This paper considers the cell-free integrated sensing and communication (CF-ISAC) networks utilizing reconfigurable intelligent surface (RIS)-mounted uncrewed aerial vehicles (UAVs). We aim to maximize the sum of weighted sum rate within the whole ISAC period by jointly optimizing access points (APs)’ transmit beamformings, RISs’ phase shifts, user-RIS association, and UAVs’ locations. To deal with a highly complex non-convex optimization problem, we propose an alternating optimization solutions by decomposing the original problem into three subproblems. In particular, for optimizing APs’ transmit beamformings, RISs’ phase shifts, and user-RIS association, we convert the log-sum problem into a quadratically constrained quadratic programming problem using the Lagrangian dual principle and multi-ratio fractional programming. For optimizing UAVs’ locations, the successive convex approximation technique is used to transform it into a convex problem. Simulation results highlight the considerable performance advantage of the proposed network compared to benchmark schemes employing fixed RISs, without RIS-mounted UAVs (URISs), and collocated network with URISs.

Unmanned aerial vehicle (UAV) is a key enabler for communication systems beyond the fifth generation due to its applications in almost every field, including mobile communications and vertical industries. However, there exist many challenges in 3-D UAV placement, such as resource and power allocation, trajectory optimization, and user association. This problem becomes even more complex as UAV changes its height, which in turn varies the channel conditions and reduces the coverage on account of high co-channel interference. To maximize the user coverage in uplink transmission, we propose to jointly optimize the 3-D UAV placement and path-loss compensation factor. Moreover, we also optimize the latter for various UAV deployment heights in the suburban environment. Simulation results have demonstrated that the joint optimization of the UAV height and path-loss compensation factor results in better coverage and throughput performance as compared to the baseline scheme.