Reinforcement learning based sensing policy optimization for energy efficient cognitive radio networks

NEUROCOMPUTING

卷:80

Article

Oksanen, Jan¹  Lunden, Jarmo^1,2  Koivunen, Visa¹ 

[1] Aalto Univ, Sch Elect Engn, SMARAD CoE, Dept Signal Proc & Acoust, FI-00076 Aalto, Finland

[2] Princeton Univ, Dept Elect Engn, Princeton, NJ 08544 USA

关键词: Cognitive radio;    Frequency hopping;    Machine learning;    Sensing policy;    Spatial diversity;    Spectrum sensing;   

DOI  :  10.1016/j.neucom.2011.07.027

来源: Elsevier

【 摘 要 】

This paper introduces a machine learning based collaborative multi-band spectrum sensing policy for cognitive radios. The proposed sensing policy guides secondary users to focus the search of unused radio spectrum to those frequencies that persistently provide them high data rate. The proposed policy is based on machine learning, which makes it adaptive with the temporally and spatially varying radio spectrum. Furthermore, there is no need for dynamic modeling of the primary activity since it is implicitly learned over time. Energy efficiency is achieved by minimizing the number of assigned sensors per each subband under a constraint on miss detection probability. It is important to control the missed detections because they cause collisions with primary transmissions and lead to retransmissions at both the primary and secondary user. Simulations show that the proposed machine learning based sensing policy improves the overall throughput of the secondary network and improves the energy efficiency while controlling the miss detection probability. (C) 2011 Elsevier B.V. All rights reserved.

【 授权许可】

Free