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Register a new userAnalytical Framework of LDGM-based Iterative Quantization with Decimation
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Qingchuan Wang
Publication date
2013
fields
Informatics Engineering
and research's language is
English
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While iterative quantizers based on low-density generator-matrix (LDGM) codes have been shown to be able to achieve near-ideal distortion performance with comparatively moderate block length and computational complexity requirements, their analysis remains difficult due to the presence of decimation steps. In this paper, considering the use of LDGM-based quantizers in a class of symmetric source coding problems, with the alphabet being either binary or non-binary, it is proved rigorously that, as long as the degree distribution satisfies certain conditions that can be evaluated with density evolution (DE), the belief propagation (BP) marginals used in the decimation step have vanishing mean-square error compared to the exact marginals when the block length and iteration count goes to infinity, which potentially allows near-ideal distortion performances to be achieved. This provides a sound theoretical basis for the degree distribution optimization methods previously proposed in the literature and already found to be effective in practice.
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The Accumulative Iterative Code (AIC) proposed in this work is a new error correcting code for channels with feedback. AIC sends the information message to the receiver in a number of transmissions, where the initial transmission contains the uncoded message and each subsequent transmission informs the receiver about the locations of the errors that corrupted the previous transmission. Error locations are determined based on the forward channel output, which is made available to the transmitter through the feedback channel. AIC achieves arbitrarily low error rates, thereby being suitablefor applications demanding extremely high reliability. In the same time, AIC achieves spectral efficiencies very close to the channel capacity in a wide range of signal-to-noise ratios even for transmission of short information messages.
We introduce a two-stage decimation process to improve the performance of neural belief propagation (NBP), recently introduced by Nachmani et al., for short low-density parity-check (LDPC) codes. In the first stage, we build a list by iterating between a conventional NBP decoder and guessing the least reliable bit. The second stage iterates between a conventional NBP decoder and learned decimation, where we use a neural network to decide the decimation value for each bit. For a (128,64) LDPC code, the proposed NBP with decimation outperforms NBP decoding by 0.75 dB and performs within 1 dB from maximum-likelihood decoding at a block error rate of $10^{-4}$.
We investigate an encoding scheme for lossy compression of a binary symmetric source based on simple spatially coupled Low-Density Generator-Matrix codes. The degree of the check nodes is regular and the one of code-bits is Poisson distributed with an average depending on the compression rate. The performance of a low complexity Belief Propagation Guided Decimation algorithm is excellent. The algorithmic rate-distortion curve approaches the optimal curve of the ensemble as the width of the coupling window grows. Moreover, as the check degree grows both curves approach the ultimate Shannon rate-distortion limit. The Belief Propagation Guided Decimation encoder is based on the posterior measure of a binary symmetric test-channel. This measure can be interpreted as a random Gibbs measure at a temperature directly related to the noise level of the test-channel. We investigate the links between the algorithmic performance of the Belief Propagation Guided Decimation encoder and the phase diagram of this Gibbs measure. The phase diagram is investigated thanks to the cavity method of spin glass theory which predicts a number of phase transition thresholds. In particular the dynamical and condensation phase transition temperatures (equivalently test-channel noise thresholds) are computed. We observe that: (i) the dynamical temperature of the spatially coupled construction saturates towards the condensation temperature; (ii) for large degrees the condensation temperature approaches the temperature (i.e. noise level) related to the information theoretic Shannon test-channel noise parameter of rate-distortion theory. This provides heuristic insight into the excellent performance of the Belief Propagation Guided Decimation algorithm. The paper contains an introduction to the cavity method.
In this paper, let $n=2m$ and $d=3^{m+1}-2$ with $mgeq2$ and $gcd(d,3^n-1)=1$. By studying the weight distribution of the ternary Zetterberg code and counting the numbers of solutions of some equations over the finite field $mathbb{F}_{3^n}$, the correlation distribution between a ternary $m$-sequence of period $3^n-1$ and its $d$-decimation sequence is completely determined. This is the first time that the correlation distribution for a non-binary Niho decimation has been determined since 1976.
The simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is capable of providing full-space coverage of smart radio environments. This work investigates STAR-RIS aided downlink non-orthogonal multiple access (NOMA) multi-cell networks, where the energy of incident signals at STAR-RISs is split into two portions for transmitting and reflecting. We first propose a fitting method to model the distribution of composite small-scale fading power as the tractable Gamma distribution. Then, a unified analytical framework based on stochastic geometry is provided to capture the random locations of RIS-RISs, base stations (BSs), and user equipments (UEs). Based on this framework, we derive the coverage probability and ergodic rate of both the typical UE and the connected UE. In particular, we obtain closed-form expressions of the coverage probability in interference-limited scenarios. We also deduce theoretical expressions in traditional RIS aided networks for comparison. The analytical results show that there exist optimal energy splitting coefficients of STAR-RISs to simultaneously maximize the system coverage and ergodic rate. The numerical results demonstrate that: 1) RISs enhance the system coverage and NOMA schemes help improve the rate performance; 2) in low signal-to-noise ratio (SNR) regions, STAR-RISs outperform traditional RISs while in high SNR regions the conclusion is opposite.
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