T. Taleb

Toward Mission-Critical ISAC: Reliable Energy-Aware Coordination in UAV Swarms

Receding-Horizon Alternating Optimization (RHAO), a four-block per-slot algorithm that decomposes the problem into: charging admission via the Hungarian algorithm, MUAV trajectory and time-split via successive convex approximation, CUAV rendezvous, and WPT power allocation, with monotone convergence guarantees, is prop...

Boundary-Contiguous Initial Placement for Structured Network-Service Embedding

Hierarchical risk-aware RL for DVFS-enabled IoE scheduling

The ER-RL-DVFS framework combines an energy-risk task-priority score, DVFS-aware cluster–PM–VM profiling, hierarchical candidate filtering, deterministic score-based assignment for clearly separated candidates, selective tabular reinforcement learning as the prescribed refinement mechanism for near-tie decisions, and m...

A Semantic-Aware Multiple Access Scheme Leveraging Spatial Redundancy for Uplink-Dominant Network Services

The transition toward semantic-aware communication offers a paradigm shift for next-generation mobile networks, promising to decouple information significance from raw data transmission. Despite advances in semantic extraction, the integration of semantic intelligence into the Medium Access Control (MAC) layer remains...

Optimal Resource Allocation for Successful Task Transmission Probability Maximization in UAV Swarm Networks

A genetic algorithm-based method for intra-swarm resource allocation to achieve the STP maximization, which ensures reliable transmission of high-priority tasks and the impact of system parameters on STP and fairness.

Moving target defense in 5G and beyond networks: A comprehensive survey and research directions

This paper presents a deployment-oriented survey of MTD for 5G, with special emphasis on learning-assisted designs that optimize what to mutate, when to mutate, and how to enforce mutation under slice-isolation, URLLC, and orchestration constraints.

Joint Power and Location Design for Energy-Efficient Covert UAV Communications

Uncrewed aerial vehicles (UAVs) have been extensively deployed in wireless communication scenarios. However, UAV communication faces the challenges of information leakage and energy limitation. Therefore, this paper studies energy-efficient covert communication in adversarial UAV-enabled wireless systems, where a UAV c...

Hierarchical Scheduling for Federated Learning Optimization over Deterministic Satellite Networks

A hierarchical Graph Attention Transformer-Proximal Policy Optimization (GAT-PPO) framework is proposed, where a high-level agent selects aggregation nodes and a low-level agent performs packet-level routing and offset control.

Joint Spectrum, Association, and Deployment Optimization for UAV Swarm-Assisted ISAC Networks

This paper jointly optimizes spectrum allocation, UAV association and deployment to maximize average system throughput while ensuring localization accuracy in ISAC networks, where sensing is realized through localization.

UAV Trajectory, Power, and User-Association Optimization for Covert Communications in ICRNs

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 Warde...

Energy-Efficient Beamforming and Adaptive Computational Task Offloading in ISCC Systems

A nonconvex energy cost minimization problem is introduced by considering a user-specific energy cost ratio coefficient that explicitly balances UE-AP energy consumption according to heterogeneous device energy states and a double-loop framework combining successive convex approximation and alternating direction method...

A Comprehensive Survey of Generative AI: Applications and Future Directions Across Domains

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.