Adaptive resource allocation in dynamic wireless environments: a systematic review

Abstract

Abstract Increasing heterogeneity, densification, mobility, and service diversification have rendered traditional static resource allocation strategies ineffective for contemporary wireless networks. The transition to 5G-Advanced and emerging 6G systems further introduces highly dynamic environments characterised by continuously evolving traffic demand, interference patterns, network topology, and application requirements. This study presents a PRISMA-based systematic review of adaptive resource allocation in dynamic wireless environments, synthesising evidence from 34 peer-reviewed studies published from 2020 onward across major databases including IEEE Xplore, Scopus, Web of Science, ACM Digital Library, ScienceDirect, and SpringerLink. Unlike prior surveys, this review provides a unified cross-domain perspective by integrating modelling approaches, allocation strategies, and architectural contexts across terrestrial, edge-enabled, non-terrestrial, and semantic communication systems. It further develops a structured taxonomy of adaptive resource allocation frameworks, encompassing optimisation-based, learning-based, and hybrid methods under non-stationary conditions. The findings reveal a strong shift toward explicit dynamic modelling and the dominance of learning-based approaches, particularly deep reinforcement learning and multi-agent reinforcement learning, due to their ability to handle uncertainty and sequential decision-making. However, persistent challenges related to training stability, generalisation, scalability, and deployment safety are driving the emergence of hybrid and constraint-aware frameworks. The review also highlights the increasing integration of adaptive allocation within advanced architectures such as Open RAN, multi-access edge computing, non-terrestrial networks, and digital-twin-assisted systems. Despite these advances, evaluation practices remain largely simulation-driven, limiting reproducibility and real-world applicability. This study uniquely identifies critical methodological gaps and outlines deployment-oriented research directions, including cross-domain optimisation, trustworthy AI integration, and reproducible validation frameworks, to support the development of reliable and scalable adaptive resource allocation mechanisms for future 6G systems.