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As vehicular communication networks embrace metaverse beyond 5G/6G systems, the rich content update via the least interfered subchannel of the optimal frequency band in a hybrid band vehicle to everything (V2X) setting emerges as a challenging optimization problem. We model this problem as a tradeoff between multi-band VR/AR devices attempting to perform metaverse scenes and environmental updates to metaverse roadside units (MRSUs) while minimizing energy consumption. Due to the computational hardness of this optimization, we formulate an opportunistic band selection problem using a multi-armed bandit (MAB) that provides a good quality solution in real-time without computationally burdening the already stretched augmented/virtual reality (AR/VR) units acting as transmitting nodes. The opportunistic use of scheduling rich content updates at traffic signals and stand-still scenarios maps well with the formulated bandit problem. We propose a Dual-Objective Minimax Optimal Stochastic Strategy (DOMOSS) as a natural solution to this problem. Through extensive computer-based simulations, we demonstrate the effectiveness of our proposal in contrast to baselines and comparable solutions. We also verify the quality of our solution and the convergence of the proposed strategy. 

Recently emerging WiGig systems experience limited coverage and signal strength fluctuations due to strict line-of-sight (LoS) connectivity requirements. In this paper, we address these shortcomings of WiGig communication by exploiting two emerging technologies in tandem, namely the reconfigurable intelligent surface (RIS) and unmanned aerial vehicles (UAVs). In ultra-dense traffic sites (referred to as hotspots) where WiGig nodes or User Devices (UDs) experience complex propagation and non-line-of-sight (non-LoS) environment, we envision the deployment of a UAV-mounted RIS system to complement the WiGig base station (WGBS) to deliver services to the UDs. However, commercially available UAVs have limited energy (i.e., constrained flight time). Therefore, the trajectory of our considered UAV needs to be locally estimated to enable it to serve multiple hotspots while minimizing its energy consumption within the WGBS coverage boundaries. Since this tradeoff problem is computationally expensive for the resource-constrained UAV, we argue that sequential learning can be a lightweight yet effective solution to locally solve the problem with a low impact on the available energy on the UAV. We formally formulate this problem as a contextual multi-armed bandit (CMAB) game. Then, we develop the linear randomized upper confidence bound (Lin-RUCB) algorithm to solve the problem effectively. We regard the UAV as the bandit learner, which attempts to maximize its attainable rate (i.e., the reward) by serving distinct hotspots in its trajectory that we treat as the arms of the considered bandit. The context is defined as the hotspots’ locations provided using GPS (global positioning system) service and the reward history of each hotspot. Our proposal accounts for the energy expenditure of the UAV in moving from one hotspot to another within its battery charge lifetime. We evaluate the performance of our proposal via extensive simulations that exhibit the superiority of our proposed.