A Survey on Applications of Model-Free Strategy Learning in Cognitive Wireless Networks

TL;DR

This survey reviews how model-free learning techniques are applied to enable adaptive, self-organizing control in cognitive wireless networks, highlighting recent advancements and diverse application scenarios.

Contribution

It provides a comprehensive overview of model-free learning applications in cognitive wireless networks, categorizing algorithms by system models and discussing their impact on network performance.

Findings

Model-free learning enhances adaptability in cognitive wireless networks.

Applications span single-agent, multi-agent, and multi-player systems.

Improves network performance over traditional non-adaptive methods.

Abstract

Model-free learning has been considered as an efficient tool for designing control mechanisms when the model of the system environment or the interaction between the decision-making entities is not available as a-priori knowledge. With model-free learning, the decision-making entities adapt their behaviors based on the reinforcement from their interaction with the environment and are able to (implicitly) build the understanding of the system through trial-and-error mechanisms. Such characteristics of model-free learning is highly in accordance with the requirement of cognition-based intelligence for devices in cognitive wireless networks. Recently, model-free learning has been considered as one key implementation approach to adaptive, self-organized network control in cognitive wireless networks. In this paper, we provide a comprehensive survey on the applications of the state-of-the-art model-free learning mechanisms in cognitive wireless networks. According to the system models that those applications are based on, a systematic overview of the learning algorithms in the domains of single-agent system, multi-agent systems and multi-player games is provided. Furthermore, the applications of model-free learning to various problems in cognitive wireless networks are discussed with the focus on how the learning mechanisms help to provide the solutions to these problems and improve the network performance over the existing model-based, non-adaptive methods. Finally, a broad