Deep Reinforcement Learning for Dynamic Spectrum Sensing and Aggregation in Multi-Channel Wireless Networks

TL;DR

This paper applies deep reinforcement learning, specifically DQN, to optimize dynamic spectrum sensing and aggregation in multi-channel wireless networks with unknown channel dynamics, achieving near-optimal performance based on partial observations.

Contribution

It introduces a DQN-based approach for spectrum sensing and aggregation in POMDP settings, demonstrating effectiveness without prior knowledge of system dynamics.

Findings

DQN achieves near-optimal success rates in simulations.

DQN outperforms traditional Q-Learning in unknown environments.

The approach effectively utilizes partial observations and ACK signals.

Abstract

In this paper, the problem of dynamic spectrum sensing and aggregation is investigated in a wireless network containing N correlated channels, where these channels are occupied or vacant following an unknown joint 2-state Markov model. At each time slot, a single cognitive user with certain bandwidth requirement either stays idle or selects a segment comprising C (C < N) contiguous channels to sense. Then, the vacant channels in the selected segment will be aggregated for satisfying the user requirement. The user receives a binary feedback signal indicating whether the transmission is successful or not (i.e., ACK signal) after each transmission, and makes next decision based on the sensing channel states. Here, we aim to find a policy that can maximize the number of successful transmissions without interrupting the primary users (PUs). The problem can be considered as a partially observable Markov decision process (POMDP) due to without full observation of system environment. We implement a Deep Q-Network (DQN) to address the challenge of unknown system dynamics and computational expenses. The performance of DQN, Q-Learning, and the Improvident Policy with known system dynamics is evaluated through simulations. The simulation results show that DQN can achieve near-optimal performance among different system scenarios only based on partial observations and ACK signals.