1

Welcome toEQ2430/EQ2440

RF lecture

Per Zetterberg

School of Electrical Engineering

2

Objective of this lecture

• Give an overview of radio communications.• Review

3

What is RF ?

• RF = Radio Frequency. • For us: 2-6GHz.

4

What is the ”channel” ?

txTX RX

Propagation channel

TX = Transmitter chain

RX =receiver chain

D/A A/D

Communication channel

ty

5

Transmitter chain (TX)

D/A

tfj TX2exp

LPF BPF HPA

LPF = Low Pass FilterBPF = Band Pass FilterHPA = High Power Amplifier

=Mixer

tfj TX2exp =Local oscillator

6

Receiver chain (RX)

LPF = Low Pass FilterBPF = Band Pass FilterLNA =Low Noise Amplifier

=Mixer

tfj RX2exp =Local oscillator

A/D

tfj RX2exp

LPFBPF LNA

7

Basic Channel Model

0PRFILT2exp ttxththftjty

Unknown offset between clocks at TX and TX

Propagation channel

Combined effect ot low-pass and band-pass filters in TX and RX.

Frequency offset between TX and RX.

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Handling basic channel model

knnknnsn IhnfTjy

002exp

Discrete time:

TRAIN TRAIN Data

Known:nI Unkown:nI

1. Sliding correlation.2. Sliding correlation, several frequency offsets, FFT.3. Several short correlations.4. Self-correlation.

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Inter-symbol interference

12.0 nnn IIy

-1.5 -1 -0.5 0 0.5 1 1.5-1.5

-1

-0.5

0

0.5

1

1.5

-4 -3 -2 -1 0 1 2 3 4-4

-3

-2

-1

0

1

2

3

4

QPSK: No problem. 16QAM: Blur.

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Inter-symbol interference sources

1. Radio propagation.2. Narrow and sharp low-pass and band-pass filters !!!!!!

(narrow=narrow compared with the bandwidth of the desired signal)3. Pulse-shaping, sampling offsets.

So why do we use these narrow filters ?

1. Limit spectrum of transmitted signal.2. Improve adjacent channel

performance.3. Reduce requirements on A/D

converters.

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Ways to combat inter-symbol interference• Interpolation between samples.• Equalizers (linear, decision feedback, viterbi, ...)• OFDM

12

Next problem

Power amplifier non-linearity

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Power-Amplifier Non-linearity

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Input/output power

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AM/AM and AM/PM model

txjtxjStxSty expexp PA

AM/AM AM/PM

AM: Amplitude Modulation PM: Phase Modulation

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Intuition AM/AM and AM/PM model• Let’s say our communication signal has 1MHz

bandwidth.• The carrier frequency is 1GHz=1000MHz.• Then every symbol lasts 1000 cycles.• During one symbol the input signal can be seen as a

CW.• A CW which is sent through a non-linearity will always

appear at the output (together with harmonics), but with a different amplitude and phase.

• The AM/AM and AM/PM models are functions of this phase offset.

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Solid State Power Amplifier Model: SSPA

pp

A

F2

12

0

A

1

:Output saturation level (unit dependent e.g. volt, dBm, LSB)

:Smoothness parameter.0Ap

LSB: Least significant bit.

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Matlab function: SSPA.m

• Available on course homepage.• Applies non-linearity to the input signal.• The parameter A0 is hardcoded inside the function.• The patameter A0 is referenced i units of LSB (least-

significant bit) of the signal sent from the D/A converter.• The smoothness parameter p is an input to the function.• Three present values of p are proposed 1,10,100 (bad,

fair, good)

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Amplifier non-linearity effects

BS1

BS2

MS1

MS2

Link 1

Link 2

cf

MHz5cf

In-band disrtorion: Detoriation of own link.Out-of-band distortion: Detoration of the others link.

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In-band/out-of-bandIn-band distortion

Out-of-band distortion

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Example of in-band distortion influence

-4 -3 -2 -1 0 1 2 3 4-4

-3

-2

-1

0

1

2

3

4

-4 -3 -2 -1 0 1 2 3 4-4

-3

-2

-1

0

1

2

3

4

With distortionWithout distortion

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Next problem

Phase-noise

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Phase-noise: Imperfect LO

A/D

ttfjt RX2expLO

LPFBPF LNA

This phase offset is a stocastic process = phase noise.

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Phase-Noise Spectrum

tjt expF)(LOF FourierFourier

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Matlab-file: add_phase_noise.m•Link on course homepage

•Generates phase-noise from given phase-noise spectrum, and multiplies it to the desired signal.

•The phase-noise spectrum is specified by input parameters phase_noise_freq and phase_noise_power.

•Three different ”pre-set” values given on course homepage (bad, fair, good) given in phase_noise_param.m.

*

*) The function is written by Alex Bar-Guy and is available on matlab central.

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Example: Influence of phase-noise

-4 -3 -2 -1 0 1 2 3 4-4

-3

-2

-1

0

1

2

3

4

-4 -3 -2 -1 0 1 2 3 4-4

-3

-2

-1

0

1

2

3

4

Without phase-noise With phase-noise

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How should you simulate ?

• Start with basic channel modelYou should be able to do this yourself.

• Introduce AM/AM and AM/PM using SSPA.m.• Introduce phase-noise using add_phase_noise.m.

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SNR and SINAD

SNR=Signal power

Thermal noise power

SINAD= Signal power

Distortion + Thermal noise

Dominates at close distance.Often proportional to transmitted power

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SINAD and SNR versus range

0 100 200 300 400 500 600 700 800 900 10000

10

20

30

40

50

60

70

80

90

Distance TX<->RX

dB

SNR

SINAD

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Estimating SNR and SINAD

1960 1980 2000 2020 2040 2060 2080 2100 2120 2140 2160

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Signal + noise

Signal + noise + distortion

Part1: Before transmission: Thermal noise only. Part2: Signal present

X= S + N + E

Estimate thermal noise power from part 1.

Estimate signal power and distortion power from part 2 e.g. Using training sequence.

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Theory versus Reality

What theory ?

Generally: Basic channel model.

Present results versus SNR not SINAD

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Voice Band Transmission

FM modulator

AM modulator

FM de-modulator

AM de-modulatorIn

OutIn

Out

Power of output may be unrelated power of input.Difficult to use previous slides in this scenario.

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Wrap-up

• Propagation channel versus communication channel distinction.

• Basic channel model.• Power amplifier distortion (AM/AM and AM/PM).• Phase-noise (in up-/down-converters)• Matlab functions• SINAD versus SNR• Voice-band transmission