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
Generative Artificial Intelligence (GenAI) has become a foundational paradigm for learning complex data distributions and synthesizing realistic content across text, image, audio, tabular, scientific, and multimodal data. Despite rapid progress, the literature remains fragmented across model families and application domains, making it difficult to compare methodological choices, evaluation practices, and domain-specific deployment constraints. This survey addresses this gap by reviewing GenAI from both an algorithmic and cross-domain perspective. Following a PRISMA-inspired search and screening procedure, we organize representative and technically relevant studies across major GenAI families, including variational autoencoders, generative adversarial networks, diffusion models, autoregressive models, flow-based models, and recent foundation and multimodal models. We synthesize applications across healthcare, agriculture, manufacturing, transportation, earth and environmental systems, computer science and networks, materials science, finance and economics, education, business and services, and creative industries. 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. Beyond summarizing applications, the paper compares domain-specific data structures, evaluation practices, robustness concerns, failure modes, and ethical risks. The survey contributes a unified reviewed abstraction of GenAI workflows, a comparison with prior surveys, a domain-aware synthesis of open gaps, and a future roadmap for trustworthy, validated, and responsible GenAI deployment.
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