The sum-rate distortion function of generalized Gaussian multiterminal source coding is shown to coincide with that of joint encoding in the high-resolution regime if and only if the source-encoder bipartite graph and the undirected graphical model (also known as Gaussian Markov network or Gaussian Markov random field) of the source distribution satisfy a certain condition.

We propose a new coding technique for sequential transmission of a stream of Gauss-Markov sources over erasure channels under a zero decoding delay constraint. Our proposed scheme is a combination (hybrid) of predictive coding with truncated memory, and quantization-and-binning. We study the optimality of our proposed scheme using an information theoretic model. In our setup, the encoder observes a stream of source vectors that are spatially independent and identically distributed (i.i.d.) and temporally sampled from a first-order stationary Gauss-Markov process. The channel introduces an erasure burst of a certain maximum length B, starting at an arbitrary time, not known to the transmitter. The reconstruction of each source vector at the destination must be with zero delay and satisfy a quadratic distortion constraint with an average distortion of D. The decoder is not required to reconstruct those source vectors that belong to the period spanning the erasure burst and a recovery window of length W following it. We study the minimum compression rate R(B, W, D) in this setup. As our main result, we establish upper and lower bounds on the compression rate. The upper bound (achievability) is based on our hybrid scheme. It achieves significant gains over baseline schemes such as (leaky) predictive coding, memoryless binning, a separation-based scheme, and a group of pictures-based scheme. The lower bound is established by observing connection to a network source coding problem. The bounds simplify in the high resolution regime, where we provide explicit expressions whenever possible, and identify conditions when the proposed scheme is close to optimal. We finally discuss the interplay between the parameters of our burst erasure channel and the statistical channel models and explain how the bounds in the former model can be used to derive insights into the simulation results involving the latter. In particular, our proposed scheme outperforms the baseline schemes over the i.i.d. erasure channel and the Gilbert-Elliott channel, and achieves performance close to a lower bound in some regimes.

Two setups involving delay-constrained sequential transmission of a vector Gauss-Markov source over a burst-erasure channel are studied. The encoder sequentially compresses the source vectors to be transmitted in a causal fashion. The channel introduces a single erasure burst of length up to B during the transmission. In streaming with controlled-interruption, the decoder reconstructs the source vectors within average distortion D and maximum delay of T, whenever the channel packets are not erased. In streaming with ideal-playback, the decoder reconstructs all the source vectors within average distortion D and maximum delay of T. Upper and lower bounds on the minimum compression rate are derived for each setup. The bounds coincide in the high resolution regime for both cases and in large delay regime for the case of ideal-playback.

We study sequential transmission of Gauss-Markov sources over erasure channels under a zero decoding delay constraint. A two-stage coding scheme which can be described as a hybrid between predictive coding with limited past and quantization & binning is proposed. This scheme can achieve significant performance gains over baseline schemes in simulations involving i.i.d. erasure channels, and in certain regimes can attain performance close to a fundamental lower bound. We consider an information theoretic model for streaming that explains the weakness of baseline schemes (e.g., predictive coding, memoryless binning, etc.) and illustrates the advantage of our proposed hybrid scheme over these. Techniques from multi-terminal source coding are used to derive a new lower bound on the compression rate and identify cases when the hybrid coding scheme is close to optimal. We discuss qualitatively the interplay between the parameters of our information theoretic model and the statistical models used in simulations.

A setup involving zero-delay sequential transmission of a vector Markov source over a burst erasure channel is studied. A sequence of source vectors is compressed in a causal fashion at the encoder, and the resulting output is transmitted over a burst erasure channel. The destination is required to reconstruct each source vector with zero-delay, but those source sequences that are observed either during the burst erasure, or in the interval of length W following the burst erasure need not be reconstructed. The minimum achievable compression rate is called the rate-recovery function. We assume that each source vector is independent identically distributed (i.i.d.) across the spatial dimension and is sampled from a stationary, first-order Markov process across the temporal dimension. For discrete sources, the case of lossless recovery is considered, and upper and lower bounds on the rate-recovery function are established. Both these bounds can be expressed as the rate for predictive coding, plus a term that decreases at least inversely with the recovery window length W. For Gauss-Markov sources and a quadratic distortion measure, upper and lower bounds on the minimum rate are established when W = 0. These bounds are shown to coincide in the high resolution limit. Finally, another setup involving i.i.d. Gaussian sources is studied and the raterecovery function is completely characterized in this case.

We study sequential streaming of Gauss-Markov sources over a burst-erasure channel. In any sliding window of length L, the channel introduces a single erasure burst of maximum length B. The encoder observes a sequence of vector Gaussian sources, where the vectors are i.i.d. across the spatial dimension and correlated across the temporal dimension. The encoder output can depend on all source vectors observed up to that time but not on any future source vectors. The decoder is required to reconstruct the source vectors instantaneously and within a quadratic distortion constraint of D, except those source vectors that either appear during the erasure periods or a recovery period of W following each erasure burst. We focus on time-invariant encoders and establish upper and lower bounds on the minimum compression rate R(L, B, W, D). Our lower bound is obtained by making connection to a Gaussian multi-terminal source coding problem. The upper bound is based on distributed source coding, but requires a careful analysis of the achievable rate. Numerical comparisons indicate that the proposed technique provides significant gains over other baseline schemes.

We introduce a framework to study fundamental limits of sequential coding of Markov sources under an error propagation constraint. An encoder sequentially compresses a sequence of vector-sources that are spatially i.i.d. but temporally correlated according to a Markov process. The channel erases up to B packets in a single burst, but reveals all other packets to the destination. The destination is required to reproduce all the source-vectors instantaneously and in a loss less manner, except those sequences that occur in a window of length B+W following the start of the erasure burst. We define a rate-recovery function R(B, W), the minimum compression rate that can be achieved in this framework, and develop upper and lower bounds for first-order Markov sources. For the special class of linear diagonally correlated deterministic sources, we propose a new coding technique -- prospicient coding -- that achieves the rate-recovery function. Finally, a lossy extension to the rate-recovery function is also studied for a class of Gaussian sources where the source is temporally and spatially i.i.d. and the decoder aims to recover a collection of past K sources with a quadratic distortion measure. The optimal rate-recovery function is compared with the sub-optimal techniques including forward error correction coding (FEC) and Wyner-Ziv coding, and performance gains are quantified.

We study three relay selection schemes for uniformly distributed wireless sensor networks: 1) optimal selection where the relays that maximize the signal-to-noise ratio (SNR) at the destination are selected, 2) geometry-based, which is based on selecting the closest nodes to the source, and 3) random selection in which the nodes are selected randomly from a certain neighborhood of the source. In all schemes, we assume that all relays operate in the amplify-and-forward mode and transmit with equal average powers and each relay has only access to its backward channel and location. For each relay selection strategy, we propose a decentralized protocol whereby proper nodes choose to act as relays without requiring any central coordinating entity or any inter-node information transfer. We derive expressions for the average SNR at the relays and destination while assuming that the source-node distances and the inter-terminal channel links are completely random. We show that, for all proposed schemes, the SNR variance at the destination converges to zero as the number of relays increases. While each selection scheme has its pros and cons, we derive a sufficient condition under which the average SNR at the destination becomes independent of the selection scheme employed.

In this paper, we analyze the impact of carrier frequency offset (CFO) on the performance of orthogonal frequency division multiplexing (OFDM) transmission employing space-frequency coding over relay channels. The challenge in such systems lies in the difficulty of canceling the interference resulting from the different CFOs that correspond to the relays involved in the transmission. We first analyze the CFO correction schemes and examine their impact on the achievable information rates. Further, we analyze the interference cancellation (IC) technique based on the so-called turbo-principle, that is, which jointly detects and decodes the received data. The increase of the rates achievable thanks to IC is assessed via parametric description of the iterative process. We provide examples that demonstrate the efficacy of the proposed scheme and numerical results are contrasted with theoretical performance limits.

We consider a multi-relay amplify-and-forward cooperative communication scheme in wireless sensor networks with uniformly distributed nodes. Fixing the average total transmission power from the network and preserving fairness among the selected relays by constraining them to transmit with equal average powers, we aim to improve the signal reception quality at the far-field receiver by means of a proper choice of the relays. Assuming that the nodes' forward channels are not known, the following three relay selection schemes are proposed and their performances are analyzed. 1) Optimal relay selection scheme that maximizes the average SNR at the receiver by exploiting K nodes with the highest SNRs as relays; 2) geometry-based relay selection scheme that is energy-efficient and achieves a close-to-optimal average SNR performance at the receiver by using K closest nodes to the source as relays; and 3) random relay selection scheme that is energy-efficient and further guarantees a fair usage of all nodes by randomly selecting K relays from a specific area around the source. By minimizing an outage probability, a strategy to determine this area is also proposed. Finally, it is shown for all relay selection schemes that the SNR variance at the receiver converges to zero as K increases.

In this paper, the rate achievable in Multiple-Input-Multiple-Output (MIMO) systems with iterative receivers based on linear front-end (FE) processing is investigated. First, the communication with Gaussian input signal is assumed and the Extrinsic Information Transfer (EXIT) chart is applied to evaluate the achievable sum rate. Then, the method for deriving the EXIT chart for a more practical case has been introduced. As a specific model, which fits the real turbo receivers better, communication with large size uniform constellations is discussed where the information being exchanged between the receiver's iterative block are Log-Likelihood-Ratios (LLRs) of the transmitted bits. It is shown that, in this situation, the iterative process does not improve the performance from achievable sum rate point of view in both high and low SNR regimes. However, the iterative process is shown to help in the medium SNR range, which is the range of interest.