Deep MIMO Detection Using ADMM Unfolding
Refereed conference paper presented and published in conference proceedings

替代計量分析
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其它資訊
摘要This paper presents a low-complexity deep neural network (DNN)based multiple-input-multiple-output (MIMO) detector for the BPSK and QPSK constellation cases. We employ deep unfolding, whose idea is to take insight from the structure of an iterative optimization algorithm and attempt to learn a better iterative algorithm. The structure of the network is obtained from an iterative algorithm arising from the application of ADMM to the maximum-likelihood MIMO detection problem. The number of parameters to be learnt in this new design is less than that of DetNet, a recently proposed DNN-based MIMO detector. Our numerical experiments illustrate that the new method outperforms DetNet and several existing MIMO detectors in the large-scale MIMO case. In particular, we show that for a 160×160 MIMO system, our DNN design, with 40 layers, can attain nearly optimal bit-error rate performance.
出版社接受日期14.04.2019
著者Man-Wai Un, Mingjie Shao, Wing-Kin Ma, P. C. Ching
會議名稱2019 IEEE Data Science Workshop (DSW)
會議開始日02.06.2019
會議完結日05.06.2019
會議地點Minneapolis, MN
會議國家/地區美國
會議論文集題名2019 IEEE Data Science Workshop (DSW)
出版年份2019
月份6
出版社IEEE
頁次333 - 337
國際標準書號978-1-7281-0708-0
語言美式英語
關鍵詞MIMO detection, deep learning, neural networks, deep unfolding, ADMM

上次更新時間 2020-25-10 於 03:23