Cross-Channel Model-Driven Learning for Massive MIMO Detection by HyperNetwork

For the signal detection problem in a multiple-input multiple-output (MIMO) system, it has been demonstrated that deep learning can improve the detection accuracy and/or reduce the complexity of traditional detection algorithms under the assumption that the channel scenario remains the same in training and test. However, this assumption is not appropriate since the communication environment in practice is constantly changing. As a result, the performance of deep-learning-based detection methods will degrade significantly due to their lack of generalization ability. To address this problem, we model the channel scenario adaptation problem as a multi-scenario learning task and propose two schemes to improve the adaptability of model-driven detection network to cross-channel scenarios. For the case where the test channel scenario has been seen in the training stage, a hypernetwork is introduced to the deep-learning-based iterative soft thresholding algorithm (DISTA) to generate a personalized set of network parameters for each channel scenario, which is named hyperDISTA. Experimental results show that hyperDISTA trained in multiple channel scenarios can not only adapt to each seen channel scenario but also outperform existing deep-learning-based detectors trained in the single channel scenario at high signal-to-noise ratio (SNR) regimes. For the case where the test channel scenario is unseen in the training stage, we propose to retrain the hyperDISTA in a semi-supervised manner. Experimental results show that the retrained hyperDISTA achieves a performance that is comparable to that of the maximum likelihood detection algorithm (MLD).