Anonymous communication networks have emerged as crucial tools for obfuscating communication pathways and concealing user identities. However, their practical deployment faces several critical challenges, including vulnerability to artificial intelligence-powered metadata analysis, difficulties in fitting decentralized architectures, and the lack of provable security guarantees. To address these limitations, this paper proposes a novel decentralized anonymous routing protocol that resists both traceability and traffic analysis. The proposed protocol is fully decentralized and eliminates reliance on the threshold model and trusted third-party setups, ensuring indistinguishable identity privacy. Different from traditional empirical or heuristic security analysis of anonymous networks, this paper rigorously proves indistinguishable identity privacy for users even in extremely adversarial environments. Furthermore, simulation results validate the protocol’s practical feasibility, demonstrating both security and efficiency. By enabling secure information exchange while preserving user privacy, the proposed protocol offers a provably secure solution for privacy-preserving communication in modern digital infrastructures.

The coverage optimization for Unmanned Aerial Vehicles (UAVs) in low-altitude intelligent networks (LAINs) poses a significant challenge due to factors such as multiple performance metrics, large-scale systems, limited global information, and potential UAV failures. To address these challenges, this paper proposes an optimal control-based framework to model the joint coverage optimization problem, which comprehensively extends system endurance, maximizes throughput, enhances global connectivity, and ensures ground user fairness in dynamic scenarios. To solve this problem, we develop a fully distributed algorithm for UAVs’ navigation and real-time adjustments of Common Pilot Channel (CPICH) transmit power. Owing to its distributed nature and ability to maintain global connectivity, the algorithm ensures robust system performance in uncertain environments. It offers advantages such as low computational complexity, strong scalability, and efficient handling of node failures, demonstrating resilience and rapid self-recovery capabilities. To validate its effectiveness, we conduct simulations in large-scale scenarios. The results confirm that the proposed algorithm achieves efficient and fast-response performance with quick self-recovery.

The frequent migration of large-scale users leads to the load imbalance of mobile communication networks, which causes resource waste and decreases user experience. To address the load balancing problem, this paper proposes a dynamic optimization framework for mobile communication networks inspired by the average consensus in multi-agent systems. In this framework, all antennas cooperatively optimize their CPICH (Common Pilot Channel) transmit power in real-time to balance their busy-degrees. Then, the coverage area of each antenna would change accordingly, and we call this framework a “breathing” mobile communication network. To solve this optimization problem, two algorithms named BDBA (Busy-degree Dynamic Balancing Algorithm) and BFDBA (Busy-degree Fast Dynamic Balancing Algorithm) are proposed. Moreover, a fast network coverage calculation method is introduced, by which each antenna's minimum CPICH transmit power is determined under the premise of meeting the network coverage requirements. Besides, we present the theoretical analysis of the two proposed algorithms’ performance, which prove that all antennas’ busy-degrees will reach consensus under certain assumptions. Furthermore, simulations carried out on three large datasets demonstrate that our cooperative optimization can significantly reduce the unbalance among antennas as well as the proportion of over-busy antennas.