Deep reinforcement learning-based power control in full-duplex cognitive radio networks

Xiangyue Meng, Hazer Inaltekin, Brian Krongold

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

10 Citations (Scopus)


This paper considers the use of full-duplex technology in cognitive radio networks to allow secondary users to sense the presence of primary users and transmit data simultaneously. This is the main advantage over half-duplex radios. In such networks, the so-called sensing-throughput trade-off exists due to the fact that while a higher transmit power results in higher secondary network throughput, sensing performance is degraded by the self-interference at the full-duplex transceiver. This paper presents a novel deep reinforcement learning-based joint spectrum sensing and power control algorithm for downlink communications in a cognitive small cell. The proposed algorithm can adapt to the unknown radio environment to transmit data opportunistically to the secondary users while avoiding interference to the primary network. Simulation results show that our algorithm achieves better performance than the traditional energy detection-based sensing method and performs close to a genie-aided method with the optimal spectrum utilization, especially in the high-SNR regime.

Original languageEnglish
Title of host publication2018 IEEE Global Communications Conference (GLOBECOM)
Subtitle of host publicationproceedings
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages7
ISBN (Electronic)9781538647271, 9781538669761
ISBN (Print)9781538647288
Publication statusPublished - 2018
Externally publishedYes
Event2018 IEEE Global Communications Conference, GLOBECOM 2018 - Abu Dhabi, United Arab Emirates
Duration: 9 Dec 201813 Dec 2018

Publication series

ISSN (Print)1930-529X
ISSN (Electronic)2576-6813


Conference2018 IEEE Global Communications Conference, GLOBECOM 2018
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi


Dive into the research topics of 'Deep reinforcement learning-based power control in full-duplex cognitive radio networks'. Together they form a unique fingerprint.

Cite this