Unmanned aerial vehicles (UAVs) are considered to be excellent candidates of airborne relays or base stations for the 3-D ad hoc networks. They can be promptly deployed to serve large bursts of communication traffic in cellular networks or provide timely network support in wireless sensor networks (WSNs). It is desirable but challenging to dynamically find the optimal deployment strategy of UAVs in the air to provide a better Quality of Service (QoS) for a UAV-assisted wireless network. In this article, we propose a novel Gibbs-sampling distributed algorithm (GSDA) to dynamically optimize the UAVs’ locations when they serve as airborne base stations for ground users. In our proposed GSDA, channel capacity is adopted as the objective function and a distributed approach is employed such that each UAV is able to optimize its location independently and asynchronously. Furthermore, we propose a polynomial-regression-based predictor to make use of users’ moving trajectories and take advantage of the predicted users’ future locations to expedite the convergence of the GSDA. Meanwhile, we also compare our proposed GSDA with the existing distributed genetic algorithm. The asynchronization of UAV location updates and the location errors are also investigated to evaluate the robustness of the GSDA. Simulation results demonstrate that our proposed novel GSDA is quite robust and superior to the existing distributed genetic algorithm.

Unmanned aerial vehicles (UAVs) employed as airborne base stations (BSs) are considered the essential components in future sixth-generation wireless networks due to their mobility and line-of-sight communication links. For a UAV-assisted ad hoc network, its total channel capacity is greatly influenced by the deployment of UAV-BSs and the corresponding coverage layouts, where square and hexagonal cells are partitioned to divide the zones individual UAVs should serve. In this paper, the total channel capacities of these two kinds of coverage layouts are evaluated using our proposed novel computationally efficient channel capacity estimation scheme. The mean distance (MD) between a UAV-BS in the network and its served users as well as the MD from these users to the neighboring UAV-BSs are incorporated into the estimation of the achievable total channel capacity. We can significantly reduce the computational complexity by using a new polygon division strategy. The simulation results demonstrate that the square cell coverage layout can always lead to a superior channel capacity (with an average increase of $7.67\%$ to be precise) to the hexagonal cell coverage layout for UAV-assisted ad hoc networks.