计算机视觉辅助URLL通信：主动服务识别和共存
Computer Vision Aided URLL Communications: Proactive Service Identification and Coexistence
摘要：
对共存的超可靠和低延迟（URLL）和增强型移动宽带（eMBB）服务的支持是当前和未来无线通信网络的关键挑战。这两种类型的服务引入了严格的、并且有时是冲突的资源分配要求，这可能导致无线网络中的可靠性、延迟和资源利用之间的功率斗争。解决这一挑战的困难可以追溯到在分配无线资源时占主导地位的反应性方法。该分配操作是基于接收到的服务请求和全局网络统计来执行的，其可以不包含\textit{proaction}的意义。因此，本文提出了一种新的框架称为\textit{service identification}开发新的主动资源分配算法。开发的框架基于视觉数据（例如由RGB相机捕获）和深度学习（例如，深度神经网络）。该框架的最终目标是使未来的无线网络具有分析用户行为、预测传入服务和执行主动资源分配的能力。为了展示所提出的框架的潜力，考虑了具有两个共存的URLL和eMBB服务的无线网络场景，并且设计了两个深度学习算法来利用RGB视频帧并预测传入服务类型及其请求时间。一个评估数据集的基础上所考虑的情况下开发和使用的两种算法的性能进行评估。结果证实了预期的价值的主动无线网络，所提出的模型，使高效的网络性能，确保超过$85\%$利用网络资源在$\sim 98\%$可靠性。这为未来的视觉辅助无线通信网络指明了一个很有前途的方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
