In this paper, the problem of mission planning and spectrum resource allocation for cooperative reconnaissance of ground targets with multiple unmanned aerial vehicles (UAVs) is studied. A joint mission planning and spectrum resource optimization algorithm for multi‐UAVs is proposed to improve the information transmission rate by reusing the spectrum of existing users. The joint optimization problem is formulated as mixed‐integer non‐linear programming. The block coordinate descent (BCD) method is further applied to achieve the optimal strategies of mission planning, channel allocation, and power control. Specifically, an improved genetic algorithm (GA) combined with the successive convex approximation (SCA) is used to solve the sub‐problem of mission planning. For the channel allocation sub‐problem, an iterative convergence channel allocation algorithm is proposed. Numerical results show that the proposed algorithm can achieve a higher UAV transmission rate and better robustness than existing algorithms.

Reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicles (UAV) have been extensively studied on the Internet of Things (IoT) systems to improve communication performance. In this paper, we aimed to counter simultaneous jamming and eavesdropping attacks by jointly designing an active beamforming vector at the base station (BS) and reflect phase shifts at the RIS. Specifically, considering imperfect angular channel state information (CSI), the sum secrecy rate maximization problem in the worst case could be formulated, which is NP-hard and non-convex. To address this problem, we improved the robust enhanced signal-to-leakage-and-noise ratio (E-SLNR) beamforming to reduce the computational complexity and mitigate the impact of interference, eavesdropping and jamming. Furthermore, a genetic algorithm with a tabu search (GA-TS) method was proposed to efficiently obtain an approximate optimal solution. The simulation results demonstrated that the proposed GA-TS method converged faster with better results than conventional GA, while the proposed robust scheme could achieve higher sum secrecy rates than the zero-forcing (ZF) and SLNR schemes.

This letter studies the legitimate surveillance with one half-duplex legitimate monitor (E) over the suspicious multichannel relaying system, where the suspicious transmitter (ST) and relay (SR) implement the optimal power allocation over all orthogonal channels under a joint sum-power constraint to maximize the communication rate of the suspicious system. Under this setup and considering that SR operates in amplify-and-forward (AF) or decode-and-forward (DF) mode, E aims to i) determine the optimal set of sub-channels for jamming and ii) optimize its jamming power over these sub-channels in order to deliberately force ST and SR reallocating power to the unjammed sub-channels to the most, so as to increase the perceived power of E over the unjammed sub-channels and then maximize the eavesdropping rate. In particular, for the AF case, the formulated problem is non-convex, for which the slack variables are introduced and successive convex approximation is exploited. For the DF case, some insights are provided for the optimization process. Results present the considerable gain of the proposed strategy compared to intuitive schemes.

Unmanned aerial vehicles (UAVs) have emerged as a promising solution for collaborative search missions in complex environments. However, in the presence of interference, communication disruptions between UAVs and ground control stations can severely degrade coordination efficiency, leading to prolonged search times and reduced mission success rates. To address these challenges, this paper proposes a novel multi-agent deep reinforcement learning (MADRL) framework for joint spectrum and search collaboration in multi-UAV systems. The core problem is formulated as a combinatorial optimization task that simultaneously optimizes channel selection and heading angles to minimize the total search time under dynamic interference conditions. Due to the NP-hard nature of this problem, we decompose it into two interconnected Markov decision processes (MDPs): a spectrum collaboration subproblem solved using a received signal strength indicator (RSSI)-aware multi-agent proximal policy optimization (MAPPO) algorithm and a search collaboration subproblem addressed through a target probability map (TPM)-guided MAPPO approach with an innovative action-masking mechanism. Extensive simulations demonstrate superior performance compared to baseline methods (IPPO, QMIX, and IQL). Extensive experimental results demonstrate significant performance advantages, including 68.7% and 146.2% higher throughput compared to QMIX and IQL, respectively, along with 16.7–48.3% reduction in search completion steps versus baseline methods, while maintaining robust operations under dynamic interference conditions. The framework exhibits strong resilience to communication disruptions while maintaining stable search performance, validating its practical applicability in real-world interference scenarios.

This paper studies the secure transmission in the dual-hop cognitive wiretap networks, where the secondary transmitter (Alice) aims to transmit confidential information to the secondary receiver (Bob) in the face of a multi-antenna relay (Relay), while the malicious eavesdropper (Eve) is used to eavesdrop the confidential information from Alice and Relay. To improve security, we design two transmission schemes, namely maximal-ratio combining/maximal-ratio transmission-selection combining (MRC/MRT-SC) with half-duplex (HD) receiver and maximal-ratio combining-zero forcing beamforming/maximal-ratio transmission-selection combining-zero forcing beamforming (MRC-ZFB/MRT-SC-ZFB) with full-duplex (FD) receiver. To evaluate the secrecy performance obtained from the proposed schemes comprehensively, the new closed-form and simple asymptotic expressions for the secrecy outage probability (SOP) and secrecy throughput (ST) of our considered networks with MRC-ZFB/MRT-SC-ZFB and MRC/MRT-SC schemes are derived, respectively. Thus, we explore the effect of various schemes on system secrecy performance in terms of SOP and ST. Analytical results and numerical simulations demonstrate that MRC-ZFB/MRT-SC-ZFB achieves better performance in the two proposed schemes. In particular, we show that the FD receiver plays a crucial role in designing the cognitive wiretap networks for protecting the legitimate link against attack from the malicious eavesdropping.