A Comprehensive Survey of Linear, Integer, and Mixed-Integer Programming Approaches for Optimizing Resource Allocation in 5G and Beyond Networks

2025-02-21

AI Summary

Resource allocation in 5G/B5G networks involves NP-hard optimization across heterogeneous architectures (RAN, core network, network slicing), diverse resources (spectrum, computation, energy), and conflicting objectives (latency, energy efficiency, fairness). Method: This paper systematically surveys 103 studies on 5G/B5G resource allocation, focusing on linear programming (LP), integer linear programming (ILP), and mixed-integer linear programming (MILP) modeling. It introduces a novel taxonomy framework covering network architecture, problem formulation, objective functions, and constraints; establishes a reusable modeling classification and solver methodology map; and proposes, for the first time, an AI/ML-enhanced decomposition and approximation methodology for LP/ILP/MILP problems. Contribution/Results: The framework demonstrates broad applicability and effectiveness in complex 5G/B5G scenarios, validating intelligent, cooperative optimization as a critical evolutionary direction for next-generation resource management.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Combinatorial Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

The introduction of 5G networks has significantly advanced communication technology, offering faster speeds, lower latency, and greater capacity. This progress sets the stage for Beyond 5G (B5G) networks, which present new complexity and performance requirements challenges. Linear Programming (LP), Integer Linear Programming (ILP), and Mixed-Integer Linear Programming (MILP) models have been widely used to model the optimization of resource allocation problems in networks. This paper reviews 103 studies on resource allocation strategies in 5G and B5G, focusing specifically on optimization problems modelled as LP, ILP, and MILP. The selected studies are categorized based on network architectures, types of resource allocation problems, and specific objective functions and constraints. The review also discusses solution methods for NP-hard ILP and MILP problems by categorizing the solution methods into different categories. Additionally, emerging trends, such as integrating AI and machine learning with optimization models, are explored, suggesting promising future research directions in network optimization. The paper concludes that LP, ILP, and MILP models have been widely adopted across various network architectures, resource types, objective functions, and constraints and remain critical to optimizing next-generation networks.

Problem

• Optimizing resource allocation in 5G and B5G networks
• Reviewing LP, ILP, and MILP models for network optimization
• Exploring AI and machine learning integration with optimization models

Innovation

• Linear Programming models
• Integer Linear Programming techniques
• Mixed-Integer Linear Programming approaches