AoI Minimization in Heterogeneous MEC Networks: A Federated Learning-Assisted Hybrid DRL and Convex Approach

Toward Low-Delay and Energy-Efficient UAV-Assisted MEC Systems Through Intelligent Resource Allocation

A Prioritized Adaptive Weighting based on Deep Deterministic Policy Gradient (PAW-DDPG) as an enhanced Deep Deterministic Policy Gradient (DDPG) algorithm to minimize both processing delay and energy consumption by jointly optimizing user scheduling, partial-task offloading, and UAV trajectory is proposed.

HALO: Hierarchical Auction-assisted Learning for Offloading in SAGIN

HALO, a hierarchical auction-assisted learning framework that combines auction-based task association with hierarchical Proximal Policy Optimization for resource allocation, is proposed, highlighting HALO's ability to maintain stable and efficient performance under varying traffic conditions, making it well-suited for delay-sensitive SAGIN environments.

AoI Minimization in Multi-UAV Edge Computing via Influence-Aware Heterogeneous Federated Multiagent Reinforcement Learning

A Novel Approach to Received Signal Power Optimization in 5G Networks using Pelican Optimization Algorithm

Findings validate the efficacy of incorporating swarm intelligence into the 5G architectures as a viable and self-optimizing solution for the promotion of connectivity and signal power performance in the next-generation high-density wireless networks.

Mobile-Edge Computing in SAGINs: A Hybrid Action Space P-DDQN Algorithm for Joint Offloading and Resource Allocation

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.

Edge Computing Control and Task Assignment in Integrated Terrestrial and Non-Terrestrial Networks

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.