  We consider the problem of dynamic channel allocation (DCA) in cognitive
communication networks with the goal of maximizing a global
signal-to-interference-plus-noise ratio (SINR) measure under a specified target
quality of service (QoS)-SINR for each network. The shared bandwidth is
partitioned into K channels with frequency separation. In contrast to the
majority of existing studies that assume perfect orthogonality or a one- to-one
user-channel allocation mapping, this paper focuses on real-world systems
experiencing inter-carrier interference (ICI) and channel reuse by multiple
large-scale networks. This realistic scenario significantly increases the
problem dimension, rendering existing algorithms inefficient. We propose a
novel multi-agent reinforcement learning (RL) framework for distributed DCA,
named Channel Allocation RL To Overlapped Networks (CARLTON). The CARLTON
framework is based on the Centralized Training with Decentralized Execution
(CTDE) paradigm, utilizing the DeepMellow value-based RL algorithm. To ensure
robust performance in the interference-laden environment we address, CARLTON
employs a low-dimensional representation of observations, generating a QoS-type
measure while maximizing a global SINR measure and ensuring the target QoS-SINR
for each network. Our results demonstrate exceptional performance and robust
generalization, showcasing superior efficiency compared to alternative
state-of-the-art methods, while achieving a marginally diminished performance
relative to a fully centralized approach.
