Improved Modeling of the Correlation Between Continuous-Valued Sources in LDPC-Based DSC

Accurate modeling of the correlation between the sources plays a crucial role in the efficiency of distributed source coding (DSC) systems. This correlation is commonly modeled in the binary domain by using a single binary symmetric channel (BSC), both for binary and continuous-valued sources. We show that "one" BSC cannot accurately capture the correlation between continuous-valued sources; a more accurate model requires "multiple" BSCs, as many as the number of bits used to represent each sample. We incorporate this new model into the DSC system that uses low-density parity-check (LDPC) codes for compression. The standard Slepian-Wolf LDPC decoder requires a slight modification so that the parameters of all BSCs are integrated in the log-likelihood ratios (LLRs). Further, using an interleaver the data belonging to different bit-planes are shuffled to introduce randomness in the binary domain. The new system has the same complexity and delay as the standard one. Simulation results prove the effectiveness of the proposed model and system.Comment: 5 Pages, 4 figures; presented at the Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, November 201

A Progressive Universal Noiseless Coder

The authors combine pruned tree-structured vector quantization (pruned TSVQ) with Itoh's (1987) universal noiseless coder. By combining pruned TSVQ with universal noiseless coding, they benefit from the “successive approximation” capabilities of TSVQ, thereby allowing progressive transmission of images, while retaining the ability to noiselessly encode images of unknown statistics in a provably asymptotically optimal fashion. Noiseless compression results are comparable to Ziv-Lempel and arithmetic coding for both images and finely quantized Gaussian sources

Unified bit-based probabilistic data association aided MIMO detection for high-order QAM constellations

A unified Bit-based Probabilistic Data Association (B-PDA) detection approach is proposed for Multiple-Input Multiple-Output (MIMO) systems employing high-order rectangular Quadrature Amplitude Modulation (QAM). The new approach transforms the symbol detection process of QAM to a bit-based process by introducing a Unified Matrix Representation (UMR) of QAM. Both linear natural and nonlinear binary reflected Gray bit-to-symbol mappings are considered. With the aid of simulation results, we demonstrate that the linear natural mapping based B-PDA approach typically attained an improved detection performance (measured in terms of both Bit Error Ratio (BER) and Symbol Error Ratio (SER)) in comparison to the conventional symbol-based PDA aided MIMO detector, despite its dramatically reduced computational complexity. The only exception is that at low SNRs, the linear natural mapping based B-PDA is slightly inferior in terms of its BER to the conventional symbol-based PDA using binary reflected Gray mapping. Furthermore, the simulation results show that the linear natural mapping based B-PDA MIMO detector may approach the best-case performance provided by the nonlinear binary reflected Gray mapping based B-PDA MIMO detector under ideal conditions. Additionally, the implementation of the B-PDA MIMO detector is shown to be much simpler in the case of the linear natural mapping. Based on these two points, we conclude that in the context of the uncoded B-PDA MIMO detector it is preferable to use the linear natural bit-to-symbol mapping, rather than the nonlinear Gray mapping