Deep Reinforcement Learning for Distributed Uncoordinated Cognitive Radios Resource Allocation

TL;DR

This paper introduces a novel deep reinforcement learning method for distributed resource allocation in cognitive radio networks, achieving near-optimal performance without coordination among agents.

Contribution

It presents the first deep reinforcement learning algorithm with proven convergence to equilibrium in a non-stationary multi-agent environment for cognitive radios.

Findings

Achieves within 3% of exhaustive search performance in finite time

Finds the optimal policy in nearly 70% of cases

Standard single-agent RL may not converge in multi-radio scenarios

Abstract

This paper presents a novel deep reinforcement learning-based resource allocation technique for the multi-agent environment presented by a cognitive radio network that coexists through underlay dynamic spectrum access (DSA) with a primary network. The resource allocation technique presented in this work is distributed, not requiring coordination with other agents. The presented algorithm is the first deep reinforcement learning technique for which convergence to equilibrium policies can be shown in the non-stationary multi-agent environment that results from the uncoordinated dynamic interaction between radios through the shared wireless environment. Moreover, simulation results show that in a finite learning time the presented technique is able to find policies that yield performance within 3 % of an exhaustive search solution, finding the optimal policy in nearly 70 % of cases. Moreover, it is shown that standard single-agent deep reinforcement learning may not achieve convergence when used in a non-coordinated, coupled multi-radio scenario.