Project

Names

Department of Electrical and Computer Engineering

Motivation

Cognitive Radio Systems (CRS)

Methodology

Future Work

Acknowledgements

Spectrum Sensing in Cognitive Radio Systems Shaun Kotikalapudi, Neeraj Venkatesan

Advisor: Dr. David Daut Slade Scholars Program

Rutgers School of Engineering

Spectrum Sensing Techniques

Figure 1: Edge detection of a

simulated signal structure with

sharp edges using continuous

wavelet transforms (CWT). The

signal has an SNR of 20 dB. The

wavelet transforms were taken for

dyadic scales 2^j, with

j=1,2,3,4,5,6,7. A Haar mother

wavelet was used to take the CWT

of the signal. As we can see in the

second graph, the edge detection

improves when the wavelet scales

increase. The algorithm is able to

detect all four edges at 200, 400,

600 and 800 MHz.

R𝑥𝛼 τ = lim

𝑇→ ∞ Rx t +

τ

2, t −

τ

2e−j2παt𝑑𝑡(1)

𝑇2

−𝑇2

𝑆𝑥𝛼 𝑓 = 𝑅𝑥

𝛼 𝜏 e−j2πfτ𝑑𝜏(2)

∞

−∞

Equation 1: Cyclic

Autocorrelation

Figure 2: BPSK Spectral Correlation Estimate, 1024 Hz Symbol Rate, 4096

Hz carrier, 16.384 KHz sampling rate, 128 signal frames, 128-point FFT

• Survey existing spectrum sensing algorithms in

Cognitive Radio Systems and examine signal

structure-based sensing algorithms as opposed to

energy detection`

Equation 2: Spectral Correlation Function

• Cognitive Radios use knowledge of their RF

environment to alter certain to attain a predefined

goal

• In recent years Cognitive Radios have been used as a

solution for an overcrowded spectrum

• One of the key aspects of Cognitive Radio Systems

is spectrum sensing, which was the focus of this

research effort

• Spectrum sensing traditionally measures the RF

energy within a certain spectral band in order to detect

the presence of a primary user, due to low complexity

• In a CRS paradigm spectrum sensing includes

obtaining spectrum characteristics across multiple

dimensions including frequency, time and space

• Some of the key spectrum sensing techniques

developed for CRS are as follows

• Energy detection: Measures the energy in the band of

interest to detect presence of a user

• Cyclostationary method: Uses cyclostationary

properties of certain signal structures to detect

presence of a user

• Matched Filter detection: Using matched filters to

correlate incoming signal with a known signal

structure to detect the presence of user

• Wavelet Detection: Converting incoming signals into

the wavelet domain and performing energy detection

in order to detect the presence of a user

• Conducted a literature review on existing spectrum

sensing techniques

• Decided to focus on implementing cyclostationary

and wavelet based spectrum sensing algorithms

• Cyclostationary Detection

• Initially tried to implement FFT

Accumulation method (FAM) algorithm to

study cyclostationary properties of certain

signal structures

• Settled on a slightly different spectral

correlation approach

• Traced cyclic domain profiles for different

modulation schemes (BPSK, BFSK and

QPSK)

• Wavelet Detection:

• Settled on a edge detection based Wavelet

approach for spectrum sensing

• Uses knowledge of frequency

discontinuities or “edges” in a signal PSD

to detect presence of primary user

• Implemented edge detection algorithm for

signals with sharp edges(using Haar

mother wavelet)

• Continue working on implementing spectrum

sensing decision algorithms for cyclostationary and

wavelet detection

• Work on alternative decision algorithms as opposed

to energy detection for wavelet and maximum

likelihood for cyclostationary

• Dr. David Daut

• IEEE