In traditional fog-radio access networks (F-RANs), deploying many enhanced remote radio heads (eRRHs) faces challenges due to site limitations and fronthaul link costs. To tackle this issue, we introduced smart helpers (SHs) in our earlier study. SHs mainly listen to communications between eRRHs and users, smartly cache popular content, and serve users without having a fronthaul link to the macro base station (MBS). In this paper, we explore using an unmanned aerial vehicle (UAV) as the SH to assess the potential benefits of integrating UAVSHs into F-RANs. Because UAVs typically have limited battery life and cover smaller areas, we leverage a reconfigurable intelligent surface (RIS) to extend the service area of the UAVSH. To assess the performance of the considered UAVSH-aided F-RAN, we formulate the problem of average delivery delay minimization. To tackle the problem, we develop an algorithm to optimize user and RIS scheduling and caching decisions using multiagent reinforcement learning (MARL). The simulation results numerically prove that integrating a UAVSH into a RIS-assisted F-RAN leads to significant improvements in delivery delay, fronthaul load, and cache hit rate metrics.

In traditional fog-radio access networks (F-RANs), enhanced remote radio heads (eRRHs) are connected to a macro base station (MBS) through fronthaul links. Deploying a massive number of eRRHs is not always feasible due to site constraints and the cost of fronthaul links. This paper introduces an innovative concept of using smart helpers (SHs) in F-RANs. These SHs do not require fronthaul links and listen to the within-coverage eRRHs’ communications. Then, they smartly select and cache popular content. This capability enables SHs to serve users with frequent on-demand service requests potentially. As such, network operators have the flexibility to easily deploy SHs in various scenarios, such as dense urban areas and temporary public events, to expand their F-RANs and improve the quality of service (QoS). To study the performance of the proposed SH-aided F-RAN, we formulate an optimization problem of minimizing the average transmission delay that jointly optimizes cache resources and user scheduling. To tackle the formulated problem, we develop an innovative 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 evaluated numerically and compared with those of traditional F-RANs.

In this paper, we consider a cognitive radio system with some of secondary users (SUs) equipped with multiple antenna full-duplex transceivers. We design a hybrid system to sense and access the spectrum opportunistically such that the SU operates on both underlay and interweave hybrid methods while the primary user (PU) may be active or inactive, respectively, to perform the sensing as agile as possible. Moreover, we maximize the throughput by allocating the spectral resources assuming limited available energy during each time frame with constraints on the interference to the primary user. We show that this problem is convex and propose a simple solution algorithm for it. Our simulations confirm the accuracy of our analytical and theoretical results and illustrate that the total achieved throughput of full-duplex cognitive radio even by considering self-interference is maximized under the assumed conditions.