Deep Temporal Sequence Prediction Neural Network for MIMO Detection

Recovering the transmitted signals in a multiple-input multiple-output (MIMO) system is known to be non-deterministic polynomial hard. It is extremely challenging to obtain a high-quality solution with fairly low computational complexity. To solve the MIMO detection problem effectively, this paper proposes to model it as a time series prediction problem, and a bidirectional temporal convolutional network (Bi- TCN) is designed to address it. In Bi- TCN, the encoder extracts the features of the received signal and the channel matrix by applying non-causal dilated convolution, and the decoder outputs the probability distribution of the recovered transmitted signal in parallel. In the experiments, we compare it with traditional and deep learning-based detectors on both i.i.d. and correlated Rayleigh fading channels, respectively. Experimental results empirically demonstrate that Bi- TCN can achieve near-optimal bit-error-rate (BER) performance with considerably low space complexity.