Intelligent shared spectrum coordination in heterogeneous networks

Atimati, Ehinomen and Crawford, David and Stewart, Robert; (2024) Intelligent shared spectrum coordination in heterogeneous networks. In: 2023 IEEE Virtual Conference on Communications (VCC). IEEE, pp. 252-257. ISBN 9798350318807 (https://doi.org/10.1109/VCC60689.2023.10474686)

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Abstract

Global connectivity requires reliable and affordable access to the internet for digital inclusiveness. Shared spectrum technologies are one of many technologies that can help provide affordable connectivity. They require available spectrum (channels) from Primary Users (PUs) and share these among future dynamic heterogeneous secondary user (SU) networks. Coordination of these transient available resources is exacerbated by dynamic SU network scenarios, thus raising the risk of poor SU experience and inefficient spectrum and power usage. Therefore, adopting reinforcement learning (RL), terrain-based propagation models, and IEEE 802.19 coexistence principles, a central intelligent real-time shared spectrum coordination algorithm is proposed to coordinate resource allocation among dynamic SUs operating at low bands. The proposed two-stage algorithm was compared to existing shared spectrum allocation techniques deployed in dynamic spectrum access (DSA) networks to quantify RL's impact on low-band wireless networks. 66% to 100% of SU nodes/access points (APs) that used the proposed algorithm experienced good quality of service (QoS) in most scenarios examined. A good QoS meant that 75% of APs receivers experienced signal-to-noise plus interference ratio (SINR) greater than 5. This was achieved using minimal AP transmission power.

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