Optimum Low-Complexity Decoder for Spatial Modulation

In this paper, a novel low-complexity detection algorithm for spatial modulation (SM), referred to as the minimum-distance of maximum-length (m-M) algorithm, is proposed and analyzed. The proposed m-M algorithm is a smart searching method that is applied for the SM tree-search decoders. The behavior...

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Vydáno v:IEEE journal on selected areas in communications Ročník 37; číslo 9; s. 2001 - 2013
Hlavní autoři: Al-Nahhal, Ibrahim, Basar, Ertugrul, Dobre, Octavia A., Ikki, Salama
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.09.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Complexity
complexity analysis
Complexity theory
Computer simulation
Decoders
Decoding
low-complexity algorithms
maximum likelihood (ML) decoder
Maximum likelihood decoding
Maximum likelihood estimation
Modulation
Monte Carlo simulation
Multiple-input multiple-output (MIMO) systems
Random variables
Receiving antennas
Reduction
spatial modulation (SM)
sphere decoder (SD)
Transmitting antennas
Variance
ISSN:0733-8716, 1558-0008
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Shrnutí:In this paper, a novel low-complexity detection algorithm for spatial modulation (SM), referred to as the minimum-distance of maximum-length (m-M) algorithm, is proposed and analyzed. The proposed m-M algorithm is a smart searching method that is applied for the SM tree-search decoders. The behavior of the m-M algorithm is studied for three different scenarios: 1) perfect channel state information at the receiver side (CSIR); 2) imperfect CSIR of a fixed channel estimation error variance; and 3) imperfect CSIR of a variable channel estimation error variance. Moreover, the complexity of the m-M algorithm is considered as a random variable, which is carefully analyzed for all scenarios, using probabilistic tools. Based on a combination of the sphere decoder (SD) and ordering concepts, the m-M algorithm guarantees to find the maximum-likelihood (ML) solution with a significant reduction in the decoding complexity compared with SM-ML and existing SM-SD algorithms; it can reduce the complexity up to 94% and 85% in the perfect CSIR and the worst scenario of imperfect CSIR, respectively, compared with the SM-ML decoder. The Monte Carlo simulation results are provided to support our findings as well as the derived analytical complexity reduction expressions.
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ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2019.2929454