Resource Management for Transmit Power Minimization in UAV-Assisted RIS HetNets Supported by Dual Connectivity

Abstract

This paper proposes a novel approach to improve the performance of a heterogeneous network (HetNet) supported by dual connectivity (DC) by adopting multiple unmanned aerial vehicles (UAVs) as passive relays that carry reconfigurable intelligent surfaces (RISs) . More specifically, RISs are deployed under the UAVs termed as UAVs-RISs that operate over the micro-wave (µW) channel in the sky to sustain a strong line-of-sight (LoS) connection with the ground users. The macro-cell operates over the µW channel based on orthogonal multiple access (OMA), while small base stations (SBSs) operate over the millimeter-wave (mmW) channel based on non-orthogonal multiple access (NOMA). We study the problem of total transmit power minimization by jointly optimizing the trajectory/velocity of each UAV, RISs' phase shifts, subcarrier allocations, and active beamformers at each BS. The underlying problem is highly non-convex and the global optimal solution is intractable. To handle it, we decompose the original problem into two subproblems, i.e., a subproblem which deals with the UAVs' trajectories/velocities, RISs' phase shifts, and subcarrier allocations for µW; and a subproblem for active beamforming design and subcarrier allocation for mmW. In particular, we solve the first subproblem via the dueling deep Q-Network (DQN) learning approach by developing a distributed algorithm which leads to a better policy evaluation. Then, we solve the active beamforming design and subcarrier allocation for the mmW via the successive convex approximation (SCA) method. Simulation results exhibit the effectiveness of the

UAV-Assisted RIS for Future Wireless Communications: A Survey on Optimization and Performance Analysis

“…In [83], the authors have proposed a multiple UAVs mounted RISs system that communicates in HetNets supported by dual connectivity. The proposed system operated with micro-wave channels at UAVs-RISs while maintaining the LoS communication with the users.…”

“…The Federated learning-based UAV-RIS model could be a possible definite solution towards various problems we talk about such as secrecy performance, energy efficiency, and spectral efficiency. In [80]- [83], the authors have identified that implementation of various DRL techniques has enhanced the energy performance of UAVs and reduced the latency in data transmission. Whereas, few other stand-alone systems work also indicated that implementing federated learning in their proposed system models has shown significant performance gain.…”

“…Whereas, few other stand-alone systems work also indicated that implementing federated learning in their proposed system models has shown significant performance gain. In [88], it is particularly Optimization × [62]- [68] Efficiency Enhancement × [69]- [72] Secrecy Performance × [74]- [77] Communication techniques × [80]- [83] Deep Reinforcement Learning × [85]- [87] IoT × mentioned that federated learning could be a potential and attractive candidate to integrate with RIS. Another similar potential technology for UAV-assisted RIS is Artificial Intelligent and Internet of Things (IoT).…”