Cognitive Radio.

Cognitive Radio
Spectrum Sensing Techniques and Cognitive Radio
Tayyab Ali Babar
Macquarie University
Sydney, Australia
[e-mail]
Abstract— There currently exists a growing global need to
enhance flexible wireless communication. This is largely due to
the fact that development of wireless networks has portrayed a
trend which will in prospect bring together both the mobile
systems and the internet systems in order to provide many
different services to the prospective customers. Spectrum is
therefore a very vital resource for the development of these
services. Cognitive radio equally has the ability to solve the lack
of or inadequate access to dynamic spectrum. Current policies on
spectrum present a major challenge in the development of
wireless communication technology. These policies have
ultimately resulted to inadequate usage of radio spectrum. In a
bid to outdo this problem, studies have revealed that cognitive
radio is the most appropriate technology that can be used to
provide an answer. Spectrum sensing appears to be the most vital
and initial step in the development of this technology. It is useful
in identifying bands of radio spectrum that are unused also called
spectrum holes. Since the emergence of CR technology, different
research works have been conducted in order to understand how
this technology can be used in managing the radio spectrum [1].
This report therefore intends to carry out a detailed survey on
the vital developments that have been made in spectrum sensing
as well as looking into its development since its emergence to its
current state. Relevant highlights have also been put on the
effectiveness and the challenges of different spectrum sensing
techniques and the common challenges that are always faced
when implementing the technology [2]
I. INTRODUCTION
The past decades have witnessed a fast rise in the number
of wireless communication devices together with the radio
spectrum static management. This has greatly led to lack of
available radio spectrum. Studies have revealed that an
estimated 50 billion devices will have a connection in the 2
years. The connections to all these devices will highly likely
be in need of accessing the internet. It is evident that
frequency spectrum is often reduced. The rising demand for
the frequency spectrum due to the constantly emerging
technologies and different uses for mobile users has been the
major reason for the limited availability of the frequency
spectrum given that it is not utilized evenly [4]. The radio
spectrum static management has no longer been effective
enough to allow access to all connected devices. Considering
this allocation, some segments of the radio spectrum are
highly utilized whereas are never used or used sparingly. The
concept of not sharing the radio spectrum with the users can
lead to the development of unwanted denial of service events.
The shortage of this technology therefore presents one of the
most critical concerns that should be looked at in the
prospective research works on networks that have not been
discussed [3].
One of the major and most vital answers to these concerns
and many others is to adopt the use of radio technology. The
radio spectrum is a technology that has been exposed to
several detailed investigations and studies by different
research communities for several decades. This technology
enables wireless devices to sense the radio spectrum. It is then
able to make a choice on the condition of the frequency
channels [5]. It then reconfigures the communication
parameters in order to attain high standard requirement of
service as it also minimizes the consumption of energy. The
devices are then able to use both unlicensed and licensed
bands whenever their main licensed users are not active hence
avoiding severe interface.
II. COGNITIVE CYCLE
Within the past decade, several technologies of sensing
have been suggested and can be grouped into 2 major
categories namely the wideband and the narrowband [6].
Wideband sensing is able to analyze several frequencies all at
one time whereas the narrowband sensing is able to analyze
just 1 frequency at one time. Examples of narrowband sensing
include detection of energy, sensing based on machine
learning as well as detection that is based on covariance.
Examples of wideband sensing includes spectrum that is
divided in several sub bands that are then sensed either
simultaneously or sequentially by the use of the techniques of
narrowband sensing. Approaches to sequential sensing are
generally not effective due to the fact that they need increased
energy and longer time due to the use of Analog to Digital
Converters of high rate (ADC). The ADCs are usually costly
and basically not practical for timely communications [7]. The
concept of simultaneous schemes of testing often need joint
synchronized functions and high amounts of sensors thus
raising the complexity of any given implementation.

The traditional schemes of licensing controls the results
and usage of frequency spectrum in instances where there is
reduced usage of the frequency spectrum. As a result of the
licensing scheme, there is emergence of spectrum holes.
Spectrum holes can be described as frequency bands that are
allocated to some locations but are mostly not exploited by the
licensed users. This therefore provides a chance to secondary
unlicensed users to utilize it [8]
The following cognitive cycle describes how a cognitive
radio functions.
Figure 1 Cognitive Cycle [9]
The major tasks that are performed from the cognitive
cycle include the following:-
 Analysis of radio scene. This handles the interference
temperature estimation as well as detecting the spectrum
holes. Is also referred to as sensing of spectrum [10].
 “Identification of channel. It handles the channel state
information (CSI) estimation. Also referred to as analysis of
spectrum”
 Power control transmit and management of dynamic
spectrum. Also known as spectrum decision.
“This research work mainly addresses the analysis of
performance for the 3 major techniques of spectrum sensing
under the category of non-cooperative [9]. The techniques
include;
 Matched filter detection
 Energy detection
 Cyclostationary feature detection”
III. SPECTRUM SENSING TECHNIQUES
This is among the most vital procedures of the cognitive
cycle. The purpose of spectrum sensing is to discover the
availability of transmissions from the basic users. There exists
3 major spectrum sensing types namely; “cooperative sensing,
non-cooperative sensing as well as interfaced based type of
sensing” [11]
Figure 2 Techniques of Spectrum Sensing [11]
A. Matched Filter Detection
“A matched filter represents a linear filter that makes best
use of the output signal to noise ratio for each input signal.
This technique is used mainly when the primary user priori
knowledge is known. During this procedure, the unknown
signal is often convolved with the response from the filter
impulse. This represents the mirror as well as the version of
the shifted time for the primary user signal. The mathematical
expression of the matched filter detection is as shown below”
𝑌ሺ𝑛ሻ= ෍ ℎሺ𝑛 − 𝑘 ሻ𝑥 (𝑘 )
∞
𝑘 = − ∞
The following represents the block diagram for the
matched filter detection
Figure 3: “Block Diagram for the Matched Filter Operation” [11]
B. Energy Detection
“Detection of energy is basically the simplest technique of
sensing mainly because it doesn’t need any earlier information
or knowledge on the signal of the primary user. During this
operation, the detection of the primary user is on the basis of
the sensed energy. During this operation, the signal that is
received goes through the band pass filter [12]. The limited
signal of the band is then integrated within a given interval of
time. What follows is the comparison of the integrated time
signal with the earlier defined threshold.” This is carried out in
order to find out the availability of the primary signal. The
expression for the hypothesis testing for the identification of
the signal is as shown below”
Where
“n – The transmitted signal by the primary users
x (n) – received signal by the secondary user
w (n) – additive white Gaussian noise”

The hypothesis H0 indicates the unavailability of the
primary user while the hypothesis H1 indicates the availability
of the primary user [13]. The energy calculation can be
achieved by the mathematical formula shown below
෍ | 𝑥 ሺ𝑛ሻ| 2
𝑁
𝑛 = 0
The energy is subsequently likened to the threshold in
order to find out the true hypothesis. The following
expressions will assist in the comparison.
The block diagram for the procedure is as shown below
Figure 4: Energy Detection Block Diagram [14]
C. Cyclostationary Feature Detection
The signal that is transmitted from the primary user always
portrays a pattern that is periodic. The pattern is described as
cyclostationary and can be used to determine the availability
of a licensed user. In case the signal autocorrelation is a
periodic function, then the signal will be described as a
cyclostationary signal. “The expansion of the autocorrelation
function in regards to the Fourier series coefficient, it results
to a function that depends only on frequency. The spectral
features for this procedure do not have any correlation to each
other. The expansion of the Fourier series is often described as
the cyclic auto-correlation function (CAF) whereas the related
frequency is known as the cyclic frequency.” “The calculation
of the cyclic spectral density (CSD) can be achieved by using
the Fourier transform of the CAF. This is a representation of
the correlation density between 2 spectral components. They
have a separation quantity that is equal to the cyclic frequency
[14]. The pattern is very vital when identifying the primary
user from the noise.
The signal that is received, x (t) is the cyclostationary
whenever the auto correlation and the mean portrays a
periodicity
Where; the auto correlation and mean period is represented
by T0. Whenever they and t are replaced with t-τ/2 and t+τ/2
in the equation, then
Where; Rax indicates the cyclic autocorrelation function
(CAF. a represents the cyclic frequency. The cyclic frequency
is often taken as a known parameter.” The calculation of the
CAF is as expressed below
The transmitted signals from the primary users are mainly
coupled by spreading codes, cyclic prefix etc. that leads to the
periodicity of the statistics like the auto correlation and mean.
When the CFD calculations for these signals are attained, they
are useful in highlighting the stated periodicities. The Fourier
transform results for the correlated signals in the frequencies
peak that are specific to the signal are important when
determining the availability of the primary users while noise
generally has a random nature. It also does not portray the
stated periodicities thus is not often highlighted whenever
correlation is carried out [15]. The block diagram for the
cyclostationary process is as shown below
Figure 5: Cyclostationary Feature Detection Block Diagram [16]
To Sum these three Techniques I have recorded the
advantages and disadvantages in the table below
Table 1: Pros and Cons of Sensing Techniques
IV. RESULTS AND ANALYSIS
All the simulations for performing an analysis for these
sensing techniques were conducted through MATLAB. The
major reason for performing the simulations was to carry out a