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In traditional Fog-Radio Access Networks (F-RANs), enhanced remote radioheads (eRRHs) are connected to a macro base station (MBS) through fronthaullinks. Deploying a massive number of eRRHs is not always feasible due to siteconstraints and the cost of fronthaul links. This paper introduces aninnovative concept of using smart helpers (SHs) in F-RANs. These SHs do notrequire fronthaul links and listen to the nearby eRRHs' communications. Then,they smartly select and cache popular content. This capability enables SHs toserve users with frequent on-demand service requests potentially. As such,network operators have the flexibility to easily deploy SHs in variousscenarios, such as dense urban areas and temporary public events, to expandtheir F-RANs and improve the quality of service (QoS). To study the performanceof the proposed SH-aided F-RAN, we formulate an optimization problem ofminimizing the average transmission delay that jointly optimizes cacheresources and user scheduling. To tackle the formulated problem, we develop aninnovative multi-stage algorithm that uses a reinforcement learning (RL)framework. Various performance measures, e.g., the average transmission delay,fronthaul load, and cache hit rate of the proposed SH-aided F-RAN are evaluatednumerically and compared with those of traditional F-RANs.

Smart Helper-Aided F-RANs: Improving Delay and Reducing Fronthaul Load