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Communications and Networking. 16th EAI International Conference, ChinaCom 2021, Virtual Event, November 21-22, 2021, Proceedings

Research Article

Joint Power Allocation and Passive Beamforming Design for IRS-Assisted Cell-free Networks

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  • @INPROCEEDINGS{10.1007/978-3-030-99200-2_21,
        author={Chen He and Xie Xie and Yangrui Dong and Shun Zhang},
        title={Joint Power Allocation and Passive Beamforming Design for IRS-Assisted Cell-free Networks},
        proceedings={Communications and Networking. 16th EAI International Conference, ChinaCom 2021, Virtual Event, November 21-22, 2021, Proceedings},
        proceedings_a={CHINACOM},
        year={2022},
        month={4},
        keywords={Intelligent reflecting surface Power allocation Fractional programming Sequential Optimization},
        doi={10.1007/978-3-030-99200-2_21}
    }
    
  • Chen He
    Xie Xie
    Yangrui Dong
    Shun Zhang
    Year: 2022
    Joint Power Allocation and Passive Beamforming Design for IRS-Assisted Cell-free Networks
    CHINACOM
    Springer
    DOI: 10.1007/978-3-030-99200-2_21
Chen He1,*, Xie Xie1, Yangrui Dong1, Shun Zhang2
  • 1: School of Information Science and Technology
  • 2: State Key Laboratory of Integrated Services Networks
*Contact email: chenhe@nwu.edu.cn

Abstract

This paper investigates multiple intelligent reflecting surface (IRSs) assisted cell-free networks, where multiple single antenna access points (APs) and IRSs are connected to a network controller, to serve multiple user equipment (UEs) simultaneously. Our objective is to maximize the sum-rate of the cell-free network by jointly designing the power allocation of APs and the passive reflecting beamforming of IRSs, while the constraints on the maximum transmit power of each AP and the phase of each phase shifter (PS) of IRS are satisfied. However, the problem is non-convex and challenging to solve. To this end, we propose an efficient framework to jointly design the power allocation vectors and the passive reflecting beamforming matrices. Particularly, we first reformulate the problem as a more tractable form by employing the fractional programming methods and then decompose the transformed problem into two subproblems. Finally, we propose an alternating iteratively (AI) algorithm to solve the two subproblems, which is guaranteed to converge to locally optimal solutions. Simulation results indicate that the advantages of leveraging IRSs in improving the performance of the conventional cell-free networks.

Keywords
Intelligent reflecting surface Power allocation Fractional programming Sequential Optimization
Published
2022-04-05
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-99200-2_21
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