论文标题
深度学习的应用在大型MIMO系统的领域解码
Application of Deep Learning to Sphere Decoding for Large MIMO Systems
论文作者
论文摘要
尽管球形解码器（SD）是多输入多输出（MIMO）系统的强大检测器，但在大量使用天线的大量MIMO系统中，它已变得越来越高。为了克服这一挑战，我们提出了快速深度学习（DL）AIDED SD（FDL-SD）和快速DL-ADED $ K $ BEST SD（KSD，FDL-KSD）算法。在其中，DL的主要应用是生成高度可靠的初始候选者，以与候选/层订购和早期拒绝结合使用SD和KSD中的搜索。与现有的DL辅助SD方案相比，我们提出的计划在离线培训和在线应用阶段中都更有优势。具体来说，与现有的DL ADED SD方案不同，它们不需要在培训阶段执行常规SD。对于带有QPSK的$ 24 \ times 24 $ MIMO系统，与常规的SD方案相比，拟议的FDL-SD可将复杂性降低超过$ 90 \％$而没有任何性能损失。对于带有QPSK的$ 32 \ times 32 $ MIMO系统，拟议的FDL-KSD仅需要$ k = 32 $才能获得具有$ k = 256 $的常规KSD的性能，其中$ k $是KSD中生存路径的数量。这意味着绩效的巨大改善 - 拟议的FDL-KSD方案的复杂性权衡。
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 $24 \times 24$ 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 $32 \times 32$ 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.
