Deep Learning-based Uplink Data Detection in User-Centric Cell-Free mMIMO Systems

This paper tackles the problem of uplink data detection in a user-centric cell-free massive multi-input multi-output (UC-CF-mMIMO). First of all, we cast the uplink data detection in UC-CF-mMIMO with large-scale fading decoding (LSFD) as a classical MIMO detection problem. Next, we develop a new detection structure, called LMDPIC, which combines linear minimum mean square error (LMMSE) and deep-learning-based parallel interference cancellation (DeepPIC) detectors for symbol detection. Simulation results demonstrate that LMDPIC outperforms other state-of-the-art MIMO detection schemes for BPSK and QPSK modulation schemes, both in the case of the perfect and in that of imperfect channel state information (CSI). We also evaluate the performance of LMDPIC for several power-control strategies. Our results show that for 5% UEs with the highest pairwise symbol error rate, the LMDPIC with half power transmission outperforms the LMMSE with full power transmission. Finally the paper shows that the deep neural network (DNN) used in the LMDPIC structure is robust against CSI time variations.