Latency-Aware Radio Resource Optimization in Learning-Based Cloud-Aided Small Cell Wireless Networks
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Low latency communication is one of the fundamental requirements for 5G wireless networks and beyond. In this paper, a novel approach for joint caching, user scheduling and resource allocation is proposed for minimizing the queuing latency in serving user's requests in cloud-aided wireless networks. Due to the slow temporal variations in user requests, a time-scale separation technique is used to decouple the joint caching problem from user scheduling and radio resource allocation problems. To serve the spatio-temporal user requests under storage limitations, a Reinforcement Learning (RL) approach is used to optimize the caching strategy at the small cell base stations by minimizing the content fetching cost. A spectral clustering algorithm is proposed to speed-up the convergence of the RL algorithm for a large content caching problem by clustering contents based on user requests. Meanwhile, a dynamic mechanism is proposed to locally group coupled base stations based on user requests to collaboratively optimize the caching strategies. To further improve the latency in fetching and serving user requests, a dynamic matching algorithm is proposed to schedule users and to allocate users to radio resources based on user requests and queue lengths under probabilistic latency constraints. Simulation results show the proposed approach significantly reduces the average delay from 21 random resource allocation and random scheduling baselines.
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