Computer Vision Aided URLL Communications: Proactive Service Identification and Coexistence

• URL: http://arxiv.org/abs/2103.10419v1
• Date: Thu, 18 Mar 2021 17:53:29 GMT
• Title: Computer Vision Aided URLL Communications: Proactive Service Identification and Coexistence
• Authors: Muhammad Alrabeiah, Umut Demirhan, Andrew Hredzak, and Ahmed Alkhateeb
• Abstract summary: Support of coexisting ultra-reliable and low-latency (URLL) and enhanced Mobile BroadBand (eMBB) services is a key challenge for wireless networks. This paper proposes a novel framework termed textitservice identification to develop novel proactive resource allocation algorithms. The framework is based on visual data (captured for example by RGB cameras) and deep learning (e.g., deep neural networks)
• Score: 17.623847356925964
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The support of coexisting ultra-reliable and low-latency (URLL) and enhanced Mobile BroadBand (eMBB) services is a key challenge for the current and future wireless communication networks. Those two types of services introduce strict, and in some time conflicting, resource allocation requirements that may result in a power-struggle between reliability, latency, and resource utilization in wireless networks. The difficulty in addressing that challenge could be traced back to the predominant reactive approach in allocating the wireless resources. This allocation operation is carried out based on received service requests and global 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 allocation algorithms. The developed framework is based on visual data (captured for example by RGB cameras) and deep learning (e.g., deep neural networks). The ultimate objective of this framework is to equip future wireless networks with the ability to analyze user behavior, anticipate incoming services, and perform proactive resource allocation. To demonstrate the potential of the proposed framework, a wireless network scenario with two coexisting URLL and eMBB services is considered, and two deep learning algorithms are designed to utilize RGB video frames and predict incoming service type and its request time. An evaluation dataset based on the considered scenario is developed and used to evaluate the performance of the two algorithms. The results confirm the anticipated value of proaction to wireless networks; the proposed models enable efficient network performance ensuring more than $85\%$ utilization of the network resources at $\sim 98\%$ reliability. This highlights a promising direction for the future vision-aided wireless communication networks.

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