International Journal of Computer Applications (0975 – 8887)
Volume 40– No.16, February 2012
37
Cognitive Radio: Spectrum Sensing Problems in
Signal Processing
J. Christopher Clement
VIT University
Vellore
Tamil Nadu, India
Kishore V. Krishnan
VIT University
Vellore
Tamil Nadu, India
A. Bagubali
VIT University
Vellore
Tamil Nadu, India
ABSTRACT
The electromagnetic radio spectrum is a natural resource.
Effective Utilization of this natural resource is a Challenging
task in Present Day Wireless Communication. Cognitive
Radio is an emerging trend in wireless communication to
Combat for spectral scarcity. Cognitive radio is an intelligent
wireless communication system that is aware of its
surrounding environment and uses the methodology of
learning, and understanding from the environment and
adapting to it. Advanced signal processing techniques are
used in Cognitive Radio Network to achieve this intelligence.
Spectrum Sensing is one of such a signal processing to
identify spectrum holes which can be temporarily used
without interfering Primary User. Every cognitive radio
receiver is incorporated with Spectrum Sensing. There are
various existing techniques to detect the spectrum of which
some widely used are presented in this thesis.
General Terms
Your general terms must be any term which can be used for
general classification of the submitted material such as Pattern
Recognition, Security, Algorithms et. al.
Keywords
CR – Cognitive Radio, CSD – Cyclic Spectrum Density, PSD
– Power Spectrum Density, RF Spectrum- Radio Frequency
Spectrum.
1. INTRODUCTION
According to the report published by The Federal
Communications Commission (FCC) in November 2002 [1].
1) Some frequency bands in the spectrum are largely
unoccupied most of the time;
2) Some other frequency bands are only partially occupied;
3) The remaining frequency bands are heavily used.
In every wireless Communication there exists a primary user
(Licensed User) who is authorized to access specific part of
the spectrum. FCC has opened a way for Cognitive radio
network to allocate this Primary users spectrum to secondary
unlicensed user without intervening primary user
communication [1]. The underutilization or the bands of
frequencies which are not used by the primary user at
particular time and at particular geographical location are
called spectrum holes[2]. Spectrum utilization can be
improved significantly by making it possible for a secondary
user (who is not being serviced) to access a spectrum hole
unoccupied by the primary user at the right location and the
time in question [2]. This paper will try to address the task of
identifying the spectrum holes in the local neighborhood of
radio receiver. The conventional way of spectrum sensing is
measuring energy of radio frequency spectrum over wide
band of interest. In cognitive radio terminology spectrum
sensing involves measuring energy in various dimensions like
time, space, frequency and code [2]. It also tries to identify
types of signal occupying the spectrum such as modulation,
bandwidth, and carrier frequency etc.
Rest of the paper is organized as follows. Section II defines
Spectrum holes as white, Gray, and Black Spaces. Section III
describes spectrum sensing techniques, their advantages, and
disadvantages. And finally section IV concludes the paper.
2. SPECTRUM HOLES
A licensed spectrum band that is not used at some geo graphic
locations and at the time of question is called spectrum hole.
Generally spectrum holes are broadly classified into temporal
spectrum holes and spatial spectrum holes as shown in Fig
respectively [3]. A temporal spectrum hole is unoccupied by
the primary user during the time of sensing [3]. Hence, this
band can be utilized by CR users in the current time slot. As it
works in time domain, spectrum sensing of this kind does not
demand complex signal processing [3]. A spatial spectrum
hole is a frequency band which is free for secondary user at
some spatial areas; and therefore can be utilized by CR users
well outside this area. Spatial sensing of spectrum hole
requires complex signal processing algorithms.
In terms of the amount of power a band possess, power
spectra of incoming RF stimuli is classified into three types as
[2]
1) Black spaces, which are occupied by high-power “local”
interferers some of the time.
2) Grey spaces, which are partially occupied by low-power
interferers.
3) White spaces, which are free of RF interferers except for
white Gaussian noise.
Among this three, White spaces (for sure) and grey spaces (to
a lesser extent) can be used by unlicensed operators if
accurate sensing technique is adapted, and Black spaces
cannot be used because usage of this space will cause
interference to the primary user