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

TL;DR

This survey comprehensively reviews the application of linear, integer, and mixed-integer programming techniques for resource allocation in 5G and Beyond 5G networks, highlighting current methods, challenges, and future directions.

Contribution

It categorizes and analyzes 103 studies on optimization models in 5G/B5G networks, emphasizing emerging trends like AI integration and identifying research gaps.

Findings

LP, ILP, and MILP are widely used in network resource optimization.

Solution methods for NP-hard problems are categorized and discussed.

Emerging trends include AI and machine learning integration.

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.