In this paper, we explore the three-node multi-terminal lossysourcecoding problem, which seems to offer a formidable mathematical complexity. We derive an inner bound to the general rate-distortion region of this pr...
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In this paper, we explore the three-node multi-terminal lossysourcecoding problem, which seems to offer a formidable mathematical complexity. We derive an inner bound to the general rate-distortion region of this problem, which is a natural extension of the seminal work by Kaspi on the interactive two-terminal sourcecoding problem. It is shown that this (rather involved) inner bound contains several rate-distortion regions of some relevant sourcecoding settings. In this way, besides the non-trivial extension of the interactive two terminal problem, our results can be seen as a generalization and hence unification of several previous works in the field. By specializing the inner bound to particular cases, we obtain some novel rate-distortion regions for several multi-terminal lossysourcecoding problems.
This paper addresses lossydistributedsourcecoding for acquiring correlated sparse sources via compressed sensing (CS) in wireless sensor networks. Noisy CS measurements are separately encoded at a finite rate by ea...
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This paper addresses lossydistributedsourcecoding for acquiring correlated sparse sources via compressed sensing (CS) in wireless sensor networks. Noisy CS measurements are separately encoded at a finite rate by each sensor, followed by the joint reconstruction of the sources at the decoder. We develop a novel complexity-constrained distributed variable-rate quantized CS method, which minimizes a weighted sum between the mean square error signal reconstruction distortion and the average encoding rate. The encoding complexity of each sensor is restrained by pre-quantizing the encoder input, i.e., the CS measurements, via vector quantization. Following the entropy-constrained design, each encoder is modeled as a quantizer followed by a lossless entropy encoder, and variable-rate coding is incorporated via rate measures of an entropy bound. For a two-sensor system, necessary optimality conditions are derived, practical training algorithms are proposed, and complexity analysis is provided. Numerical results show that the proposed method achieves superior compression performance as compared with baseline methods, and lends itself to versatile setups with different performance requirements.
This paper presents an in-depth performance analysis of lossy communications in a single-relay system, where the recovered information is not necessarily lossless in both the relay and the destination. In this system,...
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This paper presents an in-depth performance analysis of lossy communications in a single-relay system, where the recovered information is not necessarily lossless in both the relay and the destination. In this system, the relay continues transmitting the sequence with source-relay (S-R) link errors to the destination even if errors are detected after decoding, i.e., so-called lossy-forward (LF) strategy. The problem can be decomposed into two parts as follows: a point-to-point coding problem in the S-R link and a lossysourcecoding problem with an LF relay in the source-destination (S-D) and a relay-destination (R-D) links. To begin with, we derive the admissible rate region of the lossysourcecoding problem with the LF relay for a specified distortion requirement. Then, we focus on the analysis of outage probability over block Rayleigh fading channels. Finally, a practical encoding/decoding scheme is proposed for the evaluation of system performance by computer simulations. Due to the suboptimal channel coding and incomplete utilization of joint typicality, the theoretical performance cannot be achieved in the simulation;however, the tendency of curves in simulations matches that in theoretical calculation.
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