Deep Learning for Signal Detection in Uplink and Downlink Rate-Splitting Multiple Access Systems

In this paper, we propose a long short-term memory- based deep learning (DL) architecture for signal detection in uplink and downlink rate-splitting multiple access systems with multi-carrier modulation. The proposed DL detector eliminates the need for the use of successive interference cancellation, which suffers from disadvantages such as error propagation. In an orthogonal frequency division multiplexing setting, we show that the proposed DL detector outperforms the standard least squares detector and the minimum mean-squared error receiver and attains the performance of the optimal maximum likelihood detector, in terms of the symbol error rate.