Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/171818
Title: Algorithm unrolling-based distributed optimization for RIS-assisted cell-free networks
Authors: Xu, Wangyang
An, Jiancheng
Li, Hongbin
Gan, Lu
Yuen, Chau
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2023
Source: Xu, W., An, J., Li, H., Gan, L. & Yuen, C. (2023). Algorithm unrolling-based distributed optimization for RIS-assisted cell-free networks. IEEE Internet of Things Journal. https://dx.doi.org/10.1109/JIOT.2023.3288072
Project: MOE-T2EP50220-0019 
Journal: IEEE Internet of Things Journal
Abstract: The user-centric cell-free network has emerged as an appealing technology to improve the wireless communication’s capacity of the internet of things (IoT) networks thanks to its ability to eliminate inter-cell interference effectively. However, the cell-free network inevitably brings in higher hardware cost and backhaul overhead as a larger number of base stations (BSs) are deployed. Additionally, severe channel fading in high-frequency bands constitutes another crucial issue that limits the practical application of the cell-free network. In order to address the above challenges, we amalgamate the cell-free system with another emerging technology, namely reconfigurable intelligent surface (RIS), which can provide high spectrum and energy efficiency with low hardware cost by reshaping the wireless propagation environment intelligently. To this end, we formulate a weighted sum-rate (WSR) maximization problem for RIS-assisted cell-free systems by jointly optimizing the BS precoding matrix and the RIS reflection coefficient vector. Subsequently, we transform the complicated WSR problem to a tractable optimization problem and propose a distributed cooperative alternating direction method of multipliers (ADMM) to fully utilize parallel computing resources. Inspired by the model-based algorithm unrolling concept, we unroll our solver to a learning-based deep distributed ADMM (D-ADMM) network framework. To improve the efficiency of the D-ADMM in distributed BSs, we develop a monodirectional information exchange strategy with a small signaling overhead. In addition to benefiting from domain knowledge, D-ADMM adaptively learns hyper-parameters and non-convex solvers of the intractable RIS design problem through data-driven end-to-end training. Finally, numerical results demonstrate that the proposed D-ADMM achieve around 210% improvement in capacity compared with the distributed noncooperative algorithm and almost 96% compared with the centralized algorithm.
URI: https://hdl.handle.net/10356/171818
ISSN: 2327-4662
DOI: 10.1109/JIOT.2023.3288072
Schools: School of Electrical and Electronic Engineering 
Rights: © 2023 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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