Jul 2026· International Conference on Smart Communications and Networking· pp. 1-6· 0 citations· 11 references
Abstract
Integrated Terrestrial–Non-Terrestrial Networks (ITNTN), which combine terrestrial base stations (BSs), High-Altitude Platform Stations (HAPS), and Low-Earth Orbit (LEO) satellites, are key enablers of 6G communication and edge computing (EC) services. However, energy-limited BSs, particularly HAPS and satellites, pose significant sustainability challenges under continuous operation. To address this issue, we propose an on-demand EC server activation framework integrated with intelligent task offloading across ITNTN. A joint optimization problem is formulated to maximize task offloading success while satisfying energy and quality-of-service requirements. To solve it, we propose an online Q-learning policy that adaptively manages task offloading and EC server activation without prior knowledge of traffic dynamics. Simulation results show that the proposed method achieves superior task offloading success and energy efficiency compared to online heuristic and offline metaheuristic baselines. These findings highlight the importance of energyaware On-Off EC control for sustainable ITNTN systems.
The rapid growth of Internet of Vehicles (IoV) applications has imposed strict requirements on low-latency and energy-efficient computing services. This letter investigates a multi-Uncrewed Aerial Vehicle (UAV)-assisted IoV system, where multiple Mobile Edge Computing (MEC)-enabled UAVs (MUs) collaboratively provide computing services for vehicular terminals (VTs). To improve service capability, we propose an energy-efficient task offloading and load balancing scheme that jointly considers vehicle mobility, task offloading and migration, and computing resource allocation to formulate an optimization problem. To solve this problem, a collective learning (CL)-enabled multi-agent reinforcement learning (CL-MARL) algorithm is proposed, where each agent learns optimal policies through centralized training and collective cooperative learning. Simulation results demonstrate that the proposed scheme outperforms benchmark strategies in terms of energy efficiency, task completion rate, and load balancing.
Yongbin Wang, Peng Lin, Yan Liu et al.· IEEE Wireless Communications...· 0 citations
The flexible deployment of uncrewed aerial vehicles (UAVs) and the wide-area coverage of low Earth orbit (LEO) satellites make their integration in space–air–ground integrated networks (SAGINs) a promising solution for communication in resource-constrained remote areas. This paper proposes a SAGIN framework supporting mobile edge computing (MEC) with a three-layer architecture, which provides heterogeneous computing resources for ground Internet of Things (IoT) devices and enables users in remote and underdeveloped regions to access computational services. Our objective is to minimize the weighted sum of energy consumption and latency in the SAGIN subject to satellite coverage time constraints and partial task offloading requirements. The optimization problem is formulated as a mixed-integer nonlinear programming (MINLP) challenge that jointly optimizes the UAV’s three-dimensional trajectory, IoT device association, transmit power, and task assignment. The coupled optimization variables form a hybrid action space with both discrete and continuous actions. To address this challenge, a parameterized double deep Q-network (P-DDQN) algorithm based on deep reinforcement learning (DRL) is proposed. The proposed method employs the DDQN algorithm to handle discrete actions and the deep deterministic policy gradient (DDPG) algorithm to generate continuous actions. Simulation results show that the proposed algorithm outperforms several baseline schemes in terms of system cost, providing an efficient solution for highly coupled hybrid decision optimization problems in SAGINs.
Haosheng Chen, Haixia Cui, Peng Cao et al.· IEEE Transactions on Wireles...· 0 citations
A task-driven offloading algorithm based on Balanced Multi-Agent Deep Deterministic Policy Gradient (BMADDPG) that reduces average task processing latency by approximately 22.67% and decreases total system cost by at least 18.32% under high-load scenarios.
The Open Radio Access Network (O-RAN) paradigm, with its open interfaces and intelligent functions, is a key enabler for next-generation wireless systems. We investigate the deployment of O-RAN-based network slice functions over Low Earth Orbit (LEO) satellite networks with Mobile Edge Computing (MEC) capabilities. To provide energy-efficient and low-latency services through distributed data processing, we formulate a slice function data offloading problem aimed at jointly optimizing end-to-end (E2E) latency and energy consumption. We model the problem as an MDP and propose a Deep Reinforcement Learning (DRL)-based solution. The proposed DRL agent learns efficient offloading policies by balancing computation and communication costs in the dynamic satellite environment. Simulation results show that our DRL-based approach significantly outperforms conventional benchmarks, achieving enhanced latency and energy performance, enabling intelligent orchestration of O-RAN slices over LEO satellite networks.
S. Shinde, Daniele Tarchi, Carlo Fischione· International Mediterranean...· 0 citations
: Mobile edge computing (MEC) is an effective paradigm for supporting latency-sensitive and computation-intensive intelligent applications. However, in dynamic mobile-edge network scenarios, mobile terminals experience time-varying wireless links due to mobility. Tasks may also arrive unpredictably, while multiple terminals compete for limited edge resources. As a result, MEC systems may suffer from service congestion and unbalanced resource utilization, which increases end-to-end latency and energy consumption. This paper investigates cooperative task offloading in dynamic MEC networks. The considered system comprises one macro base station and multiple small base stations equipped with edge-computing resources. In each time slot, each mobile terminal selects a service option, determines the task offloading ratio, and chooses its transmit power for task uploading. This sequential decision process is formulated as a multi-agent problem with continuous action spaces. Under the centralized training and decentralized execution (CTDE) framework, the problem is further modeled as a decentralized partially observable Markov decision process (Dec-POMDP). Standard multi-agent soft actor-critic (MASAC) is not fully suitable for this problem. Its original action model does not handle bounded continuous actions well. Its exploration strength may also be unsuitable at different training stages. Frequent policy updates can further make training unstable when critic estimates are inaccurate. To address these issues, this paper develops an adaptive Beta-policy and delayed-update multi-agent soft actor-critic method, abbreviated as ABDMASAC. This method uses a Beta policy to model bounded actions. It adjusts the entropy coefficient during training and delays policy updates to reduce training oscillations. Experimental results show that, under a unified training budget and a consistent evaluation protocol, the proposed method achieves a better overall trade-off than the selected MASAC-backbone and on-policy MARL baselines under the considered simulation settings in terms of overall reward, average end-to-end latency, and average energy consumption. In the large-scale scenario, compared with MASAC, it improves the overall reward by 17.8%, reduces the average end-to-end latency by 18.0%, and lowers the average energy consumption by 11.4%.
Zheng Yao, Jie Liu, Changjun Deng et al.· Computers, Materials & C...· 0 citations
An MECN that integrates UAVs as the aerial layer and TESs as the terrestrial layer is introduced, and a quantum-inspired particle swarm optimization-based offloading strategy (QIPSO-TOS) is proposed to facilitate coverage-aware task offloading.
Marlom Bey, P. Kuila, Biswadip Bandyopadhyay et al.· Journal of Supercomputing· 0 citations