Geo-Location Information Aided Spectrum Sensing in Cellular Cognitive Radio Networks †

Apart from the received signal energy, auxiliary information plays an important role in remarkably ameliorating conventional spectrum sensing. In this paper, a novel spectrum sensing scheme aided by geolocation information is proposed. In the cellular cognitive radio network (CCRN), secondary user equipments (SUEs) first acquire their wireless fingerprints via either received signal strength (RSS) or time of arrival (TOA) estimation over the reference signals received from their surrounding base-stations (BSs) and then pinpoint their geographical locations through a wireless fingerprint (WFP) matching process in the wireless fingerprint database (WFPD). Driven by the WFPD, the SUEs can easily ascertain for themselves the white licensed frequency band (LFB) for opportunistic access. In view of the fact that the locations of the primary user (PU) transmitters in the CCRN are either readily known or practically unavailable, the SUEs can either search the WFPD directly or rely on the support vector machine (SVM) algorithm to determine the availability of the LFB. Additionally, in order to alleviate the deficiency of single SUE-based sensing, a joint prediction mechanism is proposed on the basis of cooperation of multiple SUEs that are geographically nearby. Simulations verify that the proposed scheme achieves higher detection probability and demands less energy consumption than the conventional spectrum sensing algorithms.

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

[2]  Erik G. Larsson,et al.  Overview of spectrum sensing for cognitive radio , 2010, 2010 2nd International Workshop on Cognitive Information Processing.

[3]  Bo Gao,et al.  Incentivizing spectrum sensing in database-driven dynamic spectrum sharing , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.

[4]  Wei Jiang,et al.  Linear Soft Combination for Cooperative Spectrum Sensing in Cognitive Radio Networks , 2017, IEEE Communications Letters.

[5]  Branka Vucetic,et al.  A Machine Learning-Enabled Spectrum Sensing Method for OFDM Systems , 2019, IEEE Transactions on Vehicular Technology.

[6]  Bin Gu,et al.  Spectrum Sensing and the Utilization of Spectrum Opportunity Tradeoff in Cognitive Radio Network , 2016, IEEE Communications Letters.

[7]  Zuneera Umair,et al.  Problem of Wireless Fingerprint Duplication in Fingerprinting Based Indoor Localization , 2018, 2018 IEEE 27th International Symposium on Industrial Electronics (ISIE).

[8]  Khairi Ashour Hamdi,et al.  Interference Analysis of Energy Detection for Spectrum Sensing , 2013, IEEE Transactions on Vehicular Technology.

[9]  Zan Li,et al.  Improved cooperative spectrum sensing model based on machine learning for cognitive radio networks , 2018, IET Commun..

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

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

[12]  Ling Guan,et al.  Graph-Based Safe Support Vector Machine for Multiple Classes , 2018, IEEE Access.

[13]  Kemal Tepe,et al.  Blind Spectrum Sensing Approaches for Interweaved Cognitive Radio System: A Tutorial and Short Course , 2019, IEEE Communications Surveys & Tutorials.

[14]  Petros Spachos,et al.  RSSI-Based Indoor Localization With the Internet of Things , 2018, IEEE Access.

[15]  Jihwan P. Choi,et al.  Sensing Coverage-Based Cooperative Spectrum Detection in Cognitive Radio Networks , 2019, IEEE Sensors Journal.

[16]  Ekram Hossain,et al.  Machine Learning Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks , 2013, IEEE Journal on Selected Areas in Communications.

[17]  Min Jia,et al.  Joint cooperative spectrum sensing and channel selection optimization for satellite communication systems based on cognitive radio , 2017, Int. J. Satell. Commun. Netw..

[18]  J. I. Mararm,et al.  Energy Detection of Unknown Deterministic Signals , 2022 .

[19]  Ingrid Moerman,et al.  Geolocation database beyond TV white spaces? Matching applications with database requirements , 2012, 2012 IEEE International Symposium on Dynamic Spectrum Access Networks.

[20]  Won Mee Jang,et al.  Blind Cyclostationary Spectrum Sensing in Cognitive Radios , 2014, IEEE Communications Letters.

[21]  V. Rajendran,et al.  Spectrum Sensing Using Cognitive Radio Technology , 2017, 2017 International Conference on Communication and Signal Processing (ICCSP).

[22]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[23]  A. Fanan,et al.  Implementation of combined geolocation database and infrastructure sensing in TV bands using different spectrum devices , 2017, 2017 25th Telecommunication Forum (TELFOR).

[24]  Xiaofeng Zhu,et al.  Efficient kNN Classification With Different Numbers of Nearest Neighbors , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[25]  Ismail Güvenç,et al.  A Survey on TOA Based Wireless Localization and NLOS Mitigation Techniques , 2009, IEEE Communications Surveys & Tutorials.

[26]  Kemal Tepe,et al.  Technical Issues on Cognitive Radio-Based Internet of Things Systems: A Survey , 2019, IEEE Access.

[27]  H. Vincent Poor,et al.  Collaborative Cyclostationary Spectrum Sensing for Cognitive Radio Systems , 2009, IEEE Transactions on Signal Processing.

[28]  Harry L. Van Trees,et al.  Detection, Estimation, and Modulation Theory, Part I , 1968 .

[29]  W.-C. Lin,et al.  Differential matched filter architecture for spread spectrum communication systems , 1996 .

[30]  E. Conte,et al.  Adaptive matched filter detection in spherically invariant noise , 1996, IEEE Signal Processing Letters.

[31]  Ken Cai,et al.  A SVM Multi-Class Image Classification Method Based on DE and KNN in Smart City Management , 2019, IEEE Access.

[32]  Xin Wang,et al.  SVM and Decision Stumps Based Hybrid AdaBoost Classification Algorithm for Cognitive Radios , 2019, 2019 21st International Conference on Advanced Communication Technology (ICACT).

[33]  C. Cordeiro,et al.  IEEE 802.22: the first worldwide wireless standard based on cognitive radios , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[34]  Iker Sobrón,et al.  Energy Detection Technique for Adaptive Spectrum Sensing , 2015, IEEE Transactions on Communications.

[35]  Qixun Zhang,et al.  Three regions for space-time spectrum sensing and access in cognitive radio networks , 2012, 2012 IEEE Global Communications Conference (GLOBECOM).

[36]  Antonio Napolitano,et al.  Cyclostationarity: Half a century of research , 2006, Signal Process..

[37]  Chengke Zhou,et al.  Application of K-Means method to pattern recognition in on-line cable partial discharge monitoring , 2013, IEEE Transactions on Dielectrics and Electrical Insulation.