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Early Acceptance Matching Game for User-Centric Clustering in Scalable Cell-free MIMO Networks

Ala Eddine Nouali, Mohamed Sana, Jean-Paul Jamont

TL;DR

The paper addresses the scalability challenge of cell-free MIMO by introducing a user-centric clustering paradigm and an early-acceptance many-to-many matching algorithm to form AP–UE clusters under fronthaul and QoS constraints. It models downlink transmissions with full CSI, LMMSE precoding, and QoS targets, and optimizes the expected number of QoS-satisfied UEs while limiting associations. The proposed EA-M2M framework accelerates convergence and reduces signaling compared to deferred-acceptance schemes, achieving higher UE satisfaction (up to ~99%) and an ~84% reduction in AP–UE associations in simulations with realistic network settings. These results demonstrate a scalable approach to resource allocation in dense cell-free networks, with practical implications for fronthaul load and computation. Future work will refine preference definitions and explore energy efficiency improvements.

Abstract

The canonical setup is the primary approach adopted in cell-free multiple-input multiple-output (MIMO) networks, in which all access points (APs) jointly serve every user equipment (UE). This approach is not scalable in terms of computational complexity and fronthaul signaling becoming impractical in large networks. This work adopts a user-centric approach, a scalable alternative in which only a set of preferred APs jointly serve a UE. Forming the optimal cluster of APs for each UE is a challenging task, especially, when it needs to be dynamically adjusted to meet the quality of service (QoS) requirements of the UE. This complexity is even exacerbated when considering the constrained fronthaul capacity of the UE and the AP. We solve this problem with a novel many-to-many matching game. More specifically, we devise an early acceptance matching algorithm, which immediately admits or rejects UEs based on their requests and available radio resources. The proposed solution significantly reduces the fronthaul signaling while satisfying the maximum of UEs in terms of requested QoS compared to state-of-the-art approaches.

Early Acceptance Matching Game for User-Centric Clustering in Scalable Cell-free MIMO Networks

TL;DR

The paper addresses the scalability challenge of cell-free MIMO by introducing a user-centric clustering paradigm and an early-acceptance many-to-many matching algorithm to form AP–UE clusters under fronthaul and QoS constraints. It models downlink transmissions with full CSI, LMMSE precoding, and QoS targets, and optimizes the expected number of QoS-satisfied UEs while limiting associations. The proposed EA-M2M framework accelerates convergence and reduces signaling compared to deferred-acceptance schemes, achieving higher UE satisfaction (up to ~99%) and an ~84% reduction in AP–UE associations in simulations with realistic network settings. These results demonstrate a scalable approach to resource allocation in dense cell-free networks, with practical implications for fronthaul load and computation. Future work will refine preference definitions and explore energy efficiency improvements.

Abstract

The canonical setup is the primary approach adopted in cell-free multiple-input multiple-output (MIMO) networks, in which all access points (APs) jointly serve every user equipment (UE). This approach is not scalable in terms of computational complexity and fronthaul signaling becoming impractical in large networks. This work adopts a user-centric approach, a scalable alternative in which only a set of preferred APs jointly serve a UE. Forming the optimal cluster of APs for each UE is a challenging task, especially, when it needs to be dynamically adjusted to meet the quality of service (QoS) requirements of the UE. This complexity is even exacerbated when considering the constrained fronthaul capacity of the UE and the AP. We solve this problem with a novel many-to-many matching game. More specifically, we devise an early acceptance matching algorithm, which immediately admits or rejects UEs based on their requests and available radio resources. The proposed solution significantly reduces the fronthaul signaling while satisfying the maximum of UEs in terms of requested QoS compared to state-of-the-art approaches.
Paper Structure (9 sections, 9 equations, 6 figures, 2 tables, 2 algorithms)

This paper contains 9 sections, 9 equations, 6 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. System model & problem formulation
  3. System Model
  4. Problem Formulation
  5. User-centric clustering as a many-to-many matching game
  6. Background on many-to-many matching concepts
  7. Proposed algorithm
  8. Numerical results
  9. Conclusion

Figures (6)

  • Figure 1: A user-centric clustering operation in a cell-free MIMO network. Here, $M$ geographically distributed APs jointly serve $K$ UEs. Each UE is served by a cluster of nearby preferred APs depending on its QoS requirements.
  • Figure 2: Many-to-many matching between APs and UEs.
  • Figure 3: Flowchart representing our EA user-centric clustering procedure.
  • Figure 4: Percentage of $\kappa_0$-satisfied UEs in the network.
  • Figure 5: Percentage of satisfaction level per UE in the network.
  • ...and 1 more figures

Theorems & Definitions (2)

  • Definition 1
  • Definition 2: favorable-association pair