Hybrid spectrum sensing architecture using LLCBC MAC for CR-WSN applications

A reconfigurable and hardware proficient hybrid VLSI architecture is greatly necessitated for spectrum sensing that is a combination of energy detection and time domain cyclostationary techniques for cognitive radio applications. One among the key features of Cognitive Radio is Spectrum Sensing which is exploited for allocating unused frequency band efficiently. Various research works are being carried out to investigate the different spectrum sensing methods and analyze the advantages, disadvantage and applications. Here, the novel idea is to design a hybrid architecture by combining energy detection and time domain cyclostationary techniques by exploiting the advantages of those techniques and can be selected based on the applications. Also an energy efficient Low Latency Column Bit Compressed (LLCBC) MAC is greatly utilized in this hybrid architecture to design an Application Specific Integrated Circuit more efficiently. The Proposed method thus helps in achieving significant VLSI parameters such as area, power and delay when compared with the existing time domain cyclostationary technique. The proposed Cyclostationary-based Spectrum Sensing Architecture using LLCBC MAC also yields 4.3% Area reduction, 19.5% Power reduction and with no change in Delay when compared with existing Cyclostationary-based Spectrum Sensing Architecture. Also, in Hybrid Spectrum Sensing Architecture embedding LLCBC MAC unit, a significant reduction in Area and Power corresponding to 57%, and 32% are achieved respectively, besides Hybrid Architecture is 15.28% faster than the individual architectures.

[1]  Jaakko Ojaniemi,et al.  Performance Evaluation of Cyclostationary-Based Cooperative Sensing Using Field Measurements , 2016, IEEE Transactions on Vehicular Technology.

[2]  Rahul Shrestha,et al.  Hardware implementation and VLSI design of spectrum sensor for next-generation LTE-A cognitive-radio wireless network , 2018, IET Circuits Devices Syst..

[3]  Anthony Chan Carusone,et al.  A sub-mW spectrum sensing architecture for portable IEEE 802.22 cognitive radio applications , 2017, 2017 IEEE International Symposium on Circuits and Systems (ISCAS).

[4]  Miguel López-Benítez,et al.  Improved energy detection spectrum sensing for cognitive radio , 2012, IET Commun..

[5]  Vimalathithan Rathinasabapathy,et al.  An SoC architecture for energy detection based spectrum sensing using Low Latency Column Bit Compressed (LLCBC) MAC in cognitive radio wireless sensor networks , 2019, Microprocess. Microsystems.

[6]  H. Vincent Poor,et al.  Spectrum Sensing in Cognitive Radios Based on Multiple Cyclic Frequencies , 2007, 2007 2nd International Conference on Cognitive Radio Oriented Wireless Networks and Communications.

[7]  Gyanendra Prasad Joshi,et al.  Cognitive Radio Wireless Sensor Networks: Applications, Challenges and Research Trends , 2013, Sensors.

[8]  Umesh Chandra Samal,et al.  Sensing performance of energy detector in cognitive radio networks , 2019 .

[9]  Mort Naraghi-Pour,et al.  Autocorrelation-Based Spectrum Sensing for Cognitive Radios , 2010, IEEE Transactions on Vehicular Technology.

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

[11]  Georgios B. Giannakis,et al.  Statistical tests for presence of cyclostationarity , 1994, IEEE Trans. Signal Process..

[12]  Xavier Fernando,et al.  A Hybrid Spectrum Sensing Method for Cognitive Sensor Networks , 2014, Wirel. Pers. Commun..

[13]  Anjana Sharma,et al.  Spectrum sensing based on multiple energy detector for cognitive radio systems under noise uncertainty , 2016, 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES).

[14]  G. Lakshminarayanan,et al.  An efficient hybrid spectrum sensing architecture on FPGA , 2017, 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET).

[15]  Vladimir Poulkov,et al.  Real-time adaptive spectrum sensing for cyclostationary and energy detectors , 2018, IEEE Aerospace and Electronic Systems Magazine.

[16]  Mort Naraghi-Pour,et al.  Autocorrelation-Based Spectrum Sensing Algorithms for Cognitive Radios , 2008, 2008 Proceedings of 17th International Conference on Computer Communications and Networks.

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

[18]  E. S. Pearson,et al.  On the Problem of the Most Efficient Tests of Statistical Hypotheses , 1933 .

[19]  Abhijeet A. Chincholkar,et al.  MATLAB IMPLEMENTATION OF SPECTRUM SENSING METHODS IN COGNITIVE RADIO , 2014 .

[20]  Naima Kaabouch,et al.  Performance evaluation of spectrum sensing techniques for cognitive radio systems , 2016, 2016 IEEE 7th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON).

[21]  Linda Doyle,et al.  Cyclostationary Signatures in Practical Cognitive Radio Applications , 2008, IEEE Journal on Selected Areas in Communications.

[22]  Murali Krishna Senapaty,et al.  Performance Testing and Monitoring SQL Queries for Rebuild or Reorganize Operations , 2016 .

[23]  Martin Reisslein,et al.  Cognitive Radio for Smart Grids: Survey of Architectures, Spectrum Sensing Mechanisms, and Networking Protocols , 2016, IEEE Communications Surveys & Tutorials.

[24]  Hong Wen,et al.  Adaptive Threshold Control for Energy Detection Based Spectrum Sensing in Cognitive Radios , 2012, IEEE Wireless Communications Letters.