Joint optimization of deployment, user association, channel, and resource allocation for fairness-aware multi-UAV network

This paper studies the problem of joint deployment, user association, channel, and resource allocation in unmanned aerial vehicle-enabled access network. Since different user equipments performing different tasks and have different data rate requirements, the priority-based traffic fairness problem is investigated. This problem, however, is a mixed integer nonlinear programming problem with NP-hard complexity, making it challenging to be solved. To address this issue, a self-organized and distributed framework "sense-as-you-fly" based on the decomposition process, which divides the original problem into several subproblems, is proposed. Assuming without central controller, we derive the closed-form resource allocation scheme and propose distributed many-to-one matching to optimize user association subproblem. Considering the coupled characteristics, the multi-unmanned aerial vehicle deployment and channel allocation subproblems are modelled as a local altruistic game. The existence of Nash equilibrium is proved with the aid of exact potential game and efficient best response learning-based algorithm is proposed. The original problem is finally addressed by solving the sub-problems alternately and iteratively. Simulation results verify its effectiveness. By jointly optimizing multidimensional variables, the proposed algorithm unlocks network performance gains, especially in resource-limited regimes. This paper investigates the priority-aware traffic fairness problem in a unmanned aerial vehicle-enabled wireless access network. A self-organized and distributed framework is proposed where multi-unmanned aerial vehicle deployment, user association, channel, and resource allocation are jointly considered. The performance is analysed, and extensive simulations verify the effectiveness and robustness of the proposed algorithm. image