Multi-Agent Deep Reinforcement Learning Approach for Efficient Spectrum Utilization in 5G and Future Cognitive Networks

The sheer increase in the number of connected
devices of the 5G and beyond wireless networks has
compounded the pressure on smart and dynamic spectrum
sharing. Conventional spectrum allocation algorithms
such as static assignment, game-theoretic optimization,
and single-agent reinforcement learning struggle with the
high-dimensional, diverse, and time-varying
characteristics of cognitive radio environments. To
overcome these difficulties, the proposed study proposes a
Multi-Agent Deep Reinforcement Learning (MADRL)
framework that can be used to facilitate cooperative and
decentralized spectrum management between multiple
cognitive radio users. The agents set policies in a
collaborative manner, and each can learn the best policies
using Deep Q-Networks (DQN) with Actor-Critic system
aggregation around each agent independent of the other
agent, observing local environmental states, which
include channel occupancy, signal-to-interference-plusnoise ratio (SINR), and transmission power levels of the
agent. The suggested framework exploits the idea of interagent communication and experience sharing and reduces
spectral collisions, increases throughput, and maximizes
energy efficiency. The proposed MADRL-based model
under dynamic traffic and user mobility conditions has a
32% improvement in spectrum utilization efficiency, 25%
decrease in interference, and 20% energy saving as
compared to conventional Q-learning and single-agent
DRL frameworks. Combining multi-agent coordination
and deep reinforcement learning offers scalability,
adaptability and robustness needed in next-generation
cognitive 6G networks to provide intelligent, selforganizing, and sustainable spectrum management. This
research is expected to result in a very adaptive spectrum
allocation scheme that will allow improving the
performance, reliability, and spectral fairness of a
network in heterogeneous and high-density
communication scenarios.
Keywords: Multi Agent Deep Reinforcement Learning
(MADRL), Deep Q-Networks, Actor-critic system,
utilization efficiency, Q-learning, spectrum management