Replicated Q-learning Based Sub-band Selection for Wideband Spectrum Sensing in Cognitive Radios

被引:0
|
作者
Aref, Mohamed A. [1 ]
Machuzak, Stephen [1 ]
Jayaweera, Sudharman K. [1 ]
Lane, Steven [2 ]
机构
[1] Univ New Mexico, Dept Elect & Comp Engn, CISL, Albuquerque, NM 87131 USA
[2] US Air Force, Res Lab, Albuquerque, NM USA
来源
2016 IEEE/CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA (ICCC) | 2016年
关键词
Cognitive radios; wide-band spectrum scanning; sub-band selection; partially observable Markov decision processes; Q-learning; replicated Q-learning;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Spectrum sensing is a key basic function in any wideband cognitive radio (CR) for detecting the presence of any spectral activities. However, due to hardware constraints, the instantaneous sensing bandwidth is limited to a single sub-band out of all sub-bands in the spectrum of interest. Hence, sub-band selection is an important step in wideband spectrum sensing. In this paper we develop a partially observable Markov decision process (POMDP) to model the sub-band dynamics and propose an efficient sub-band selection policy based on replicated Q-learning. It is shown through simulations that the proposed selection policy has reasonably low computational complexity and significantly outperforms the random sub-band selection policy.
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页数:6
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