A robust conscious model for enhancing cognitive radio quality of service

Cognitive radios (CRs) use learning algorithms (LAs) located in the cognition engine (CE) to adapt their behaviour. CRs use LAs for spectrum prediction to enhance their quality of service (QoS). The CR's CE consumes battery power while classifying LAs. The LA classification reduces CR data transmission power and limits CR throughput. This paper proposes a framework to enhance CR QoS in LTE-Advanced (LTE-A). The proposed framework reduces battery power expended in LA classification and increases CR data transmission power. It introduces the radio resource control (RRC); RRC_COGNITIVE state in which the CR pauses LA classification. The framework's performance is evaluated using the CR transmit power and throughput. Simulations show that the proposed framework reduces LA classification power by 65 % on average. The reduction of LA classification power enhances CR transmit power. The CR throughput is enhanced by 23% and 80% when CRs are and are not secondary users (SUs) respectively.

[1]  Panagiotis Demestichas,et al.  Self-Organizing Maps for advanced learning in cognitive radio systems , 2012, Comput. Electr. Eng..

[2]  Kala Praveen Bagadi,et al.  Multiuser Detection in SDMA–OFDM Wireless Communication System Using Complex Multilayer Perceptron Neural Network , 2014, Wirel. Pers. Commun..

[3]  Youssef Tawk,et al.  Reconfigurable Antennas: Design and Applications , 2015, Proceedings of the IEEE.

[4]  Harald Haas,et al.  Using Echo State Networks to Characterise Wireless Channels , 2013, 2013 IEEE 77th Vehicular Technology Conference (VTC Spring).

[5]  Kyung-Geun Lee,et al.  CSIT: channel state and idle time predictor using a neural network for cognitive LTE-Advanced network , 2013, EURASIP J. Wirel. Commun. Netw..

[6]  Jianke Fan,et al.  UE power saving with RRC semi-connected state in LTE , 2014, 2014 Wireless Telecommunications Symposium.

[7]  Hamid Aghvami,et al.  Wireless Standard Classification in Cognitive Radio Networks Using Self-Organizing Maps , 2013, ISWCS.

[8]  Thomas J. Afullo,et al.  Optimizing Neural Network for TV Idle Channel Prediction in Cognitive Radio Using Particle Swarm Optimization , 2013, 2013 Fifth International Conference on Computational Intelligence, Communication Systems and Networks.

[9]  Michael M. Marefat,et al.  Metacognitive Radio Engine Design and Standardization , 2015, IEEE Journal on Selected Areas in Communications.

[10]  Michael M. Marefat,et al.  Metacognition and the next generation of cognitive radio engines , 2016, IEEE Communications Magazine.

[11]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[12]  C. Koch,et al.  Consciousness: here, there and everywhere? , 2015, Philosophical Transactions of the Royal Society B: Biological Sciences.

[13]  A. Holcombe,et al.  The claustrum’s proposed role in consciousness is supported by the effect and target localization of Salvia divinorum , 2014, Front. Integr. Neurosci..

[14]  Jeffrey H. Reed,et al.  Metacognition: Enhancing the performance of a cognitive radio , 2011, 2011 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA).

[15]  Newton Howard.  Rethinking Artificial Intelligence , 2015, ICCI*CC.

[16]  A. Citri,et al.  Attention: the claustrum , 2015, Trends in Neurosciences.

[17]  Chung-Ju Chang,et al.  Analysis of DRX power saving with RRC states transition in LTE networks , 2015, 2015 European Conference on Networks and Communications (EuCNC).

[18]  J. Ryan,et al.  Where is my mind? How sponges and placozoans may have lost neural cell types , 2015, Philosophical Transactions of the Royal Society B: Biological Sciences.

[19]  Wei Cheng,et al.  Spectrum prediction in cognitive radio networks , 2013, IEEE Wireless Communications.

[20]  Sudharman K. Jayaweera,et al.  A Survey on Machine-Learning Techniques in Cognitive Radios , 2013, IEEE Communications Surveys & Tutorials.

[21]  Abdulla K. Al-Ali,et al.  TFRC-CR: An equation-based transport protocol for cognitive radio networks , 2013, 2013 International Conference on Computing, Networking and Communications (ICNC).

[22]  Necmi Taspinar,et al.  Neural Network Based Receiver for Multiuser Detection in MC-CDMA Systems , 2013, Wirel. Pers. Commun..