Optimum Low-Complexity Decoder for Spatial Modulation
Copyright policy )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 of the m-M algorithm is studied for three different scenarios: i) perfect channel state information at the receiver side (CSIR), ii) imperfect CSIR of a fixed channel estimation error variance, and iii) 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 to 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 to the SM-ML decoder. Monte Carlo simulation results are provided to support our findings as well as the derived analytical complexity reduction expressions.
12 pages, 15 figures. To appear on IEEE Journal on Selected Areas in Communications
- Memorial University of Newfoundland Canada
- Koç University Turkey
- St. John's University United States
- Lakehead University Canada
Engineering, electrical and electronic; Telecommunications, FOS: Computer and information sciences, Low-complexity algorithms, Computer Science - Information Theory, Information Theory (cs.IT), Sphere decoder (SD), Multiple-input multiple-output (MIMO) systems, Computational Complexity (cs.CC), electrical and electronic, Multiple-input multiple-output (MIMO) systems; Spatial modulation (SM); Maximum likelihood (ML) decoder; Sphere decoder (SD); Low-complexity algorithms; Complexity analysis, Maximum likelihood (ML) decoder, Computer Science - Computational Complexity, Engineering, Spatial modulation (SM), Telecommunications, Complexity analysis
Engineering, electrical and electronic; Telecommunications, FOS: Computer and information sciences, Low-complexity algorithms, Computer Science - Information Theory, Information Theory (cs.IT), Sphere decoder (SD), Multiple-input multiple-output (MIMO) systems, Computational Complexity (cs.CC), electrical and electronic, Multiple-input multiple-output (MIMO) systems; Spatial modulation (SM); Maximum likelihood (ML) decoder; Sphere decoder (SD); Low-complexity algorithms; Complexity analysis, Maximum likelihood (ML) decoder, Computer Science - Computational Complexity, Engineering, Spatial modulation (SM), Telecommunications, Complexity analysis
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