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Fairness Optimization for Intelligent Reflecting Surface Aided Uplink Rate-Splitting Multiple Access

Shanshan Zhang, Wen Chen, Qingqing Wu, Ziwei Liu, Shunqing Zhang, Jun Li

Abstract

This paper studies the fair transmission design for an intelligent reflecting surface (IRS) aided rate-splitting multiple access (RSMA). IRS is used to establish a good signal propagation environment and enhance the RSMA transmission performance. The fair rate adaption problem is constructed as a max-min optimization problem. To solve the optimization problem, we adopt an alternative optimization (AO) algorithm to optimize the power allocation, beamforming, and decoding order, respectively. A generalized power iteration (GPI) method is proposed to optimize the receive beamforming, which can improve the minimum rate of devices and reduce the optimization complexity. At the base station (BS), a successive group decoding (SGD) algorithm is proposed to tackle the uplink signal estimation, which trades off the fairness and complexity of decoding. At the same time, we also consider robust communication with imperfect channel state information at the transmitter (CSIT), which studies robust optimization by using lower bound expressions on the expected data rates. Extensive numerical results show that the proposed optimization algorithm can significantly improve the performance of fairness. It also provides reliable results for uplink communication with imperfect CSIT.

Fairness Optimization for Intelligent Reflecting Surface Aided Uplink Rate-Splitting Multiple Access

Abstract

This paper studies the fair transmission design for an intelligent reflecting surface (IRS) aided rate-splitting multiple access (RSMA). IRS is used to establish a good signal propagation environment and enhance the RSMA transmission performance. The fair rate adaption problem is constructed as a max-min optimization problem. To solve the optimization problem, we adopt an alternative optimization (AO) algorithm to optimize the power allocation, beamforming, and decoding order, respectively. A generalized power iteration (GPI) method is proposed to optimize the receive beamforming, which can improve the minimum rate of devices and reduce the optimization complexity. At the base station (BS), a successive group decoding (SGD) algorithm is proposed to tackle the uplink signal estimation, which trades off the fairness and complexity of decoding. At the same time, we also consider robust communication with imperfect channel state information at the transmitter (CSIT), which studies robust optimization by using lower bound expressions on the expected data rates. Extensive numerical results show that the proposed optimization algorithm can significantly improve the performance of fairness. It also provides reliable results for uplink communication with imperfect CSIT.
Paper Structure (27 sections, 74 equations, 7 figures, 1 table)

This paper contains 27 sections, 74 equations, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related works
  3. Motivations
  4. Contributions
  5. Organization
  6. Notation
  7. System Descriptions
  8. System Model
  9. Successive Group Decoding (SGD)
  10. Resource Allocation for Fairness with known CSIT
  11. Problem Formulation
  12. Optimizing Receive Beamforming
  13. Optimizing Phase-shift Beamforming
  14. Optimizing Power Allocation
  15. Optimizing Decoding Order
  16. ...and 12 more sections

Figures (7)

  • Figure 1: The architecture of uplink RSMA with the SGD receiver.
  • Figure 2: The minimum rate versus the number of iterations. There are SNR$=10$ dB and $L=4$. We consider the CSIT to be known.
  • Figure 3: The minimum rate versus SNR. Scheme 1 is the proposed scheme. Scheme 2 is without IRS-aided scheme and Scheme 3 adopts SDP to optimize the receive beamforming. The setup is as follows: (a). $K=12,M=16,N=8,I=2$; (b). $K=8,M=16,N=16,I=2$. In both cases, we consider the CSIT to be known.
  • Figure 4: The minimum rate versus SNR. There are $N=M=16$ and $K=12$ with different $I$ and $L$. In this case, the CSIT is known.
  • Figure 5: The minimum rate versus the number of devices $K$. There are $M=16$ and SNR$=$$10$ dB with different $N$ and $L$. The numerical simulation considers two cases: known CSIT and imperfect CSIT.
  • ...and 2 more figures