QoS driven Channel Selection Algorithm for Cognitive Radio Network: Multi-User Multi-armed Bandit Approach

Abstract : In this paper, we deal with the problem of opportunistic spectrum access (OSA) in infrastructure-less cognitive networks. Each secondary user (SU) Tx is allowed to select one frequency channel at each transmission trial. We assume that there is no information exchange between SUs, and they have no knowledge of channel quality, availability and other SUs actions, hence, each SU selfishly tries to select the best band to transmit. This particular problem is designed as a multi-user restless Markov multi-armed bandit (MAB) problem, in which multiple SUs collect a priori unknown reward by selecting a channel. The main contribution of the paper is to propose an online learning policy for distributed SUs, that takes into account not only the availability criterion of a band but also a quality metric linked to the interference power from the neighboring cells experienced on the sensed band. We also prove that the policy, named distributed restless QoS-UCB (RQoS-UCB), achieves at most logarithmic order regret, for a single-user in a first time and then for multi-user in a second time. Moreover, studies on the achievable throughput, average bit error rate obtained with the proposed policy are conducted and compared to well-known reinforcement learning algorithms.
Document type :
Journal articles
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-01492886
Contributor : Philippe Mary <>
Submitted on : Monday, March 20, 2017 - 5:46:49 PM
Last modification on : Thursday, February 7, 2019 - 4:57:32 PM

File

QoS_driven_Channel_Selection_A...
Files produced by the author(s)

Identifiers

Citation

Navikkumar Modi, Philippe Mary, Christophe Moy. QoS driven Channel Selection Algorithm for Cognitive Radio Network: Multi-User Multi-armed Bandit Approach. IEEE Transactions on Cognitive Communications and Networking, IEEE, 2017, ⟨10.1109/TCCN.2017.2675901⟩. ⟨hal-01492886⟩

Share

Metrics

Record views

1770

Files downloads

237