Optimal Entropy-Based Cooperative Spectrum Sensing for Maritime Cognitive Radio Networks

Maritime cognitive radio networks (MCRNs) have recently been proposed for opportunistic utilization of the licensed band. Spectrum sensing is one of the key issues for the successful deployment of the MCRNs. The maritime environment is unique in terms of radio wave propagation over water, surface reflection and wave occlusions. In order to deal with the challenging maritime environment, we proposed an optimal entropy-based cooperative spectrum sensing. As the results of spectrum sensing are sensitive to the number of samples in an entropy-based local detection scheme, we first calculated the optimal number of samples. Next, a cooperative spectrum sensing scheme considering the conditions of the sea environment is proposed. Finally, the throughput optimization of the m-out-of-n rule is considered. Results revealed that although the existing schemes work well for the lower sea states, they fail to perform at higher sea states. Moreover, simulation results also indicated the robustness of the entropy-based scheme and the proposed cooperative spectrum sensing scheme at higher sea states in comparison with the traditional energy detector.

[1]  Yonghong Zeng,et al.  Sensing-Throughput Tradeoff for Cognitive Radio Networks , 2008, IEEE Trans. Wirel. Commun..

[2]  Oriol Sallent,et al.  Operating point selection for primary and secondary users in cognitive radio networks , 2009, Comput. Networks.

[3]  Tommaso Melodia,et al.  A frequency-domain entropy-based detector for robust spectrum sensing in cognitive radio networks , 2010, IEEE Communications Letters.

[4]  Siba K. Udgata,et al.  Spectrum sensing based on entropy estimation using cyclostationary features for Cognitive radio , 2012, 2012 Fourth International Conference on Communication Systems and Networks (COMSNETS 2012).

[5]  Ming-Tuo Zhou,et al.  TRITON: High speed maritime mesh networks , 2008, 2008 IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications.

[6]  W. Pierson,et al.  A proposed spectral form for fully developed wind seas based on the similarity theory of S , 1964 .

[7]  K. B. Letaief,et al.  Optimization of cooperative spectrum sensing with energy detection in cognitive radio networks , 2009, IEEE Transactions on Wireless Communications.

[8]  Hyung Seok Kim,et al.  Improved local spectrum sensing for cognitive radio networks , 2012, EURASIP J. Adv. Signal Process..

[9]  W. Elliott Results of a VHF propagation study , 1981 .

[10]  Qinyu Zhang,et al.  Entropy-based robust spectrum sensing in cognitive radio , 2010, IET Commun..

[11]  Helena Rifà-Pous,et al.  Review of Robust Cooperative Spectrum Sensing Techniques for Cognitive Radio Networks , 2012, Wirel. Pers. Commun..

[12]  Hongxing Xia,et al.  Spectral Entropy Based Primary User Detection in Cognitive Radio , 2009, 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing.

[13]  Fritz Bekkadal,et al.  Emerging maritime communications technologies , 2009, 2009 9th International Conference on Intelligent Transport Systems Telecommunications, (ITST).

[14]  Thomas Kunz,et al.  Challenges and opportunities in managing maritime networks , 2008, IEEE Communications Magazine.

[15]  Ian F. Akyildiz,et al.  Cooperative spectrum sensing in cognitive radio networks: A survey , 2011, Phys. Commun..

[16]  Hyung Seok Kim,et al.  Cooperative Spectrum Sensing for Cognitive Radio Networks Application: Performance Analysis for Realistic Channel Conditions , 2013 .

[17]  Ming-Tuo Zhou,et al.  Cognitive maritime wireless mesh/ad hoc networks , 2012, J. Netw. Comput. Appl..

[18]  Ian J. Timmins,et al.  Marine Communications Channel Modeling Using the Finite-Difference Time Domain Method , 2009, IEEE Transactions on Vehicular Technology.

[19]  Santosh V. Nagaraj,et al.  Entropy-based spectrum sensing in cognitive radio , 2009, Signal Process..

[20]  Ryu Miura,et al.  High Speed Maritime Ship-to-Ship/Shore Mesh Networks , 2007, 2007 7th International Conference on ITS Telecommunications.

[21]  Hyung Seok Kim,et al.  Optimal entropy-based spectrum sensing for cognitive radio networks under severe path loss conditions , 2013, 8th International Conference on Cognitive Radio Oriented Wireless Networks.

[22]  Shunsuke Ihara,et al.  Information theory - for continuous systems , 1993 .

[23]  Hüseyin Arslan,et al.  A survey of spectrum sensing algorithms for cognitive radio applications , 2009, IEEE Communications Surveys & Tutorials.

[24]  Erik G. Larsson,et al.  Spectrum Sensing for Cognitive Radio : State-of-the-Art and Recent Advances , 2012, IEEE Signal Processing Magazine.