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Efficient Resource Allocation in 5G Massive MIMO-NOMA Networks: Comparative Analysis of SINR-Aware Power Allocation and Spatial Correlation-Based Clustering

Samar Chebbi, Oussama Habachi, Jean-Pierre Cances, Vahid Meghdadi, Essaid Sabir

TL;DR

This paper tackles resource allocation in downlink 5G MIMO-NOMA networks by evaluating multiple clustering and power-control strategies under identical spatially correlated conditions. It introduces a SINR-aware power-allocation framework and compares CIA, KUC, and GWO clustering approaches, using Zero-Forcing beamforming to mitigate inter-cluster interference. CIA excels at maximizing the number of served users, especially in dense, spatially correlated environments, while GWO delivers the best energy efficiency and scalability; KUC offers a practical, moderate-performance alternative. The findings highlight a trade-off between computational complexity and performance, underscoring the potential of metaheuristic and ML-informed clustering for enabling massive connectivity with QoS guarantees in next-generation wireless networks.

Abstract

With the evolution of 5G networks, optimizing resource allocation has become crucial to meeting the increasing demand for massive connectivity and high throughput. Combining Non-Orthogonal Multiple Access (NOMA) and massive Multi-Input Multi-Output (MIMO) enhances spectral efficiency, power efficiency, and device connectivity. However, deploying MIMO-NOMA in dense networks poses challenges in managing interference and optimizing power allocation while ensuring that the Signal-to-Interference-plus-Noise Ratio (SINR) meets required thresholds. Unlike previous studies that analyze user clustering and power allocation techniques under simplified assumptions, this work provides a comparative evaluation of multiple clustering and allocation strategies under identical spatially correlated network conditions. We focus on maximizing the number of served users under a given Quality of Service (QoS) constraint rather than the conventional sum-rate maximization approach. Additionally, we consider spatial correlation in user grouping, a factor often overlooked despite its importance in mitigating intra-cluster interference. We evaluate clustering algorithms, including user pairing, random clustering, Correlation Iterative Clustering Algorithm (CIA), K-means++-based User Clustering (KUC), and Grey Wolf Optimizer-based clustering (GWO), in a downlink spatially correlated MIMO-NOMA environment. Numerical results demonstrate that the GWO-based clustering algorithm achieves superior energy efficiency while maintaining scalability, whereas CIA effectively maximizes the number of served users. These findings provide valuable insights for designing MIMO-NOMA systems that optimize resource allocation in next-generation wireless networks.

Efficient Resource Allocation in 5G Massive MIMO-NOMA Networks: Comparative Analysis of SINR-Aware Power Allocation and Spatial Correlation-Based Clustering

TL;DR

This paper tackles resource allocation in downlink 5G MIMO-NOMA networks by evaluating multiple clustering and power-control strategies under identical spatially correlated conditions. It introduces a SINR-aware power-allocation framework and compares CIA, KUC, and GWO clustering approaches, using Zero-Forcing beamforming to mitigate inter-cluster interference. CIA excels at maximizing the number of served users, especially in dense, spatially correlated environments, while GWO delivers the best energy efficiency and scalability; KUC offers a practical, moderate-performance alternative. The findings highlight a trade-off between computational complexity and performance, underscoring the potential of metaheuristic and ML-informed clustering for enabling massive connectivity with QoS guarantees in next-generation wireless networks.

Abstract

With the evolution of 5G networks, optimizing resource allocation has become crucial to meeting the increasing demand for massive connectivity and high throughput. Combining Non-Orthogonal Multiple Access (NOMA) and massive Multi-Input Multi-Output (MIMO) enhances spectral efficiency, power efficiency, and device connectivity. However, deploying MIMO-NOMA in dense networks poses challenges in managing interference and optimizing power allocation while ensuring that the Signal-to-Interference-plus-Noise Ratio (SINR) meets required thresholds. Unlike previous studies that analyze user clustering and power allocation techniques under simplified assumptions, this work provides a comparative evaluation of multiple clustering and allocation strategies under identical spatially correlated network conditions. We focus on maximizing the number of served users under a given Quality of Service (QoS) constraint rather than the conventional sum-rate maximization approach. Additionally, we consider spatial correlation in user grouping, a factor often overlooked despite its importance in mitigating intra-cluster interference. We evaluate clustering algorithms, including user pairing, random clustering, Correlation Iterative Clustering Algorithm (CIA), K-means++-based User Clustering (KUC), and Grey Wolf Optimizer-based clustering (GWO), in a downlink spatially correlated MIMO-NOMA environment. Numerical results demonstrate that the GWO-based clustering algorithm achieves superior energy efficiency while maintaining scalability, whereas CIA effectively maximizes the number of served users. These findings provide valuable insights for designing MIMO-NOMA systems that optimize resource allocation in next-generation wireless networks.

Paper Structure

This paper contains 21 sections, 1 theorem, 33 equations, 10 figures, 2 tables, 4 algorithms.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. System Model and Preliminaries
  4. Zero-Forcing Beamforming
  5. Spatial correlation
  6. Power Allocation Algorithm
  7. User Clustering
  8. User Pairing
  9. Near-Far user pairing
  10. Correlation-based user pairing
  11. Correlation-based Iterative Algorithm (CIA)
  12. K-means++ user clustering algorithm (KUC)
  13. GWO-based User Clustering
  14. Computational Complexity Analysis
  15. Performance Evaluation
  16. ...and 6 more sections

Key Result

Proposition 1

In order to meet the power requirements of all clusters, the total allocated power should be bounded by $P_{\text{Max}}$ and $P_{\text{min}}$, where $P_{\text{min}}$ represents the minimum power required to meet the SINR thresholds and QoS requirements in all clusters. $P_{\text{min}}$ is illustrate

Figures (10)

  • Figure 1: 2-user PD_NOMA
  • Figure 2: Comparison of user pairing approaches
  • Figure 3: Example of K-Means++ Clustering.
  • Figure 4: GWO hunting process.
  • Figure 5: Number of served while varying the total number of users for $5$ RF chains.
  • ...and 5 more figures

Theorems & Definitions (2)

  • Proposition 1
  • proof