Low-Complexity Sphere Decoding of Polar Codes Based on Optimum Path Metric

Sphere decoding (SD) of polar codes is an efficient method to achieve the error performance of maximum likelihood (ML) decoding. But the complexity of the conventional sphere decoder is still high, where the candidates in a target sphere are enumerated and the radius is decreased gradually until no...

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Veröffentlicht in:IEEE communications letters 2014-02, Vol.18 (2), p.332-335
Hauptverfasser: Niu, Kai, Chen, Kai, Lin, Jiaru
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description Sphere decoding (SD) of polar codes is an efficient method to achieve the error performance of maximum likelihood (ML) decoding. But the complexity of the conventional sphere decoder is still high, where the candidates in a target sphere are enumerated and the radius is decreased gradually until no available candidate is in the sphere. In order to reduce the complexity of SD, a stack SD (SSD) algorithm with an efficient enumeration is proposed in this paper. Based on a novel path metric, SSD can effectively narrow the search range when enumerating the candidates within a sphere. The proposed metric follows an exact ML rule and takes the full usage of the whole received sequence. Furthermore, another very simple metric is provided as an approximation of the ML metric in the high signal-to-noise ratio regime. For short polar codes, simulation results over the additive white Gaussian noise channels show that the complexity of SSD based on the proposed metrics is up to 100 times lower than that of the conventional SD.
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subjects Algorithms
Applied sciences
Approximation methods
Coding, codes
Complexity theory
Exact sciences and technology
Information, signal and communications theory
Maximum likelihood decoding
maximum likelihood rule
Measurement
Polar codes
Signal and communications theory
Signal to noise ratio
sphere decoding
successive cancellation decoding
Telecommunications and information theory
Vectors
title Low-Complexity Sphere Decoding of Polar Codes Based on Optimum Path Metric
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