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When operated in the mmWave band, user channels get highly correlated whichcan be exploited in mmWave-NOMA systems to cluster a set of "correlated" userstogether. Identifying the set of users to cluster greatly affects the viabilityof NOMA systems. Typically, only channel state information (CSI) is used tomake these clustering decisions. When any problem arises in accessingup-to-date and accurate CSI, user clustering will not properly function due toits hard-dependency on CSI, and obviously, this will negatively affect therobustness of the NOMA systems. To improve the robustness of the NOMA systems,we propose to utilize emerging trends such as location-aware andcamera-equipped base stations (CBSs) which do not require any extra radiofrequency resource consumption. Specifically, we explore three differentdimensions of feedback that a CBS can benefit from to solve the user clusteringproblem, namely CSI-based feedback and non-CSI-based feedback, comprised ofuser equipment (UE) location and the CBS camera feed. We first investigate howthe vision assistance of a CBS can be used in conjunction with other dimensionsof feedback to make clustering decisions in various scenarios. Later, weprovide a simple user case study to illustrate how to implement vision-assisteduser clustering in mmWave-NOMA systems to improve robustness, in which a deeplearning (DL) beam selection algorithm is trained on the images captured by theCBS to perform NOMA clustering. We demonstrate that user clustering without CSIcan achieve comparable performance to accurate CSI-based solutions, and userclustering can continue to function without much performance loss even in thescenarios where CSI is severely outdated or not available at all.

Vision-Assisted User Clustering for Robust mmWave-NOMA Systems