Application of deep learning to sphere decoding for large MIMO systems
Nguyen, Nhan Thanh; Lee, Kyungchun; Dai, Huaiyu (2021-05-06)
N. T. Nguyen, K. Lee and H. DaiIEEE, "Application of Deep Learning to Sphere Decoding for Large MIMO Systems," in IEEE Transactions on Wireless Communications, vol. 20, no. 10, pp. 6787-6803, Oct. 2021, doi: 10.1109/TWC.2021.3076527
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https://urn.fi/URN:NBN:fi-fe2021122162809
Tiivistelmä
Abstract
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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