Application of Deep Learning to Sphere Decoding for Large MIMO Systems
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Although the sphere decoder (SD) is a powerful detector for multiple-input multiple-output (MIMO) systems, it has become computationally prohibitive in massive MIMO systems, where a large number of antennas are employed. To overcome this challenge, we propose fast deep learning (DL)-aided SD (FDL-SD) and fast DL-aided K-best SD (KSD, FDL-KSD) algorithms. Therein, the major application of DL is to generate a highly reliable initial candidate to accelerate the search in SD and KSD in conjunction with candidate/layer ordering and early rejection. Compared to existing DL-aided SD schemes, our proposed schemes are more advantageous in both offline training and online application phases. Specifically, unlike existing DL-aided SD schemes, they do not require performing the conventional SD in the training phase. For a 24 × 24 MIMO system with QPSK, the proposed FDL-SD achieves a complexity reduction of more than 90% without any performance loss compared to conventional SD schemes. For a 32 × 32 MIMO system with QPSK, the proposed FDL-KSD only requires K = 32 to attain the performance of the conventional KSD with K=256, where K is the number of survival paths in KSD. This implies a dramatic improvement in the performance–complexity tradeoff of the proposed FDL-KSD scheme.
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