A novel orchestration scheme for game-theoretic UARA in HetNets that closely approximates the optimal policy for the considered operational objectives, while delivering higher network throughput than conventional association methods.
Abstract
Managing dynamic User Association and Resource Allocation (UARA) in modern Heterogeneous Cellular Networks (HetNets) remains a critical open challenge. Existing mathematical optimization and Reinforcement Learning approaches face limitations in handling low-latency decision-making under dynamic traffic conditions. This paper introduces a novel orchestration scheme for game-theoretic UARA in HetNets. The proposed bilevel framework distributes UARA decisions to User Equipment through a multi-objective non-cooperative game. Overlaying the distributed game, a centralized Deep Reinforcement Learning controller orchestrates network performance by dynamically configuring the game's utility parameters, enabling transitions between power awareness, coverage enhancement, and balanced operation. Evaluated on urban HetNet topologies with 3GPP TR 38.901-compliant channel modeling, the proposed framework closely approximates the optimal policy for the considered operational objectives, while delivering higher network throughput than conventional association methods. Furthermore, it incurs low computational overhead and maintains stable performance across the evaluated traffic densities without retraining.
As the Multimedia Internet of Things (M-IoT) evolves, the orchestration of numerous resources that offer support for high-bandwidth, low-latency applications arises as a key challenge. Architecturally, the edge-cloud framework alleviates structural concerns, but the linked nature of compute and data transfer poses problems of resource management. Approaches that tackle task offloading and adaptive transmission that think independently of each other tend to have problems such as user-server cross-region overloads or network congestion. This paper presents JATO, a framework to jointly tackle the problems of adaptive task offloading and transmission optimization using Deep Reinforcement Learning. JATO offers a mono-faceted solution, learning a policy to simultaneously determine the best offloading target and the transmission quality. The framework was implemented for evaluation with a combination of different edge devices in a testbed alongside a simulation environment. JATO recorded a result of 0.9321 as the holistic score of the overall framework endpoint, a score significantly better than that of all the other frameworks that were used as functional baselines. JATO was able to resource optimally with a network lag of 131.65 milliseconds and a network freeze of 0.09% with the resources utilized. This is evidence that offloading and rate control in combination provides better resource elasticity for M-IoT systems.
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