This paper investigates the pilot assignment and power control problems for secure UAV communications in cell-free massive MIMO network with the user-centric scheme, where numerous distributed access points (APs) simultaneously serve multiple UAVs and terminal users. Meanwhile, there exists one UAV acting as an eavesdropper which can perform pilot spoofing attack. Considering a mixture of Rayleigh and Ricean fading channels, the APs respectively perform MMSE estimation and distributed conjugate beamforming for uplink training and downlink data transmission. Using random matrix theory, the closed-form expression for a tight lower bound on the achievable secrecy rate is derived, which enables the impact analysis of key parameters, such as power, antenna configuration, UAV height, etc. Taking into account both performance and complexity, a novel pilot assignment scheme is proposed by combining weighted graphic framework and genetic algorithm, which can actualize global search with limited iterations. The max-min power control with security constraints is then studied in parallel, which can not only enhance the network fairness but also ensure the security. Accordingly, successive convex approximation and fractional optimization are jointly utilized to solve this non-convex problem. Simulation results numerically verify the analytical results and indicate the superiority of the proposed pilot assignment and power control schemes.

Abstract In unmanned aerial vehicle (UAV)‐assisted wireless sensor networks (WSNs), UAVs are employed to collect sensing data from each sensor node (SN). Because of limited battery capacity, shortening the time of data collection by the UAV is necessary. Notably, the task completion time is related to UAV trajectory and spectrum allocation. In this paper, joint trajectory design and spectrum allocation is studied to minimize the task completion time of UAVs while ensuring the target upload data amount for each SN. The cases that all SNs are located within the communication range of a UAV is explored first; the formulated problem is non‐convex and difficult to solve directly. Hence, an iterative algorithm based on block coordinate descent and successive convex approximation is proposed to decompose and transformed the original problem into two convex optimizations. Furthermore, the proposed algorithm is applied to the general case that all SNs are widely distributed by leveraging the spiral algorithm and traveling salesman problem technique. Simulation results show that the proposed algorithm can effectively reduce the task time of UAV compared to other benchmark algorithms.

This paper investigates the secure communication in reconfigurable intelligent surface aided cell-free massive MIMO system in the presence of active eavesdropping, where the access points (APs) are equipped with low resolution analogto-digital/digital-to-analog converters (ADCs/DACs). Specifically, the additive quantization noise model (AQNM) is explored to capture the impacts of coarse ADCs/DACs. To reduce the channel estimation overhead, all APs employ a minimum mean-squared-error channel estimator which estimates the aggregated channel including both the direct and indirect channels. With the available imperfect channel state information (CSI), conjugate beamforming and random beamforming are applied at the APs and the RISs for downlink data transmission. Moreover, the closed-form expression of the achievable ergodic secrecy rate is derived which can be used to evaluate the impact of the number of the APs, the number of the RISs and the ADCs/DACs resolution on the systems performance. Finally, numerical and simulation results are presented to verify the analytical results.