Underwater Communication Goes Cognitive

Wang Yonggang, Student Member IEEE, Tang Jiansheng, Pan Yue, Huangfu Li Systems Research Engineering Institute

Email: yg.wang@ieee.org

Abstract- Underwater acoustic channel is the most challenging

channel in the world due to its time varying and frequency-selective characters. These facts obstruct the realization of most of intelligent and high bit rate underwater communication systems. Cognitive underwater communication systems build up rules of modulation method and receiver algorithm over time through learning from continuous experiential interactions with the underwater environment. Underwater acoustic channel can be proved to be a comb filter where passband and stopband appear alternately. The transmitter can intelligently chooses sub-channels whose SNR exceed a certain threshold which can be called as the passband of the channel to carry information and otherwise the stopband of the channel to carry little or null. But it also has some technical bottlenecks like the need of feedback channel. In future applications, Transform-domain communication system (TDCS) and blind receiver will be adopted to improve system performance.

Keywords: cognitive radio; underwater communication; adaptive modulation; channel state estimation; OFDM; TDCS

I. INTRODUCTION

As the development of underwater sensor networks, there is a pressing requirement for high data rate and reliable digital communication to transmit information over an underwater acoustic channel in the littoral environment. The acoustic signals are subject to time-varying multipath [1], which may result in severe intersymbol interference (ISI) and large Doppler shift and spread.

Classic underwater acoustic communication systems apply powerful adaptive algorithms in receiver end to overcome time-varying fading[2][3], but it cannot conceive the surrounding underwater environment and predict the channel changes to update modulation strategy in transmitter end and algorithm in receiver end adaptively which cognitive systems blend themselves to.

Cognitive underwater communication systems can be defined as follows, inspired by Dr. S. Haykin[4], that

Cognitive underwater communication systems build up rules of modulation method and receiver algorithm over time through learning from continuous experiential interactions with the underwater environment, and thereby deal with channel uncertainties and possible adversary jamming by making corresponding changes in certain operating parameters (e.g., transmit-power, carrier-frequency, and modulation strategy) in real-time .

In sensor networks, unmanned or autonomous underwater vehicles (UUVs, AUVs), equipped with sensors, can gather environment data in collaborative monitoring missions and support cognitive systems to build their rules.

It can be seen in this definition that in cognitive underwater communication systems, not only the receiver is adaptive to environment, but also the transmitter.

Recent research result[5][6] in underwater communication reveals that multicarrier modulation method is an efficient way to combat multipath fading which is nature in littoral underwater channel. Another character make multicarrier modulation appealing to underwater cognitive system is its convenience to manage each subcarrier’s modulation alphabet and power which is significant in adaptive modulation.

Cognitive Radio which is still a conceptual technology owes its development to Dr. Joseph Mitola. It can pick up authorized user’s spectrum holes to transmit signal. If we extend this concept to underwater environment, authorized user’s spectrum can be seen as the stopband of the underwater acoustic channel while the authorized user’s spectrum holes is the passband of the channel. The underwater cognitive communication system’s goal is to find “the spectrum hole of the underwater channel” by sensing the environment and channel estimation.

The rest of this paper will organized as follows: In section 2, cognitive underwater communication structure is presented. Example of applications of cognitive communication is given in section 3. The bottleneck obstruct realization of cognitive system is discussed in section 4. In section 5, future development of such systems is predicted.

II. COGNITIVE UNDERWATER COMMUNICATION

In cognitive underwater communication systems, transmitter and receiver can preserve and predict environment changes and can both work adaptively according to channel changes. In this section, the basic structure of such system is described. A. Underwater Acoustic Channel

Multicarrier signal transmitted in underwater acoustic channel can be seen as some sinusoids travel in parallel sub-channels. Underwater acoustic channel is proved to be a frequency selective channel, which means each sub-channel has its own attenuation or has its own SNR. Fig1 presents that the frequency domain of channel estimation result of underwater acoustic channel is a comb filter which means that passband and stopband appear alternately.

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(a) (b)

Figure 1. channel estimation result and error location distribute Figure 1(a) is the channel estimation result from an OFDM

signal consist of 300 subcarriers in space of 10Hz and range from 12KHz to 15KHz in Underwater Channel Simulation Pool from Harbin Engineering University. The channel estimation method is based on the LS algorithm, given by Van de Beek. It can be seen in Figure 1(a) that subchannels around subcarrier No.100 suffers deep fading and its amplitude of frequency response is rather low. In figure 1(b), we can see that subcarrier No. 94 make 34.8 percent of the total error in this single carrier. So we can tell subcarriers around No.100 is the stopband of the channel and information in these subchannels will suffer deep fading, so adaptive modulation algorithm should avoid allocate much information in these subcarriers.

B. System Processing Cycle

In traditional multicarrier systems, transmitter and receiver do not have a interactive connection, so transmitter do not have the knowledge that which subchannel is the passband and which is stopband. It will allocate same amount of information and power to different subcarriers identically. A cognitive communication system, on the other hand, would be able to identify subbands of the spectrum that are currently in deep fading and assign them less information or null. So it is a paramount important strategy to detect passband and stopband of the channel in a cognitive way to start a reliable communication.

When the system switch on, the system becomes linked to its surrounding environment in the sense that the environment has a strong and continuous influence on the signal traveled in it. In so doing, the system builds up its knowledge of the environment from one communication to next.

Sensors built in transmitter, receiver and other nodes in the underwater communication network can acquire and conceive the surrounding environment like sound speed profile and etc. changing as well as interference from other user or from a possible adversary jammer. Unmanned Autonomous Vehicle (UAV) which equipped with sensors mounted or towed can explore the underwater environment, collect information and report it to certain communication nodes and help them to make up the modulation strategy. From one communication to next, the underwater nodes continuously learn about the environment and platform relevant move through experience gained from interactions with the environment. In a corresponding way, the cognitive underwater communication

system continually updates the transmitter and receiver with relevant information on the environment.

Channel state information is the key feather to find the passband and stopband which fatal to cognitive systems. There are 2 traditional methods to deal with channel state estimation problem. The first is differential detection and the second is pilot transmission. Differential detection uses a kind of decision direct channel estimation method. At least one known symbol must be transmitted. This enables the receiver to attain channel estimates for all subcarriers, which are then used to detect the data in the following symbol. Decision direct channel estimation method is easy to implement but at the cost of a significant degradation in frame error rate. Pilot transmission will insert some pilot which is known to the receiver periodically to estimate the channel state. This will improve receiver performance, but the use of pilot in data frame is a waste of data rate. In underwater environment, the channel state is time varying and transmitter and receiver have a relevant move is common, so pilots distribute in every frame is necessary.

At the beginning of the communication, the transmitter send several frame of data which are known to the receiver as training sequence to have a initial view of the channel state. Channel capacity C is calculated using Shannon’s information capacity theorem by estimate the SNR. Instead of sending C directly, it is practical to quantized C to several transmission rates [10]. Allocation of information and power transmit in each subchannel is done in consideration of the fading temperature in each subchannel and the channel capacity. This information allocation map is send back to the transmitter through the feedback channel, so the transmitter can have a modulation strategy according to the environment. Pilots are also inserted in following data frames so the receiver can continuously estimate the channel states. Channel state estimation and prediction models can be found in [10]. By adopting a state-space model of the underwater environment and updating the state vector representing an estimation of certain parameters pertaining to the underwater environment recursively, cognitive systems can predict the channel changes via the previous knowledge of the environment.

Training sequence is sent to receiver in several other frames to adjust channel state estimation result. So the communication system continuously learns about the environment through experience gained from training sequence and, in a corresponding way, continually updates the transmitter and receiver adaptively with relevant information on the environment. The transmitter adjusts its modulation strategy in an intelligent manner according to underwater environment, taking into account of location and relevant velocity of transmitter and receiver.

III. EXAMPLES OF UNDERWATER COGNITIVE SYSTEMS

A. Underwater acoustic network Underwater acoustic network is an appealing technology for

ocean sampling, environmental monitoring, assistant

navigation and underwater surveillance. To accomplish these goals, underwater acoustic communication is the major obstacle not only because the signal transmitted in underwater channel usually in deep fading, but also the interference between their own nodes. There are some multi-user access strategies for underwater acoustic networks like CDMA, FDMA and etc. It is straightforward for an OFDM system to use FDMA multi-user access strategy for its easy to implement in frequency domain. Like a common cognitive communication systems, cognitive underwater acoustic system can find the spectrum hole which is not occupied by other users, and make rules to adapt it while other users change their frequency band. In this scenario, cognition is some kind of dynamic frequency select strategy which tries to avoid possible interference between nodes in the same network.

B. Underwater anti-jammer communication Underwater acoustic channel is an open environment that

there is no physical barrier to prevent adversary jamming authorized communication. In many cases, the receiver cannot tell the authorized signal from jamming signal for their similar signature in many feathers.

Cognitive systems can perceive this interference, conceive the jamming frequency and make dynamic rules to transmit signal in jammer’s spectrum holes and change adaptively according to interference changes.

IV. OBSTACLE FOR REALIZATION OF COGNITIVE UNDERWATER COMMUNICATION SYSTEMS

Although cognitive underwater communication seems appealing to future underwater acoustic networks, it still has some technical obstacles to realize this goal.

A. Making rules through the surrounding environment Underwater channel is time-varying and the key feather of

underwater cognitive systems can perceive this change and modify modulation strategy adaptively according to some certain rules. Channel state information can be achieved in the receiver by pilots, but it is hard to build the channel state equation with environment information. Recent state of the art to track the channel is based on state-space-model comprised of two equations which are processing equation and measurement equation [11]. In cognitive communication networks, we use a large number of sensors to monitor the surrounding environment, so factors that can affect underwater acoustic communication such as sound profile, seabed character, sea state and many other type of information can be obtained in real time. To develop a new kind of channel state prediction model in consideration of the surrounding environment and predict the channel varying according to environment changes is still a territory which we are not familiar with.

B. Feedback channel Another obstacle of developing an underwater cognitive

communication system is how to build a stable feedback channel. The receiver and the transmitter are connected by

feedback channel and thereby make it possible for the transmitter to adapt itself to the environment in light of the information passed on to it by the receiver. In an underwater acoustic network, spectrum resource is limited. Feedback channel will take precious available spectrum resource to transmit environment information which may cause possible interference to other users.

The structure of the whole underwater cognitive communication system can be seen as a closed-loop feedback control wireless system. When the system is healthy, the feedback channel will transmit true environment information and modulation alphabet, so the system will work properly. In contrast with it, transmitting fake channel information or the feedback channel is in a deep fading situation will increase system crack down. So the stability of the feedback is fatal to a successful system. In order to keep the system robust, extra effort should be taken, like advanced error control coding.

V. FUTURE DEVELOPMENT OF UNDERWATER COGNITIVE COMMUNICATION

A. Transform-domain communication system Transform-domain communication system (TDCS) is

suitable for future underwater communication, because its robustness in frequency selective channels and low probability of intercept (LPI).

TDCS is a kind of underwater cognitive communication system for it can adaptively change its waveform to avoid frequency selective fading and possible jamming.

TDCS, like other underwater cognitive systems, can sense the surrounding environment and find the spectrum hole of the channel, and generate fundamental modulation waveforms in the time domain by IDFT using only the sub-channels in the passband. Unlike OFDM, subcarriers in TDCS do not carry any information, so a pseudorandom (PR) phase can be multiplied in each subcarrier in order to reduce time domain peak-to-average power ratio (PAR) which is major flaw of OFDM. The waveform of TDCS with PR phase is more noise-like, so the possibility of interception is reduced.

Information modulation in TDCS has considered in two ways. Traditionally, an antipodal signal can take 1 bit information where positive and negative signals represent 0 or 1 respectively. Another way is called cyclic shift keying (CSK) which takes advantage of noise-like TDCS’ noise like waveform properties which can be find in [12][13].

B. Blind receiver A blind receiver is necessary for cognitive communication

system because of its adaptive nature in both receiver and transmitter. A blind receiver can demodulate detected signal without predetermined knowledge of central frequency, subcarrier spacing and modulation alphabet.

By exploiting the redundancy of cyclic prefix in OFDM signal, receiver can estimate the subcarrier spacing of a certain frame of signal. Estimation of central frequency is

straightforward and if we know both the central frequency and subcarrier spacing we can find the subcarrier location.

Recently, many literatures have focused on adaptive modulation classification (AMC) technology and two general classes of AMC algorithms can be crystallized, which are likelihood-based (LB) and feature-based (FB) methods. LB based algorithms is optimal in the Bayesian sense, which minimizes the probability of false classification but suffers from computational complexity. FB based algorithms make decision through several feathers of the observed value. Although FB algorithm is not optimal, it is less computational and with proper design it can give near optimal performance.

In cognitive communication system, transmitter is also adaptive, and receiver does not have the complete knowledge of transmitter’s modulation method. So the ability that a blind receiver can demodulate signals without prior knowledge is an important power of cognitive systems.

VI. CONCLUSION

Underwater cognitive communication system is an appealing technology for the future. It builds up rules of modulation method and receiver algorithm over time through learning from continuous experiential interactions with the underwater environment. It can be used in underwater acoustic network, underwater anti-jamming communication, and many other underwater circumstances where need high reliable communication. But it still has some obstacles to realize and much more effort should be paid to build new channel prediction model and improve the system stability.

VII. ACKNOWLEDGEMENT

The authors would like to thank Prof. Sun Dajun at Harbin Engineering University for his great help on experiments.

REFERENCES [1] J. Catipovic, D. Brady, and 5. Etchemendy, "Development of

Underwater Acoustic Modems and Networks," Oceanography, vol. 6, Mar. 1993, pp. 112-19.

[2] M. Stojanovic, "Recent Advances In High-Speed Underwater Acoustic Communications," IEEE I. Oceanic Eng., vol. 21, Apr. 1996, pp. 125-36.

[3] D. B. Kilfoyle and A. B. Baggeroer, "The State of the Art In Underwater Acoustic Telemetry," IEEE I. Oceanic Eng., vol. 25, 2000, pp. 4-27.

[4] Simon Haykin, “Cognitive Dynamic Systems”, ICASSP 2007 IEEE, pp. 1369-1372

[5] Byung-Chul Kim and I-Tai Lu.” Parameter studies of OFDM underwater communications systems”. in Proc.MTS/IEEE Oceans 2000, vo1.2 pp. 1251-1255, Sept.2000.

[6] Baosheng Li, Shengli Zhou, Milica Stojanovic, Lee Freitag. “Pilot-tone based ZP-OFDM Demodulation for an Underwater Acoustic Channel”.,in Proc. MTS/IEEE Oceans 2006

[7] J. J. van de Beek, O. Edfors, M. Sandell, S. K. Wilson, and P. O. Borjesson. “On channel estimation in OFDM systems” in Proc. IEEE Vehicular Technology Conf., vol. 2, Chicago, IL, July 1995, pp. 815–819.

[8] S. Haykin and M. Moher, “Modern Wireless Communications”, New York: Prentice-Hall, 2004.

[9] Richard van Nee, Ramjee Prasad. “OFDM for Wireless Multimedia Communications”’, Artech House, Boston London, 1999.

[10] Simon Haykin, “Cognitive Radio: Brain-Empowered Wireless Communications”, IEEE Journal on Selected Areas in Communications, VOL. 23, NO. 2, Feb. 2005, pp. 201-220

[11] S. Haykin, K. Huber, and Z. Chen, “Bayesian sequential state estimation for MIMO wireless communications,” Proc. IEEE (Special Issue on Sequential State Estimation), vol. 92, no. 3, pp. 439–454, Mar. 2004.

[12] P. J. Swackhammer et al., “Performance Simulation of a Transform Domain Communication System for Multiple Access Application,” MILCOM ’99, Nov. 1999.

[13] M. J. Lee, “Wavelet Domain Communication System (WDCS): Packet-Based Wavelet Spectral Estimation and M-ARY Signaling,” Masters’ thesis, AFIT/GE/ENG/02M-14, Air Force Inst. Tech., Mar. 2001, DTIC: ADA401433, approved for public release.