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Factor Graph Based Iterative Receiver Design for Underwater Acoustic Communications（基于因子图的水声通信迭代接收算法研究）
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In this article, a new category of soft-input soft-output (SISO) minimum-mean square error (MMSE) finite-impulse response (FIR) decision feedback equalizers (DFEs) with iteration-wise static filters (i.e. iteration variant) is investigated. It has been recently shown that SISO MMSE DFE with dynamic filters (i.e. time-varying) reaches very attractive operating points for high-data rate applications, when compared to alternative turbo-equalizers of the same category, thanks to sequential estimation of data symbols [1]. However the dependence of filters on the feedback incurs high amount of latency and computational costs, hence SISO MMSE DFEs with static filters provide an attractive alternative for computational complexityperformance trade-off. However, the latter category of receivers faces a fundamental design issue on the estimation of the decision feedback reliability for filter computation. To address this issue, a novel approach to decision feedback reliability estimation through online prediction is proposed and applied for SISO FIR DFE with either a posteriori probability (APP) or expectation propagation (EP) based soft feedback. This novel method for filter computation is shown to improve detection performance compared to previously known alternative methods, and finite-length and asymptotic analysis show that DFE with static filters still remains well-suited for high-spectral efficiency applications.
Algorithms that must deal with complicated global functions of
many variables often exploit the manner in which the given functions
factor as a product of “local” functions, each of which
depends on a subset of the variables. Such a factorization can be
visualized with a bipartite graph that we call a factor graph, In this
tutorial paper, we present a generic message-passing algorithm, the
sum-product algorithm, that operates in a factor graph. Following a
single, simple computational rule, the sum-product algorithm
computes-either exactly or approximately-various marginal functions
derived from the global function. A wide variety of algorithms developed
in artificial intelligence, signal processing, and digital
communications can be derived as specific instances of the sum-product
algorithm, including the forward/backward algorithm, the Viterbi
algorithm, the iterative “turbo” decoding algorithm, Pearl's
(1988) belief propagation algorithm for Bayesian networks, the Kalman
filter, and certain fast Fourier transform (FFT) algorithms
Random matrix theory has found many applications in physics, statistics and engineering since its inception. Although early developments were motivated by practical experimental problems, random matrices are now used in fields as diverse as Riemann hypothesis, stochastic differential equations, condensed matter physics, statistical physics, chaotic systems, numerical linear algebra, neural networks, multivariate statistics, information theory, signal processing and small-world networks. Random Matrix Theory and Wireless Communications is the first tutorial on random matrices which provides an overview of the theory and brings together in one source the most significant results recently obtained. Furthermore, the application of random matrix theory to the fundamental limits of wireless communication channels is described in depth. The authors have created a uniquely comprehensive work that provides the reader with a full understanding of the foundations of random matrix theory and demonstrates the trends of their applications, particularly in wireless communications. Random Matrix Theory and Wireless Communications is a valuable resource for all students and researchers working on the cutting edge of wireless communications.
In this paper we propose a novel turbo equalizer based on the expectation propagation (EP) algorithm. Optimal equalization is computationally unfeasible when high-order modulations and/or large memory channels are used. In these scenarios, low-cost and suboptimal equalizers, such as those based on the linear minimum mean square error (LMMSE), are commonly used. The LMMSE-based equalizer can be efficiently implemented with a Kalman smoother (KS), i.e., a forward and backward Kalman filtering whose predictions are merged in a posterior smoothing step. Recently, it was shown that applying EP at the forward and backward stages of a KS equalizer could significantly improve its performance. In this paper, we investigate applying EP at the smoothing level instead. Also, we propose some further modifications to better exploit the information coming from the channel decoder in turbo equalization schemes. Overall, we remarkably reduce the computational complexity while highly improving the performance in terms of bit error rate.
Faster-than-Nyquist (FTN) signaling is an attractive technology to improve the spectral efficiency. In this paper, an efficient FTN transceiver is proposed for underwater acoustic (UWA) communications. At the transmitter, the FTN signaling is implemented via the serial concatenation of a sample-rate-convertor (SRC) based on the well-known Farrow filter and the Nyquist pulse shaping. Compare with the conventional FTN signaling, it facilitates the convolution operation and thus achieves the efficient FTN signaling. On the receiver side, the direct-adaptive turbo equalization is employed to combat the hybrid inter-symbol interference (ISI) of the artificial ISI introduced by FTN signaling and the UWA channel-ISI. Wherein the improved proportionate normalized least mean squares (IPNLMS)-based direct-adaptive equalizer (DAE) with the data reuse (DR) aid is adopted as the soft equalizer. As for the channel coding, an irregular convolutional code (IrCC) is designed by matching the extrinsic information transfer (EXIT) curve of the DR-IPNLMS-DAE, leading to a low signal-to-noise ratio (SNR) threshold of the turbo iteration. Underwater experimental results verify the advantage of the FTN transmission over the higherorder modulation in terms of improving the spectral efficiency, and they show the proposed FTN transceiver scheme has the better detection performance than the traditional turbo equalization.
Deep sea manned submersible is a key technology for deep sea exploring and exploitation. Chinese “Jiaolong” is the deepest one among manned submersible on duty around the word. The functions of Jiaolong’s acoustic system include underwater communication, positioning, obstacle avoidance, target searching, high resolution bathymetric scanning, etc. Its advanced high speed underwater acoustic communication system can transmit images, data, text, voice and Morse codes, and its advanced high resolution bathymetric side scan sonar can acquire high resolution 3D map of the sea bottom. The overall performance of Jiaolong acoustic system is superior to any other manned submersible. In future, manned submersible acoustic system will develop to full ocean depth, higher underwater communication capability, higher navigation and position accuracy, more measurement functions and modules. And for China, acoustic technologies and instruments will be localized.
The sparse direct adaptive equalization technique recently received many attentions for single-carrier underwater acoustic communications. By taking advantage of the sparse (nonuniform) structure of a direct adaptive equalizer (DAE), one obtains a sparse DAE with improved performance and/or reduced complexity compared with its nonsparse counterpart. In this article, the sparse DAE is revisited with two contributions made: First, a comprehensive comparison is made for existing sparse DAEs designed with the proportionate-updating (PU) or the zero-attracting (ZA) adaptive filtering principles. The comparison concludes that the PU-type DAE outperforms the ZA-type DAE in terms of complexity, performance, and operability, thus shall be favored in practical use. Moreover, it motivates the design of a sparse DAE, named the selective ZA improved proportionate normalized least mean squares DAE (SZA-IPNLMS-DAE), based on the combination of the PU and ZA principles. The SZA-IPNLMS-DAE outperforms existing sparse DAEs armed with only one sparsity-promoting mechanism; second, a partial tap update (PTU) scheme via hard thresholding is introduced to sparse DAEs for reducing their complexity without sacrificing performance. The resulting low-complexity and high-performance sparse PTU-DAE schemes are strong candidates for single-carrier UWA communications. Experimental results of multiple-input multiple-output UWA communications are presented to corroborate all above claims.