Deep learning

Algorithm

Channel (broadcasting)

Artificial intelligence

Block code

Composite material

Computational complexity theory

Materials science

Sequential decoding

Range (aeronautics)

List decoding

MIMO

Hypersphere

Mathematics

Computer science

Artificial neural network

Decoding methods

Telecommunications

Concatenated error correction code

IEEE Transactions on Wireless Communications

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 the 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 the simulation for high-dimensional multiple-input multiple-output (MIMO) systems, using high-order modulations.

Deep Learning-Based Sphere Decoding