What question did this study set out to answer?

The aim is to enhance spectrum allocation efficiency using deep reinforcement learning in dynamic wireless environments.

March 10, 2026

Optimizing Spectrum Allocation Using Deep Reinforcement Learning in Heterogeneous Wireless Networks

Key Points

The aim is to enhance spectrum allocation efficiency using deep reinforcement learning in dynamic wireless environments.
Implemented deep reinforcement Q-networks (DRQNs) for spectrum management.
Incorporated various node types including legacy and learning-based agents.
Conducted simulations to evaluate performance metrics against traditional models.
The DRQN-based method showed superior performance compared to LTE, SUM, DLMA, and TSRA.
It minimized frequency collisions and optimized throughput.
Improved metrics included reduced packet expiry durations over episodes.

Abstract

ABSTRACT Especially in heterogeneous wireless networks (HetNets), the demand for efficient spectrum utilization has increased exponentially. Traditional spectrum allocation methods struggle to cope with dynamic environments and underutilized frequency bands. DRQNs are used to intelligently manage spectrum access by secondary users without interfering with that of primary users in this paper. The system model incorporates various types of nodes, including legacy, greedy, and learning‐based agents. DRL's adaptive decision‐making and memory capabilities allow the proposed method to minimize collisions and maximize performance. A simulation study shows that the proposed DRQN‐based method outperforms traditional models, including LTE, SUM, DLMA, and TSRA, on multiple metrics, including packet expiry durations and throughput over episodes.

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Cite This Study

Dutta et al. (Fri,) studied this question.

synapsesocial.com/papers/69af95cf70916d39fea4dbed https://doi.org/https://doi.org/10.1002/itl2.70242

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