A Deep MIMO Detector Based on MQAM Signal Decomposition and Channel Ordering

Abstract

The recently proposed learning-to-learn iterative search algorithm (LISA) for multiple-input multiple-output (MIMO) detection can achieve excellent BER performance for low-order modulation signals, such as BPSK/QPSK, but its performance is degraded as the modulation order increases. This limits its application in high data rate transmission. Motivated by this observation, this paper proposes a decomposition-ordering-based LISA (DO-LISA) to improve the accuracy of LISA for higher-order QAM signals. Firstly, the detection of high-order QAM signal is transformed into the detection of QPSK signal to exploit the facility of binary classification. To further exploit the sequential features of a bidirectional long short-term memory (BiLSTM) network, channel ordering is adopted to enhance the detection accuracy. Moreover, to make the proposed method more practical, low-rank decomposition and channel pruning are adopted to accelerate and compress the neural network. Numerical experiments show that the proposed method can achieve quasi-ML BER performance. Compared with the full model, the compact model achieves $2.37\times $ compression ratio and $2.38\times $ acceleration ratio with limited accuracy loss. In addition, the proposed DO-LISA shows its good generalization ability by preserving very good BER performance both in the correlated channel condition and imperfect channel condition.

Keywords

• MIMO detection
• deep learning
• LSTM
• V-BLAST
• compression