Cooperative Spectrum Sensing with Deep Q-Network for Multimedia Applications
Pages 57 - 63
Abstract
With the gradually stricter requirement for multimedia applications, spectrum inefficiencies are urgent to be relieved by sensing and utilizing Spectrum Holes (SHs) over a wide spectrum. Cognitive Radio Sensor Network (CRSN) has drawn a lot of attention, which determines the state of Primary Users (PUs) by implementing Cooperative Spectrum Sensing (CSS), further overcoming various noise and fading issues in the radio environment. A survey on the application of Reinforcement Learning (RL) technology for CSS is conducted, especially through handling the performance optimization problem that cannot be achieved by traditional methods. Specifically, we transformed the traditional Fusion Center (FC) into an intelligent Agent that is responsible for making fusion decisions based on the results of Energy Detection (ED) technology. In this way, through learning from experience, the system performance in global probabilities can be improved by making fusion decisions as accurately as possible. Compared with traditional methods, comparison studies demonstrate the effectiveness of the proposed method in improving the CSS system performances, as well as its robustness in the face of various environments. The combination and complement of the traditional and the proposed scheme are also suggested in this paper.
References
[1]
Muhammad Amjad, Mubashir Husain Rehmani, and Shiwen Mao. Wireless multi&media cognitive radio networks: A comprehensive survey. IEEE Communications Surveys & Tutorials, 20(2):1056--1103, 2018.
[2]
Steven M Kay. Fundamentals of statistical signal processing: estimation theory. Prentice-Hall, Inc., 1993.
[3]
Ming Jin, Qinghua Guo, Jiangtao Xi, Youming Li, Yanguang Yu, and Defeng Huang. Spectrum sensing using weighted covariance matrix in rayleigh fading channels. IEEE Transactions on Vehicular Technology, 64(11):5137--5148, 2014.
[4]
Muhammad Zeeshan Shakir, Anlei Rao, and Mohamed-Slim Alouini. General&ized mean detector for collaborative spectrum sensing. IEEE Transactions on Communications, 61(4):1242--1253, 2013.
[5]
Abbass Nasser, Hussein Al Haj Hassan, Jad Abou Chaaya, Ali Mansour, and Koffi-Clément Yao. Spectrum sensing for cognitive radio: Recent advances and future challenge. Sensors, 21(7):2408, 2021.
[6]
Kai Fang, Tingting Wang, Xiaolong Zhou, Yaping Ren, Hongfei Guo, and Jianqing Li. A topsis-based relocalization algorithm in wireless sensor networks. IEEE Transactions on Industrial Informatics, 18(2):1322--1332, 2021.
[7]
Tingting Wang, Jianqing Li, Wei Wei, Wei Wang, and Kai Fang. Deep-learning&based weak electromagnetic intrusion detection method for zero touch networks on industrial iot. IEEE Network, 36(6):236--242, 2022.
[8]
Qingying Wu, Benjamin K Ng, and Chan-Tong Lam. Energy-efficient cooperative spectrum sensing using machine learning algorithm. Sensors, 22(21):8230,
[9]
Andrew Y Ng. Shaping and policy search in reinforcement learning. University of California, Berkeley, 2003.
[10]
Haibo He and He Jiang. Deep learning based energy efficiency optimization for distributed cooperative spectrum sensing. IEEE Wireless Communications, 26(3):32--39, 2019.
[11]
Jiang Zhu, Yonghui Song, Dingde Jiang, and Houbing Song. Multi-armed ban&dit channel access scheme with cognitive radio technology in wireless sensor networks for the internet of things. IEEE Access, 4:4609--4617, 2016.
[12]
Marc Lelarge, Alexandre Proutiere, and M Sadegh Talebi. Spectrum bandit optimization. In 2013 IEEE Information Theory Workshop (ITW), pages 1--5. IEEE, 2013.
[13]
Pouya Tehrani, Qing Zhao, and Lang Tong. Multi-channel opportunistic spectrum access in unslotted primary systems with unknown models. In 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pages 157--160. IEEE, 2011.
[14]
Sina Maleki, Geert Leus, Symeon Chatzinotas, and Björn Ottersten. To and or to or: How shall the fusion center rule in energy-constrained cognitive radio networks? In 2014 IEEE International Conference on Communications (ICC), pages 1632--1637. IEEE, 2014.
[15]
Sina Maleki, Geert Leus, Symeon Chatzinotas, and Björn Ottersten. To and or to or: On energy-efficient distributed spectrum sensing with combined censoring and sleeping. IEEE Transactions on Wireless Communications, 14(8):4508--4521, 2015.
[16]
Erik Axell, Geert Leus, Erik G Larsson, and H Vincent Poor. Spectrum sensing for cognitive radio: State-of-the-art and recent advances. IEEE signal processing magazine, 29(3):101--116, 2012.
[17]
Krzysztof Cicho?, Adrian Kliks, and Hanna Bogucka. Energy-efficient cooperative spectrum sensing: A survey. IEEE Communications Surveys & Tutorials, 18(3):1861-- 1886, 2016.
[18]
Chunhua Sun, Wei Zhang, and Khaled Ben Letaief. Cluster-based cooperative spectrum sensing in cognitive radio systems. In 2007 IEEE International Conference on Communications, pages 2511--2515. IEEE, 2007.
[19]
N Armi, N Mohammad Saad, and Mohammad Arshad. Hard decision fusion based cooperative spectrum sensing in cognitive radio system. ITB Journal of Information and Communication Technology, 3(2):109--122, 2009.
[20]
Srinivas Nallagonda, Y Rakesh Kumar, and Pasham Shilpa. Analysis of hard&decision and soft-data fusion schemes for cooperative spectrum sensing in rayleigh fading channel. In 2017 IEEE 7th International Advance Computing Conference (IACC), pages 220--225. IEEE, 2017.
[21]
Pramod K Varshney. Distributed detection and data fusion. Springer Science & Business Media, 2012.
[22]
Lu Lu, Xiangwei Zhou, Uzoma Onunkwo, and Geoffrey Ye Li. Ten years of research in spectrum sensing and sharing in cognitive radio. EURASIP Journal on Wireless Communications and Networking, 2012(1):1--16, 2012.
[23]
Sina Maleki, Ashish Pandharipande, and Geert Leus. Energy-efficient distributed spectrum sensing with convex optimization. In 2009 3rd IEEE International Work&shop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pages 396--399. IEEE, 2009.
[24]
Edward CY Peh, Ying-Chang Liang, Yong Liang Guan, and Yiyang Pei. Energy&efficient cooperative spectrum sensing in cognitive radio networks. In 2011 IEEE Global Telecommunications Conference-GLOBECOM 2011, pages 1--5. IEEE, 2011.
[25]
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013.
[26]
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al. Human-level control through deep reinforcement learning. nature, 518(7540):529--533, 2015.
[27]
Frederick James. Monte carlo theory and practice. Reports on Progress in Physics, 43(9):1145, 1980.
[28]
Christophe Andrieu, Arnaud Doucet, and Roman Holenstein. Particle markov chain monte carlo for efficient numerical simulation. In Monte Carlo and quasiMonte Carlo methods 2008, pages 45--60. Springer, 2009
Index Terms
- Cooperative Spectrum Sensing with Deep Q-Network for Multimedia Applications
Recommendations
Towards energy-efficient cooperative spectrum sensing for cognitive radio networks: an overview
Cognitive radio has been proposed as a promising technology to resolve the spectrum scarcity problem by dynamically exploiting underutilized spectrum bands. Cognitive radio technology allows unlicensed users to exploit the spectrum vacancies at any time ...
A survey on security threats in cognitive radio networks based on cooperative spectrum sensing
Cognitive radio networks (CRNs) are a technological revolution that allows unlicensed users (SUs) to opportunistically use the freely licensed spectrum bands of a primary user (PU). To avoid interference with the PU, SUs need to do accurate spectrum ...
Soft Cooperative Spectrum Sensing using Quantization Method in the Presence of Smart PUE Attack
To overcome the frequency spectrum scarcity, cognitive radio (CR) network is proposed where secondary users opportunistically use vacant frequency band. CR intrinsic properties impose threats to these wireless networks. Smart primary user emulation ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
October 2023
83 pages
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 29 October 2023
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
- Macao Polytechnic University
Conference
MM '23
Sponsor:
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 46Total Downloads
- Downloads (Last 12 months)21
- Downloads (Last 6 weeks)0
Reflects downloads up to 27 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in