Incorporating Sensing into MIMO-NOMA Communications: Theoretical Joint Rates Bound and Learning-Based Optimal Design

This paper focuses on non-orthogonal multiple ac-cess (NOMA) scenarios with emerging sensing targets, where beamforming is utilized to integrate sensing and NOMA communications (NOMA-ISAC). This scheme does not require ded-icated sensing signals, avoids further degradation of system performance in the overloaded regime, and facilitates massive device connectivity in future wireless networks. We investigate the performance region of NOMA-ISAC from an information-theoretic perspective and propose a learning-based design to approach the given performance bound. Specifically, the upper bounds for the sensing estimation rate and communication information rate are derived to evaluate the achievable rate region of NOMA-ISAC. Guided by mutual information, the proposed end-to-end learning architecture for NOMA-ISAC enables joint transmitter-receiver optimization to achieve bound-approaching performance. Furthermore, we design a sensing mutual information neural estimation unit (SMEU) for the proposed architecture to evaluate the available sensing mutual information. Simulation results show that the proposed scheme can approach the theoretical bounds of NOMA-ISAC.