This thesis is organized as the following. Chapter 2 summarizes the state-of-the-art

work in UWA communications. In Chapter 3, we consider the UWA channel modeling and the

intrinsic effects on the signal propagation, such as path loss, noise, multipath, and the Doppler effect.

Chapter 4 describes the OFDM principle, the system parameters, OFDM system design, and block

diagrams of the transmitter and receiver based on cyclic prefix (CP). In this chapter, we also propose

the use of differentially encoding and differentially coherent detection. Chapter 5 introduces the

system implementation approach on FPGA, design work-flow using MathWorks Simulink, HDL

code generation, block diagrams built in Vivado Design Suite, and software developed in Xilinx

SDK. Additionally, Chapter 5 discusses the internal architecture of the Zynq SoC device, the process

of targeting the hardware and software parts into the PL and the PS, respectively, and the Advanced

eXtensible Interface (AXI)4-Stream protocol used to communicate the PL with the PS sections. In

Chapter 6, we present and analyze the system performance results from testings in tank and pool.

Finally, we summarize our conclusions and future work in Chapter 7.

3

Chapter 2

State of the Art

Underwater wireless communications is of great interest to the military, industry, and the

scientific community, as it plays an important role in tactical surveillance, pollution control, oil control

and maintenance, offshore explorations, climate change monitoring, and oceanography research [5].

The transmission of information through underwater channels is performed using electromagnetic

waves, optical waves, and acoustic waves. Even tough electromagnetic waves propagate at extra

low frequency ranges (30 Hz to 300 Hz), they require large antennas, high transmitting power, and

are suited for very short distances. Optical waves are mainly affected by scattering, temperature

fluctuations, dispersion and beam steering. Consequently, optical underwater communications are

also limited to short distances due to severe water absorption at optical-frequency bands [6]. However,

acoustic waves can establish relative long range links (in the order of km), and currently is the most

common technique used for underwater communications.

Underwater acoustic signals occupy bandwidths in the order of few kHz and are placed on

center frequencies of similar order. This is a feature of wideband systems. The acoustic signal that

propagates through the underwater channel is mainly affected by absorption, which depends on the

signal frequency and distance, multipath propagation, and the relative low speed of sound (1500 m/s)

that creates severe Doppler distortions. Thus, all these combined effects establishes one of the most

challenging communications mediums [7].

Although, the first underwater acoustic communication systems were based on analog

amplitude modulation, the advent of digital communication techniques and the availability of

Digital Signal Processing (DSP) boards allowed the development of more sophisticated underwater

communication devices. Initially, non-coherent systems, such as Frequency Shift Keying (FSK),

reported high robustness to the severe UWA channel impairments, low bandwidth efficiency, and

4

CHAPTER 2. STATE OF THE ART

range in the order of few kilometers. Then, coherent detection techniques were introduced to increase

the bandwidth efficiency, but the receiver complexity also increased considerably [7].

Research efforts have been made during the last two decades in terms of the communication

range and data throughput. Multi-carrier modulation techniques emerged to provide high data rate

transmission, computational efficiency, and high performance against the frequency-selectivity of

the channel. Also, spatial modulation methods, which were widely used in radio systems, presented

even more improvement on the overall system performance. Nowadays, the investigation is focused

on the development of multi-agent underwater networks [8].

In the next sections, we present a detailed review of the state of the art work on the

characterization of the underwater acoustic channel, system requirements, system design, modulation

techniques, and underwater networks.

2.1 Underwater Acoustic Communication Channel Models

UWA channels are essential part of system design and prospective system deployment. An

appropriate channel modeling based on the physical wave propagation features impacts on the overall

system performance [9]. In a communication system, the transmitter sends information through a

signal that propagates through the medium, where the transmitted signal experiences several effects

due to the channel impairments. The signal distortions are caused by the frequency-dependent path

loss, multipath propagation, and the low speed of sound in water, 1500 m/s. Also, the relative

transmitter-receiver motion introduces extreme Doppler distortions, namely frequency shifting and

spreading [10].

In addition to the deterministic properties of wave propagation on the UWA channel, other

random signal fluctuations occur due to surface waves, internal turbulence, variations in the sound

speed, and other small-scale phenomena. Thus, a complete statistical channel model must encompass

the deterministic physical characterization along with the random channel variations [11].

Even though UWA communications lacks standardization, there are several studies in the

literature regarding channel modeling. A deterministic approach grounded on ray tracing theory is

shown in [12]. Also, several channel modeling studies using stochastic theory were performed by

processing data collected from real experiments. These approaches are based in fitting Ricean fading

and Rayleigh fading to experimental measurements [13].

The statistical characterization of the underwater channel is described by the large-scale

fluctuations, which are caused by random displacements in the order of many wavelengths; and

5


small-scale effects caused by displacements in the order of few wavelengths. An example of large-

scale analysis is shown in [14]. And a rigorous model that studies not only large-scale fluctuations,

but also small-scale variations is presented in [9]. However, small-scale propagation analysis is still

currently an on-going research topic.

2.2 Range, bandwidth, and system requirements

In terms of range, UWA communication systems are classified in: very long, long, medium,

short, and very short distances [7]. However, there is evidence that the available bandwidth highly

depends on the operational frequency and link distance (Figure 1 in [15]). This fact creates the

necessity of adequate modulation schemes which exploits the available limited bandwidth of the

channel to its maximum. Additionally, the bandwidth-distance dependence has further implications

in underwater network design. Table 2.1 summarizes the classification of UWA communication

systems according to the range and feasible bandwidth.

Type Range [km] Bandwidth [kHz]

very long > 100 < 1

long 10− 100 2− 5

medium 1− 10 ≈ 10

short 0.1− 1 20− 50

very short < 0.1 > 100

Table 2.1: Available bandwidth for UWA communication ranges [7].

Regarding the system requirements, the most common applications in underwater channels

are: remote control of objects, telemetry, digital voice communication, and video transmission.

Table 2.2 outlines these applications along with the appropriate transmission data rates and BER [7].

The performance of underwater acoustic communication systems is measured through

the BER and the bandwidth efficiency (bits per seconds per Hertz), which have been subject of

improvement for more than two decades of research work. The researchers have focused on different

modulation techniques, starting from single carrier non-coherent modulation, coherent modulation,

and nowadays, most of the scientific production is related with multi-carrier modulation techniques.

6


Application Rate [kbit/s] BER

remote control 1 < 10−4

telemetry ∼ 10 10−3 − 10−4

digital voice communication ∼ 1 10−2

video transmission < 64 10−3 − 10−4

Table 2.2: Underwater communications applications and corresponding required BER [8].

2.3 System Design

In order to combat against the severe fluctuations of the underwater acoustic channel, the

early communication systems were designed based on non-coherent modulation techniques. Even

though non-coherent systems in the form of FSK rely on signal energy detection and offer high

robustness to channel impairments, they are not bandwidth efficient. An example of non-coherent

system was known as DATS (Digital Acoustic Telemetry System) in the 80s [16].

Coherent systems were introduced in the 90s to increase bandwidth efficiency. Phase Shift

Keying (PSK) and Quadrature Amplitude Modulation (QAM) are examples of coherent techniques,

which increase the rate per bandwidth use. However these methods require a receiver able to track

the time-varying channel and perform coherent detection.

Despite of the noticeable improvement provided by coherent systems, their receiver struc-

ture is subject to use long equalizers to correct the extreme channel impairments. Therefore, research

focused on the development of reduced complexity systems that effectively use the limited bandwidth

channel such as multi-carrier modulation schemes [7].

Apart from single transmitter and single receiver systems, usually referred to Single Input

Single Output (SISO) systems, Multiple-Input Multiple-Output (MIMO) structures were introduced

to increase the bandwidth efficiency and improve the system performance (BER) [17].

2.3.1 Non-Coherent Modulation

Non-coherent systems in the form of FSK require low complexity receivers based on energy

detection. These techniques have worked on rapid phase variation channels in shallow-water in both,

long and medium links. The multipath effect of the UWA channel, which introduces Inter-symbol

Interference (ISI), is compensated by inserting guard times between consecutive symbols in the

transmitted signal. In addition, frequency channels that are separated by more than the coherence

bandwidth Bc can be used at the same time [8]. Table 2.3 recaps the research work observed on

7


non-coherent systems, particularly the bandwidth efficiency does not exceed 0.47.

Publication Rate[kbit/s]

Bandwidth[kHz]

BandwidthEfficiency

Range[km]

BER

Catipovic (1984) [18] 1.2 5 0.24 3 (shallow) ∼ 10−2

Freitag (1990) [19] 2.5 20 0.13 3.7 (deep) 10−4

Freitag (1991) [20] 0.6 5 0.12 2.9 (deep) 10−3

Mackelburg (1991) [21] 1.25 10 0.13 2 (deep) N/AScussel (1997) [22] 0.6− 2.4 5 0.47 10 (deep) -

5 (shallow)N/A

Table 2.3: Evolution of non-coherent modulation techniques [8].

2.3.2 Coherent Modulation

As the increase of bandwidth efficiency was required, then phase-coherent modulation

schemes were introduced, namely PSK and QAM. In the early developments, the rapid phase

variations of the UWA channel stopped the utilization of coherent techniques; however, the arrival

of sophisticated phase-tracking algorithms allowed the implementation of coherent receivers and

consequently data rates were considerable boosted. Table 2.4 comprises the evolution of coherent

modulation techniques.

Publication Rate[kbit/s]

Bandwidth[kHz]

BandwidthEfficiency

Range [km] BER

Mackelburg (1981) [23] 4.8 8− 14 0.6 4.8 (deep) 10−6

Olson (1985) [24] 2 2− 10 1.0 6 (deep) < 10−3

Howe (1992) [25] 1.6 10− 50 0.16 0.1 (shallow) < 10−3

Suzuki (1992) [26] 16 8− 20 2.0 6.5 (deep) 10−4

Jones (1997) [27] 20 10− 50 2.0 1.0 (deep) 10−2

Table 2.4: Evolution of coherent modulation techniques [8].

Phase-coherent systems are classified into two categories: differentially coherent and

purely phase-coherent. An advantage of using differentially encoded PSK (DPSK) with differentially

coherent detection is that it allows simple carrier recovery [7]. DPSK serves as an intermediate

solution between non-coherent and purely coherent systems in terms of bandwidth efficiency [28].

2.3.3 Multi-carrier Modulation

Multi-carrier modulation techniques are used to overcome the frequency-selective effects

of the UWA channel. Multi-carrier techniques in the form of OFDM have been widely used in

8


radio communication systems and is currently adopted in UWA systems. OFDM offers remarkable

robustness against the frequency-selective channel as it translates the frequency-selective channel

into several narrowband channels, each one experiencing flat fading, and thus, eliminating the need

of long equalizers in the time domain. In addition, the allure of OFDM relies on its computationally

efficient FFT processing and that it easily scales to different bandwidths.

OFDM is also favorable for MIMO processing, adaptive modulation, differentially coherent

detection, and partial-band interference suppression. Major issues of OFDM include: high sensitivity

to frequency offset and time-variation of the channel, sensitivity to non-lineal distortions, and power

efficiency.

Additionally, OFDM incorporates two stages: pre-FFT and post-FFT processing. The

front-end resampling is required when the motion-induced Doppler frequency shifting exceeds the

sub-carrier spacing. Even though initial resampling is performed, some Doppler shifting can still

remain because of the slow velocity of propagation in the acoustic channel. A collection of achievable

data rates for multi-carrier modulation techniques is illustrated in Table 2.5.

Publication Modulation Rate [kbit/s] Bandwidth[kHz]

Range [km] BER

Stojanovic (2006) [29] QPSK 30 24 2.5 (shallow) ∼ 0

Li (2008) [30] QPSK 1.5− 25 3− 50 0.5 (deep) 10−5

Li (2008) [30] 16-QAM 12, 25, 50 12, 25, 50 0.5 (deep) 10−5

J. Huang (2011) [31] QPSK,16-QAM

5.2, 10.4 9.77 1.0 (shallow) 10−3

Table 2.5: Evolution of multi-carrier modulation techniques [8].

2.3.4 Spatial Modulation

Limited bandwidth and low spectral efficiency are the main shortcomings in UWA commu-

nications. Certainly, coherent modulation schemes demonstrated an exceptional boost in the field as

it enabled to increase the bandwidth efficiency. However, the spatial structure of the UWA channel

can introduce additional bandwidth efficiency gain [32].

Spatial modulation is a technique that controls the spatial distribution of signal energy, such

that the UWA channel supports multiple parallel independent communication channels [32]. The

exploitation of the several parallel channels, best known as diversity gain, leads to an improvement in

the performance of the whole system and thus increasing the data rate transmission [33]. A system

9


that uses multiple transmitters and multiple receivers is referred to as MIMO system. MIMO systems

have been developed over UWA channels using single-carrier and multi-carrier modulation schemes.

2.4 Networks

Even though the bandwidth-distance dependence in UWA communications is a consider-

able obstacle, it allows the idea of implementing underwater multi-hopping and further networks.

In addition, the transmission power and the low speed of sound are major issues that influence the

design of network protocols. Also, large delays between transactions must be taken in account so

that it can reduce the throughput of the system considerably [15].

Additionally, power efficiency is a desirable characteristic for underwater networks since

underwater instruments are battery-powered. Optimization of energy consumption can be accom-

plished by transmitting at higher rate or reducing the number of re-transmissions. However, in a

random access network, where several users are enabled to continuously transmit information, this

task is limited by the channel latency [34].

In summary, the intrinsic propagation characteristics of UWA channels, power, and data

rate must be taken into consideration in network design.

10

Chapter 3

Underwater Acoustic Channel Modeling

The underwater acoustic channel is mainly described by a path loss which depends on the

transmission distance and signal frequency ([35], [36]). The UWA channel offers a limited bandwidth

that is also conditioned on the transmission distance. For instance, the available bandwidth is reduced

to some kHz when the distance is in the order of few tens of kilometers [37].

The acoustic signal that propagates through the UWA channel has the following limitations:

the time-varying multipath propagation and the low speed of sound (1500 m/s) [11]. Moreover,

the inherently wideband feature of UWA communication systems, in which the bandwidth and the

center frequency are in the same order, can cause severe frequency-selective signal distortions and

motion-induced Doppler spreading and shifting [15]. These effects together are contrasted with the

worst properties of radio channels: poor link quality of a terrestrial system, and high latency of a

satellite system [11].

3.1 Path loss

The path loss of the acoustic channel is characterized by its dependency of the signal

frequency, and it is expressed as

A(l, f) =( llr

)ka(f)l−lr (3.1)

where l is the distance between transmitter and receiver with a reference lr, and f is the signal

frequency. The path loss exponent k usually takes values between 1 and 2 for cylindrical and

11

CHAPTER 3. UNDERWATER ACOUSTIC CHANNEL MODELING

spherical spreading, respectively. The absorption coefficient a(f), measured in dB/km, is evaluated

according to the Thorp’s empirical formula ([38]). Figure 3.1 shows its rapid growth with frequency.

0 100 200 300 400 500 600 700 800 900 1000

0

50

100

150

200

250

300

350

Figure 3.1: Absorption Coefficient.

3.2 Noise

Noise in an acoustic channel is generated from four sources: turbulence Nt(f), shipping

Ns(f), waves Nω(f), and thermal noise Nth(f) [11]. The empirical approximations of the noise

PSD as function of frequency f [kHz] are defined in ([35], [39]) and measured in [dB re µ Pa per

Hz].

10 log10Nt(f) = 17− 30 log10(f) (3.2)

10 log10Ns(f) = 40 + 20(s− 0.5) + 26 log10(f)− 60 log10(f + 0.03) (3.3)

10 log10Nω(f) = 50 + 7.5√vω + 20 log10(f)− 40 log10(f + 0.4) (3.4)

10 log10Nth(f) = −15 + 20 log10(f) (3.5)

In equation (3.3), the shipping activity s ranges from 0 to 1, for low and high activity

respectively, and the wind speed vω is measured in m/s (equation (3.4)). The total noise PSD N(f)

12


is given by

N(f) = Nt(f) +Ns(f) +Nω(f) +Nth(f) (3.6)

Figure 3.2 depicts the noise power spectral density for different levels of shipping traffic

and wind speed. The the total noise PSD N(f) decays at a rate of approximately 18 dB/decade.

100 101 102 103 104 105 1060

10

20

30

40

50

60

70

80

90

100

110

turbulence

low activity(s=0)

medium activity(s=0.5)

high activity(s=1)

wind at 10m/s

wind at 0m/s

thermal

overall

Figure 3.2: Empirical model of the noise power spectral density for UWA channel [40].

3.3 Signal-to-Noise ratio

The signal-to-noise ratio (SNR) depends not only on the frequency, but also on the trans-

mitting distance. To illustrate this distance-frequency dependence, using the path loss A(l, f), noise

PSD N(f), and assuming that the transmitted signal is a tone with frequency f and power P , then

the SNR at the receiver can be evaluated as below.

SNR(l, f) =P

A(l, f)N(f)∆f(3.7)

where ∆f is the narrowband received bandwidth around the frequency f .

13


Figure 3.3: Frequency-distance dependence part of the SNR. The spreading factor is k = 1.5 forthe path-loss. The noise PSD is calculated for moderate shipping activity (s = 0.5) and no wind(vω = 0 m/s) [36].

The frequency-distance dependence of the SNR is determined by the factor A(l, f)N(f)

(equation (3.7)). For each transmitting distance there exists a narrow range of optimal frequencies

(bandwidth) B(l) where the received SNR level is maximum [36]. Figure 3.3 depicts plots of

the function 1/A(l, f)N(f) for different distances. The bandwidth is extremely limited by the

transmitting distance. For instance, at 100km the available bandwidth is approximately 1kHz, while

for shorter distances the bandwidth increases.

3.4 Multipath

In UWA channels, multipath is generated by: sound reflection at the surface, bottom and

any objects, and sound refraction in the water [15]. The geometry of the channel and its reflection

properties define the impulse response of an acoustic channel, including the important propagation

paths and delays. The frequency response of the pth path is denoted by

Hp(f) =Γp√A(lp, f)

(3.8)

where Γp is the total reflection coefficient along the p-th propagation path and A(lp, f) is the

14


propagation loss that belongs to this path and acts as a low-pass filter. Moreover, the overall channel

frequency response is

H(f) =

P−1∑

p=0

Hp(f)e−j2πfτp (3.9)

and its corresponding channel impulse response

h(t) =

P−1∑

p

hp(t− τp) (3.10)

With the aim of illustrating the multipath effect as in [36], let us define a system with

transmitter and receiver, both located at 75m depth from the bottom and separated by 3 km. The

spreading factor is k = 1.5 and 3dB loss associated with each bottom reflection.

Bottom

TX RXp0

p1

p2

p3 p4

p5

3 km

75 m

Figure 3.4: Geometry of the channel. [36].

The transmitted signal propagates through different paths p with length lp. Assuming that

the speed of propagation c is constant then the first arrival p0 occurs at t0 = l0/c, which is the

reference time used to calculate the other relative path delays as τp = lp/c− t0. Additionally, ideal

surface reflection coefficient amount to -1, while the bottom reflection coefficients depend on the

material type and grazing angle.

15


0

0.02

0.04

0.06

0.08

0.1

0 0.02 0.04 0.06 0.08 -200 -100 0 100 200

0

1

2

3

4

510

-4

Figure 3.5: Channel response functions [36].

Figure 3.5 illustrates the overall transfer function and response magnitudes |H(f)| and

|h(t)|. The total multipath spread Tmp is specified by the longest path delay, and typically ranges on

the order of tens of milliseconds [15].

3.5 The Doppler Effect

The relative motion between the transmitter and receiver contributes to the variability of

the channel impulse response. The Doppler effect causes frequency shifting and frequency spreading

in the acoustic signal. This phenomena is proportional to the ratio a = v/c of the relative velocity

between transmitter and receiver, and the speed of sound. The speed of sound in underwater channels

is slow (around 1500 m/s), thus the motion-induced Doppler distortion in underwater channels is far

more severe than wireless radio channels.

To gain deep understanding of motion-induced Doppler effects, let us suppose that the

transmitter and receiver move relative to each other, and the transmitted signal s(t) = <{g(t)ej2πfct}contains a rectangular pulse g(t) which is modulated onto a carrier of frequency fc. Neglecting the

multipath effect and path dispersion of the channel, the single-path received signal is modeled as

r(t) = s(t0 + t− l(t0)− vt

c

)(3.11)

16


where the relative velocity v is constant and l(t0) is the distance traveled by the signal arriving at t0.

Reordering the variables of equation (3.11) we have that

r(t) = s((1 + a)t− τ

)= <{g((1 + a)t− τ)ej2πfc[(1+a)t−τ ]} (3.12)

where τ = l(t0)/c− t0 is the signal delay. Hence, the equivalent baseband received signal is defined

as

v(t) = e−j2πaτg((1 + a)t− τ)ej2πafc (3.13)

There are two effects on the signal causing distortion. First, the motion-induced Doppler

spreading where the pulse g(t) is scaled in time by (1 + a) and delayed by τ , so that the original

pulse duration T is observed at the receiver as T/(1 + a). Similar to the transmitted bandwidth B

which is observed as B(1 + a). Second, the motion-induced Doppler shifting where a frequency

offset afc is introduced. The effects of frequency shifting and spreading are illustrated in Figure 3.6.

Figure 3.6: Motion-induced Doppler effects in time and frequency domains [36].

17

Chapter 4

Orthogonal Frequency Division

Multiplexing and System Design

OFDM is a multi-carrier modulation technique widely used in wireless radio systems.

It has also been implemented in acoustic communications due to its high bandwidth efficiency,

scalable system design, computational efficiency of FFT-based processing, and its robustness against

frequency-selective channels.

Unlike the single carrier systems, OFDM splits the available signal bandwidth into a

collection of equally-spaced narrowband chunks, which are referred to as inter-carrier frequency

separation. Thus, each narrowband carrier experiences flat fading, and therefore, eliminates the use

of long equalizers. In addition, OFDM is suitable for adaptive modulation and differentially coherent

detection techniques.

OFDM is highly sensitive to frequency offset and time-variation of the channel. Frequency

Doppler shifts are easily introduced in UWA channels due to its wideband nature, where the ratio

between the carrier frequency and the bandwidth is small.

4.1 OFDM Data Transmission and Reception

Let us consider a signal defined in a bandwidth B. The signal is transmitted through a

channel with coherence bandwidth Bc < B, hence the signal experiences frequency-selective fading.

The OFDM technique splits the bandwidth B into a collection of K narrowband sub-channels, each

one defined over ∆f = B/K. The value of K is selected such that ∆f << Bc, this ensures that

each sub-channel only faces flat fading.

18

CHAPTER 4. ORTHOGONAL FREQUENCY DIVISION MULTIPLEXING AND SYSTEM DESIGN

Datasource

Serial-to-Parallel(S-P)

∑

SymbolMapping

×

ej2π0∆ft

d0

SymbolMapping

×

ej2π(K−1)∆ft

dK−1

×

g(t)

×u(t)

ej2πf0t

<{·} s(t)

Figure 4.1: OFDM transmitter scheme.

Figure 4.1 illustrates the OFDM scheme. The incoming stream of data is allocated in K

segments and converted into aK parallel stream of symbols. Each symbol dk belongs to a pre-defined

alphabet, e.g. PSK, and is modulated by a carrier frequency k∆f and defined over a bandwidth ∆f .

The equivalent baseband signal u(t) results from the sum of the K modulated segments and the

multiplication by a rectangular pulse g(t). The passband signal is the consequence of taking the real

part of the up-shifted version of u(t) by the initial carrier frequency f0,

s(t) = <{K−1∑

k=0

dkej2πfktg(t)

}(4.1)

s(t) = <{K−1∑

k=0

dkej2πk∆ftg(t)ej2πf0t

}(4.2)

s(t) = <{u(t)ej2πf0t

}(4.3)

u(t) =

K−1∑

k=0

dkej2πk∆ftg(t) (4.4)

where g(t) is a rectangular pulse with duration T = 1/∆f and defined in T ′ = T + Tg, where Tg is

the guard interval and must be greater than the multipath delay spread of the channel Tg > Tmp. The

signal s(t) passes through a channel h(t, τ) and Gaussian noise n(t) is added, the noise variance is

σ2/2. The impulse response of the channel is defined as in [41], where τ is the delay, t is the channel

observation time and P the number of propagation paths. Additionally, hp(t) indicates the p-th path

19


real-valued gain and τp(t) is the propagation delay per path.

h(t, τ) =

P−1∑

p=0

hp(t)δ(t− τp(t)) (4.5)

The received passband r(t) and baseband v(t) signals (see Figure 4.2 ) are given as

r(t) =

P−1∑

p=0

hp(t)s(t− τp(t)) + n(t) (4.6)

=P−1∑

p=0

hp(t)<{u(t− τp(t))ej2πf0(t−τp(t))

}+ n(t)

= <{[

P−1∑

p=0

hp(t)u(t− τp(t))e−j2πf0τp(t) + ω(t)

]ej2πf0t

}

= <{v(t)ej2πf0t

}

where v(t) is inferred as the complex baseband equivalent received signal and ω(t) is zero-mean

additive Gaussian noise with variance σ2.

v(t) =P−1∑

p=0

hp(t)u(t− τp(t))e−j2πf0τp(t) + ω(t) (4.7)

=

P−1∑

p=0

hp(t)

[K−1∑

k=0

dkej2πk∆f(t−τp(t))g(t− τp(t))

]e−j2πf0τp(t) + ω(t)

=

K−1∑

k=0

dk

[P−1∑

p=0

hp(t)e−j2πfkτp(t)

]ej2πk∆ftg(t− τp(t)) + ω(t)

=K−1∑

k=0

dkHk(t)ej2πk∆ftg(t− τp(t)) + ω(t)

The channel transfer function at frequency fk is defined as

Hk(t) =∑

p

hp(t)e−j2πfkτp(t) (4.8)

20


Channelh(t, τ)

s(t)+

n(t)

×r(t)

e−j2πf0t

×

e−j2π0∆ftv(t)

×

e−j2π(K−1)∆ft

∫T

y0

∫T

yK−1

Figure 4.2: OFDM receiver scheme.

yk(a)=

1

T

∫ T ′

0v(t)e−j2πk∆ftg(t)dt (4.9)

(b)=

1

T

∫ T

0

[K−1∑

l=0

dlHl(t)ej2πl∆ft + ω(t)

]e−j2πk∆ftdt

(c)=

K−1∑

l=0

dlHl

[1

T

∫ T

0ej2π(l−k)∆ftdt

]+ zk

(d)=

K−1∑

l=0

dlHlδkl + zk

(e)= dkHk + zk

In equation (4.9), the equality (a) comes from the fact that g(t) is defined over the interval

t ∈ [0, T ]. Moreover, we assume that the channel is time-invariant over the interval of an OFDM

block, Hl(t) = Hl for t ∈ [0, T ] as stated in (b). The orthogonality property between sub-carriers

is expressed in (c), so the integral results in a delta-function dkl which is one when k = l and

zero otherwise (d). The last expression (e) describes the effect of a flat channel Hk on each carrier

frequency fk, meaning that OFDM decomposes the wideband channel into a set of narrowband

orthogonal sub-channels.

Finally, the multi-carrier modulation and demodulation schemes, illustrated on Figure 4.1

and Figure 4.2, are replaced by the Inverse Discrete Fourier Transform (IDFT) and Discrete Fourier

Transform (DFT) respectively. The IDFT and DFT are implemented through the Inverse Fast Fourier

21


Transform (IFFT) and FFT algorithms.

4.2 Differentially coherent detection

Channel estimation is an indispensable part of a coherent OFDM system, and its accuracy

greatly influences the overall system performance. For a time-invariant channel, estimates can be

obtained in the initial OFDM block, which are then applied to detect the data throughout a frame

of OFDM blocks. While for a time-varying channel, channel estimates have to be computed and

updated in every OFDM block. The methods for estimating the channel coefficients are frequently

based on pilot sub-carriers or decision-feedback, and the algorithms can range from the traditional

least squares to more complex ones, such as basis and matching pursuit which exploit the sparsity of

the acoustic channel [42]. However, the long delay spread, time-varying channels, and the wideband

nature of the UWA signals are factors that contribute to an imperfect channel estimation in coherent

systems. And thus, this motivates the use of differentially coherent detection as a low complexity

method which eliminates the need for channel estimation [3].

Differentially coherent detection is based on the assumption that the channel response does

not change significantly either between adjacent carriers or between blocks. The first one implies

differential encoding and detection in frequency, while the latter involves differential encoding

and detection in time ([2],[43]). We consider encoding/detection in frequency domain which has

the following advantages: 1) reinforces the carrier coherence assumption, and 2) increases the

transmission rate [44]. On the transmitter side, the data symbols dk are obtained by differential

encoding the original i.i.d. symbols bk, i.e. dk = dk−1bk, where bk and dk belong to the same

PSK alphabet, with d0 = 1. While on the receiver, the estimates of the differentially-encoded data

symbols bk are obtained as

bk =y∗k−1yk

|yk−1|2, k = 0, . . . ,K − 1 (4.10)

In equation (4.10), we can observe two facts: 1) consecutive channel coefficients can be

approximated as Hk ≈ Hk−1, and 2) the received signal yk−1 acts as the channel estimate, i.e.

y∗k−1/|yk−1|2 is the normalized channel coefficient. To better understand the concept of differentially

coherent detection, consider the received signals on consecutive carriers yk−1 = dk−1Hk−1 and

yk = dkHk where zk−1 = zk = 0. Hence, the right side of the equality (4.10) becomes,

22


y∗k−1yk

|yk−1|2(a)=d∗k−1H

∗k−1dkHk

|dk−1Hk−1|2(4.11)

(b)=d∗k−1H

∗k−1dk−1Hk−1bk

d∗k−1H∗k−1dk−1Hk−1

(c)= bk

where equality (a) is obtained by replacing yk and yk−1. The assumptions Hk = Hk−1 and

dk = dk−1bk are observed in equality (b). Finally, equality (c) capitalizes the concept of differentially-

coherent detection.

In addition, differentially coherent detection is closely associated with the efficient use

of system bandwidth, which is given by the ratio of the symbol rate to bandwidth, R/B ∼ 1/(1 +

TgB/K), where Tg is the guard interval. As K increases for a given bandwidth B, both the

bandwidth efficiency and the coherence between adjacent carrier increase, this causes the carrier

spacing ∆f = B/K to become smaller. Thus, favoring the use of differential encoding in frequency

[2]. However, K is limited by the time-coherence of an OFDM system. Since the block duration

T = 1/∆f increases along with K, so does the motion-induced Doppler as well as the channel

response fluctuates within one block. In consequence, the orthogonality between carriers is lost and

ICI must be compensated.

In the literature, two methods have been introduced to overcome the motion-induced

Doppler and the ICI in UWA OFDM systems. The authors in [3] propose four methods for ICI

mitigation. This study suggests the implementation of differentially-coherent detection to reduce the

overall system complexity. While in [4], a method for compensating the motion-induced Doppler

is establish by applying differentially-coherent detection. We briefly overview these methods in

Section 4.2.1 and Section 4.3.1, respectively.

4.2.1 Method based on multiple fast Fourier transform (FFT) demodulation

This study assumes the notion that the channel variations may be decomposed based on a

set of pre-defined functions. The received signal is projected onto these functions, and the projections

are passed on to FFT demodulation and subsequent to a combiner block along with differentially

coherent detection. The equivalent baseband received signal is expressed as in (4.7), where the

channel coefficients Hk(t) permit a decomposition onto a set of known functions Φi(t), such that

23


Hk(t) ≈∑I−1

i=0 Hk,iΦi(t). If this decomposition can be performed then vk ≈∑I−1

i=0 Hk,iyk,i, where

yk,i is obtained by taking the FFT of the product Φ∗i (t)v(t) as

yk,i =

∫ T

0Φ∗i (t)v(t)e−j2πk∆ftdt (4.12)

T = K/B

FFT FFT FFT

Combiner

Differentially Coherent Detection

T/I

k1y k2y kIy

kx

kb

Figure 4.3: Partial FFT demodulation scheme [3].

Figure 4.3 illustrates a total of I FFT operations to be performed. Since the channel

Hk(t) fluctuates slowly, then I can be small. The outputs yk,i are fed into a combiner and further

differentially decoded.

4.3 Doppler compensation for mobile communications

The main obstacle in OFDM systems applied to UWA acoustic channels are the severe

Doppler distortions induced by the relative motion between the transmitter and receiver. The

wideband nature of UWA communication systems causes non-uniform shifting on each frequency fk,

so that the shifted amount is not equal for all sub-carriers. Figure 4.4 illustrates the Doppler effect in

a wideband acoustic system creating non-uniform frequency shifting [15].

The motion-induced Doppler shifting can be mitigated by resampling the received signal by

a factor a. The Doppler scaling factor is estimated as a = Trx/Ttx − 1, where Ttx is the transmitted

frame duration and Trx is the estimated received frame duration [15].

24


f

f∆

0f 1f 2f kf 1Kf −

B K f= ∆

(1 )kf a+

f

Figure 4.4: Motion-induced Doppler shift is not uniform in a wideband system [15].

The resampling process of the signal at a constant rate a only removes the Doppler

distortions created by sources moving at a constant speed. However, the Doppler distortions are

time-varying due to the random motion of the transmitter-receiver and channel time variations. Thus,

initial resampling performs coarse Doppler compensation of the entire received signal (frame of

OFDM blocks) and leaves a residual Doppler factor that must be corrected on each OFDM block for

best ICI reduction ([3],[45]).

The received signal is bandpass filtered, synchronized, resampled, and downshifted by the

lowest carrier frequency f0. The passband resampled signal is obtained as r(t) = r(t/(1 + a)) and

the equivalent received baseband signal is expressed as

v(t) = ejβtK−1∑

k=0

dkHk(t)ej2πk∆ft + ω(t) (4.13)

where β is the unknown residual frequency offset. In summary, two steps must be taken for

compensating Doppler distortions: 1) front-end resampling of the entire frame of OFDM blocks and

2) high-resolution Doppler compensation on each block to mitigate the ICI.

25


4.3.1 Beta (β) algorithm

A low complexity method based on hypothesis testing and differentially coherent detection

[4]. Several hypothesized frequency offset values are used in steps of a fraction of ∆f , the length of

the fraction depends on the accuracy of the final estimated β and the computational complexity. The

hypothesized value β is used to compensate and demodulate the signal as

yk =

∫

Tv(t)e−jβte−j2πk∆ftdt (4.14)

where k = 0, . . . ,K−1, and the values yk are used to find the estimates of the differentially-encoded

data symbols by using equation (4.10).

By using equally-spaced pilot data symbols bk, k ∈ κp, the composite error is defined as

E(β) =∑

k∈κp

|bk − bk|2 (4.15)

and the estimate of β∗ is obtained as

β∗ = arg minβE(β) (4.16)

4.4 Transmitter design

The OFDM transmitted signal consists of K carriers occupying a bandwidth B = K∆f ,

which is expressed as

s(t) = Re

{K−1∑

k=0

dkej2πfkt

}, t ∈ [0, T + Tg] (4.17)

where fk = f0 +k∆f is the k-th sub-carrier frequency, f0 is the first carrier frequency, and the carrier

spacing ∆f is assumed to be small enough so thatHp(f) ≈ Hp(fk) for f ∈ [fk−∆f/2, fk+∆f/2].

The OFDM block duration T = 1/∆f must be greater than the multipath spread of the channel Tmp;

and if an efficient use of system bandwidth is required then ∆f should be on the order of 100 Hz or

less [15].

This system assumes the use of CP to preserve the orthogonality of the carriers and to

eliminate inter-block interference between consecutive OFDM blocks [46]. The cyclic prefix duration

26


Tg, also referred to as guard interval, must be chosen such that its value is longer than the multipath

spread of the channel, Tg ≥ Tmp.An illustration of the structure of the CP-OFDM frame is depicted in Figure 4.5. Each

frame includes a preamble, Nb OFDM blocks, and a postamble. The synchronization preamble

and postamble are short signals formed from a pseudo-noise m-sequence with m = 5 mapped to a

unit-amplitude Binary Phase Shift Keying (BPSK) alphabet, i.e., {−1,+1}. The carrier frequency

fc of the preamble-postamble is fc = f0 +B/2 where f0 is the first carrier frequency of the OFDM

signal and B is the acoustic bandwidth. The pause interval between the preamble and the OFDM

signal is Tpse.

CP CP CP#2 #Nb

Tg >> TmpPreamble Postamble

Tpse Tpse

T = K/Bt

#1

Figure 4.5: Illustration of the structure of a sample CP-OFDM frame.

A complete block diagram of the transmitter is showed in Figure 4.6, where the codeword

bits c are mapped into PSK data symbols bk. The data symbols dk are obtained by differential

encoding the original i.i.d. symbols bk, i.e. dk = dk−1bk, where bk and dk belong to the same PSK

alphabet, with d0 = 1. Taking IFFT on the differentially-encoded data symbols and inserting CP

yields the baseband signal u(t). Finally, u(t) is upshifted by the first carrier frequency f0.

SymbolMapping

DifferentialEncoding

IFFT

CyclicPrefix

× <{·}c bk dk

ej2πf0t

s(t)

Figure 4.6: Block diagram of the CP-OFDM transmitter.

27


× Lowpassfiltering

Framesynchronization

Doppler scalecoarse estimation

× Bandpassresampling

× Frequencyoffset correction

Cyclic prefixremoving

FFT

DifferentialDecoding

r(t)

e−j2πfct ej2πfct

e−j2πfot

v(t) yk bk

Figure 4.7: Block diagram of the CP-OFDM receiver.

4.5 Receiver design

The passband received signal, modeled as in equation (4.6), is downshifted to baseband

by demodulating with a carrier at frequency fc = f0 + B/2. Then the signal is filtered using a

lowpass filter (LPF) with cutoff frequency fcutoff = B/2. At this point, the signal is correlated

with the transmitted preamble to find the beginning of the OFDM frame. After synchronization,

the signal is up-shifted again so that it lives in the frequency band [0, B]. The cyclic prefix (CP) is

removed, as in equation (4.7), and using the fact that carriers are orthogonal over t ∈ [0, T ] then the

demodulation is performed through the FFT to obtain yk = dkHk + zk k = 0, . . . ,K − 1, where

Hk is the channel frequency response and zk contains the equivalent complex baseband noise. The

differentially coherent detection is applied on yk to obtain estimates of the data symbols bk.

28

Chapter 5

Underwater Platform Overview

In this chapter we introduce the hardware development of underwater communication

devices using the FPGA technology. This device implements high performance signal processing in

hardware level. We also outline the hardware, HDL and software design of this platform.

The main part of this development is the Microzed board, which includes the Xilinx Zynq-

7000 All Programmable SoC (Zynq SoC). The Zynq SoC is made of two components, the PS and

the PL parts. The PS is formed around a dual-core Advanced RISC Machine (ARM) processor and

can support software routines and/or operating systems, while the PL section comprises an FPGA,

ideal to implement high-speed logic. The PL-PS communication is established by using industrial

standard AXI connections [47].

A complete system platform is illustrated from its design in MathWorks Simulink, HDL

code generation, Xilinx Vivado Design Suite system integration, and application development in

Xilinx SDK.

5.1 Hardware Overview

5.1.1 Microzed Board

Microzed board (Figure 5.1) is a low-cost development kit that enables hardware and

software developers to create and evaluate Zynq-7000 All Programmable SoC designs [48]. The

most important features that Microzed support are:

• Processor: Zynq-7000 All Programmable SoC XC7Z020-CLG400-1

• Memory: 1 GB DDR3, 128 Mb Quad-SPI Flash, 4GB mcroSD card

29

CHAPTER 5. UNDERWATER PLATFORM OVERVIEW

• Communication: 10/100/1000 Ethernet, USB 2.0 and USB-UART

• General purpose I/O and PMOD capability

• Clocking: 33.33 MHz clock source for PS

• Xilinx Platform Cable JTAG connector

Figure 5.1: Microzed board [48].

The Zynq 7020 SoC includes a dual ARM Cortex-A9 CPU based PS and Xilinx hardware

PL. The ARM cores are the Cortex A9 MPcore which are able to run up to 1 GHz. The PS includes

L2 cache and enables its embedded computing capability using DDR2 and DDR3 SDRAM memory,

Flash memory, Gigabit Ethernet, general purpose I/O, and UART technologies. Also, the Xilinx AXI

Interface provides high-speed memory-access between the PS and the PL [47].

5.1.1.1 Processing System (PS)

The processing system of the Zynq SoC includes a dual core embedded ARM Cortex-A9

processor operating at up to 1 GHz. It can support memory controllers and I/O peripherals.

The processing system core acts as a logical connection between the PS and the PL and

allows to integrate custom and embedded Intellectual Property (IP) cores with the processing system

unit inside Vivado Design Suite [49].

30


5.1.1.2 Programmable Logic (PL)

The PL section is composed of general purpose FPGA logic fabric, slices and Configurable

Logic Blocks (CLB), and Input/Output Blocks (IOBs) for interfacing. Some of these terms are

explained below [47].

• Configurable Logic Block(CLB) are small groupings of logic elements that are laid out in a

two-dimensional array on the PL, and connected to other similar resources via programmable

interconnects. Each CLB is positioned next to a switch matrix and contains two logic slices.

• Slice a sub-unit within the CLB, which contains resources for implementing combinatorial

and sequential logic circuits. Zynq slices are composed of 4 Lookup Tables, 8 Flip-Flops, and

other logic.

• Lookup Table (LUT) is a flexible resource that can implement a logic function of up to six

inputs, Read Only Memory (ROM), Random Access Memory (RAM), or a shift register.

• Flip-Flop (FF) a 1-bit register with reset functionality.

• Switch Matrix each one sits next to one CLB and provides a flexible routing facility for

making connections.

• Input/Output Blocks (IOBs) are resources that provide interfacing between the PL logic

resources, and external circuitry. Each IOB can handle a 1-bit input or output signal.

There are also two special purpose components at the PL level: Block RAMs and DSP48E1

slices for high-speed arithmetic.

• Block RAM (BRAM) is a configurable memory module used to store data and implements

Random Access Memory (RAM), Read Only Memory (ROM), and First In First Out (FIFO)

buffers. Each BRAM can store up to 36 Kb of information.

• DSP48E1 slices are dedicated hardware implementing high-speed arithmetic on signals with

medium to long arithmetic word lengths. Typically, they comprise a pre-adder/subtractor,

multiplier, and post-adder/subtractor with logic unit.

31


5.1.1.3 Interfacing the Processing System with the Programmable Logic

The ability to connect the PL with the PS is an appealing feature that Zynq SoC implements

to form a completely integrated system. AXI interconnects and interfaces are used to connect the PS

and the PL, and there are three different types of AXI communication, which are presented below:

• AXI4 is suited for memory-mapped links, provides a data burst transfer of up to 256 data

words.

• AXI4-Lite supports only one data transfer per connection, it is also a memory-mapped

communication.

• AXI4-Stream is designed for high-speed streaming data, supports burst transfers of size in

the order of few Mega-bytes and is not a memory-mapped based communication.

For this thesis the AXI4-Stream communication protocol is used, which is described next.

5.1.1.4 AXI4-Stream Interface

In general the AXI protocol is implemented to communicate the Programmable Logic

with the Processing System, depending on the application the designer can use either AXI4-Lite

or AXI4-Stream as described before. AXI4-Stream best fits when the target application requires

high-data throughput. More often a bus of 32-bit width is used, which implies 4 byte per clock cycle

in hardware. For instance, if the PL is running a clock of 100 MHz, the data is transferred at a rate of

400 Mega-bytes per second [50].

The AXI4-Stream protocol consists of a bus of signals specifically designed such that

the two interfaces, usually known as master and slave, can understand each other and make a data

transfer. These signals include: TVALID, TDATA, TREADY, TLAST and TUSER.

Figure 5.2 illustrates two packet transfers using the AXI4-Stream protocol (reference

Xilinx AXI reference guide). The first transaction consists of four data cycles D0 to D3. The last

data clock is marked by TLAST asserted signal. It is clear that during the transaction both TVALID

and TREADY are always high. Similarly, a second transaction of n cycles is shown. The signals

TVALID and TREADY are used for flow control. The transmitter (master) controls how much data

is transferred by asserting the TVALID signal and the receiver (slave) de-asserts TREADY signal

when it is not ready to receive more data.

32


D0 D1 D2 D4

CLK

TVALID

TREADY

TLAST

TDATA D0 D1 Dn-1 Dn

First Transfer Second Transfer

Figure 5.2: AXI4-Stream Signal Diagram [51].

5.2 Software Tools

The process of targeting custom designs on the Microzed board is carried out by using

commercially available tools from MathWorks and Xilinx. MathWorks Simulink is used to create

and simulate the models; it also provides the HDL Coder tool which allow us to target the PL section

of the Zynq [52]. Through the HDL Coder we are able to generate IP cores from the Simulink

models [47]. The IP core generated is integrated along with other components such as the Processing

System unit, AXI Direct Memory Access (DMA)s, and AXI Interconnects inside Xilinx Vivado

Design Suite software. The Xilinx Vivado is a tool that allows designers to define the PL section in a

complete block diagram, where each element is interconnected with others using wires in a graphical

fashion. Xilinx Vivado performs two operations, synthesis and implementation, to finally generate

the bitstream file that is further programmed on the target board.

Additionally, the PS part is performed by Xilinx SDK, that makes use of the generated

hardware file (bitstream). Xilinx SDK is based on Eclipse and provides a friendly graphical user

interface to develop C/C++ code that can run on the dual-core ARM processor of the Zynq.

Figure 5.3 depicts the workflow used to develop this project. There are two paths clearly

defined; the first one targets the PL section, while the second path targets the PS part of the Zynq

device. Following the PL targeting procedure, the Simulink models are compiled and converted to

HDL code, namely Verilog or VHDL code. The IP cores are created in Vivado Design Suite using

the previously generated HDL code, and the final step encompasses the creation of a complete block

33


Software Tool Version

Matlab Simulink R2017bHDL Coder Compatible with R2013b to R2019a

Vivado Design Suite 2017.4Xilinx SDK 2017.4

Table 5.1: Software tools and corresponding versions.

design in Vivado Design Suite which further generates the bitstream used to target the PL.

Furthermore, the PS targeting process is carried out by using Xilinx SDK where the

application is developed using C language. The software tool versions are described in Table 5.1.

IP Core

HDL Code

MathWorks Simulink™ Model

PS

HDL Coder™

FPGA Bitstream

Xilinx Vivado®

PL

Zynq® SoC

ARM Executable

Xilinx SDK®

Application

Host PC running SW tools

Figure 5.3: High-Level SW Tool Workflow for Zynq PL and PS.

5.3 HDL Design

Let us consider an OFDM baseband signal defined by equation (4.4) and bandwidth B,

which is sampled at frequency fs > 2(f0 + B), as illustrated in Figure 5.4; f0 is the first carrier

frequency of the passband signal and fs = 1/Ts.

34


fB f0 f0 +Bfc. . .

Nyquist

Lim

it

fs

Figure 5.4: OFDM digital implementation.

The resulting expressions (5.1) and (5.2) are obtained by replacing ∆f = 1/T and

T = NsTs in equation (4.4).

u(nTs) =K−1∑

k=0

dkej2πk∆fnTs (5.1)

u(n) =

K−1∑

k=0

dkej2πknNs , n = 0, . . . , Ns (5.2)

For convenience, fs = LB > 2(f0 +B) so that L is a factor multiple of two with Ns =

LK; equation (5.3) is interpreted as the IDFT of length Ns by zero-padding the sequence {dk}K−10

as stated in equation (5.4). The IDFT and DFT operations are performed by the corresponding

efficient algorithms IFFT and FFT respectively.

u(n) =

Ns−1∑

k=0

dkej2πknNs , n = 0, . . . , Ns (5.3)

u(n) = IFFTNs

{[d0 . . . dK−1︸︷︷︸

K elements

0 0 . . . 0︸︷︷︸(L-1)K elements

]T}

(5.4)

The OFDM passband sampled signal is defined by,

s(n) = <{u(n)ej2πf0nTs} (5.5)

which is converted to the analog domain through the DAC and further transmitted through the

channel. On the receiver side, the passband signal is sampled by the ADC at frequency fs such that

the Nyquist sampling theorem is satisfied. Let v(n) = v(nTs), n = 0, . . . , Ns − 1 be samples of the

35


received signal after performing several operations over the signal (described in 4.5) in the digital

domain. The demodulation process is implemented through the FFT (equation (??)).

yk =1

NsFFTNs

{[v0 . . . vK−1︸︷︷︸

K elements

vK . . . vNs−1︸︷︷︸(L-1)K elements

]T}

(5.6)

the resulting vector yk from (5.6) contains Ns elements, so the differentially coherent detection is

performed only over the first K samples.

The Simulink models for the transmitter and receiver are built using equations (5.4),

(5.5), and (5.6). An illustration of the models is described in Figure 5.5, the transmitter includes

the following sub-systems: Symbol mapping, differential encoder, IFFT, cyclic prefix insertion,

preamble insertion, and baseband to passband. The receiver encompasses the following blocks:

Passband to baseband, frame synchronization, cyclic prefix removal, FFT, differential decoding, and

detection. These sub-systems are explained in the next section.

5.3.1 Simulink Models

The OFDM models are built in Simulink which provides tools and libraries that ease the

simulation and prototyping of the UWA platform. We develop two independent models, transmitter

and receiver, each one is split into several sub-systems. The transmitter model is observed in

Figure 5.5(a) where each small system performs some defined operations that are explained below:

• Symbol Mapping

This block performs a mapping operation between a sequence of information bits c and a

sequence of symbols bk, according to a predefined linear modulation scheme, i.e. Quadrature

Phase Shift Keying (QPSK).

• Differential Encoder

The encoder works at symbol level, so that differentially encoded data symbols dk are obtained

from the original symbols bk, i.e. dk = dk−1bk, with d0 = 1.

• IFFT

This sub-system implements the operation described by the equation (5.3). A sequence of K

differentially encoded symbols {dk}K−10 is zero-padded by a sequence of (L− 1)K elements

(equation (5.4)) and then the IFFT operation is applied. The result is referred to as OFDM

block or symbol.

36


AXI4-Stream

Interface

SymbolMapping

DifferentialEncoding

OFDMMapper IFFT

CyclicPrefix andPreambleInsertion

Basebandto Pass-

band

OFDM TX IP Core

To DACFrom PS

(a)

Passbandto Base-

band

FrameSynchro

OFDMDemapper FFT

DifferentialDecoding Detection

AXI4-Stream

Interface

OFDM RX IP Core

To PSFrom ADC

(b)

Figure 5.5: OFDM transmitter and receiver models.

• Cyclic Prefix Insertion

The first Ng samples of each OFDM block are inserted between consecutive OFDM blocks.

The number Ng is chosen according to Tg = TsNg where Tg is the guard interval or cyclic

prefix duration (described in Chapter 4).

• Preamble Insertion

A pseudo-noise m-sequence, where m = 5, is used to perform synchronization on the receiver

side. In addition, a pause time interval Tpse is inserted between the preamble-first OFDM

block, and the last OFDM block-postamble (preamble of the next frame).

• Baseband to Passband

The stream of OFDM blocks are up-shifted by the first carrier frequency f0 as defined by

equation (5.5) and the preamble-postamble sequences are placed at fc.

The sub-systems of the receiver model (Figure 5.5(b)) are defined:

• Passband to Baseband

The samples of the received signal from the ADC are downshifted and filtered to obtain the

complex baseband received signal.

37


• Frame Synchronization

The complex baseband signal is correlated with the transmitted preamble sequence. The

OFDM blocks are synchronized by finding the maximum of the correlation function.

• Cyclic Prefix Removal

The last Ng samples of each OFDM block, which correspond to the cyclic prefix, are removed.

• FFT

This system describes the OFDM demodulation stated in equation (5.6). The length of the

FFT and the output sequence yk is Ns, so the first K elements of yk are kept and the others

are discarded.

• Differential Decoding

The samples {yk}K−1k=0 are differentially decoded by using equation (??) to obtain the decoded

symbols bk.

• Detection

The differentially decoded symbols bk are converted to a sequence of information bits c.

In addition to the OFDM sub-systems, there are two blocks designed to interface the

PS with the PL and vice versa, and use the AXI4-Stream protocol. On the transmitter model, a

user-defined bit sequence c is transferred from the PS to the PL. Similarly, on the receiver side the

detected bit-sequence c is transferred from the PL to the PS.

Simulink Model

Custom Parameters- Number of Carriers- Number of Blocks

- Sampling Frequency...

ofdmSystemParameters.mat

MATLAB function

Figure 5.6: System Parameters. A Matlab function, that includes the underwater system parameters,such as number of carriers, number of blocks, sampling frequency, etc, generates a .mat file called”ofdm SystemParameters” and this file is further called inside the Simulink models.

The model presented in Figure 5.5 has the ability to work with different system parameters,

such as bandwidth, number of carriers, number of OFDM blocks, and sampling frequency. The

system parameters are defined by a Matlab function that generates a structured file to be called inside

38


the Simulink model (see Figure 5.6). Even though changing parameters is possible, we have to

consider that they will further define the resources used in the PL, which are limited.

AXI DMAOFDM TX

IP Core

ZYNQ Processing System

DR

AM

M

emor

y

DR

AM

C

ontr

olle

r

ARM

ARM

ZYNQ Programmable Logic

control

AXI4-Stream

Microzed Board

ToDAC

AXI DMAOFDM RX

IP Core

DR

AM

M

emor

y

ZYNQ Programmable Logic

control

AXI4-Stream

Microzed Board

ZYNQ Processing System

DR

AM

C

ontr

olle

r

ARM

ARM

FromADC

Figure 5.7: OFDM Transmitter and Receiver Platform.

5.3.2 Block Diagram Design

The block diagrams inside the Zynq SoC are built such that the PS and PL can interact

with each other. The so-called OFDM IP cores generated from the Simulink models through the

39


HDL Coder tool, are targeted to the PL and designed to communicate with the platform end-user.

Thus, the end-user send or receive information from the OFDM IP cores through an user application

running on the PS.

The process of communicating PS and PL is carried out by the AXI DMA core and is

defined in the Xilinx Vivado Design Suite (see Figure 5.7). AXI DMA provides high-bandwidth

direct memory access between memory and AXI4-Stream target peripherals. For instance, user data

is placed on the on-chip memory DRAM, then the AXI DMA bridges such data from memory to the

AXI4-Stream channel that connects the OFDM IP core, all sequentially controlled by the PS. On the

other side, the AXI DMA receives data from the AXI4-Stream channel and places on DRAM that

can be further accessed by an application running on the PS.

40

Chapter 6

Results

This chapter describes the architecture of the UWA prototype which includes hardware

components such as DAC and ADC boards, power amplifiers, and acoustic transducers. We briefly

point out the most relevant technical characteristics of each hardware element, and a complete block

diagram of the prototype is presented.

In addition, we report experimental results from a system deployed in both indoor en-

vironment (water tank) and outdoor environment (sea water pool). The performance of the UWA

prototype is evaluated in terms of data detection mean squared error (MSE) and bit error rate (BER)

from a system designed to use the 120− 130kHz band.

6.1 UWA Modem

The UWA platform prototype is built around the Microzed board that incorporates the

Zynq SoC device. The prototype also contains other components, namely analog-to-digital and

digital-to-analog converters (ADC and DAC respectively), power amplifiers, and acoustic transducers.

Table 6.1 summarizes the hardware components with their corresponding models.

Hardware Device Model

DAC board DC2459A Evaluation BoardADC board DC528A Demonstration Circuit

Power Amplifier ZHL-6A+Pre-amplifier Teledyne VP2000Hydrophone Teledyne Reson TC4013

Table 6.1: Hardware components of UWA prototype.

41

CHAPTER 6. RESULTS

6.1.1 Hardware components

• DC2459A Evaluation Board

This demonstration circuit features the LTC1668 of 16-bit input and sampling frequency of

50Msps. The Microzed board generates a digital signal of 16-bit resolution that drives the

input of the DAC evaluation circuit. The DAC analog output is amplified by the ZHL-6A+

device.

• DC528A Demonstration circuit

The DC528A evaluation board based on the LTC1740 which has 14-bit resolution and sampling

frequency up to 6Msps. The ADC board converts the analog signal received from the transducer

to a 14-bit digital pattern that is fed to the Microzed board.

• Mini-Circuits ZHL-6A+ Power Amplifier

The Mini-Circuits ZHL-6A+ is an amplification device that offers a gain up to 10dB. The

operational band ranges from 2.5kHz to 50MHz.

• Teledyne VP2000 Pre-amplifier

The Teledyne VP2000 serves both in amplifying and filtering the received signal. It also

provides variable gain selection in six levels from 0 to 50dB. The filter offers a range of 12

high-pass and 12 low-pass filters with variable cutoff frequencies, so that it allows ideal band

pass filtering.

• Hydrophone TC4013

The Teledyne Reson TC4013 is used for both transmission and reception as it offers a relative

wideband usable frequency range of 1Hz to 170kHz (see Figure 6.1). The TC4013 also

provides uniform omnidirectional sensitivities in both horizontal and vertical planes up to high

frequencies.

6.1.2 Prototype

The development of the UWA prototype consists in two parts: 1) the FPGA modeling

explained in Chapter 5 and 2) the hardware components. The workflow used to design the FPGA

model (illustrated in Figure 5.3) produces the bitstream file that is targeted to the Zynq 7000 SoC

enclosed in the Microzed board. The FPGA DSP capabilities are integrated with real-world analog

42

CHAPTER 6. RESULTS

Figure 6.1: Hydrophone TC4013 frequency response [53].

signals through the use of hardware components, namely ADC and DAC devices. Figure 6.2 depicts

hardware block diagram, a power amplifier is used in both the transmitter and receiver sides.

• Transmitter

The output of the Microzed board is a bus of 16-bit digital outputs that are connected to the

digital input ports of the DAC DC2459A Evaluation Board. The analog output signal of the

DAC is amplified by the Mini-Circuits ZHL-6A+ power amplifier, and is further connected to

the Teledyne Reson TC4013 transducer.

• Receiver

The signal received by the Teledyne Reson TC4013 is pre-amplified through the Teledyne

VP2000. The pre-amplified signal is sampled by the ADC DC528A board, where each sample

is represented by 14-bit length words, and thus, a digital bus of 14-bits is connected between

the ADC and the Microzed board.

43

CHAPTER 6. RESULTS

Microzed Board – Xilinx-7000 SoC

DACDC2459A-A ZHL-6A+

TransducerTC-4013

FPGABoard

16 Digital Outputs

AnalogSignal

K

Power Amplifier

DACTransducer

Microzed Board – Xilinx-7000 SoC

ADCDC528A

TeledyneVP2000

TransducerTC-4013

FPGABoard

14 Digital Outputs

AnalogSignal

K

Pre Amplifier

ADCTransducer

Receiver

Transmitter

Figure 6.2: Hardware block diagrams.

44

CHAPTER 6. RESULTS

6.2 Performance Evaluation

To asses the system performance, we performed several experimental testings in tank and

pool, whose dimensions are showed in Figure 6.3. The experimental OFDM signal was transmitted

in the band of 120kHz to 130kHz where the transducer transmitting sensitivity response is relatively

flat as depicted in Figure 6.1.

TX RXd

295 cm

95 cm

150 cm

100cm

600cm

TX RXd

Figure 6.3: Tank and pool dimensions.

6.2.1 System parameters

The signal bandwidth is B = 10kHz placed at the central frequency fc = 125kHz. The

CP-OFDM system has a guard interval of Tg = 25ms and total number of carriers K = 1024. The

carrier spacing ∆f = 9.7656Hz and the OFDM block duration is T = 102.4ms. Each OFDM frame

contains Nb = 8 OFDM blocks and a total of Nd = 8192 data symbols. Table 6.2 summarizes the

system parameters.

6.2.2 Experimental Result

The experiment consists of two parts: 1) measurement of the channel impulse response

based on the preamble signal, and 2) measurement of the system performance.

45

CHAPTER 6. RESULTS

first carrier frequency f0 [KHz] 120

bandwidth B [KHz] 10

guard interval Tg [ms] 25

pause interval Tpse [ms] 25

preamble carrier frequency fc [KHz] f0 +B/2

preamble generating polynomial x5 + x4 + 1

number of carriers K 1024

number of OFDM blocks Nb 8

Table 6.2: Experiment Signal Parameters.

-0.5 0 0.5 1 1.5

0

0.2

0.4

0.6

-0.5 0 0.5 1 1.5

0

0.2

0.4

0.6

(a)

-0.5 0 0.5 1 1.5

0

0.2

0.4

0.6

-0.5 0 0.5 1 1.5

0

0.2

0.4

0.6

(b)

Figure 6.4: Channel profile based on preamble correlation measured in (a) tank and (b) pool.

6.2.2.1 Channel impulse response

The approximate channel impulse response is obtained by correlating the preamble signal,

which is a pseudo-noise sequence with m = 5. The normalized correlation of the received preambles

with the transmitted one is illustrated in Figure 6.4. The first-row plot shows the magnitude square of

the correlation of the preambles overlapped as a function of the delay time. While the second-row

plot depicts the average correlation. In both figures, the direct path signal occurs at τ = 0ms where

the correlation function peaks. The plots also show some signal replicas that occur in different path

delays. The total multipath spread Tmp of the channel is governed by the longest path delay. The tank

multipath spread (Figure 6.4(a)) reports an approximate of Tmp ≈ 1.1ms, while the pool multipath

spread (Figure 6.4(b)) accounts for Tmp ≈ 0.75ms.

46

CHAPTER 6. RESULTS

A

B D

C

(0,0)

295 cm

95 cmE

E(200,50)

A(120,80)

B(100,50)

C(150,50)

D(120,20)

TxRx

(a)

TxRx

A

B

C

D

cm380≈

cm380≈

cm550≈

(b)

Figure 6.5: Geometry of the experiment in (a) tank and (b) pool.

6.2.2.2 System performance

The experiment involves multiple transmissions, each containing the OFDM signal whose

parameters are listed in Table 6.2. There is a total of 50 transmissions taken from each four fixed

positions in the tank and three locations in the pool. Figure 6.5 illustrates the geometry of the

experiment in tank and pool setups. In the tank, the transmitter is placed on point E, and the receiver

is set on points A, B, C, and D. In the pool, the transmitter is specified by point D, while the receiver

is located on A, B, and C.

The performance of the system is presented in terms of data detection mean squared error

(MSE) and bit error rate (BER). We report on the estimated cumulative density function (CDF) of

the MSE measured in each signal frame. Furthermore, we show both the bit error rate (BER) and

block error rate (BLER) of the system without channel coding.

The MSE corresponding to an OFDM signal with K carriers is measured in the n-th block

of the i-th frame as

MSEi(n,K) =1

K − 1

K−1∑

k=1

|bik(n)− bik(n)|2 (6.1)

and the MSE per frame is obtained as

47

CHAPTER 6. RESULTS

MSEi(K) =1

Nb

Nb∑

n=1

MSEi(n,K) (6.2)

The estimated cumulative distribution function (CDF) of the MSE per block, measured

in the tank and the pool, is illustrated in Figure 6.6(a) and Figure 6.6(c), respectively. Each figure

reports measurements from four points in tank and three locations in pool (Figure 6.5). Moreover,

the average MSE per frame received is showed in Figure 6.6(b) and Figure 6.6(d) for tank and pool

setups, accordingly.

The MSE measured in 90% of the total OFDM blocks and frames in the tank is summarized

in Table 6.3. The best performance is observed on position D, whose MSE per block is less than

−20.66dB and MSE per frame is below −21.14dB.

Position MSE [dB] per block MSE [dB] per frame

A < −16.86 < −17.67

B < −15.01 < −15.86

C < −16.46 < −17.2

D < −20.66 < −21.14

Table 6.3: MSE performance for 90% of the OFDM blocks and frames recorded in tank.

Similarly, the experiment in pool for 90% of the total OFDM blocks and frames is outlined

in Table 6.4. The best MSE result is reported on position C, whose MSE per block is less than

−9.27dB and MSE per frame is less than −10.09dB.

Position MSE [dB] per block MSE [dB] per frame

A < −9.09 < −9.92

B < −7.85 < −8.35

C < −9.27 < −10.09

Table 6.4: MSE performance for 90% of the OFDM blocks and frames recorded in pool.

In Figure 6.7, we demonstrate the performance of the system in terms of bit error rate BER

versus frame index and average block error rate (BLER) versus block index.

The results from tank experiments (Figure 6.7(a) and (b)) describe an average BLER

uniformly distributed on each OFDM block. The best average BLER is observed on position D,

which is in the order of 3 × 10−4. While the worst average BLER results from position B, which

48

CHAPTER 6. RESULTS

accounts for 1.7× 10−3. The BER measurements on each frame transmission present similar error

rates on average.

Equivalently, the experiments in pool (Figure 6.7(c) and (d)) present identical average

BLER values across the OFDM block index. The best average BLER performance reaches 2.6×10−3

on position C. The worst average BLER is seen on position B which accounts for 3.6× 10−3. The

BER measurements on each frame transmission present similar error rates on average.

-25 -20 -15 -10

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(a) (b)

-15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5

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(c) (d)

Figure 6.6: The estimated CDFs of the MSE per block and MSE per frame measured in tank (top)and pool (bottom) setups. The experiments, both in tank and pool, consist of 50 frame transmissionsand each frame is composed of 8 OFDM blocks. The CDFs in (a) and (c) reflect the recorded MSEon each OFDM block, while the CDFs in (b) and (d) reflect the average MSE per frame on eachreceiving point during the experiment.

49

CHAPTER 6. RESULTS

1 2 3 4 5 6 7 810

-4

10-3

10-2

0 5 10 15 20 25 30 35 40 45 5010

-4

10-3

10-2

(a) (b)

1 2 3 4 5 6 7 810

-3

10-2

0 5 10 15 20 25 30 35 40 45 5010

-3

10-2

(c) (d)

Figure 6.7: Average block error rate (BLER) and bit error rate (BER) measured in tank (top) andpool (bottom) setups. The results in (a) and (c) reflect the average BLER for 50 frame transmissionsrecorded on each position, in tank (A, B, C, and D) and in pool (A, B, and C). The plots (b) and (d)indicate the BER calculated on each OFDM frame transmission.

50

Chapter 7

Conclusion

In this research work, we designed and implemented an UWA platform based on CP-

OFDM which incorporates differentially coherent detection in frequency domain. Particularly, this

detection technique eliminates the need for channel estimation, and thus, increases the system

robustness on rapidly varying UWA channels. Differentially coherent detection has reported excep-

tional performance results in terms of MSE of the data detected and its appealing obeys to the low

computational resources used.

Furthermore, we developed hardware and software for the UWA platform. By exploiting

the Simulink capabilities, the CP-OFDM system is modeled and simulated, which also allows us

to conveniently change the system parameters, such as bandwidth, number of carriers, number of

OFDM blocks per frame, and the sampling frequency. The HDL code is then generated from the

Simulink model and further the OFDM IP cores are created for the transmitter and the receiver.

The OFDM IP cores are interconnected with other IPs, namely AXI DMA and ZYNQ Processing

System, in a block design using the Vivado Design Suite. This software tool synthesizes, implements,

and generates the bitstream file that is targeted on the Microzed. The software part is used to

interface the end user with the UWA platform and it is generated using the Xilinx SDK using C

programming language. The Microzed board is the main component of the final UWA prototype,

which encompasses several hardware components, namely ADC and DAC boards to perform the

conversion between the digital and analog domain or vice versa, power amplifiers, and acoustic

transducers.

Finally, we evaluated the performance of the UWA prototypes in both indoor (water tank)

and outdoor (sea water pool) environments. The experiment was split in two parts. First, we

conducted several transmissions of the preamble signal, which allowed us to obtain the channel

51

CHAPTER 7. CONCLUSION

impulse response and measure the multipath delay spread of the channel. The second part consisted

in assessing the system performance. The results are obtained from about 50 real time transmissions

during the experiment. The experiments in tank reported an average MSE below −20.66dB for

90% of OFDM blocks and achieves BLER as low as 3 × 10−4 without channel coding. Also, the

experiments in pool showed an average MSE below−9.27dB for 90% of OFDM blocks and achieves

BLER as low as 2.6× 10−3 without channel coding.

Future work

The current FPGA-based UWA prototypes can establish a fixed point-to-point real-time

communication scenario. However, we plan to implement the Doppler compensation algorithm,

described in Section 4.3.1, as a future work. This high-resolution Doppler compensation method will

allow us to deploy mobile underwater acoustic communication and further multichannel implementa-

tion.

52

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