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Rate-Splitting for Cell-Free Massive MIMO: Performance Analysis and Generative AI Approach

Jiakang Zheng, Jiayi Zhang, Hongyang Du, Ruichen Zhang, Dusit Niyato, Octavia A. Dobre, Bo Ai

TL;DR

A generative AI algorithm is improved to address this complex and non-convexity problem by using a diffusion model to obtain solutions, and results demonstrate its effectiveness and practicality in mitigating interference, especially in dynamic environments.

Abstract

Cell-free (CF) massive multiple-input multipleoutput (MIMO) provides a ubiquitous coverage to user equipments (UEs) but it is also susceptible to interference. Ratesplitting (RS) effectively extracts data by decoding interference, yet its effectiveness is limited by the weakest UE. In this paper, we investigate an RS-based CF massive MIMO system, which combines strengths and mitigates weaknesses of both approaches. Considering imperfect channel state information (CSI) resulting from both pilot contamination and noise, we derive a closed-form expression for the sum spectral efficiency (SE) of the RS-based CF massive MIMO system under a spatially correlated Rician channel. Moreover, we propose low-complexity heuristic algorithms based on statistical CSI for power-splitting of common messages and power-control of private messages, and genetic algorithm is adopted as a solution for upper bound performance. Furthermore, we formulate a joint optimization problem, aiming to maximize the sum SE of the RS-based CF massive MIMO system by optimizing the power-splitting factor and power-control coefficient. Importantly, we improve a generative AI (GAI) algorithm to address this complex and nonconvexity problem by using a diffusion model to obtain solutions. Simulation results demonstrate its effectiveness and practicality in mitigating interference, especially in dynamic environments.

Rate-Splitting for Cell-Free Massive MIMO: Performance Analysis and Generative AI Approach

TL;DR

A generative AI algorithm is improved to address this complex and non-convexity problem by using a diffusion model to obtain solutions, and results demonstrate its effectiveness and practicality in mitigating interference, especially in dynamic environments.

Abstract

Cell-free (CF) massive multiple-input multipleoutput (MIMO) provides a ubiquitous coverage to user equipments (UEs) but it is also susceptible to interference. Ratesplitting (RS) effectively extracts data by decoding interference, yet its effectiveness is limited by the weakest UE. In this paper, we investigate an RS-based CF massive MIMO system, which combines strengths and mitigates weaknesses of both approaches. Considering imperfect channel state information (CSI) resulting from both pilot contamination and noise, we derive a closed-form expression for the sum spectral efficiency (SE) of the RS-based CF massive MIMO system under a spatially correlated Rician channel. Moreover, we propose low-complexity heuristic algorithms based on statistical CSI for power-splitting of common messages and power-control of private messages, and genetic algorithm is adopted as a solution for upper bound performance. Furthermore, we formulate a joint optimization problem, aiming to maximize the sum SE of the RS-based CF massive MIMO system by optimizing the power-splitting factor and power-control coefficient. Importantly, we improve a generative AI (GAI) algorithm to address this complex and nonconvexity problem by using a diffusion model to obtain solutions. Simulation results demonstrate its effectiveness and practicality in mitigating interference, especially in dynamic environments.
Paper Structure (18 sections, 2 theorems, 65 equations, 9 figures, 1 algorithm)

This paper contains 18 sections, 2 theorems, 65 equations, 9 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Channel Estimation
  4. Rate-Splitting Strategy
  5. Downlink Data Transmission
  6. Sum Spectral Efficiency
  7. Power-Splitting Factors
  8. Power-Control Coefficients
  9. Joint Optimization With GAI
  10. Problem Formulation
  11. Generative AI Approach
  12. Forward diffusion
  13. Reverse diffusion
  14. GAI as solution
  15. Complexity Analysis
  16. ...and 3 more sections

Key Result

Theorem 1

Using MR precoding ${{\mathbf{v}}_{il}} = {{{\mathbf{\hat{g}}}}_{il}}$ for private messages and superposition-based precoding ${{\mathbf{v}}_{{\mathrm{c}},l}} = \sum\nolimits_{i = 1}^K {{{{\mathbf{\hat{g}}}}_{il}}}$ for common messages, a lower bound on the sum SE is with ${{\mathrm{SINR}}_k^{\mathrm{c}}}$ and ${\mathrm{SINR}}_k^{\mathrm{p}}$ are given by where each term is expressed as Moreove

Figures (9)

  • Figure 1: GAI for the RS-based CF massive MIMO system under dynamic environments.
  • Figure 2: CDF of the sum SE for RS-based CF massive MIMO systems ($K=4$, $L=20$, $N=4$, $\bar{K} = 5$ dB, $\tau_p=K/2$).
  • Figure 3: Sum SE for RS-based CF massive MIMO systems against transmit power per AP ($K=4$, $L=20$, $N=4$, $\bar{K} = 5$ dB). (a) Imperfect CSI with pilot contamination and noise interference; (b) Perfect CSI without pilot contamination and noise interference.
  • Figure 4: Sum SE for RS-based CF massive MIMO systems against initial power-splitting factor with different power-splitting algorithms ($K\!=\!4$, $L\!=\!20$, $N\!=\!4$, $\tau_p\!=\!K/2$). (a) Rician channel with $\bar{K} \!=\! 5$ dB; (b) Rayleigh channel.
  • Figure 5: Sum SE for RS-based CF massive MIMO systems against initial power-splitting factor with different power-control algorithms ($K=4$, $L=20$, $N=4$, $\bar{K} = 5$ dB, $\tau_p=K/2$). (a) Rician channel with $\bar{K} \!=\! 5$ dB; (b) Rayleigh channel.
  • ...and 4 more figures

Theorems & Definitions (5)

  • Remark 1
  • Theorem 1
  • Corollary 1
  • Remark 2
  • Remark 3