Deep Echo State Q-Network (DEQN) and Its Application in Dynamic Spectrum Sharing for 5G and Beyond

Area of Science:

• Artificial Intelligence
• Telecommunications Engineering
• Machine Learning

Background:

• Deep reinforcement learning (DRL) combined with recurrent neural networks (RNNs) shows promise for non-Markovian environments.
• Training DRL and RNNs typically requires substantial data, posing challenges in dynamic, data-scarce applications like 5G cellular communication.
• Dynamic spectrum sharing (DSS) is crucial for enhancing spectrum utilization in 5G and future 6G networks.

Purpose of the Study:

• To develop a DRL strategy that efficiently handles dynamic environments with limited training data.
• To introduce a novel DRL framework, the Deep Echo State Q-Network (DEQN), for rapid adaptation.
• To evaluate DEQN's performance in a realistic 5G/6G scenario.

Main Methods:

• The study introduces the Deep Echo State Q-Network (DEQN), integrating DRL with echo state properties.
• DEQN is designed to capture temporal correlations in dynamic environments with reduced training overhead.
• Performance is evaluated using the dynamic spectrum sharing (DSS) scenario.

Main Results:

• DEQN demonstrates the ability to adapt to highly dynamic environments rapidly.
• The proposed method achieves effective learning with limited training data.
• DEQN shows promise for efficient spectrum management in DSS.

Conclusions:

• The Deep Echo State Q-Network (DEQN) offers an efficient DRL framework for dynamic environments with limited data.
• DEQN is a viable solution for improving spectrum utilization in 5G/6G through dynamic spectrum sharing.
• This work highlights the potential of efficient DRL in challenging, real-world communication systems.