Cooperative underwater acoustic communications

This article presents a contemporary overview of underwater acoustic communication (UWAC) and investigates physical layer aspects on cooperative transmission techniques for future UWAC systems. Taking advantage of the broadcast nature of wireless transmission, cooperative communication realizes spatial diversity advantages in a distributed manner. The current literature on cooperative communication focuses on terrestrial wireless systems at radio frequencies with sporadic results on cooperative UWAC. In this article, we summarize initial results on cooperative UWAC and investigate the performance of a multicarrier cooperative UWAC considering the inherent unique characteristics of the underwater channel. Our simulation results demonstrate the superiority of cooperative UWAC systems over their point-to-point counterparts. © 1979-2012 IEEE

Cooperative Communication over Underwater Acoustic Channels

As diverse and data-heavy underwater applications emerge, demanding requirements are further imposed on underwater wireless communication systems. Future underwater wireless communication networks might consist of both mobile and stationary nodes which exchange data such as control, telemetry, speech, and video signals among themselves as well as a central node located at a ship or onshore. The submerged nodes, which can, for example, take the form of an autonomous underwater vehicle/robot or diver, can be equipped with various sensors, sonars, video cameras, or other types of data acquisition instruments. Innovative physical layer solutions are therefore required to develop efficient, reliable, and high-speed transmission solutions tailored for challenging and diverse requirements of underwater applications. Building on the promising combination of multi-carrier and cooperative communication techniques, this dissertation investigates the fundamental performance bounds of cooperative underwater acoustic (UWA) communication systems taking into account the inherent unique characteristics of the UWA channel. We derive outage probability and capacity expressions for cooperative multi-carrier UWA systems with amplify-and-forward and decode-and-forward relaying. Through the derived expressions, we demonstrate the effect of several system and channel parameters on the performance. Furthermore, we investigate the performance of cooperative UWA systems in the presence of non-uniform Doppler distortion and propose receiver designs to mitigate the degrading Doppler effects

Doppler-Resilient Schemes for Underwater Acoustic Communication Channels.

In this thesis we consider Orthogonal Frequency Division Multiplexing (OFDM) technique by taking into account in the receiver design the fundamental and unique characteristics of Underwater Acoustic (UWA) channels in the context of Relay-Assisted (RA) systems. In particular, OFDM technique is used to combat the problem of Intersymbol Interference (ISI), while to handle the Intercarrier Interference (ICI), a pre-processing unit is used prior to the Minimum Mean Squared Error (MMSE) frequency-domain equalization called Multiple Resampling (MR), which minimizes the effect of time variation. This pre-processor consists of multiple branches, each corresponds to a Doppler scaling factor of a path/user/cluster, and performs of frequency shifting, resampling, and Fast Fourier Transform (FFT) operation. As a suboptimal alternative to MR pre-processing, Single Resampling (SR) pre-processing is also used to reduce the effect of ICI in the system, and it consists of only one branch that performs frequency shifting, resampling, and FFT operation, which corresponds to one approximated resampling factor, that is a function of one or more of the actual Doppler scaling factors. The problem of bandwidth scarcity is considered in the context of Two Way Relaying (TWR) systems in an attempt to increase the bandwidth efficiency of the system, while the problem of fading is considered in the context of Distributed Space-Time Block Coding (D-STBC) to boost the system reliability. Also, joint TWR-D-STBC system is proposed to extract the advantages of both schemes simultaneously. Second, motivated by the fact that OFDM is extremely sensitive to time variation, which destroys the orthogonality between the subcarriers, we consider another candidate to UWA channels and competitor to OFDM scheme, namely, block-based Single Carrier (SC) modulation with Frequency Domain Equalization (FDE). We start by the Point-to-Point (P2P) systems with path-specific Doppler model and Multiple Access Channel (MAC) system with user-specific Doppler model. The Maximum Likelihood (ML) receiver in each case is derived, and it is shown that a MR pre-processing stage is necessary to handle the effect of time variation, as it is the case in OFDM. Different from OFDM, however, the structure of this pre-processing stage. Specifically, it consists of multiple branches and each branch corresponds to a Doppler scaling factor per path or per user, and performs frequency shifting, resampling, and followed by and integration. FFT operation is not a part of the pre-processor. The goal of this pre-processing stage is to minimize the level of time variation in the time domain. So, the output of the pre-processor will still be time-varying contaminated by ISI, and hence an equalization stage is required. To avoid the complexity of the optimum Maximum Likelihood Sequence Detector (MLSD), we propose the use of MMSE FDE, where the samples are transformed to the frequency domain by means of FFT operation, and after the FDE transformed back to the time domain, where symbol-by-symbol detection becomes feasible. Also, the channels are approximated such that all paths or all users have the same Doppler scaling factor, and the pre-processing stage in this case consists of only one branch and it is called SR. Having the basic structure of SC-FDE scheme, we then consider the corresponding schemes that are considered for OFDM systems, namely: TWR, D-STBC, and TWR-D-STBC schemes. A complete complexity analysis, bandwidth efficiency, and extensive Average Bit Error Rate (ABER) simulation results are given. It is shown that MR schemes outperforms its SR counterparts within a given signaling scheme (i.e., OFDM or SC-FDE). However, this superiority in performance comes at the expense of more hardware complexity. Also, for uncoded systems, MR-SC-FDE outperforms its OFDM counterpart with less hardware complexity, because in SC-FDE systems, FFT operation is not part of the MR pre-processor, but rather a part of the equalizer. Finally, under total power constraint, it is shown that TWR-D-STBC scheme serves as a good compromise between bandwidth efficiency and reliability, where it has better bandwidth efficiency with some performance loss compared to D-STBC, while it has better performance and the same bandwidth efficiency compared to TWR

Compressive Sensing for Multi-channel and Large-scale MIMO Networks

Compressive sensing (CS) is a revolutionary theory that has important applications in many engineering areas. Using CS, sparse or compressible signals can be recovered from incoherent measurements with far fewer samples than the conventional Nyquist rate. In wireless communication problems where the sparsity structure of the signals and the channels can be explored and utilized, CS helps to significantly reduce the number of transmissions required to have an efficient and reliable data communication. The objective of this thesis is to study new methods of CS, both from theoretical and application perspectives, in various complex, multi-channel and large-scale wireless networks. Specifically, we explore new sparse signal and channel structures, and develop low-complexity CS-based algorithms to transmit and recover data over these networks more efficiently. Starting from the theory of sparse vector approximation based on CS, a compressive multiple-channel estimation (CMCE) method is developed to estimate multiple sparse channels simultaneously. CMCE provides a reduction in the required overhead for the estimation of multiple channels, and can be applied to estimate the composite channels of two-way relay channels (TWRCs) with sparse intersymbol interference (ISI). To improve end-to-end error performance of the networks, various iterative estimation and decoding schemes based on CS for ISI-TWRC are proposed, for both modes of cooperative relaying: Amplify-and-Forward (AF) and Decode-and-Forward (DF). Theoretical results including the Restricted Isometry Property (RIP) and low-coherent condition of the discrete pilot signaling matrix, the performance guarantees, and the convergence of the schemes are presented in this thesis. Numerical results suggest that the error performances of the system is significantly improved by the proposed CS-based methods, thanks to the awareness of the sparsity feature of the channels. Low-rank matrix approximation, an extension of CS-based sparse vector recovery theory, is then studied in this research to address the channel estimation problem of large-scale (or massive) multiuser (MU) multiple-input multiple-output (MIMO) systems. A low-rank channel matrix estimation method based on nuclear-norm regularization is formulated and solved via a dual quadratic semi-definite programming (SDP) problem. An explicit choice of the regularization parameter and useful upper bounds of the error are presented to show the efficacy of the CS method in this case. After that, both the uplink channel estimation and a downlink data recoding of massive MIMO in the interference-limited multicell scenarios are considered, where a CS-based rank-q channel approximation and multicell precoding method are proposed. The results in this work suggest that the proposed method can mitigate the effects of the pilot contamination and intercell interference, hence improves the achievable rates of the users in multicell massive MIMO systems. Finally, various low-complexity greedy techniques are then presented to confirm the efficacy and feasibility of the proposed approaches in practical applications

Design and Implementation of Belief Propagation Symbol Detectors for Wireless Intersymbol Interference Channels

In modern wireless communication systems, intersymbol interference (ISI) introduced by frequency selective fading is one of the major impairments to reliable data communication. In ISI channels, the receiver observes the superposition of multiple delayed reflections of the transmitted signal, which will result errors in the decision device. As the data rate increases, the effect of ISI becomes severe. To combat ISI, equalization is usually required for symbol detectors. The optimal maximum-likelihood sequence estimation (MLSE) based on the Viterbi algorithm (VA) may be used to estimate the transmitted sequence in the presence of the ISI. However, the computational complexity of the MLSE increases exponentially with the length of the channel impulse response (CIR). Even in channels which do not exhibit significant time dispersion, the length of the CIR will effectively increase as the sampling rate goes higher. Thus the optimal MLSE is impractical to implement in the majority of practical wireless applications. This dissertation is devoted to exploring practically implementable symbol detectors with near-optimal performance in wireless ISI channels. Particularly, we focus on the design and implementation of an iterative detector based on the belief propagation (BP) algorithm. The advantage of the BP detector is that its complexity is solely dependent on the number of nonzero coefficients in the CIR, instead of the length of the CIR. We also extend the work of BP detector design for various wireless applications. Firstly, we present a partial response BP (PRBP) symbol detector with near-optimal performance for channels which have long spanning durations but sparse multipath structure. We implement the architecture by cascading an adaptive linear equalizer (LE) with a BP detector. The channel is first partially equalized by the LE to a target impulse response (TIR) with only a few nonzero coefficients remaining. The residual ISI is then canceled by a more sophisticated BP detector. With the cascaded LE-BP structure, the symbol detector is capable to achieve a near-optimal error rate performance with acceptable implementation complexity. Moreover, we present a pipeline high-throughput implementation of the detector for channel length 30 with quadrature phase-shift keying (QPSK) modulation. The detector can achieve a maximum throughput of 206 Mb/s with an estimated core area of 3.162 mm^{2} using 90-nm technology node. At a target frequency of 515 MHz, the dynamic power is about 1.096 W. Secondly, we investigate the performance of aforementioned PRBP detector under a more generic 3G channel rather than the sparse channel. Another suboptimal partial response maximum-likelihood (PRML) detector is considered for comparison. Similar to the PRBP detector, the PRML detector also employs a hybrid two-stage scheme, in order to allow a tradeoff between performance and complexity. In simulations, we consider a slow fading environment and use the ITU-R 3G channel models. From the numerical results, it is shown that in frequency-selective fading wireless channels, the PRBP detector provides superior performance over both the traditional minimum mean squared error linear equalizer (MMSE-LE) and the PRML detector. Due to the effect of colored noise, the PRML detector in fading wireless channels is not as effective as it is in magnetic recording applications. Thirdly, we extend our work to accommodate the application of Advanced Television Systems Committee (ATSC) digital television (DTV) systems. In order to reduce error propagation caused by the traditional decision feedback equalizer (DFE) in DTV receiver, we present an adaptive decision feedback sparsening filter BP (DFSF-BP) detector, which is another form of PRBP detector. Different from the aforementioned LE-BP structure, in the DFSF-BP scheme, the BP detector is followed by a nonlinear filter called DFSF as the partial response equalizer. In the first stage, the DFSF employs a modified feedback filter which leaves the strongest post-cursor ISI taps uncorrected. As a result, a long ISI channel is equalized to a sparse channel having only a small number of nonzero taps. In the second stage, the BP detector is applied to mitigate the residual ISI. Since the channel is typically time-varying and suffers from Doppler fading, the DFSF is adapted using the least mean square (LMS) algorithm, such that the amplitude and the locations of the nonzero taps of the equalized sparse channel appear to be fixed. As such, the channel appears to be static during the second stage of equalization which consists of the BP detector. Simulation results demonstrate that the proposed scheme outperforms the traditional DFE in symbol error rate, under both static channels and dynamic ATSC channels. Finally, we study the symbol detector design for cooperative communications, which have attracted a lot of attention recently for its ability to exploit increased spatial diversity available at distributed antennas on other nodes. A system framework employing non-orthogonal amplify-and-forward half-duplex relays through ISI channels is developed. Based on the system model, we first design and implement an optimal maximum-likelihood detector based on the Viterbi algorithm. As the relay period increases, the effective CIR between the source and the destination becomes long and sparse, which makes the optimal detector impractical to implement. In order to achieve a balance between the computational complexity and performance, several sub-optimal detectors are proposed. We first present a multitrellis Viterbi algorithm (MVA) based detector which decomposes the original trellis into multiple parallel irregular sub-trellises by investigating the dependencies between the received symbols. Although MVA provides near-optimal performance, it is not straightforward to decompose the trellis for arbitrary ISI channels. Next, the decision feedback sequence estimation (DFSE) based detector and BP-based detector are proposed for cooperative ISI channels. Traditionally these two detectors are used with fixed, static channels. In our model, however, the effective channel is periodically time-varying, even when the component channels themselves are static. Consequently, we modify these two detector to account for cooperative ISI channels. Through simulations in frequency selective fading channels, we demonstrate the uncoded performance of the DFSE detector and the BP detector when compared to the optimal MLSE detector. In addition to quantifying the performance of these detectors, we also include an analysis of the implementation complexity as well as a discussion on complexity/performance tradeoffs

Compressive Sensing for Multi-channel and Large-scale MIMO Networks

Compressive sensing (CS) is a revolutionary theory that has important applications in many engineering areas. Using CS, sparse or compressible signals can be recovered from incoherent measurements with far fewer samples than the conventional Nyquist rate. In wireless communication problems where the sparsity structure of the signals and the channels can be explored and utilized, CS helps to significantly reduce the number of transmissions required to have an efficient and reliable data communication. The objective of this thesis is to study new methods of CS, both from theoretical and application perspectives, in various complex, multi-channel and large-scale wireless networks. Specifically, we explore new sparse signal and channel structures, and develop low-complexity CS-based algorithms to transmit and recover data over these networks more efficiently. Starting from the theory of sparse vector approximation based on CS, a compressive multiple-channel estimation (CMCE) method is developed to estimate multiple sparse channels simultaneously. CMCE provides a reduction in the required overhead for the estimation of multiple channels, and can be applied to estimate the composite channels of two-way relay channels (TWRCs) with sparse intersymbol interference (ISI). To improve end-to-end error performance of the networks, various iterative estimation and decoding schemes based on CS for ISI-TWRC are proposed, for both modes of cooperative relaying: Amplify-and-Forward (AF) and Decode-and-Forward (DF). Theoretical results including the Restricted Isometry Property (RIP) and low-coherent condition of the discrete pilot signaling matrix, the performance guarantees, and the convergence of the schemes are presented in this thesis. Numerical results suggest that the error performances of the system is significantly improved by the proposed CS-based methods, thanks to the awareness of the sparsity feature of the channels. Low-rank matrix approximation, an extension of CS-based sparse vector recovery theory, is then studied in this research to address the channel estimation problem of large-scale (or massive) multiuser (MU) multiple-input multiple-output (MIMO) systems. A low-rank channel matrix estimation method based on nuclear-norm regularization is formulated and solved via a dual quadratic semi-definite programming (SDP) problem. An explicit choice of the regularization parameter and useful upper bounds of the error are presented to show the efficacy of the CS method in this case. After that, both the uplink channel estimation and a downlink data precoding of massive MIMO in the interference-limited multicell scenarios are considered, where a CS-based rank-q channel approximation and multicell precoding method are proposed. The results in this work suggest that the proposed method can mitigate the effects of the pilot contamination and intercell interference, hence improves the achievable rates of the users in multicell massive MIMO systems. Finally, various low-complexity greedy techniques are then presented to confirm the efficacy and feasibility of the proposed approaches in practical applications