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A Near BER-Optimal Decoding Algorithm for Convolutionally Coded Relay Channels With the Decode-and-Forward Protocol

delete2017-03-01
delete9
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Wai Ho Mow
DOI：10.1109/TWC.2017.2654343delete
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Abstract

Abstract

En 中文
Relay-assisted communication has been shown to be an effective technique to improve the reliability and throughput of real-world wireless communication networks. It enables single-antenna users to form a virtual antenna array without installing multiple antennas at the transmitter or the receiver. This paper re-examines the decoding problem in the convolutionally coded relay channel with the classical decode-and-forward protocol. By tackling the challenge of modeling the error propagation effect at the relay, a near BER-optimal decoding (NBOD) algorithm at the destination is derived, assuming the availability of perfect receiver channel state information. Its decoding complexity is linear in the information block length, while the exact BER-optimal decoding algorithm with a sub-exponential complexity is still unknown. The major approximation involved in the derivation of the NBOD algorithm is the pairwise error probability approximation that causes a practically insignificant degradation from the optimal BER performance. Our simulation result confirms that the proposed NBOD algorithm can perform close to the maximum likelihood performance bound on BER in the three-node one-way relay channel scenario. In addition, we have further extended the proposed algorithm for more general single-source single-destination decode-and-forward-based relay networks. Simulation results further verify that the proposed NBOD algorithm can outperform existing decoding algorithms based on maximal-ratio combining and selective decode-and-forward in various relay channel scenarios at the cost of higher complexity.
Keywords:
Relay networks
decode-and-forward
convolutional codes
BER-optimal decoding
BCJR algorithm
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Journal

IEEE Transactions on Wireless Communications cover
IEEE Transactions on Wireless Communications
IF:
10.7
Papers:
1.3W
Citations:
5.3W

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