# Learning to Coordinate in a Decentralized Cognitive Radio Network in Presence of Jammers

arXiv: https://arxiv.org/abs/1803.06810 · HTML: https://mlsanity.com/paper/1803.06810/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2018.

## Built on by (most cited first, 4 of 4)

- [A survey on multi-player bandits](https://mlsanity.com/paper/2211.16275/) (2022)
- [Competing for Shareable Arms in Multi-Player Multi-Armed Bandits](https://mlsanity.com/paper/2305.19158/) (2023)
- [Deep Learning Frameworks for Cognitive Radio Networks: Review and Open Research Challenges](https://mlsanity.com/paper/2410.23949/) (2024)
- [Multi-Player Bandits Robust to Adversarial Collisions](https://mlsanity.com/paper/2211.07817/) (2022)

## Abstract (arXiv)

Efficient utilization of licensed spectrum in the cognitive radio network is challenging due to lack of coordination among the Secondary Users (SUs). Distributed algorithms proposed in the literature aim to maximize the network throughput by ensuring orthogonal channel allocation for the SUs. However, these algorithms work under the assumption that all the SUs faithfully follow the algorithms which may not always hold due to the decentralized nature of the network. In this paper, we study distributed algorithms that are robust against malicious behavior (jamming attack). We consider both the cases of jammers launching coordinated and uncoordinated attacks. In the coordinated attack, the jammers select non-overlapping channels to attack in each time slot and can significantly increase the number of collisions for SUs. We setup the problem in each scenario as a multi-player bandit and develop algorithms. The analysis shows that when the SUs faithfully implement proposed algorithms, the regret is constant with high probability. We validate our claims through exhaustive synthetic experiments and also through a realistic USRP based experiments.