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Network Slicing Resource Management in Uplink User-Centric Cell-Free Massive MIMO Systems

Manobendu Sarker, Soumaya Cherkaoui

TL;DR

These approaches provide a practical and computationally efficient solution for resource-constrained network slicing scenarios, where QoS feasibility is often violated under dense deployments and limited bandwidth, necessitating graceful degradation and fair QoS preservation rather than solely maximizing the aggregate sum-rate.

Abstract

This paper addresses the joint optimization of per-user equipment (UE) bandwidth allocation and UE-access point (AP) association to maximize weighted sum-rate while satisfying heterogeneous quality-of-service (QoS) requirements across enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) slices in the uplink of a network slicing-enabled user-centric cell-free (CF) massive multiple-input multiple-output (mMIMO) system. The formulated problem is NP-hard, rendering global optimality computationally intractable. To address this challenge, it is decomposed into two sub-problems, each solved by a computationally efficient heuristic scheme, and jointly optimized through an alternating optimization framework. We then propose (i) a bandwidth allocation scheme that balances UE priority, spectral efficiency, and minimum bandwidth demand under limited resources to ensure fair QoS distribution, and (ii) a priority-based UE-AP association assignment approach that balances UE service quality with system capacity constraints. Together, these approaches provide a practical and computationally efficient solution for resource-constrained network slicing scenarios, where QoS feasibility is often violated under dense deployments and limited bandwidth, necessitating graceful degradation and fair QoS preservation rather than solely maximizing the aggregate sum-rate. Simulation results demonstrate that the proposed scheme achieves up to 52% higher weighted sum-rate, 140% and 58% higher QoS success rates for eMBB and URLLC slices, respectively, while reducing runtime by up to 97% compared to considered benchmarks.

Network Slicing Resource Management in Uplink User-Centric Cell-Free Massive MIMO Systems

TL;DR

These approaches provide a practical and computationally efficient solution for resource-constrained network slicing scenarios, where QoS feasibility is often violated under dense deployments and limited bandwidth, necessitating graceful degradation and fair QoS preservation rather than solely maximizing the aggregate sum-rate.

Abstract

This paper addresses the joint optimization of per-user equipment (UE) bandwidth allocation and UE-access point (AP) association to maximize weighted sum-rate while satisfying heterogeneous quality-of-service (QoS) requirements across enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) slices in the uplink of a network slicing-enabled user-centric cell-free (CF) massive multiple-input multiple-output (mMIMO) system. The formulated problem is NP-hard, rendering global optimality computationally intractable. To address this challenge, it is decomposed into two sub-problems, each solved by a computationally efficient heuristic scheme, and jointly optimized through an alternating optimization framework. We then propose (i) a bandwidth allocation scheme that balances UE priority, spectral efficiency, and minimum bandwidth demand under limited resources to ensure fair QoS distribution, and (ii) a priority-based UE-AP association assignment approach that balances UE service quality with system capacity constraints. Together, these approaches provide a practical and computationally efficient solution for resource-constrained network slicing scenarios, where QoS feasibility is often violated under dense deployments and limited bandwidth, necessitating graceful degradation and fair QoS preservation rather than solely maximizing the aggregate sum-rate. Simulation results demonstrate that the proposed scheme achieves up to 52% higher weighted sum-rate, 140% and 58% higher QoS success rates for eMBB and URLLC slices, respectively, while reducing runtime by up to 97% compared to considered benchmarks.
Paper Structure (23 sections, 3 theorems, 8 equations, 4 figures, 2 algorithms)

This paper contains 23 sections, 3 theorems, 8 equations, 4 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. Description of Network Configuration
  4. Channel Model
  5. Channel Estimation Model
  6. Uplink Data Transmission Model
  7. Delay Model
  8. Problem Formulation
  9. Proposed Solution
  10. Per-UE Bandwidth Allocation
  11. Proposed Per-UE Bandwidth Allocation Scheme
  12. Computational Complexity Analysis
  13. UE-AP Association Assignment
  14. Proposed UE-AP Association Assignment Scheme
  15. Computational Complexity Analysis
  16. ...and 8 more sections

Key Result

Theorem 1

P0 is a non-convex mixed-integer nonlinear programming (MINLP) problem that is NP-hard.

Figures (4)

  • Figure 1: Average weighted sum-rate performance of different schemes for varying UE $K$ with $\tau_p = 10$ and $M = 100$ APs.
  • Figure 2: Average success rate performance of different schemes for eMBB UEs with $\tau_p = 10$ and $M = 100$ APs.
  • Figure 3: Average success rate performance of different schemes for URLLC UEs with $\tau_p = 10$ and $M = 100$ APs.
  • Figure 4: Average runtime of Hybrid and Proposed schemes for varying UE $K$ with $\tau_p = 10$ and $M = 100$ APs.

Theorems & Definitions (4)

  • Theorem 1
  • proof
  • Proposition 1
  • Proposition 2