Journal
IEEE TRANSACTIONS ON SIGNAL PROCESSING
Volume 69, Issue -, Pages 781-796Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2020.3048232
Keywords
MIMO communication; Optimization; Signal processing algorithms; Massive MIMO; Training; Neural networks; Deep learning; MIMO detection; one-bit MIMO; homotopy optimization; deep unfolding
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Funding
- General Research Fund (GRF) of the Research Grant Council (RGC), Hong Kong [CUHK 142017318]
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In this paper, the one-bit ML MIMO detection problem under binary symbol constellations is investigated, and a homotopy optimization algorithm is proposed. It is shown that the algorithm has advantages in performance and computational complexity.
In this paper we consider maximum-likelihood (ML) MIMO detection under one-bit quantized observations and binary symbol constellations. This problem is motivated by the recent interest in adopting coarse quantization in massive MIMO systems-as an effective way to scale down the hardware complexity and energy consumption. Classical MIMO detection techniques consider unquantized observations, and many of them are not applicable to the one-bit MIMO case. We develop a new non-convex optimization algorithm for the one-bit ML MIMO detection problem, using a strategy called homotopy optimization. The idea is to transform the ML problem into a sequence of approximate problems, from easy (convex) to hard (close to ML), and with each problem being a gradual modification of its previous. Then, our attempt is to iteratively trace the solution path of these approximate problems. This homotopy algorithm is well suited to the application of deep unfolding, a recently popular approach for turning certain model-based algorithms into data-driven, and performance enhanced, ones. While our initial focus is on one-bit MIMO detection, the proposed technique also applies naturally to the classical unquantized MIMO detection. We performed extensive simulations and show that the proposed homotopy algorithms, both non-deep and deep, have satisfactory bit-error probability performance compared to many state-of-the-art algorithms. Also, the deep homotopy algorithm has attractively low computational complexity.
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