Optimizing Caching in a C-RAN With a Hybrid Millimeter-Wave/Microwave Fronthaul Link via Dynamic Programming

Abstract

Placing selected content at the edge of the network close to the users, known as caching, can lower network latency and congestion in the fronthaul link. Unlike most works that assume a fixed or limited variation in file popularities, to better address user requests, we consider a time-varying popularity resulting in hidden-mode Markov decision processes. In fact, each mode captures environmental changes, and we optimize the fetching and dropping (of files) decisions to minimize a long-term network cost in a cloud radio access network. Importantly, the primary fronthaul link is a millimeter (mmWave) link with large capacity supported by a microwave backup link in case of blockage. Since caching decisions are coupled over time and can affect the future, we introduce a dynamic programming approach to solve the caching problem. We approximate the future cost of each cache state in each mode. To reduce the complexity of calculating the future cost, we introduce two approximation approaches and illustrate the accuracy of the approximations under different environmental conditions. Finally, our simulation results confirm the effectiveness of our proposed algorithm in finding effective caching and fetching decisions to lower the total network cost while dealing with time-varying popularities.