Resource management in dynamic shared spectrum networks

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Atimati, Ehinomen and Nyasulu, Tawachi and Crawford, David and Stewart, Robert; (2025) Resource management in dynamic shared spectrum networks. In: 2025 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN). IEEE, Piscataway, N.J., pp. 13-19. ISBN 9798331533632 (https://doi.org/10.1109/DySPAN64764.2025.11115965)

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Abstract

With the ever-increasing demand for wireless connectivity for a growing range of applications, there is a compelling need to maximize the available radio spectrum where and when it is needed. In certain situations, the traditional approach of static license allocation leads to spectrum shortage. This paper proposes a tailored approach where base stations/nodes are dynamically assigned spectrum based on their real-time requests/demand needs. It develops a dynamic shared spectrum environment for training its central Deep Reinforcement Learning (DRL) agent, a resource management model (RMM) to assign limited spectrum and transmit power resources to many nodes based on their needs and specifications. The model learned to manage interference among nodes according to IEEE 802.19 coexistence management principles. RMM showed a 20% improvement in request performance compared to the traditional exclusive resource sharing approach. Its assignment performance was better than other methods, such as random, recursive (non-optimization techniques), and a staged Q-learning algorithm (optimization technique), as it consistently provided a sufficient number of requesting nodes with good quality of service (QoS). This improvement diminished with the increased number of bands. Future work will focus on enhancing DRL performance for larger state-action pair sizes.

ORCID iDs

Atimati, Ehinomen ORCID logoORCID: https://orcid.org/0000-0002-7563-0926, Nyasulu, Tawachi ORCID logoORCID: https://orcid.org/0000-0002-5039-9457, Crawford, David ORCID logoORCID: https://orcid.org/0000-0002-2698-594X and Stewart, Robert;
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