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

Neurocomputing

Volumn 80, Issue , 2012, Pages 102-110

  Oksanen, Jan ^a   Lundén, Jarmo ^a,b   Koivunen, Visa ^a  

^a AALTO UNIVERSITY   (Finland)

^b Princeton University   (United States)

Author keywords

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

Indexed keywords

COGNITIVE RADIO; COGNITIVE RADIO NETWORK; DYNAMIC MODELING; ENERGY EFFICIENT; HIGH DATA RATE; MACHINE-LEARNING; MISS DETECTION PROBABILITY; MISSED DETECTIONS; MULTI-BAND SPECTRUM; ON-MACHINES; POLICY OPTIMIZATION; PRIMARY TRANSMISSION; RADIO SPECTRA; RETRANSMISSIONS; SECONDARY NETWORKS; SPATIAL DIVERSITY; SPECTRUM SENSING; SUB-BANDS;

CHEMICAL DETECTION; COMPUTER SIMULATION; FREQUENCY HOPPING; RADIO; RADIO COMMUNICATION; RADIO SYSTEMS; REINFORCEMENT LEARNING;

ENERGY EFFICIENCY;

ARTICLE; COMPUTER SIMULATION; MACHINE LEARNING; MATHEMATICAL ANALYSIS; PRIORITY JOURNAL; PROBABILITY; QUALITY CONTROL; RADIOFREQUENCY; REINFORCEMENT LEARNING; REMOTE SENSING; SENSOR; WIRELESS COMMUNICATION;

EID: 84855250511     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.07.027     Document Type: Article

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