Deep Q Network-Aided Adaptive MM Wave Multi-User Noma User Selection and Symbol detection

Given the benefit of massive connectivity, improved user fairness and spectral efficiency, non-orthogonal multiple access (NOMA) has become a promising candidate of multiple access (MA) technology for beyond 5G networks. By exploiting the channel disparities, NOMA is capable of serving multiple

users sharing same time-frequency resources by exploiting superposition coding at the transmitter as well as successive interference cancellation (SIC) at the receiver. Furthermore, with the directional transmission employed for mmWave systems, it is highly likely that multiple users share the same spatial beam. In this scenario, the NOMA assisted transmission can be employed for MA by exploiting the power-domain, i.e. channel disparities, of the users sharing the specific beam.

In the state-of-the-art NOMA systems, SIC assisted detection is employed relying on the simplifying assumption of having perfect CSI at the receiver. However, in the face of channel impairments, the SIC assisted detection degrades the performance because of the error propagation nature observed in SIC, which is highly sensitive to the CSI imperfections.

Additionally, as the number of users in the cluster grow large, the co-channel interference further degrades the performance owing to both SIC complexity and error propagation. This degradation is even more pronounced if the non-linear components introduced by the hardware are considered. Owing to these reasons, it becomes crucial to employ machine learning techniques for jointly modeling the CSI impairments and SIC

Author(s): Satyanarayana Katla

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