Machine Learning for Spectrum Sharing: A Survey

TL;DR

This survey addresses the rising need for efficient spectrum management in 5G/6G by examining how machine learning can transform spectrum sensing, allocation, access, and handoff. It provides a structured mapping of ML methods to spectrum-sharing subproblems, outlines fundamental ML techniques, and chronicles state-of-the-art applications across sensing, allocation, access, and security. The work highlights open questions, trends, and challenges—such as scalability, real-time constraints, and security threats—while offering a roadmap for integrating ML into cognitive and dynamic spectrum management. By connecting core ML concepts with concrete spectrum-sharing use cases, the paper underscores the practical potential of ML to enable robust, high-throughput wireless systems in future networks, including mmWave, MIMO, and RIS-enabled architectures in 6G and beyond.

Abstract

The 5th generation (5G) of wireless systems is being deployed with the aim to provide many sets of wireless communication services, such as low data rates for a massive amount of devices, broadband, low latency, and industrial wireless access. Such an aim is even more complex in the next generation wireless systems (6G) where wireless connectivity is expected to serve any connected intelligent unit, such as software robots and humans interacting in the metaverse, autonomous vehicles, drones, trains, or smart sensors monitoring cities, buildings, and the environment. Because of the wireless devices will be orders of magnitude denser than in 5G cellular systems, and because of their complex quality of service requirements, the access to the wireless spectrum will have to be appropriately shared to avoid congestion, poor quality of service, or unsatisfactory communication delays. Spectrum sharing methods have been the objective of intense study through model-based approaches, such as optimization or game theories. However, these methods may fail when facing the complexity of the communication environments in 5G, 6G, and beyond. Recently, there has been significant interest in the application and development of data-driven methods, namely machine learning methods, to handle the complex operation of spectrum sharing. In this survey, we provide a complete overview of the state-of-theart of machine learning for spectrum sharing. First, we map the most prominent methods that we encounter in spectrum sharing. Then, we show how these machine learning methods are applied to the numerous dimensions and sub-problems of spectrum sharing, such as spectrum sensing, spectrum allocation, spectrum access, and spectrum handoff. We also highlight several open questions and future trends.

Figures (9)

• Figure 1: Coexistence of different technologies in a spectrum sharing scenario.
• Figure 2: Relationship among spectrum sharing mechanisms.
• Figure 3: Density of the keywords presented in the cited references of this survey.
• Figure 4: Examples of supervised, unsupervised, and reinforcement learning problems. First, we have a classification problem solved using supervised and unsupervised learning, respectively. Then, we show an example of the reinforcement learning process between an agent with the environment in a cellular networks.
• Figure 5: ML approaches for spectrum sensing covered in this survey.