Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Joint User Association and Power Control for Cell-Free Massive MIMO

Chongzheng Hao, Tung Thanh Vu, Hien Quoc Ngo, Minh N. Dao, Xiaoyu Dang, Chenghua Wang, Michail Matthaiou

TL;DR

This work tackles joint user association and power control in downlink CFmMIMO with user-centric AP clusters and PPZF precoding under practical fronthaul and QoS constraints. It delivers a dual-path solution: a convex-approximation SCA method for small-scale systems, supplemented by a DL surrogate (JointCFNet) to drastically reduce online computation, and a low-complexity APG algorithm for large-scale networks. Numerical results show that JointCFNet can match SCA performance with up to three orders of magnitude faster runtimes in small systems, while APG achieves near-SCA SE with significantly lower complexity in large systems, outperforming heuristic baselines by substantial margins. The findings demonstrate that scale-aware optimization is viable for CFmMIMO deployments, with potential extensions to uplink UA/PC. Key mathematical elements include the mixed-integer nonconvex formulation for sum SE maximization, PPZF-based downlink precoding, and convex surrogate techniques used in SCA and APG to enforce QoS and fronthaul constraints within feasible regions, all framed under limited CSI and fronthaul realities.

Abstract

This work proposes novel approaches that jointly design user equipment (UE) association and power control (PC) in a downlink user-centric cell-free massive multiple-input multiple-output (CFmMIMO) network, where each UE is only served by a set of access points (APs) for reducing the fronthaul signalling and computational complexity. In order to maximize the sum spectral efficiency (SE) of the UEs, we formulate a mixed-integer nonconvex optimization problem under constraints on the per-AP transmit power, quality-of-service rate requirements, maximum fronthaul signalling load, and maximum number of UEs served by each AP. In order to solve the formulated problem efficiently, we propose two different schemes according to the different sizes of the CFmMIMO systems. For small-scale CFmMIMO systems, we present a successive convex approximation (SCA) method to obtain a stationary solution and also develop a learning-based method (JointCFNet) to reduce the computational complexity. For large-scale CFmMIMO systems, we propose a low-complexity suboptimal algorithm using accelerated projected gradient (APG) techniques. Numerical results show that our JointCFNet can yield similar performance and significantly decrease the run time compared with the SCA algorithm in small-scale systems. The presented APG approach is confirmed to run much faster than the SCA algorithm in the large-scale system while obtaining an SE performance close to that of the SCA approach. Moreover, the median sum SE of the APG method is up to about 2.8 fold higher than that of the heuristic baseline scheme.

Joint User Association and Power Control for Cell-Free Massive MIMO

TL;DR

This work tackles joint user association and power control in downlink CFmMIMO with user-centric AP clusters and PPZF precoding under practical fronthaul and QoS constraints. It delivers a dual-path solution: a convex-approximation SCA method for small-scale systems, supplemented by a DL surrogate (JointCFNet) to drastically reduce online computation, and a low-complexity APG algorithm for large-scale networks. Numerical results show that JointCFNet can match SCA performance with up to three orders of magnitude faster runtimes in small systems, while APG achieves near-SCA SE with significantly lower complexity in large systems, outperforming heuristic baselines by substantial margins. The findings demonstrate that scale-aware optimization is viable for CFmMIMO deployments, with potential extensions to uplink UA/PC. Key mathematical elements include the mixed-integer nonconvex formulation for sum SE maximization, PPZF-based downlink precoding, and convex surrogate techniques used in SCA and APG to enforce QoS and fronthaul constraints within feasible regions, all framed under limited CSI and fronthaul realities.

Abstract

This work proposes novel approaches that jointly design user equipment (UE) association and power control (PC) in a downlink user-centric cell-free massive multiple-input multiple-output (CFmMIMO) network, where each UE is only served by a set of access points (APs) for reducing the fronthaul signalling and computational complexity. In order to maximize the sum spectral efficiency (SE) of the UEs, we formulate a mixed-integer nonconvex optimization problem under constraints on the per-AP transmit power, quality-of-service rate requirements, maximum fronthaul signalling load, and maximum number of UEs served by each AP. In order to solve the formulated problem efficiently, we propose two different schemes according to the different sizes of the CFmMIMO systems. For small-scale CFmMIMO systems, we present a successive convex approximation (SCA) method to obtain a stationary solution and also develop a learning-based method (JointCFNet) to reduce the computational complexity. For large-scale CFmMIMO systems, we propose a low-complexity suboptimal algorithm using accelerated projected gradient (APG) techniques. Numerical results show that our JointCFNet can yield similar performance and significantly decrease the run time compared with the SCA algorithm in small-scale systems. The presented APG approach is confirmed to run much faster than the SCA algorithm in the large-scale system while obtaining an SE performance close to that of the SCA approach. Moreover, the median sum SE of the APG method is up to about 2.8 fold higher than that of the heuristic baseline scheme.
Paper Structure (29 sections, 2 theorems, 80 equations, 12 figures, 4 tables, 3 algorithms)

This paper contains 29 sections, 2 theorems, 80 equations, 12 figures, 4 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Review of Related Literature
  3. Power Control
  4. UE Association
  5. Joint Power Control and UE Association
  6. Research Gap and Main Contributions
  7. Paper Organization and Notation
  8. System Model And Problem Formulation
  9. Uplink Channel Estimation
  10. Downlink Data Transmission with PPZF Precoding
  11. Step (S1)
  12. Step (S2)
  13. Problem Formulation
  14. Solution For Small-scale CFmMIMO Systems
  15. SCA-Based Algorithm
  16. ...and 14 more sections

Key Result

Proposition 1

The value $Q_{\lambda}$ of $Q$ at the solution of P:SE:equiv:2 corresponding to $\lambda$ converges to $0$ as $\lambda \rightarrow +\infty$. Also, problem P:SE:equiv has strong duality, i.e., Then, P:SE:equiv:2 is equivalent to P:SE:equiv at the optimal solution $\lambda^* \geq0$ of the sup-min problem in Strong:Dualitly:hold:1.

Figures (12)

  • Figure 1: Architecture of the CFmMIMO network. The distributed APs connect to the CPU via fronthaul links, while the APs may jointly serve UEs.
  • Figure 2: Structure of the designed JointCFNet for the joint PC and UA.
  • Figure 3: The training curves of MSE with $M=25, K=7, \widehat{K}=5$, and $M=36, K=5, \widehat{K}=3$.
  • Figure 4: CDF of the sum SE in a small-scale CFmMIMO system with $M=36, K=5$, and $\widehat{K}=3$.
  • Figure 5: CDF of the sum SE in a small-scale CFmMIMO system with $M=25, K=7$, and $\widehat{K}=5$.
  • ...and 7 more figures

Theorems & Definitions (3)

  • Proposition 1
  • proof
  • Proposition 2