This contribution considers the non-Bayesian Cramer-Rao bound (CRB) related to parameter estimation from a linearly modulated signal observed in additive white Gaussian noise. We compare the exact CRB expression for c...
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This contribution considers the non-Bayesian Cramer-Rao bound (CRB) related to parameter estimation from a linearly modulated signal observed in additive white Gaussian noise. We compare the exact CRB expression for coded modulation with two ad hoc CRB (ACRB) expressions;the first ACRB is obtained by substituting in the exact CRB expression for un-coded modulation the a priori symbol probabilities by the extrinsic symbol probabilities;the second ACRB, which has received some attention in recent scientific publications, additionally assumes that the real and imaginary parts of the symbols are independent. Our exposition focuses on the particular case of phase shift estimation. By means of examples we show that although for some coded modulation schemes the exact and ACRBs yield virtually the same numerical result, for other coded modulation schemes the ACRBs differ considerably among themselves and from the exact CRB. We provide some explanations for this behavior. We also argue that both ACRBs are expected to virtually coincide with the exact CRB in the case of bit-interleaved coded modulation combined with a rectangular constellations with independent in-phase and quadrature mapping and a binary code for which the factor graph does not contain short cycles.
Low-density parity-check (LDPC) codes, proposed by Gallager, emerged as a class of codes which can yield very good performance on the additive white Gaussian noise channel as well as on the binary symmetric channel. L...
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Low-density parity-check (LDPC) codes, proposed by Gallager, emerged as a class of codes which can yield very good performance on the additive white Gaussian noise channel as well as on the binary symmetric channel. LDPC codes have gained lots of importance due to their capacity achieving property and excellent performance in the noisy channel. Belief propagation (BP) algorithm and its approximations, most notably min-sum, are popular iterative decoding algorithms used for LDPC and turbo codes. The trade-off between the hardware complexity and the decoding throughput is a critical factor in the implementation of the practical decoder. This article presents introduction to LDPC codes and its various decodingalgorithms followed by realisation of LDPC decoder by using simplified message passing algorithm and partially parallel decoder architecture. Simplified message passing algorithm has been proposed for trade-off between low decoding complexity and decoder performance. It greatly reduces the routing and check node complexity of the decoder. Partially parallel decoder architecture possesses high speed and reduced complexity. The improved design of the decoder possesses a maximum symbol throughput of 92.95Mbps and a maximum of 18 decoding iterations. The article presents implementation of 9216 bits, rate-1/2, (3, 6) LDPC decoder on Xilinx XC3D3400A device from Spartan-3A DSP family.
Wireless broadcast advantage (WBA) and wireless cooperative advantage (WCA) are exploited in this paper for finding a cooperative route in a wireless mesh network with minimum energy consumption considering practical ...
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ISBN:
(纸本)9781479974986
Wireless broadcast advantage (WBA) and wireless cooperative advantage (WCA) are exploited in this paper for finding a cooperative route in a wireless mesh network with minimum energy consumption considering practical coding and modulation schemes. In doing so we have assumed a multihop wireless mesh network consisting of nodes capable of adjusting their transmit power. An optimization problem to allocate minimum sum power for a given route subject to a desired end-to-end throughput and outage probability constraints is formulated. WBA and WCA are taken into account separately and together during the resource allocation. Afterwards, optimal and suboptimal route selection is done using a classical Dijkstra's algorithm. Turbo codes with iterative decoding algorithms are implemented with various QAM modulation schemes.
Multiple-Input Multiple-Output - Orthogonal Frequency Division Multiplexing (MIMO-OFDM) is adopted to vehicular networks to increase the capacity, reliability and speed. In this paper, iterative demodulation and decod...
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ISBN:
(纸本)9781479960798
Multiple-Input Multiple-Output - Orthogonal Frequency Division Multiplexing (MIMO-OFDM) is adopted to vehicular networks to increase the capacity, reliability and speed. In this paper, iterative demodulation and decodingalgorithms are studied to approach the capacity of MIMO-OFDM vehicular networks. By analysing the drawbacks of the Gaussian approximation on the interference cancellation, Non-Gaussian approximation is proposed to enhance the performance of interference cancellation based detectors with large constellations. Simulation results demonstrate that the proposed non-Gaussian algorithm can achieve a significant performance gain over existing ones with high order constellations.
In this paper, the traditional BP decoding algorithm of LDPC code is investigated in detail. The large computation load is a shortcoming of traditional BP decoding algorithm, and an improved BP decoding algorithm is t...
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In this paper, the traditional BP decoding algorithm of LDPC code is investigated in detail. The large computation load is a shortcoming of traditional BP decoding algorithm, and an improved BP decoding algorithm is then proposed to deal with the problem. In the iterativedecoding process, the wrong bit information only needs to be updated for the decoding algorithm, which consequently improves the efficiency and reduces time-delay of decoding. According to the improved BP decoding algorithm, the high-speed parallel encoder is designed, which can be used in various distributed sensor network applications. Furthermore, the error correction performance of this high speed parallel encoder in the Gaussian channel is studied. The simulation is provided to illustrate that the improved high speed parallel encoder has lower computational complexity and higher decoding speed based on the premise of a little decoding performance loss.
In this paper, we present a new approach for the analysis of iterative node-based verification-based (NB-VB) recovery algorithms in the context of compressed sensing. These algorithms are particularly interesting due ...
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In this paper, we present a new approach for the analysis of iterative node-based verification-based (NB-VB) recovery algorithms in the context of compressed sensing. These algorithms are particularly interesting due to their low complexity (linear in the signal dimension n). The asymptotic analysis predicts the fraction of unverified signal elements at each iteration l in the asymptotic regime where n -> l. The analysis is similar in nature to the well-known density evolution technique commonly used to analyze iterative decoding algorithms. To perform the analysis, a message-passing interpretation of NB-VB algorithms is provided. This interpretation lacks the extrinsic nature of standard message-passing algorithms to which density evolution is usually applied. This requires a number of nontrivial modifications in the analysis. The analysis tracks the average performance of the recovery algorithms over the ensembles of input signals and sensing matrices as a function of l. Concentration results are devised to demonstrate that the performance of the recovery algorithms applied to any choice of the input signal over any realization of the sensing matrix follows the deterministic results of the analysis closely. Simulation results are also provided which demonstrate that the proposed asymptotic analysis matches the performance of recovery algorithms for large but finite values of n. Compared to the existing technique for the analysis of NB-VB algorithms, which is based on numerically solving a large system of coupled differential equations, the proposed method is more accurate and simpler to implement.
In this paper, we present a new approach for the analysis of iterative node-based verification-based (NB-VB) recovery algorithms in the context of compressive sensing. These algorithms are particularly interesting due...
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ISBN:
(纸本)9781457705953
In this paper, we present a new approach for the analysis of iterative node-based verification-based (NB-VB) recovery algorithms in the context of compressive sensing. These algorithms are particularly interesting due to their low complexity (linear in the signal dimension n). The asymptotic analysis predicts the fraction of unverified signal elements at each iteration l in the asymptotic regime where n -> infinity. The analysis is similar in nature to the well-known density evolution technique commonly used to analyze iterative decoding algorithms. To perform the analysis, a message-passing interpretation of NB-VB algorithms is provided. This interpretation lacks the extrinsic nature of standard message-passing algorithms to which density evolution is usually applied. This requires a number of non-trivial modifications in the analysis. The analysis tracks the average performance of the recovery algorithms over the ensembles of input signals and sensing matrices as a function of l. Concentration results are devised to demonstrate that the performance of the recovery algorithms applied to any choice of the input signal over any realization of the sensing matrix follows the deterministic results of the analysis closely. Simulation results are also provided which demonstrate that the proposed asymptotic analysis matches the performance of recovery algorithms for large but finite values of n. Compared to the existing technique for the analysis of NB-VB algorithms, which is based on numerically solving a large system of coupled differential equations, the proposed method is much simpler and more accurate.
That the computation load is big is a shortcoming of traditional BP decoding algorithm. According to this shortcoming we give an improved BP decoding algorithm. The decoding algorithm in the iterativedecoding process...
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ISBN:
(纸本)9780769536866
That the computation load is big is a shortcoming of traditional BP decoding algorithm. According to this shortcoming we give an improved BP decoding algorithm. The decoding algorithm in the iterativedecoding process, only need to update the possibility of a wrong bit of information without having to update the information whose reliability is very high, raises the decoding efficiency and reduces decoding time delay. The simulation result shows: the calculation load of improved BP decodingalgorithms descends distinctively with the increasing iteration numbers;Along with the times increasing of LDPC code iterations upper limit, coding performance can be obviously improved.
Source coding with correlated decoder side information is considered. We impose the practical constraint that the encoder be unaware of even the statistical dependencies between source and side information. Two classe...
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Source coding with correlated decoder side information is considered. We impose the practical constraint that the encoder be unaware of even the statistical dependencies between source and side information. Two classes of rate-adaptive distributed source codes, both based on low-density parity-check (LDPC) codes, are developed and their design is studied. Specific realizations are shown to be better than alternatives of linear encoding and decoding complexity. The proposed rate-adaptive LDPC accumulate (LDPCA) codes and sum LDPC accumulate (SLDPCA) codes (of length 6336 bits) perform within 10% and 5% of the Slepian-Wolf bound in the moderate and high rate regimes, respectively. (c) 2006 Elsevier B.V. All rights reserved.
The effects of clipping and quantization on the performance of the min-sum algorithm for the decoding of low-density parity-check (LDPC) codes at short and intermediate block lengths are studied. It is shown that in m...
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The effects of clipping and quantization on the performance of the min-sum algorithm for the decoding of low-density parity-check (LDPC) codes at short and intermediate block lengths are studied. It is shown that in many cases, only four quantization bits suffice to obtain close to ideal performance over a wide range of signal-to-noise ratios. Moreover, we propose modifications to the min-sum algorithm that improve the performance by a few tenths of a decibel with just a small increase in decoding complexity. A quantized version of these modified algorithms is also studied. It is shown that, when optimized, modified quantized min-sum algorithms perform very close to, and in some cases even slightly outperform, the ideal belief-propagation algorithm at observed error rates.
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