Jul 2026· IEEE International Conference on Engineering of Complex Computer Systems· pp. 1-5· 0 citations· 20 references
Abstract
This work investigates the joint optimization of Age of Information (AoI) and energy harvesting (EH) in wireless edge computing systems, where edge servers not only process IoT data but also act as wireless power suppliers via simultaneous wireless information and power transfer (SWIPT). Building upon the asynchronous model-free fractional multi-agent reinforcement learning framework and the Lyapunov drift-plus-penalty (DPP) concept, we design a fractional-based reward function for AoI and construct a virtual queue to enforce long-term energy stability under battery storage constraints. The overall reward is formulated as a weighted sum, capturing the trade-off between timeliness and energy sustainability, with update decisions, task offloading, and power splitting ratios as key control variables. Simulation results demonstrate that the developed multi-agent deep reinforcement learning approach achieves superior AoI–energy trade-offs compared to related baseline algorithms. These findings highlight the effectiveness of our framework in balancing information freshness and sustainable energy harvesting under resource-constrained edge environments.
This paper addresses the joint task offloading and resource allocation problem in multi-user MEC systems and proposes a decentralized control framework based on Multi-Agent Reinforcement Learning (MARL), which achieves lower total system cost and faster convergence than the full-local, full-offload, and heuristic baselines.
Youssef Oukissou, Mohamed Amine Meddaoui, Ayoub Belaidi et al.· International journal of Com...· 0 citations
6G mobile edge networks are emerging as a key infrastructure for ubiquitous large language model (LLM) inference services. However, conventional edge routing to nearby or well-connected servers falls short for efficient edge LLM inference, as it may miss the user’s KV cache and trigger costly prefill recomputation. To address this challenge, this paper studies an edge inference system assisted by an embodied UAV agent swarm, where UAVs actively sense user mobility and neighboring UAV states to make local decisions on trajectory control, user association, and inference-request routing. The goal is to improve KV-cache reuse while maintaining reliable wireless connectivity, thereby maximizing the system effective token throughput under energy and QoS constraints. We then formulate the joint optimization as a mixed-integer non-linear program and further cast the sequential UAV decision-making process as a decentralized partially observable Markov decision process. To obtain scalable decentralized policies under partial observations, we propose Q-MAA2C, a quantum-enhanced multi-agent advantage actor-critic algorithm for embodied UAV swarm control and inference routing. Q-MAA2C uses quantum actors for local action selection and an entangled split critic for swarm-level value estimation, enabling coordinated policies from partial observations with reduced raw observation exchange. Simulation results indicate that Q-MAA2C yields comparable reinforcement learning rewards to the fully classical baseline while reducing the number of convergence episodes by about 43%. Additionally, the proposed method enhances the system effective token throughput by up to about 134% over other competing methods.
Xiangdong Zheng, Long Luo, Hongfang Yu et al.· IEEE Transactions on Cogniti...· 1 citation
Massive MIMO systems require simultaneous optimization of energy efficiency, latency, and handover performance, yet existing approaches address these objectives in isolation across disparate parameter spaces. This paper proposes a multi-state parameter self-optimization framework that jointly optimizes across five interdependent operational states—channel, mobility, system configuration, power, and latency—using deep reinforcement learning. We formulate the problem as a multi-objective Markov decision process and implement five optimization approaches: Hybrid Action Space Reinforcement Learning, Q-Learning with Kalman Filter prediction, LSTM Autoencoder for PAPR reduction, bio-inspired Integrated Fruit Fly Salp Swarm Optimization for power allocation, and a proposed Multi-Agent Deep Q-Network (MA-DQN) with experience replay. Simulation results across antenna configurations from 16 to 256 elements and user counts from 5 to 40 show that the proposed MA-DQN achieves a composite performance score of $83 \pm 1.8 / 100$ across all five states (averaged over 10 seeded runs), outperforming the best single-objective method by $\mathbf{2 6} \boldsymbol{\%}$. The framework delivers 29-73% energy efficiency improvement over fixed baselines, with the learned policy favoring moderate power (0.1-0.5W) and lower antenna counts (16-32)—consistent with analytical models that show circuit power dominance at high antenna counts.
Madhu Kumari Ray, Sasmita Mohapatra, C. J.· 2026 11th International Conf...· 0 citations
Unmanned Aerial Vehicles (UAVs) are promising relay platforms due to their flexible deployment and high probability of line-of-sight (LoS) connectivity. This paper compares three deep reinforcement learning (DRL) algorithms-Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), and Recurrent PPO with LSTM memory-for joint UAV trajectory and energy optimization in UAV based relay systems. The problem formulated is a non-convex optimization problem that minimizes UAV propulsion energy while satisfying Quality of Service (QoS) and mobility constraints under realistic 3GPP channel conditions. Simulation results show that all methods achieve over 99% QoS satisfaction. SAC exhibits the fastest convergence, whereas the proposed Recurrent PPO achieves the lowest energy consumption (44.72 kJ), reducing energy usage by 5.1% compared with PPO. These results highlight the trade-off between convergence speed and energy efficiency in DRL-based UAV relay optimization.
Aniket Subbanwar, Ojas Joshi, Amit Agarwal· International Conference on...· 0 citations
This work investigates a reinforcement learning-based control framework for the autonomous movement and coordination of multiple Unmanned Aerial Vehicles (UAVs) in a wireless communication environment. The considered system includes UAVs performing sensing and relaying tasks, where mobility decisions directly affect the overall network performance. The main objective is to improve the communication quality of ground users by maximizing aggregate network throughput. To achieve this objective, a Double Deep Q-Network (DDQN) architecture is employed, where each UAV is assigned an individual learning agent. The agents learn role-specific movement policies while coordinating through interactions with the shared environment. Learning performance is further improved by using adaptive scaling and a custom reward function designed to capture variations in network utility. Simulation results show that the proposed approach outperforms baseline movement strategies in terms of utility. In addition, different task configurations, agent behaviors, and hyperparameter selections are examined to improve convergence speed and training stability. Overall, the results indicate that reinforcement learning is a promising method for cooperative UAV positioning in dynamic and interference-sensitive wireless communication scenarios.
Berke Kilinç, M. Ö. Efe· International Conference on...· 0 citations
Deep reinforcement learning (DRL) has recently gained a great attention due to its real-time adaptation and effectiveness in complex optimization problems. This paper investigates the optimal deployment of millimeter-wave (mmWave) base stations (BSs) in a realistic, non-convex campus topology. The optimization problem is NP-hard, due to the non-convex, non-smooth nature of the max-min fairness objective. To overcome these constraints, we formulate the BS placement as a Markov Decision Process (MDP) and systematically benchmark four DRL schemes: a discrete single-agent Deep Q-Network (DQN), a spatially partitioned Multi-Agent DQN, a continuous single-agent Deep Deterministic Policy Gradient (DDPG), and a geographically partitioned multi-agent DDPG framework. Numerical evaluations reveal that the multi-agent DDPG approach substantially outperforms single-agent in dense scenarios. Additionally full coverage is achieved, and a fairness Jain's index of 0.94 is obtained. Finally, the multi-agent demonstrates highly efficient computational convergence of dense scenarios with $400$ users.
Omar Rady, Mohamed Ayman, Ali Arafa et al.· 0 citations