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The support of coexisting ultra-reliable and low-latency (URLL) and enhancedMobile BroadBand (eMBB) services is a key challenge for the current and futurewireless communication networks. Those two types of services introduce strict,and in some time conflicting, resource allocation requirements that may resultin a power-struggle between reliability, latency, and resource utilization inwireless networks. The difficulty in addressing that challenge could be tracedback to the predominant reactive approach in allocating the wireless resources.This allocation operation is carried out based on received service requests andglobal network statistics, which may not incorporate a sense of\textit{proaction}. Therefore, this paper proposes a novel framework termed\textit{service identification} to develop novel proactive resource allocationalgorithms. The developed framework is based on visual data (captured forexample by RGB cameras) and deep learning (e.g., deep neural networks). Theultimate objective of this framework is to equip future wireless networks withthe ability to analyze user behavior, anticipate incoming services, and performproactive resource allocation. To demonstrate the potential of the proposedframework, a wireless network scenario with two coexisting URLL and eMBBservices is considered, and two deep learning algorithms are designed toutilize RGB video frames and predict incoming service type and its requesttime. An evaluation dataset based on the considered scenario is developed andused to evaluate the performance of the two algorithms. The results confirm theanticipated value of proaction to wireless networks; the proposed models enableefficient network performance ensuring more than $85\%$ utilization of thenetwork resources at $\sim 98\%$ reliability. This highlights a promisingdirection for the future vision-aided wireless communication networks.

Computer Vision Aided URLL Communications: Proactive Service Identification and Coexistence