Khan, Ahsan Raza and Yadav, Poonam orcid.org/0000-0003-0169-0704 (2025) SemQNet: Semantic-Aware Quantised Network for mmWave Beam Prediction. In: Workshop on Sustainable and Intelligent Green Internet of Things for 6G and Beyond (6GSIoT):IEEE Wireless Communications and Networking Conference. 2025 IEEE Wireless Communications and Networking Conference, 24-27 Mar 2025, Mico Milano Congressi. IEEE Communications Society , ITA
Abstract
Millimetre-wave (mmWave) communication systems use large antenna arrays and narrow beams to achieve strong signal power. However, this approach requires extensive beam training, which leads to high overhead. Recently proposed vision-aided beam prediction methods show promising results, reducing this overhead. However, these techniques have considerable computational complexity, hindering practical deployment. To address this issue, we propose a Semantic-Aware Quantised Network (SemQNet) framework that leverages image compression and a lightweight computer vision model to extract semantic information used for training a fully connected neural network (FCNN). Additionally, the proposed SemQNet also uses quantisation-aware training (QAT), which enables low-precision arithmetic operation, reducing the model size in the training process. Our tests on the DeepSense 6G dataset show that SemQNet achieves almost the same top-1 accuracy as existing vision-based methods while reducing the model size by 74.21\%. This smaller model size reduces the communication overhead, making SemQNet a practical and efficient solution for energy-constrained mmWave communication systems.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Funding Information: | Funder Grant number EPSRC EP/Y019229/1 EPSRC EP/X040518/1 |
Depositing User: | Pure (York) |
Date Deposited: | 21 Mar 2025 11:00 |
Last Modified: | 27 Mar 2025 01:13 |
Status: | Published |
Publisher: | IEEE Communications Society |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:224684 |