A LAGRANGIAN DUAL RELAXATION APPROACH TO ML MIMO DETECTION: REINTERPRETING REGULARIZED LATTICE DECODING
Refereed conference paper presented and published in conference proceedings

香港中文大學研究人員

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摘要This paper describes a new approximate maximum-likelihood (ML) MIMO detection approach by studying a Lagrangian dual relaxation (LDR) of ML. Unlike many existing relaxed ML methods, the proposed LDR employs a discrete domain for the problem formulation. We find that the proposed LDR exhibits an intriguing relationship to the lattice decoders (LDs) and the lattice reduction aided (LRA) detectors, both of which have caught much attention recently. Specifically, regularization in LDs, which was proposed to mitigate out-of-bounds symbol effects, can alternatively be interpreted as a way to constrain the symbol decision within bounds in a Lagrangian sense. We handle the LDR problem by using a projected subgradient method. The resultant method may physically be viewed as an adaptive regularization control in which a sequence of LDs are involved. Based on this newly developed insight, we propose two additional iterative LDR-based detectors using LRA decision-feedback (DF) and "lazy" DF. By simulation results, we show that the LDR LRA-DF and lazy-DF detectors yield better symbol error rate performance than the MMSE-regularized LRA-DF and DF detectors, respectively, where the SNR gaps can be more than 3dB.
著者Pan JX, Ma WK
會議名稱IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
會議開始日22.05.2011
會議完結日27.05.2011
會議地點Prague
會議國家/地區捷克共和國
詳細描述organized by IEEE Signal Processing Society,
出版年份2011
月份1
日期1
出版社IEEE
頁次3084 - 3087
電子國際標準書號978-1-4577-0539-7
國際標準期刊號1520-6149
語言英式英語
關鍵詞Lagrangian duality; lattice decoding; lattice reduction; MIMO detection; regularization
Web of Science 學科類別Acoustics; Engineering; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology

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