
Research Article
Joint Power Allocation and Passive Beamforming Design for IRS-Assisted Cell-free Networks
@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
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.