—Unmanned aerial vehicle (UAV) swarm-assisted integrated sensing and communication (ISAC) networks are a crucial technology for providing communication and sensing services in emergency rescue scenarios without base station support. However, the strong coupling between communication and sensing resources in such networks fundamentally limits the communication and sensing performance of ISAC systems. This paper jointly optimizes spectrum allocation, UAV association and deployment to maximize average system throughput while ensuring localization accuracy in such networks, where sensing is realized through localization. We begin by deriving an analytical expression for localization accuracy, which explicitly captures the joint effects of link quality and anchor geometry under shared communication-localization spectrum resources. We then formulate average system throughput maximization as a mixed-integer nonlinear and non-convex optimization problem with the constraints of localization accuracy, sub-channels, UAV association, UAV deployment and signal-to-interference-plus-noise ratio. We further develop an alternating iterative optimization method to solve this complex optimization problem. Within this method, a particle swarm optimization-based method is developed to jointly optimize spectrum allocation and UAV association, and a dueling double deep Q-network-based method is further employed for UAV deployment optimization. Finally, extensive simulation results are presented to validate the efficiency of our optimization method, and also to illustrate how key parameters influence average system throughput and localization accuracy.
Zhuojia Yang, Wei Su, Bin Yang et al.· IEEE Transactions on Mobile...· 0 citations
This paper studies covert communications in an uncrewed aerial vehicle (UAV)-assisted interweave cognitive radio network (ICRN). Specifically, a secondary user (UAV) opportunistically utilizes the idle spectrum resource of a primary user (Warden) to covertly transmit short-packets to ground receivers (GRs), while Warden tries to detect the existence of the UAV covert communications. To ensure fairness of covert communications, this paper maximizes the minimum covert throughput from UAV to GRs, which can be formulated as an optimization problem with the constraints of covertness, UAV trajectory, power and user-association. We further simplify the optimization problem by determining the optimal transmission power of UAV. To solve this optimization problem, we derive the optimal transmission power of UAV and user-association index as analytical expressions of UAV’s positions, respectively. We also define UAV trajectory as a successive hovering-and-flying structure. Based on these results, we employ a successive convex approximation method to obtain the maximum value of the minimum covert throughput. The numerical results are presented to illustrate the impact of system parameters on the minimum covert throughput.
Riyu Wang, Bin Yang, Shikai Shen et al.· IEEE Transactions on Cogniti...· 0 citations
Integrated sensing, communication, and computation (ISCC) enables next-generation wireless networks to perform environmental perception while processing massive data under stringent quality-of-service (QoS) requirements. Energy consumption is a crucial indicator for the ISCC system design. However, accounting for energy heterogeneity in ISCC system design is an open problem. Specifically, battery-constrained user equipments (UEs) and energy-abundant access points (APs) require fundamentally different energy allocation strategies based on device computational capabilities, battery states, and QoS constraints. In this paper, we introduce a nonconvex energy cost minimization problem by considering a user-specific energy cost ratio coefficient that explicitly balances UE-AP energy consumption according to heterogeneous device energy states. To efficiently address this problem, a double-loop framework combining successive convex approximation and alternating direction method of multipliers is also developed. Numerical results demonstrate that the proposed scheme significantly outperforms the fixed offloading baselines (full offloading, full local and half offloading) in terms of the total energy cost. In particular, the proposed scheme achieves up to $25-47.6\%$ energy cost reduction at moderate latency constraints over fixed offloading baselines, thereby supporting time-sensitive applications. Moreover, this work provides an effective solution for energy-efficient and QoS-aware 6G ISCC systems serving diverse devices with conflicting energy priorities.
Kai Dong, Lei Wang, S. Vorobyov et al.· IEEE Transactions on Wireles...· 0 citations
The review shows that diffusion and autoregressive foundation models increasingly dominate high-fidelity image, language, and multimodal generation, while GANs, VAEs, and flow-based models remain important in data-limited, structured, scientific, and privacy-aware settings.
A. Javadpour, F. Ja’fari, T. Taleb et al.· IEEE Access· 0 citations
A predictive multi-agent Reinforcement Learning (RL) framework that proactively maintains SLA stability in UAV-enabled MEC through coordinated trajectory control and computation resource allocation and designs an SLA-aware reward function that explicitly penalizes both violation probability and duration across slices.
M. Farhoudi, Zeinab Sasan, Masoud Shokrnezhad et al.· 0 citations
A compact reasoning model trained with verifier-based self-verification and periodically refined online via shadow updates is deployed, showing manageable, near-linear control-plane overhead as domains scale and during domain joins, and robust decision quality, including recovery after objective changes.
Masoud Shokrnezhad, T. Taleb· IEEE Network· 0 citations
This paper investigates a dynamic heterogeneous mobile edge computing network (HMECN), where mobile devices (MDs) could offload their full tasks to a small base station (SBS) directly or the macro base station (MBS) in direct or relay mode. As age of information (AoI) is a comprehensive and accurate metric to capture the freshness of computation results, we formulate a long-term weighted sum AoI (LWSA) minimization problem in the HMECN by jointly optimizing the offloading decisions of MDs as well as the bandwidth and computation resource allocation of all base stations, subject to energy, delay and peak AoI constraints. To address the formulated non-convex mixed integer nonlinear programming problem, we decompose it into the offloading decision optimization (ODO) top-problem and the resource allocation optimization (RAO) sub-problem. Based on the decomposition, we propose a federated learning (FL)-assisted hybrid DRL and convex approach that is comprised of a safe multi-agent DRL algorithm, convex optimization and FL. The ODO top-problem is solved by the safe multi-agent DRL algorithm, which strictly ensures that the actions of each agent do not exceed its energy constraint and then paves the way for using convex optimization to solve the RAO sub-problem. FL is used to alleviate the training instability problem aggravated by multi-agent settings via breaking the limitation of partial knowledge for each individual agent. Simulation results demonstrate the superiority of the proposed approach in terms of the LWSA, convergence, scalability and robustness in dynamic environments.
Xiaoying Liu, Junhao Zheng, Kechen Zheng et al.· IEEE Transactions on Mobile...· 8 citations