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Unmanned aerial vehicles (UAVs) have been widely used in wireless communication in recent years and are a promising component of future wireless systems. UAVs are easily deployable and can provide reliable, robust, and costeffective communication to any desired location. However, the performance parameters of UAV cannot attain the desired level due to additional path loss caused by diffraction in non-line of sight (NLOS) propagation during atmospheric turbulence. In this paper, UAV operated communication system with multihop radio frequency (RF)-free-space optical (FSO) link is proposed. In order to provide better network connectivity and coverage, relays which also act as low altitude platform are configured between source and destination. These relays are modeled using RF and FSO technologies and also maintain decode and forward protocol and linear network coding. Based on this setup, communication channel is classified into three segments namely, ground to UAV, UAV to UAV, and UAV to ground channel. Initially, the optimal altitude of UAVs is derived using RF and FSO channel parameters for reliable system performance. The proposed model is verified using Monte Carlo simulation method, and the performance of the model is measured in terms of outage probability and symbol error rate, assuming that coherent detection takes place at receiver which employs M-ary phase shift keying modulation techniques. Numerical results demonstrate that the proposed multihop RF-FSO link outperforms the relevant benchmark schemes for a given signal to noise ratio value. K E Y W O R D S free-space optical (FSO), Monte Carlo simulation, outage probability, symbol error rate (SER), unmanned aerial vehicle (UAV) 1 | INTRODUCTION A rapid evolution has occurred in the field of wireless communication over the years due to which unmanned aerial vehicles (UAVs) found extensive applications in military, commercial, scientific and agricultural sectors. UAVs such as

Summary Unmanned aerial vehicle (UAV)‐aided aerial base stations have emerged as a promising technique to provide rapid on‐demand wireless coverage for ground communicating devices in a geographical area. However, existing works on UAV‐enabled wireless communication systems overlook optimal deployment of UAVs under quality of service (QoS)‐aware device‐to‐device (D2D) communication. Therefore, this work proposes a UAV‐supported self‐organized device‐to‐device (USSD2D) network that employs multiple UAVs as relays for reliable D2D data transmission. The aim is to maximize the total instantaneous transmission rate of the USSD2D network by jointly optimizing devices' association with UAV, UAVs' channel selection, and their deployed location under signal‐to‐interference‐noise ratio (SINR) threshold. As this joint optimization problem is nonconvex and combinatorial, the formulated problem is transformed into a Markov decision process (MDP) that effectively splits up it into three individual optimization subproblems: devices association, UAVs' channel selection indicator, and UAVs' location at each instance. Finally, a reinforcement learning (RL) based on a low‐complexity iterative state–action–reward–state–action (SARSA) algorithm is developed to update UAVs' policy to solve this formulated problem. UAVs adopt the system parameters according to the current state and corresponding action to maximize the generated long‐term discounted reward under the current policy without prior knowledge about the environment. Numerical results validate the proposed approaches and provide various insights on optimal UAV deployment. This investigation demonstrates that the total instantaneous transmission rate of the USSD2D network can be improved by 75.25%, 51.31%, and 13.96% with respect to RS‐FORD, ES‐FIRD, and AOIV schemes, respectively.

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Unmanned aerial vehicles (UAVs) are capable of surveying expansive areas, but their operational range is constrained by limited battery capacity. The deployment of mobile recharging stations using unmanned ground vehicles (UGVs) significantly extends the endurance and effectiveness of UAVs. However, optimizing the routes of both UAVs and UGVs, known as the UAV-UGV cooperative routing problem, poses substantial challenges, particularly with respect to the selection of recharging locations. Here in this paper, we leverage reinforcement learning (RL) for the purpose of identifying optimal recharging locations while employing constraint programming to determine cooperative routes for the UAV and UGV. Our proposed framework is then benchmarked against a baseline solution that employs Genetic Algorithms (GA) to select rendezvous points. Our findings reveal that RL surpasses GA in terms of reducing overall mission time, minimizing UAV-UGV idle time, and mitigating energy consumption for both the UAV and UGV. These results underscore the efficacy of incorporating heuristics to assist RL, a method we refer to as heuristics-assisted RL, in generating high-quality solutions for intricate routing problems.