David Matas

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This paper deals with the joint optimal design of the power allocation and modulation stages for a convolutionally encoded bit interleaved coded modulation with iterative decoding (BICM-ID). The design optimizes the spectral efficiency subject constraints in the average power and the BER at the decoder output. The proposed approach is based on the use of(More)
We introduce a new family of graph-based source codes that can be regarded as a nonlinear generalization of LDPC codes, and apply them to the compression of asymmetric binary memoryless sources. Simulation results and the application of density evolution show that the proposed family presents a performance very close to the theoretical limits, clearly(More)
Waterfilling and mercury/waterfilling power allocation policies maximize the mutual information of independent parallel Gaussian-noise channels under an average power constraint for Gaussian signals and for practical discrete constellations respectively. In this paper, the same criterion is considered for joint power allocation and bit loading in more(More)
We propose a method to bound the syndrome entropy of linear block codes from their factor graph representation. It is specially suited for sparse graphs such as those of low density parity check codes. It is based on the chain rule decomposition of the entropy and the confinement of dependencies within code subgraphs. After forcing or assuming the subgraphs(More)
This paper aims at computing tight upper bounds for the maximum a posteriori threshold of low-density parity check codes in the asymptotic blocklength regime for the transmission over binary-input memoryless symmetric-output channels. While these bounds are already known, we propose a novel derivation based on a completely different approach: based solely(More)
This paper introduces a joint bit loading and power allocation algorithm for systems combining bit-interleaved coded modulation (BICM) with multicarrier transmission. The proposed algorithm maximizes the mutual information, so it can be regarded as a generalization of mercury/waterfilling policy that incorporates bit loading. The followed approach relies on(More)
In this paper, a new power allocation and bit loading policy is defined for those systems working with a preselected binary channel code and specific bit error rate (BER) requirements. It consists on the maximization of the spectral efficiency with a constraint on the average mutual information per coded bit (bit MI), exploiting the relationship of the bit(More)
We study the behavior of a new family of nonlinear graph-based codes, previously introduced for compression of asymmetric binary memoryless sources, for the joint source-channel coding scenario in which the codewords are transmitted through an additive white Gaussian noise channel. We focus on low entropy sources (with high redundancy) and compression(More)
In this paper we propose a low complexity adaptive algorithm for lossless compressive sampling and reconstruction of sparse signals. Consider a sparse non-negative real signal x containing only k ≪ n non-zero values. The sampling process obtains m measurements by a linear projection y = Ax and, in order to minimize the complexity, we quantize them to(More)
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