Attention-Guided Encoder-Decoder Design for Efficient RIS Phase Configuration in Time-Varying IoT Networks

The integration of Artificial Intelligence (AI) into wireless communication has enabled adaptive, efficient, robust, and scalable system designs. Reconfigurable Intelligent Surfaces (RIS) offer a promising paradigm for AI-driven control by dynamically adjusting the wireless environment through Phase...

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Bibliographic Details
Published in:IEEE transactions on artificial intelligence pp. 1 - 12
Main Authors: Bhardwaj, Vikash Kumar, Singh, Abhisekh, Sahoo, Omm Prakash, Shukla, Mahendra K., Pandey, Om Jee
Format: Journal Article
Language:English
Published: IEEE 2025
Subjects:
Artificial intelligence
Bandwidth
Base Station (BS)
Decoding
Internet of Things
Internet of Things (IoT)
Noise
Noise reduction
Reconfigurable Intelligent Surface (RIS)
Reconfigurable intelligent surfaces
Rician channels
Training
Wireless communication
ISSN:2691-4581, 2691-4581
Online Access:Get full text
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Summary:The integration of Artificial Intelligence (AI) into wireless communication has enabled adaptive, efficient, robust, and scalable system designs. Reconfigurable Intelligent Surfaces (RIS) offer a promising paradigm for AI-driven control by dynamically adjusting the wireless environment through Phase Configuration Sequence (PCS) bits sent from the Base Station (BS). By steering signals intelligently, RIS can improve coverage and direct transmissions to specific locations. However, the increasing size of RIS and rapidly changing IoT device positions in dynamic environments impose significant feedback bandwidth constraints due to frequent PCS bits updates. To address this, we propose a novel Attention-Guided Encoder-Decoder with Normalization Enhancement (AGENE) framework that compresses PCS bits at the BS using a lightweight encoder and reconstructs them at the RIS controller using a decoder. Our design incorporates a custom attention mechanism and Generalized Divisive Normalization (GDN)/Inverse GDN layers to enhance feature extraction and normalization. We also evaluate our method under different noise models, including Gaussian and Rician noise, to test robustness in practical scenarios. Finally, to evaluate the effectiveness of our proposed method (AGENE), we compared its performance with existing methods across different compression ratios and noise conditions, focusing on loss reduction and Normalized Mean Square Error (NMSE). In Additive White Gaussian Noise (AWGN) conditions, AGENE achieved a loss reduction of 28.12% compared to PSC-DN, 43.9% compared to DL-CsiNet, and 58.18% compared to CsiNet at a compression ratio of 2/9. Under Rician noise, AGENE showed a reduction of 19.39% compared to PSC-DN, 37.01% compared to DL-CsiNet, and 45.21% compared to CsiNet, again at the same compression ratio. These results consistently demonstrate that AGENE outperforms existing methods by a significant margin across both metrics and noise conditions.
ISSN:2691-4581
2691-4581
DOI:10.1109/TAI.2025.3610389