Channel Equalization in Underwater Acoustic Communications using Multiple Antennas
A Thesis
Presented in Partial Fulfillment of the Requirements for the
Degree of Master of Engineering Science
with a
Major in Electrical Engineering
in the
College of Graduate Studies
University of Idaho
by
Leili Baghaei Rad
August 2006
Major Professor: Professor Richard Wells.
AUTHORIZATION TO SUBMIT
THESIS
This thesis of Leili Baghaei Rad, submitted for the degree of Master of Science with a major in Electrical Engineering and titled " Channel Equalization in Underwater Acoustic Communications using Multiple Antennas," has been reviewed in final form. Permission, as indicated by the signatures and dates given below, is now granted to submit final copies to the College of Graduate Studies for approval.
Major Professor ______Date______
Richard B. Wells
Committee Members
______Date______
Egolf, David
______Date______
Anderson, Michael
Department Administrator
______Date______
Brian Johnson
Discipline's College Dean
______Date______
Aicha Elshabini
Final Approval and Acceptance by the College of Graduate Studies
______Date______
Margrit von Braun
ACKNOWLEDGMENTS
Firstly, I would like to thank my supervisor Professor Richard B. Wells for proof reading this theses. Also for his guidance, patience and constant support throughout the time I have worked with him. I am very grateful for every thing I have learned from him both inside and outside the classroom.
Next, I would like to thank Professor David Egolf for his support and help throughout my degree at the University of Idaho and also for all of his encouragement in my studies. Thank you to Professor Michael Anderson for helpful discussions on problems related to the work in this thesis and also his support. Thank you to Isaac Spurgeon Kodavaty from the Center for Intelligent Systems Research at the University of Idaho for providing the data used to model the underwater channel.
I would like to thank my mother-in-law, Gaye Downes, for proof reading my thesis at very short notice. I am also grateful to Mr. and Mrs. Lynch for enabling me to come to the USA through the Rebecca Lynch Memorial Scholarship. Last but not least, I would like to greatly thank my family, especially my parents for their unwavering support and enthusiasm throughout my life.
This work is dedicated to my husband and best friend, Ian Downes. Without his love and support none of this work would have been possible.
Contents
1.Introduction
1.1 Motivation and problem description
1.2 Literature Review
1.3Thesis outline
1.4 Contributions
2. Underwater Acoustic Communication and Channel Model
2.1 Channel Model
2.2 The Baseband channel
2.3 FSK Signal modulation and demodulations:
3. Receiver and Transmitter Diversity Techniques:
3.1 Antenna Diversity
3.2 Temporal Diversity
3.3 Branch Combining
3.4 Diversity gain and failure
3.5 Conventional multiple antenna systems
3.6 Parallel stream MIMO
4. Review of Adaptive Equalizers
4.1 Linear equalization
4.1.1 Peak distortion criterion
4.1.2 Mean square error criterion
4.1.3 Fractionally spaced equalizer
4.2 Decision feedback equalization
4.2.1 Mean square error optimization criterion
4.3 Filter coefficient update methods
4.3.1 LMS
4.3.2 RLS
4.4 Selected experimental results
5 Diversity Equalization
5.1 Baseband communication system model
5.2 Joint channel equalizer (JCE)
5.3 Diversity with separate optimization using an independent channel equalizer (ICE)
6 Conclusions and Topics for Future Research
6.2Further research directions
References
ABSTRACT
Currently available commercial underwater acoustic modems have been optimized for long distance communications, on the order of tens to hundreds of kilometers, trading transmission rate for increased range, reliability and robustness. However, new under-water applications are arising which do not require such long distance links. One example is the use of small fleets of closely spaced Autonomous Underwater Vehicles (AUVs) for tasks such as mine detection where the vehicle spacing is more likely to be measured in hundreds of meters at most.
This thesis investigates ways to improve transmission rates over this much shorter link. In particular, it considers the use of an additional hydrophone, currently used for navigation, to achieve diversity at the receiver. We propose an adaptive receiver structure that is capable of reliable asynchronous communication with improved efficiency.
Existing underwater acoustic equalization studies are limited to optimizing the mini-mum Mean-Square Error (MSE) jointly among all spatial diversity channels, called the Joint Channel Equalizer (JCE). In this study we propose a new sub-optimal equalizer that separately optimizes the diversity channels. We have called this the Independent Channel Equalizer (ICE). It ultimately results in a higher MSE but the system is more robust to step changes. This is beneficial to allow rapid re-establishment of communications. Re-sults are presented both in terms of the MSE and the probability of Bit Error Rate (BER). The latter is important, as it is the ultimate measure for a digital communication system.
1
1.Introduction
1.1 Motivation and problem description
As technology progresses our ability to utilize and operate within the underwater environment is advancing. Once confined to merely operating on the surface we are now rap-idly exploring the depths of the oceans for military, commercial and scientific purposes.
The use of Autonomous Underwater Vehicles (AUVs) is showing great potential among many new areas of underwater research. Possible applications for this technology include dangerous and/or routine tasks such as minesweeping and reconnaissance, neither of which is suitable for human operators. However, there are many challenges that need to be addressed. These include the autonomous control of a fleet of AUVs and consequently the underwater communications required for this.
In [1,2] the problem of control algorithms for the formation flying of AUVs is considered. As part of the solution a pair of hydrophones is used to provide relative headings to aid in maintaining the formation. The presence of the two hydrophones also present possible opportunities to improve communications between AUVs, using techniques such as receiver diversity, transmitter diversity or Multiple-Input-Multiple-Output (MIMO) communications.
In this application the hydrophones are located at the bow and stern of the AUV. Due to the physical dimensions of the AUV the separation is approximately one meter. This is much closer than typical diversity receiver separations [3]. However, other researchers have shown that receiver separations as low as 0.35 meters can provide additional sources of information in the underwater acoustic channel [4].
The fading and multi-path underwater acoustic channel has always been a great impediment to building reliable underwater communication systems. Many complex physical phenomena cause the propagating acoustic wave intensity and phase to vary temporally and spatially. Thus, one advantage of spatial diversity equalization is its ability to improve the limited signal-to-noise ratio at the receiver through coherent combining. The multi-path spread in these channels is largely caused by reflections from ocean boundaries and refraction due to sound speed variations as a function of depth. Since the degradation in the signal is caused by multi-path intersymbol interference (ISI), simply increasing the signal-to-noise-ratio will not alleviate the problem.
It is well known that coherent equalization techniques improve the bandwidth efficiency of the communication system, thus increasing the data rate [6]. Equalization techniques are well understood in radio communications [5]-[7]. A variety of equalization techniques are available, such as zero-forcing equalization, MMSE equalization, and block equalization. Decision Feedback Equalization (DFE) can be considered an effective technique because it can help to eliminate causal ISI in addition to compensating for the channel [5].
When the communication channel is unknown to the designer, adaptive equalization techniques can be used to first extract the channel response from a training sequence and then compensate the channel distortion in the incoming data symbols [7]. In this thesis the problems above are considered and a sub-optimal decision feedback adaptive equalizer with spatial diversity has been proposed and the results are compared to the optimal equalizer.
1.2 Literature Review
A general overview of the current state of underwater communications is presented in [8]. The underwater channel and its characteristics are discussed and examples of a number of communication systems of various ranges are given. It also outlines some of the current research topics, namely, receiver complexity reduction, interference cancellation and multi-user communication, system self-optimization, modulation and coding and mobile underwater communication.
Among current research a common approach to improving communication rate and reliability is to use multiple diversity channels and a jointly optimized receiver structure. Researchers have shown that for the underwater acoustic channels receiver separations as low as 0.35 meters can provide valid diversity channels [4]. In [3] a joint channel equalizer is formulated where the equalizer is optimized across all diversity channels with the receivers spaced apart in depth by between 9.4 and 55.2 meters. The separation between transmitter and receiver is 8 nautical miles. This separation is significantly greater than any expected separation within the AUV fleet. The receiver was found to have excellent performance. However, it was computationally complex and thus a sub-optimal design was presented with much reduced complexity. This sub-optimal equalizer consisted of a set of single channel equalizers followed by a MMSE combiner. Satisfactory performance was found with a DFE equalizer composed of 25 feed-forward taps and 15 feedback taps.
In [9] and [10] the authors argue that carrier phase is the most rapidly changing parameter in the underwater channel. The variation in phase can be higher than the convergence rate of the equalizer leading to tap rotation and poor performance. The authors suggest one possible solution is to jointly perform synchronization and equalization. They present a receiver that jointly performs MMSE multi-channel combining, carrier phase recovery and fractionally spaced decision feedback equalization. Their results indicate that superior performance for coherent reception can be obtained through joint diversity combining and equalization. The resulted presented are limited to coherent PSK and QAM constellations.
A further problem is the possibility of the equalizer entering a so-called degenerative state, where the output does not depend of the input. In [11], this phenomenon is discussed in the context of radio communications. The authors present an overview of current receiver structures that attempt to avoid this problem. The paper continues further to present a new algorithm for blind decision feedback equalization, which is based on constrained optimization and does not admit degenerative solutions. The possibility of carrying these ideas over to underwater acoustic communication is explored as part of the research presented in this thesis.
1.3Thesis outline
This study will investigate several possible potential benefits of multiple hydrophones with the goal of improving the communication data rate, either by increasing the raw data rate or by improving efficiency.
This thesis is organized into four main chapters following the introductory chapter as below.
Chapter 2 first outlines a selection of relevant aspects of underwater acoustic communications, in particular the modeling of the underwater channel. It also provides an overview of wireless channel multipath fading characteristics.
Chapter 3 provides background information for transmit and receive diversity. It introduces the main techniques and receiver structures that are used to gain benefit from channels containing diversity. It analyzes the trade offs between the complexity and performance for each technique.
Chapter 4 provides an overview of adaptive signal processing. It introduces the concept of an adaptive channel equalizer and formulates several equalizer structures. Simple linear equalizers are discussed before the more suitable but nonlinear decision feedback equalizers are introduced.
Chapter 5 presents the main work of this thesis. It investigates possible receiver structures to utilize the second hydrophone including a fully jointly optimized equalizer and a sub-optimal equalizer. Performance of the two equalizers is evaluated both in terms of MSE and robustness to step changes. The improved robustness of the sub-optimal equalizer is demonstrated.
Finally the conclusion and future research directions are outlined in chapter 6.
1.4 Contributions
The contributions of this thesis to the underwater acoustics communications community can be summarized in two parts. The first part is a comprehensive review of the current state of underwater acoustic communications. The progression from simple receiver structure to more complex receiver structures employing receiver diversity and joint optimization is discussed. Further discussion investigates the possible trade off in robustness against lower mean square error performance for alternative receiver structures.
The second and major part of the contribution includes the development and analysis of a new diversity equalizer structure. The equalizer is sub-optimal in the mean square sense. However it is shown to be more robust to sudden changes in the impulse response of the channel. The receiver structure consists of two or more separately optimized feed-forward filters, which are equally combined and then followed by a feedback filter.
2. Underwater Acoustic Communication and Channel Model
In general, the shallow underwater acoustic channel is a challenging environment for communication. The channel experiences a large degree of multipath interference due to reflections from the water surface and the sea floor as well as other medium changes. The multi-path signals can be of comparatively large amplitude and spread over a significant time period (many symbols), leading to severe intersymbol interference. Signal fading is present due to several complex physical phenomena, which cause the propagating acoustic wave phase and intensity to vary temporally and spatially. These phenomena include variation in sound speed and channel geometry. In addition, noise is present from ocean surface waves, shipping noise, bubbles, and aquatic wildlife [3], [4], and [16].
2.1 Channel Model
The method-of-images was used for modeling the acoustic pressure in an infinite-range underwater channel with a pressure-release surface and rigid bottom wave-guide. It has perfect reflection of the sound waves from the surface and bottom of the channel [13]. Acoustic pressure caused by a harmonic point source located in the r-z plane is given by
(2-1)
where k is the wave number given by k=/c, c is the wave speed, and is the distance traveled by the corresponding image source. Index l is given as 0, 1, 2… , m = [1, 2, 3, 4] and i = .
Figure 2.1 shows how the reflected waves can be thought of as being transmitted from separate virtual sources located above and below the real source. The channel impulse response is formed by the summation of the received direct and reflected signals. The signal phase is such that two out of phase arrivals can add either destructively or constructively.
Figure 2-1: Method of images.
It was assumed that this method-of-images model suffices for development of acoustic communication systems at short range because it provided a conservative estimate of multipath effects. The geometric positioning of a pair of AUVs is shown in Figure 2.2.
s
Figure 22: View of the AUVs.
D is the depth of the ocean, Zs is the distance of the transmitter from the ocean floor, Zr is the distance of the receiver from the ocean floor and R is the distance between the AUVs. Throughout this research the water depth is 100 meters. The AUVs are nominally located at a depth of 30 meters and are separated by approximately 30-50 meters. Figure 2.3 shows the view of a pair of AUVs from behind. It illustrates a possible change in the channel due to a relative motion of one of the AUVs.
Figure 23: Diagram of the relative AUV positions.
A channel impulse response is initially obtained for the passband carrier frequency of 8 kHz. The research was performed using the equivalent baseband model to reduce computational requirement and hence the channel was converted to its baseband equivalent.
2.2 The Baseband channel
The analytic equivalent channel is given as
(2-2)
where is the Hilbert transform . The baseband equivalent channel at any carrier frequency cis given by
.(2-3)
For a valid baseband representation the carrier frequency should be sufficiently large to ensure that has no significant energy at frequencies greater than the carrier frequency. The baseband equivalent channel is found in Matlab by using the Hilbert function to obtain the analytic equivalent channel. This channel is then multiplied by the exponential over the time period [0;M] with samples spaced at 1/fs. M is the desired length of the impulse response. Figure 2.4 and Figure 2.5 show examples of the passband and baseband equivalent channels for D=100, R=50, Zr=50 and Zs=50 and 55 respectively.
1
1
1
Figure 2.4 and 2.5 illustrate the dramatic change in the channel that can occur even over small translations. In this case initially a pair of AUVs were both located at the mid-point between the ocean surface and the ocean floor. When one of the AUVs is moved away from this mid-point the multipath arrivals will not add at the same instant leading to a more complex channel.
Figure 2.6 shows the effect of the channel on a burst of data symbols. The intersymbol interference caused by the channel is evident. However, the equalizer effectively removes this ISI. Further details about the equalizer are discussed in chapter 4.