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

arXiv: https://arxiv.org/abs/2007.13965 · HTML: https://mlsanity.com/paper/2007.13965/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2020.

## Built on by (most cited first, 6 of 6)

- [Deep Reinforcement Learning for Simultaneous Sensing and Channel Access in Cognitive Networks](https://mlsanity.com/paper/2110.14541/) (2021)
- [Deep Learning Frameworks for Cognitive Radio Networks: Review and Open Research Challenges](https://mlsanity.com/paper/2410.23949/) (2024)
- [Collaborative Wideband Spectrum Sensing and Scheduling for Networked UAVs in UTM Systems](https://mlsanity.com/paper/2308.05036/) (2023)
- [Federated Learning-based Collaborative Wideband Spectrum Sensing and Scheduling for UAVs in UTM Systems](https://mlsanity.com/paper/2406.01727/) (2024)
- [DeepEdge: A Deep Reinforcement Learning based Task Orchestrator for Edge Computing](https://mlsanity.com/paper/2110.01863/) (2021)
- [Channel-Token Attention for Reliable Dynamic Spectrum Access under Bursty Primary-User Traffic](https://mlsanity.com/paper/2608.22992/) (2026)

## Abstract (arXiv)

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.