Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Joint Max-Min Power Control and Clustering in Cell-Free Wireless Networks: Design and Analysis

Achini Jayawardane, Rajitha Senanayake, Erfan Khordad, Jamie Evans

TL;DR

This work tackles max-min SINR fairness in cell-free wireless networks by jointly optimizing uplink transmit powers and dynamic user-centric AP clusters using a centralized CPU with an MRC receiver. It retools a non-linear Perron-Frobenius–based fixed-point algorithm to handle variable AP associations, and frames the problem as a conditional eigenvalue problem whose solution is characterized by the spectral radius of a centrally constructed matrix. Theoretical results prove convergence under standard interference-function assumptions, and a spectral-radius formulation links the optimal fairness solution to AP clustering choices, yielding unique, positive power allocations at the optimum. Numerical experiments show that carefully designed AP clustering can significantly exceed fixed or naive clustering strategies, with distributed antenna deployments offering notable gains through macro-diversity, while exhaustive per-user clustering provides the best fairness in distributed settings under reasonable complexity. The approach offers a scalable, convergent method for achieving fair, high-quality service in cell-free MIMO systems and points to future work on adaptive clustering in heterogeneous networks.

Abstract

Cell-free wireless networks have attracted significant interest for their ability to eliminate cell-edge effects and deliver uniformly high service quality through macro-diversity. In this paper, we develop an algorithm to jointly optimize uplink transmit powers and dynamic user-centric access point (AP) clusters in a centralized cell-free network. This approach aims to efficiently mitigate inter-user interference and achieve higher max-min signal-to-interference-plus-noise ratio (SINR) targets for users. To this end, we re-purpose an iterative power control algorithm based on non-linear Perron-Frobenius theory and prove its convergence for the maximum ratio combiner (MRC) receiver under various AP subset selection schemes. We further provide analytical results by framing the joint optimization as a conditional eigenvalue problem with power and AP association constraints, and leveraging Perron-Frobenius theory on a centrally constructed matrix. The numerical results highlight that optimizing each user's serving AP cluster is essential to achieving higher max-min SINR targets with the simple MRC receiver.

Joint Max-Min Power Control and Clustering in Cell-Free Wireless Networks: Design and Analysis

TL;DR

This work tackles max-min SINR fairness in cell-free wireless networks by jointly optimizing uplink transmit powers and dynamic user-centric AP clusters using a centralized CPU with an MRC receiver. It retools a non-linear Perron-Frobenius–based fixed-point algorithm to handle variable AP associations, and frames the problem as a conditional eigenvalue problem whose solution is characterized by the spectral radius of a centrally constructed matrix. Theoretical results prove convergence under standard interference-function assumptions, and a spectral-radius formulation links the optimal fairness solution to AP clustering choices, yielding unique, positive power allocations at the optimum. Numerical experiments show that carefully designed AP clustering can significantly exceed fixed or naive clustering strategies, with distributed antenna deployments offering notable gains through macro-diversity, while exhaustive per-user clustering provides the best fairness in distributed settings under reasonable complexity. The approach offers a scalable, convergent method for achieving fair, high-quality service in cell-free MIMO systems and points to future work on adaptive clustering in heterogeneous networks.

Abstract

Cell-free wireless networks have attracted significant interest for their ability to eliminate cell-edge effects and deliver uniformly high service quality through macro-diversity. In this paper, we develop an algorithm to jointly optimize uplink transmit powers and dynamic user-centric access point (AP) clusters in a centralized cell-free network. This approach aims to efficiently mitigate inter-user interference and achieve higher max-min signal-to-interference-plus-noise ratio (SINR) targets for users. To this end, we re-purpose an iterative power control algorithm based on non-linear Perron-Frobenius theory and prove its convergence for the maximum ratio combiner (MRC) receiver under various AP subset selection schemes. We further provide analytical results by framing the joint optimization as a conditional eigenvalue problem with power and AP association constraints, and leveraging Perron-Frobenius theory on a centrally constructed matrix. The numerical results highlight that optimizing each user's serving AP cluster is essential to achieving higher max-min SINR targets with the simple MRC receiver.

Paper Structure

This paper contains 11 sections, 5 theorems, 28 equations, 3 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Problem Formulation
  4. Access Point Subset Selection Schemes
  5. Proposed Algorithm: Power Control and User-Centric Access Point Assignment
  6. Proof of Convergence
  7. Properties of SINR Expressions
  8. Convergence of the Algorithm
  9. Theoretical Characterization of Max-Min SINR
  10. Numerical Examples
  11. Conclusion

Key Result

Corollary 1

SINR expressions of the form $\mathrm{SINR}_n(\mathbf{p}) = \frac{p_n}{T_n(\mathbf{p})}$, where $T_n(\mathbf{p})$ is a standard interference function, fulfill the properties in Assumption 5_assumption, $\forall \mathbf{p} > \mathbf{0}$.

Figures (3)

  • Figure 1: Comparison of Proposition \ref{['proposition_5_2']} and proposed Algorithm \ref{['5_algo']} with various AP clustering schemes on a simple network setup
  • Figure 2: Performance with various AP clustering schemes and candidate set sizes using the proposed Algorithm \ref{['5_algo']}
  • Figure 3: Performance with various AP clustering schemes on different network setups in Table \ref{['table_3_2']}, using the proposed Algorithm \ref{['5_algo']}

Theorems & Definitions (5)

  • Corollary 1
  • Theorem 1
  • Proposition 1
  • Proposition 2
  • Proposition 3