Source Symbol Purging-Based Distributed Conditional Arithmetic Coding

A distributed arithmetic coding algorithm based on source symbol purging and using the context model is proposed to solve the asymmetric Slepian–Wolf problem. The proposed scheme is to make better use of both the correlation between adjacent symbols in the source sequence and the correlation between the corresponding symbols of the source and the side information sequences to improve the coding performance of the source. Since the encoder purges a part of symbols from the source sequence, a shorter codeword length can be obtained. Those purged symbols are still used as the context of the subsequent symbols to be encoded. An improved calculation method for the posterior probability is also proposed based on the purging feature, such that the decoder can utilize the correlation within the source sequence to improve the decoding performance. In addition, this scheme achieves better error performance at the decoder by adding a forbidden symbol in the encoding process. The simulation results show that the encoding complexity and the minimum code rate required for lossless decoding are lower than that of the traditional distributed arithmetic coding. When the internal correlation strength of the source is strong, compared with other DSC schemes, the proposed scheme exhibits a better decoding performance under the same code rate.

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Distributed Arithmetic Coding Over Binary Erasure Channel

10.21203/rs.3.rs-1090955/v1 ◽

2021 ◽

Author(s):

Xianyu Wang ◽

Cong Li ◽

Jinlin Tan ◽

Rui Zhang ◽

Zhifeng Liang ◽

...

Keyword(s):

Side Information ◽

Experimental Results ◽

Arithmetic Coding ◽

Maximum A Posteriori ◽

A Posteriori ◽

Distributed Arithmetic ◽

Residual Network ◽

Lt Codes ◽

Erasure Channel ◽

Binary Erasure Channel

Abstract In this paper, the Binary Erasure Channel (BEC) is researched by Distributed Arithmetic Coding (DAC) based on Slepian-Wolf coding framework. The source and side information are modelled as a virtual BEC. The DAC decoder uses maximum a posteriori (MAP) as the criterion to recover the source. A deep residual network is used to boost the DAC decoding process. The experimental results show that our algorithm nearly achieves the same performance with LT codes under different erasure probabilities.

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BER Performance Analysis of Non-Coherent Q-Ary Pulse Position Modulation Receivers on AWGN Channel

Sensors ◽

10.3390/s21186102 ◽

2021 ◽

Vol 21 (18) ◽

pp. 6102

Author(s):

Xianhua Shi ◽

Yimao Sun ◽

Jie Tian ◽

Maolin Chen ◽

Youjiang Liu ◽

...

Keyword(s):

Gaussian Noise ◽

Additive White Gaussian Noise ◽

Theoretical Formula ◽

Pulse Position Modulation ◽

Error Performance ◽

Optimal Receiver ◽

Awgn Channel ◽

Simulation Results ◽

The Relationship ◽

Better Than

This paper introduces the structure of a Q-ary pulse position modulation (PPM) signal and presents a noncoherent suboptimal receiver and a noncoherent optimal receiver. Aiming at addressing the lack of an accurate theoretical formula of the bit error rate (BER) of a Q-ary PPM receiver in the additive white Gaussian noise (AWGN) channel in the existing literature, the theoretical formulas of the BER of a noncoherent suboptimal receiver and noncoherent optimal receiver are derived, respectively. The simulation results verify the correctness of the theoretical formulas. The theoretical formulas can be applied to a Q-ary PPM system including binary PPM. In addition, the analysis shows that the larger the Q, the better the error performance of the receiver and that the error performance of the optimal receiver is about 2 dB better than that of the suboptimal receiver. The relationship between the threshold coefficient of the suboptimal receiver and the error performance is also given.

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