Joint Source-Channel Coding with Time-Varying Channel and Side-Information

Transmission of a Gaussian source over a time-varying Gaussian channel is studied in the presence of time-varying correlated side information at the receiver. A block fading model is considered for both the channel and the side information, whose states are assumed to be known only at the receiver. The optimality of separate source and channel coding in terms of average end-to-end distortion is shown when the channel is static while the side information state follows a discrete or a continuous and quasiconcave distribution. When both the channel and side information states are time-varying, separate source and channel coding is suboptimal in general. A partially informed encoder lower bound is studied by providing the channel state information to the encoder. Several achievable transmission schemes are proposed based on uncoded transmission, separate source and channel coding, joint decoding as well as hybrid digital-analog transmission. Uncoded transmission is shown to be optimal for a class of continuous and quasiconcave side information state distributions, while the channel gain may have an arbitrary distribution. To the best of our knowledge, this is the first example in which the uncoded transmission achieves the optimal performance thanks to the time-varying nature of the states, while it is suboptimal in the static version of the same problem. Then, the optimal \emph{distortion exponent}, that quantifies the exponential decay rate of the expected distortion in the high SNR regime, is characterized for Nakagami distributed channel and side information states, and it is shown to be achieved by hybrid digital-analog and joint decoding schemes in certain cases, illustrating the suboptimality of pure digital or analog transmission in general.Comment: Submitted to IEEE Transactions on Information Theor

Distortion Exponent in MIMO Fading Channels with Time-Varying Source Side Information

Transmission of a Gaussian source over a time-varying multiple-input multiple-output (MIMO) channel is studied under strict delay constraints. Availability of a correlated side information at the receiver is assumed, whose quality, i.e., correlation with the source signal, also varies over time. A block-fading model is considered for the states of the time-varying channel and the time-varying side information; and perfect state information at the receiver is assumed, while the transmitter knows only the statistics. The high SNR performance, characterized by the \textit{distortion exponent}, is studied for this joint source-channel coding problem. An upper bound is derived and compared with lowers based on list decoding, hybrid digital-analog transmission, as well as multi-layer schemes which transmit successive refinements of the source, relying on progressive and superposed transmission with list decoding. The optimal distortion exponent is characterized for the single-input multiple-output (SIMO) and multiple-input single-output (MISO) scenarios by showing that the distortion exponent achieved by multi-layer superpositon encoding with joint decoding meets the proposed upper bound. In the MIMO scenario, the optimal distortion exponent is characterized in the low bandwidth ratio regime, and it is shown that the multi-layer superposition encoding performs very close to the upper bound in the high bandwidth expansion regime.Comment: Submitted to IEEE Transactions on Information Theor

ADAPTIVE CHANNEL AND SOURCE CODING USING APPROXIMATE INFERENCE

Channel coding and source coding are two important problems in communications. Although both channel coding and source coding (especially, the distributed source coding (DSC)) can achieve their ultimate performance by knowing the perfect knowledge of channel noise and source correlation, respectively, such information may not be always available at the decoder side. The reasons might be because of the time−varying characteristic of some communication systems and sources themselves, respectively. In this dissertation, I mainly focus on the study of online channel noise estimation and correlation estimation by using both stochastic and deterministic approximation inferences on factor graphs.In channel coding, belief propagation (BP) is a powerful algorithm to decode low−density parity check (LDPC) codes over additive white Gaussian noise (AWGN) channels. However, the traditional BP algorithm cannot adapt efficiently to the statistical change of SNR in an AWGN channel. To solve the problem, two common workarounds in approximate inference are stochastic methods (e.g. particle filtering (PF)) and deterministic methods (e.g. expectation approximation (EP)). Generally, deterministic methods are much faster than stochastic methods. In contrast, stochastic methods are more flexible and suitable for any distribution. In this dissertation, I proposed two adaptive LDPC decoding schemes, which are able to perform online estimation of time−varying channel state information (especially signal to noise ratio (SNR)) at the bit−level by incorporating PF and EP algorithms. Through experimental results, I compare the performance between the proposed PF based and EP based approaches, which shows that the EP based approach obtains the comparable estimation accuracy with less computational complexity than the PF based method for both stationary and time−varying SNR, and enhances the BP decoding performance simultaneously. Moreover, the EP estimator shows a very fast convergence speed, and the additional computational overhead of the proposed decoder is less than 10% of the standard BP decoder.Moreover, since the close relationship between source coding and channel coding, the proposed ideas are extended to source correlation estimation. First, I study the correlation estimation problem in lossless DSC setup, where I consider both asymmetric and non−asymmetric SW coding of two binary correlated sources. The aforementioned PF and EP based approaches are extended to handle the correlation between two binary sources, where the relationship is modeled as a virtual binary symmetric channel (BSC) with a time−varying crossover probability. Besides, to handle the correlation estimation problem of Wyner−Ziv (WZ) coding, a lossy DSC setup, I design a joint bit−plane model, by which the PF based approach can be applied to tracking the correlation between non−binary sources. Through experimental results, the proposed correlation estimation approaches significantly improve the compression performance of DSC.Finally, due to the property of ultra−low encoding complexity, DSC is a promising technique for many tasks, in which the encoder has only limited computing and communication power, e.g. the space imaging systems. In this dissertation, I consider a real−world application of the proposed correlation estimation scheme on the onboard low−complexity compression of solar stereo images, since such solutions are essential to reduce onboard storage, processing, and communication resources. In this dissertation, I propose an adaptive distributed compression solution using PF that tracks the correlation, as well as performs disparity estimation, at the decoder side. The proposed algorithm istested on the stereo solar images captured by the twin satellites systemof NASA’s STEREO project. The experimental results show the significant PSNR improvement over traditional separate bit−plane decoding without dynamic correlation and disparity estimation

Joint Source-Channel Coding over a Fading Multiple Access Channel with Partial Channel State Information

In this paper we address the problem of transmission of correlated sources over a fast fading multiple access channel (MAC) with partial channel state information available at both the encoders and the decoder. We provide sufficient conditions for transmission with given distortions. Next these conditions are specialized to a Gaussian MAC (GMAC). We provide the optimal power allocation strategy and compare the strategy with various levels of channel state information. Keywords: Fading MAC, Power allocation, Partial channel state information, Correlated sources.Comment: 7 Pages, 3 figures. To Appear in IEEE GLOBECOM, 200