Deep Learning Based Sphere Decoding

Abstract

In this paper, a deep learning (DL)-based sphere decoding algorithm is proposed, where the radius of the decoding hypersphere is learned by a deep neural network (DNN). The performance achieved by the proposed algorithm is very close to the optimal maximum likelihood decoding (MLD) over a wide range of signal-to-noise ratios (SNRs), while the computational complexity, compared to existing sphere decoding variants, is significantly reduced. This improvement is attributed to DNN's ability of intelligently learning the radius of the hypersphere used in decoding. The expected complexity of the proposed DL-based algorithm is analytically derived and compared with existing ones. It is shown that the number of lattice points inside the decoding hypersphere drastically reduces in the DL-based algorithm in both the average and worst-case senses. The effectiveness of the proposed algorithm is shown through simulation for high-dimensional multiple-input multiple-output (MIMO) systems, using high-order modulations.

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• Neural Lattice Decoders
• Intelligent Radio Signal Processing: A Survey
• Decision Directed Channel Estimation Based on Deep Neural Network k-step Predictor for MIMO Communications in 5G
• Sphere Decoder with Box Optimization for FTN Non-orthogonal FDM System
• EQ-Net: A Unified Deep Learning Framework for Log-Likelihood Ratio Estimation and Quantization