Application of Compressive Sensing in Wideband Cognitive Radio Network

Bharadwaj, Diksha (2018) Application of Compressive Sensing in Wideband Cognitive Radio Network. MTech thesis.

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Abstract

This Thesis presents an energy efficient Compressive spectrum sensing technique for Wide-band Cognitive Radio(CR) Network. In this work we have presented cooperative spectrum sensing method to determine the spectral location of Primary User(PU) in a wide-band spectrum. The Wide-band Spectrum is first divided into narrow-bands of equivalent bandwidth. CR sensors which behave as Secondary user are arbitrarily placed in various geographic location. Here, we assume that the CR sensors have overlapping spectrum sensing range to exploit spatial diversity and to alleviate deep fade problem.Further, we allot a probabilistic active and sleep state for each CR sensors. The CR sensor when in active state will send the sensed data to fusion center wherein final decision for spectral occupancy of PU signal is made. In sleep state CR sensor won’t participate in sensing.Assuming the sparse occupancy of PU user in wide-band we have applied the compressive sensing technique to identify the spectral location of PU with limited information of fewer set of measurements. We have evaluated the performance of our system through simulation on MATLAB. Further as prior knowledge of actual sparsity order is unknown in practical scenario, the number of measurements required is determined using maximum sparsity order which can be large. Hence in order to reduce this wastage of sampling resources we also present a two-step process wherein we first estimate the actual sparsity order with relatively few measurements than sparse signal recovery and then we adjust the number of measurements required for reconstruction of sparse vector.

Item Type:Thesis (MTech)
Uncontrolled Keywords:Cognitive radio; Spectrum sensing; Compressive sensing
Subjects:Engineering and Technology > Electronics and Communication Engineering > Signal Processing
Engineering and Technology > Electronics and Communication Engineering > Artificial Neural Networks
Divisions: Engineering and Technology > Department of Electronics and Communication Engineering
ID Code:9884
Deposited By:IR Staff BPCL
Deposited On:02 May 2019 12:08
Last Modified:02 May 2019 12:08
Supervisor(s):Deshmukh , Siddharth

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