A Comprehensive Survey of Linear, Integer, and Mixed-Integer Programming Approaches for Optimizing Resource Allocation in 5G and Beyond Networks
Naveed Ejaz, Salimur Choudhury
TL;DR
The paper surveys 103 studies on resource allocation in 5G and Beyond networks modeled with LP, ILP, and MILP, providing a taxonomy by network architectures, resources, objectives, constraints, and solution methods. It demonstrates the broad applicability of LP variants across diverse architectures (RAN, Cloud-RAN, HetNets, MEC, SDN/NFV) and resource types (spectrum, slicing, RRM, energy, QoS), while highlighting the NP-hard nature of ILP/MILP and the consequent reliance on decompositions, heuristics, approximations, and AI-assisted approaches. The review also details a spectrum of solution techniques—from direct LP formulations to advanced decompositions (Benders, Dantzig-Wolfe), metaheuristics, and reinforcement learning—emphasizing the trend toward scalable, real-time optimization. Looking forward, the authors identify AI/ML integration, distributed and parallel optimization, and quantum-classical hybrids as promising directions to tackle the complexity of dense 5G/B5G networks and future 6G scenarios.
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.