Computational Complexity Analysis of FBMC-OQAM under different Strategic Conditions

The wireless communication has undergone a revolution due to advancements in technology. For each new user or application to be a part of communication network, the preliminary requirement is the allocation of frequency spectrum band. This frequency band is a limited resource and it is impossible to expand its boundaries. So the need is to employ intelligent, adaptive and reconfigurable communication systems which can investigate the requirements of the end user and assign the requisite resources in contrast to the traditional communication systems which allocate a fixed amount of resource to the user under adaptive, autonomic and opportunistic cognitive radio environment. Cognitive Radio (CR) Technology has emerged from software defined radios wherein the key parameters of interest are frequency, power and modulation technique adopted. The Role of Cognitive Radio is to alter these parameters under ubiquitous situations. The Spectrum Sensing is an important task to determine the availability of the vacant channels to be utilized by the secondary users without posing any harmful interference to the primary users. In Multicarrier Communication System using Digital Signal Processing Techniques, Filter Bank Multi Carrier has an edge over other technologies in terms of Bandwidth and Spectral Efficiency. The present paper deals with Computational Complexity Analysis of FBMC-OQAM under different Strategic Conditions. The study comprises of the effect of variation of overlapping factor and the number of sub-carriers used in Filter Bank Multicarrier approach with Orthogonal Quadrature Amplitude Modulation pre and post processing techniques for the physical layer of Cognitive Radio under AWGN fading channel environment.

[1]  Haijian Zhang,et al.  Spectral Efficiency Analysis in OFDM and OFDM/OQAM Based Cognitive Radio Networks , 2009, VTC Spring 2009 - IEEE 69th Vehicular Technology Conference.

[2]  Ian F. Akyildiz,et al.  NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey , 2006, Comput. Networks.

[3]  Joseph Mitola,et al.  Cognitive radio: making software radios more personal , 1999, IEEE Wirel. Commun..

[4]  Laurence B. Milstein,et al.  Analysis and Simulation of Sensing Deception in Fading Cognitive Radio Networks , 2010, 2010 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM).

[5]  Ying-Chang Liang,et al.  Optimal Power Allocation for Fading Channels in Cognitive Radio Networks under Transmit and Interference Power Constraints , 2008, 2008 IEEE International Conference on Communications.

[6]  Hengzhu Liu,et al.  Cognitive radio simulation environment realization based on autonomic communication , 2011, 2011 IEEE 3rd International Conference on Communication Software and Networks.

[7]  Ajay K. Sharma,et al.  BER Performance Analysis of Cognitive Radio Physical Layer over Rayleigh fading Channel , 2011 .

[8]  Andre B. J. Kokkeler,et al.  Towards Cognitive Radio for emergency networks , 2006 .

[9]  Faouzi Bader,et al.  Computationally Efficient Power Allocation Algorithm in Multicarrier-Based Cognitive Radio Networks: OFDM and FBMC Systems , 2010, EURASIP J. Adv. Signal Process..

[10]  Anuj Batra,et al.  Cognitive radio techniques for wide area networks , 2005, Proceedings. 42nd Design Automation Conference, 2005..

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

[12]  Faouzi Bader,et al.  Comparison of OFDM and FBMC performance in multi-relay cognitive radio network , 2012, 2012 International Symposium on Wireless Communication Systems (ISWCS).

[13]  Behrouz Farhang-Boroujeny,et al.  Multicarrier communication techniques for spectrum sensing and communication in cognitive radios , 2008, IEEE Communications Magazine.

[14]  Dirk Dahlhaus,et al.  Optimized Paraunitary Filter Banks for Time-Frequency Channel Diagonalization , 2010, EURASIP J. Adv. Signal Process..

[15]  Andrea M. Tonello,et al.  Design of Orthogonal Filtered Multitone Modulation Systems and Comparison among Efficient Realizations , 2010, EURASIP J. Adv. Signal Process..

[16]  Markku Renfors,et al.  Channel Equalization in Filter Bank Based Multicarrier Modulation for Wireless Communications , 2007, EURASIP J. Adv. Signal Process..