Vision-Assisted User Clustering for Robust mmWave-NOMA Systems

NOMA is an emerging paradigm for B5G systems to support a large number and variety of connected users, simultaneously. When operated in the mmWave band and higher bands, user channels get highly correlated which can be exploited in mmWave-NOMA systems to cluster a set of correlated users together and serve them in one beam in the same time slot. Identifying the set of users to cluster together greatly affects the viability of NOMA systems. Typically, only CSI is used to make these clustering decisions. When any problem arises in accessing up-to-date and accurate CSI, user clustering will not properly function due to its hard-dependency on CSI, and obviously, this will negatively affect the robustness of these NOMA systems. To improve the robustness of the NOMA systems, in this paper, we propose to utilize emerging trends such as location-aware and camera-equipped base stations (CBSs) which do not require any extra radio frequency resource consumption. Specifically, we explore three different dimensions of feedback that a CBS can benefit from to solve the user clustering problem, namely CSI-based feedback and non-CSI-based feedback, comprised of UE location and the CBS camera feed. We first investigate how the vision assistance of a CBS can be used in conjunction with other dimensions of feedback to make clustering decisions in various scenarios. Later, we provide a simple user case study to illustrate how to implement vision-assisted user clustering in mmWave-NOMA systems to improve robustness, in which a DL beam selection algorithm is trained on the images captured by the CBS to perform NOMA clustering. We demonstrate that the user clustering without CSI can achieve comparable performance to accurate CSI-based user clustering solutions, and user clustering can continue to function without much performance loss even in the scenarios where CSI is severely outdated or not available at all.