a survey on spectrum sensing techniques in cognitive...

A Survey on Spectrum Sensing Techniques in Cognitive Radio

Anita Garhwal and Partha Pratim Bhattacharya

Department of Electronics and Communication Engineering

Faculty of Engineering and Technology

Mody Institute of Technology & Science (Deemed University) Lakshmangarh, Dist. Sikar, Rajasthan,

Pin – 332311, India [email protected] [email protected]

Abstract

The limited available spectrum and the inefficiency in

the spectrum usage necessitate a new communication

technology, referred to as cognitive radio (CR)

networks. The key characteristic of CR system is that it

senses the electromagnetic environment to adapt their

operation and dynamically vary its radio operating

parameters. A cognitive radio must detect the presence

of primary user to avoid interference. Spectrum

sensing helps to detect the spectrum holes (unutilized

bands of the spectrum) providing high spectral

resolution capability. Different spectrum sensing

techniques for cognitive radio are discussed in this

paper.

1. Introduction Demand of radio spectrum is increasing day by day

due to increase in wireless devices and applications.

Current radio spectrum allocation is not efficient due to

regulations that have limited spectrum access, spatial

restriction on frequency usage. Peak traffic planning

causes temporal under utilization since spectrum

demand vary with time. A cognitive radio must detect

the presence of primary user to avoid interference. The

use of allocated spectrum varies at different times and

over different geographical regions. In accordance to a

report by Spectrum Policy Task Force of FCC, the

spectrum is under or scarcely utilized and this

situation is due to the static allocation of the

spectrum rather than the physical shortage of the

spectrum as shown in Figure 1 [1]. Thus, to overcome

the spectrum deficiencies and the inefficient utiliz-

ation of the allocated frequencies, it is necessary to

introduce new communication models through which

frequency spectrum can be utilized whenever the

opening (hole) is available. Thus to resolve the

spectrum inefficiency problem, the concept of dynamic

spectrum access and cognitive radio is introduced.

These dynamic techniques for spectrum access are

known as Cognitive Radios (CR).

The concept of CR as proposed by Mitola in 1998

may also be defined as a radio that is aware of its

environment and the internal state and with knowledge

of these elements and any stored pre-defined objectives

can make implement decisions about it.

Figure 1. Spectrum concentration

2. Spectrum sensing The important requirement of cognitive radio

network is to sense the spectrum hole. Cognitive radio

has an important property that it detects the unused

spectrum and shares it without harmful interference to

other users. It determines which portion of the

spectrum is available and detects the presence of

licensed users when a user operates in licensed band.

Partha Pratim Bhattacharya et al, International Journal of Computer Science & Communication Networks,Vol 1(2), 196-206

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mailto:%[email protected]@gmail.com

mailto:%[email protected]@gmail.com

The spectrum sensing enables the cognitive radio to

detect the spectrum holes. Spectrum sensing

techniques can be classified as frequency domain

approach and time domain approach. In frequency

domain method estimation is carried out directly

from signal so this is also known as direct method. In

time domain approach, estimation is performed using

autocorrelation of the signal. Another way of

categorizing the spectrum sensing and estimation

methods is by making group into model based

parametric method and periodgram based non-

parametric method [2]. Another way of classification

depends on the need of spectrum sensing as stated

below [3]:

2.1. Spectrum sensing for spectrum

opportunities a. Primary transmitter detection: In this approach,

detection of a signal from a primary transmitter is

based on the received signal at CR users whether it is

present or not. It is also known as non-cooperative

detection. This method includes matched filter based

detection, energy based detection, cyclostationary

based detection, and radio identification based

detection.

b. Cooperative or collaborative detection: It refers

to spectrum sensing methods where information

from multiple Cognitive radio users is incorporated

for primary user detection. This approach includes

either centralized access to the spectrum coordinated

by a spectrum server or distributed approach.

2.2. Spectrum sensing for interference

detection a. Interference temperature detection: In this

method the secondary users are allowed to transmit

with lower power then the primary users and

restricted by interference temperature level so that

there is no interference. Cognitive radio works in the

ultra wide band (UWB) technology.

b. Primary receiver detection: In this method, the

interference and/or spectrum opportunities are

detected based on primary receiver's local oscillator

leakage power.

2.3. Classification of spectrum sensing

techniques

Figure 2. Classification of spectrum sensing

techniques [4]

Figure 2 shows the detailed classification of

spectrum sensing techniques. They are broadly

classified into three main types, transmitter detection

or non cooperative sensing, cooperative sensing and

interference based sensing. Transmitter detection

technique is further classified into energy detection,

matched filter detection and cyclostationary feature

detection [5].

2.3.1. Non-cooperative detection This technique is

based on the detection of the weak signal from a

primary transmitter. In primary transmitter based

detection techniques, a cognitive user determines

signal strength generated from the primary user. In

this method, the location of the primary receivers are

not known to the cognitive users because there is no

signaling between the primary users and the

cognitive users. Basic hypothesis model for

transmitter detection can be defined as follows [6]

x(t) = 𝑛 𝑡 𝐻0,

ℎ𝑠 𝑡 + 𝑛 𝑡 𝐻1, (1)

where x(t) is the signal received by the cognitive

user, s(t) is the transmitted signal of the primary

user, n(t)is the AWGN(Additive White Gaussian

Noise) and h is the amplitude gain of the channel. H0

is a null hypothesis, H1 is an alternative hypothesis.

2.3.1.1. Energy detection This technique is

suboptimal and can be applied to any signal.

Conventional energy detector consists of a low pass

filter to reject out of band noise and adjacent signals.

Implement-ation with nyquist sampling A/D

converter, square-law device and integrator as shown

in Figure 3(a) [7] –[15]. An energy detector can be

implemented similar to a spectrum analyzer by

averaging frequ-ency bins of a FFT.

Without loss of generality, we can consider a

complex baseband equivalent of the energy detector.

The detection is the test of the following two hypo-

theses: [15]

H0: Y [n] = W[n] signal absent

H1: Y [n] = X[n] +W[n] signal present

n=1,…,N; where N is observation interval

(2)

The noise samples W[n] are assumed to be additive

white Gaussian (AWGN) with zero mean and

variance σw. In the absence of coherent detection,

the signal samples X[n] can also be modeled as

Gaussian random process with variance σx. Note

that over-sampling would correlate noise samples

and in principle, the model could be always reduced

into equation (2).


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A decision statistic for energy detector is:

T= 𝑌 𝑛 𝑁2 (3)

Figure 3.(a) Implementation with analog pre-filter

and square-law device (b) implementation using

periodogram: FFT magnitude squared and averaging

[15]

Note that for a given signal bandwidth B, a

prefilter-matched to the bandwidth of the signal

needs to be applied. For narrowband signals and

sinewaves this implementation is simple as shown in

Figure 3 (a). An alternative approach could be

proposed by using a periodogram to estimate the

spectrum via squared magnitude of the FFT, as

depicted in Figure 3(b). This architecture also

provides the flexibility to process wider bandwidths

and sense multiple signals simultaneously. As a

consequence, an arbitrary band-width of the

modulated signal could be processed by selecting

corresponding frequency bins in the periodogram.

In this architecture, to improve signal detection we

have two degrees of freedom. The frequency

resolution of the FFT increases with the number of

points K (equivalent to changing the analog pre-

filter), which effectively increases the sensing time.

As the number of averages N increases, estimation of

signal energy also increases. In practice, to meet the

desire resolution with a moderate complexity and

low latency , fixed size FFT is choosed. Then, the

number of spectral averages becomes the parameter

used to meet the detector performance goal.

If the number of samples used in sensing is not

limited, an energy detector can meet any desired 𝑃𝑑

and 𝑃𝑓𝑎 simultaneously. The minimum number of

samples is a function of the signal to noise ratio

SNR= 𝜎𝑥2/ 𝜎𝑤

2 :

N= 2[𝑄−1 (𝑃𝑓𝑎 ) -𝑄−1 (𝑃𝑑 ))𝑆𝑁𝑅−1 - 𝑄−1 (𝑃𝑑 ) ]2

(4)

In the low SNR << 1, number of samples required

for the detection, that meets specified Pd and Pfa,

scales as O(1/SNR2). This inverse quadratic scaling

is significantly inferior to the optimum matched filter

detector whose sensing time scales as O(1/SNR).

(a) QPSK sensing

(b) Sinewave sensing

Figure 4. Required sensing time vs. signal input level

for fixed Pd and Pfa [15]

Figure 4(a) shows how the sensing time scales with

input signal level for QPSK signal sensing.

Measurements also shows that when the signal

becomes too weak then increasing the number of

averages does not improve the detection. This result

is expected and is explained by the SNR wall

existence. The limit happens at -110dBm (SNRwall=

-25dBm). From the theoretical analysis, we know

that SNR wall=-25dB corresponds to less than 0.03

dB of noise uncertainty. Results for sinewave energy

detection show interesting behavior as shown in

Figure 4 (b). Slope of the scaling law is improved by

increasing the FFT size. For 1024 pt. FFT. We

obtained N~1/𝑆𝑁𝑅 1.5. For 256 pt. FFT coherent

gain is negligible and we observe scaling law of

N~1/𝑆𝑁𝑅2.

This technique has the advantages that it has low

complexity, ease of implementation and faster

decision making probability. In addition, energy

detection is the optimum detection if the primary

user signal is not known. It also have disadvantages :

(1) The threshold used in energy selection depends

on the noise variance. (2) Inability to differentiate the


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interference from other secondary users sharing the

same channel and with the primary user. (3) It has

poor performance under low SNR conditions.This is

because the noise variance is not accurately known at

the low SNR, and the noise uncertainty may render

the energy detection useless.(4) It doesnot work for

the spread spectrum techniques like direct sequence

and frequency hopping.

2.3.1.2. Matched filter detection Matched-filtering

is known as the optimum method for detection of

primary users when the transmitted signal is known

[16]. The main advantage of matched filtering is the

short time to achieve a certain probability of false

alarm or probability of misdetection [17]. Block

diagram of matched filter is shown in Figure 5.

Figure 5. Block diagram of matched filter [18]

Initially the input signal passes through a band-

pass filter; this will measure the energy around

the related band, then output signal of BPF is

convolved with the match filter whose impulse

response is same as the reference signal. Finally

the matched filter out value is compared to a

threshold for detecting the existence or absence of

primary user.

The operation of matched filter detection is

expressed as[1]:

𝑌 𝑛 = ℎ 𝑛 − 𝑘 𝑥[𝑘]∞𝑘=−∞ (5)

Where ‗x‘ is the unknown signal (vector) and is

convolved with the ‗h‘, the impulse response of

matched filter that is matched to the reference signal

for maximizing the SNR. Detection by using

matched filter is useful only in cases where the

information from the primary users is known to the

cognitive users.

This technique has the advantage that it requires

less detection time because it requires less time for

higer processing gain. The required number of

samples grows as O(1/SNR) for a target probability

of false alarm at low SNRs for matched-filtering

[17]. However, matched-filtering requires cognitive

radio to demodulate received signals. Hence, it

requires perfect knowledge of the primary users

signaling features such as bandwidth, operating

frequency, modulation type and order, pulse shaping,

and frame format. Moreover, since cognitive radio

needs receivers for all signal types, the implem-

entation complexity of sensing unit is impractically

large [19]. Another disadvantage of match filtering is

large power consumption as various receiver algorit-

hms need to be executed for detection. Further this

technique is feasible only when licensed users are

cooperating. Even in the best possible conditions,

the results of matched filter technique are bound by

the theoretical bound .

2.3.1.3. Cyclostationary feature detection It have

been introduced as a complex two dimensional

signal processing technique for recognition of

modulated signals in the presence of noise and

interference [19]. To identify the received primary

signal in the presence of primary users it exploits

periodicity of modulated signals couple with sine

wave carriers, hopping sequences, cyclic prefixes

etc. Due to the periodicity, these cyclostationary

signals exhibit the features of periodic statistics and

spectral correlation, which is not found in stationary

noise and interference [20] – [28]. Block diagram is

shown in Figure 6.

Figure 6. Cyclostationary feature detector block

diagram [18]

The received signal is assumed to be of the

following simple form

y(n)=s(n)+w(n) (6)

The cyclic spectral density (CSD) function of a

received signal (6) can be calculated as [29]

S(f,α)= 𝑅𝑦𝛼∞

𝜏=−∞ (𝜏)𝑒−𝑗2𝜋𝑓𝜏 (7)

where 𝑅𝑦𝛼 ( 𝜏) = E[y(n + τ)𝑦∗ (n – 𝜏) 𝑒−𝑗2𝜋𝑓𝑛 ]

(8)

is the cyclic autocorrelation function (CAF) and α

is the cyclic frequency. The CSD function outputs

peak values when the cyclic frequency is equal to the

fundamental frequencies of transmitted signal

x(n).Cyclic frequencies can be assumed to be known

[23], [25] or they can be extracted and used as

features for identifying transmitted signals [24].

The main advantage of the feature detection is that

it can discriminate the noise energy from the

modulated signal energy. Furthermore,

cyclostationary feature detection can detect the

signals with low SNR . This technique also have

disadvanages that the detection requires long

observation time and higher computational


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complexity [20], [30]. In addition, feature detection

needs the prior knowledge of the primary users .

2.3.2. Cooperative or collaborative detection In

this tecchnique cognitive radio users are incoop-

erated, for this two methods Gains and Group

Intelligence are discussed .

2.3.2.1. Gains The performance of spectrum sensing

is limited by noise uncertainty, shadowing, and

multi-path fading effect.When the received primary

SNR is too low, there exists a SNR wall, below

which reliable spectrum detection is impossible even

with a very long sensing time. If secondary users

cannot detect the primary transmitter, while the

primary receiver is within the secondary user‘s

transmission range, a hidden primary user problem

will occur, and the primary user‘s transmission will

be interfered. Cooperative sensing decreases the

probabilities of misdetection and false alarm. In

addition, cooperation can solve hidden primary user

problem and also decreases sensing problem [9]-

[10],[15]. This also provides higher spectrum

capacity gains then local sensing. In this pilot

channel (control channel ) can be implemented using

different methologies such as dedicated band, an

unlicensed band such as ISM (Industrial Scientific

and Management) , UWB(Ultra Wide Band ) i.e.

under lay system [32].

Collaborative spectrum sensing is most effective

when collaborating cognitive radios observe

independent fading or shadowing [15], [33]. The performance degradation due to correlated

shadowing is investigated in [14], [34] in terms of

missing the opportunities. This techniqur is more

benifical when some amount of users collebrating

over a large area then over a small area. To,

overcome the effect of shadowing, beam foarming

and directional antenna is used [15].

(a) Sinewave sensing

Figure 8 (a) and (b) shows collaborative gain as a

function of the number of colleborative radios for

sine wave and QPSK signals.

(b) QPSK sensing

For a given probability of false alarm for the

cooperating system, an estimated threshold was com-

puted for each location based on the idle spectrum

data. Then, these thresholds were used to compute

the probability of detection for each location.

(a) Impact of spatial separation

(b) Impact of number of antennas

Figure 9. Small scale collaborative gain and benefit

of multiple antennas[15]


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Figure 9(a) shows that it is benifical to space the

antennas at distance larger the λ/2 to maximize the

gain. Figure 9 (b) shows that performance improves

with number of antennas , 2 antenna improves 𝑄𝐷 by

4 % and 4 antennas maximize it by 25% gain.

2.3.2.2. Group intelligence A spectrum sensing

techniq-ue using group intelligence is proposed

where multiple users, each with incomplete

information, can learn from the group‘s wisdom to

reach a supposedly correct conclusion. He proposed

adaptive threshold based on group intelligence

accuracy of the spectral occupancy has improved by

training the Secondary User (SU).This training is

done with the help of the global decision derived

from other SUs in the network. This approach is

suitable in two ways. (1)To indicate the presence or

absence of the PU separate training signal are not

required. Training can be done continually, not

limited to the training signal. (2)Training signal

derived from spatially diverse SUs should be robust

and less sensitive to local channel fading. Together

the SUs are expected to reach a unanimous decision,

which should be correct in all situations [35].

Group Intelligence emerges from the collaboration

and competition among many individuals, enhancing

the social pool of existing knowledge. The concept

of Group Intelligence obtains prominence in the

context of Multi Agent scenarios where in the global

decision made is an essence of the all the individual

local decisions. In the Cognitive Adhoc Network

(CAN) scenario, the group intelligence in terms of

the accuracy of the spectral information learned by

the group of SUs. In other words lesser the number

of decision errors, wiser is the network [36].

Figure 10. 𝑃𝑚 vs SNR with SNR estimate error=5dB,

mean SNR of group= 5dB [35]

Figure 10 shows the simulation result using 10(N)

SUs. The mean operating SNR for the SUs is 5 dB

with a variance of 5 dB. The SNR estimate error at

the SUs is 5 dB. In the first case, it assumed that

each SU broadcasts its hard decision (PU present or

absent). The training signal for each SU is obtained

by fusing these decisions using majority logic

decision. In the second case, knowledge of (Pd, Pf)

for each SU is assumed and training signal is

obtained using Log Likelihood Ratio Test (LLRT).

Figure 11. 𝑃𝑚 vs SNR with SNR Estimate

Error=1dB, mean SNR of group= 5dB [35]

Figure 11 shows the simulation results under same

conditions as before, except here the error in SNR

estimation is smaller 1 dB. It can be see that the

method is relatively insensitive to SNR estimation

error and continues to perform better than the

distributed sensing methods. Thus the decision

margin is an indicator of the confidence we have in

our final decision and can also be interpreted as

reliability of the result. Figure 12 represents this

scenario.

Figure 12. Reliability versus no. of SUs [35]

2.3.3. Advantages of cooperation Advantages of

Cooperation technique are given below.

2.3.3.1. Plummenting sensitivity requirements

Channel distortions like multipath fading, shadowing

and building penetration losses impose high

sensitivity requirements on cognitive radios.

Sensitivity in cognitive radio is limited by cost and

power requirement, power is limited by statistical

uncertainities in noise and signal characteristics, a

CR can detect this minimum power called SNR

walls. If there is cooperation between them ensitivity


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requirement decreases. Figure 13 shows the sensiti-

vity benefits obtained from a partially cooperative

coordinated centralized scheme consid-ered by [37].

This also shows a -25 dBm reduction in sensitivity

threshold obtained by this scheme [38].

Figure 13. Reduction in sensitivity threshold [38]

2.3.3.2. Agility improvement using totally

cooperative centralized coordinated scheme The

major challe-nge in CR is reduction in overall

detection time. Totally cooperative centralized

scheme is highly agile of all coopertaive schems.

They have been to shown to be over 35 percent more

agile compared to the partially cooperative schemes. Totally cooper-ative schemes achieve high agility by

pairing up ―weak users‖ with ―strong ones‖. Figure

14 shows the benefits of total cooperation over

partial cooper-ation. Cooperative sensing has to be

performed frequently and small benefits too will

have a large impact on system performance [39].

Figure 14. The benefits of total cooperation over

partial cooperation [38]

2.3.3.3 Cognitive relaying It is the fact that 80

percent of the spectrum remains unused at any point

of time [37]. As the number of CR users increases,

probabilty of finding spectrum hole decrease with

time. CR users would have to scan a wider range of

spectrum to find a hole resulting in undesirable

overhead and system requirements. An alternate

solution to this is cognitive relaying proposed by

[39] shown in Fihure 15. In cognitive relaying the

secondary user selflessly relays the primary users

transmission thereby decreasing the primary users

transmission time. Thus cognitive relaying in effect

creates ―spectrum holes‖.

This method is not practical because of (1). The

primary user don‘t have any information about

secondary user decode its transmission due to security related issues. (2). The cognitive users are

generally ad hoc energy constrained devices, they

might not relay primary users transmission. This

technique is a very goodway of creating transmission

opportunities when spectrum gets scarce.

Figure 15. Illustration of cognitive relaying [38]

2.3.4. Disadvantages of cooperation Cooperative

sensing schems are not beneficial due to following

reasons [38].

2.3.4.1. Scalability Even though cooperation has its

benefits, too many users cooperating have reverse

effects. It was shown in [37] as the number of users

increases that cooperative centralized coordinated

schemes follows the law of diminishing returns.

However a totally cooperative centralized coordi-

nated scheme was considered in [40] where with

the number of nodes participating benefits of

cooperation increased.

2.3.4.2. Limited Bandwidth CR usres might not

have dedicated hardware for cooperation because of

low cost low power devices. So data and cooperation

information would have to be multiplixed, there is

degredation of throughput for the cogntive users.

2.3.4.3. Short Timescale The CR user would have

to do sensing at periodic intervals as sensed

information will become stale fast due to factors like

mobility, channel impairments etc. This increases the

over-head.


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2.3.4.4. Large Sensory Data Cognitive radio have

the ability to use any ununsed spectrum hole so it

will have to scan wide range of spectrum, resulting

in large amount of data.

2.4. Interference based sensing For interference

based spectrum sensing techniques there are two

methods proposed. (a) Interference Temperature

Management (b) Primary Receiver Detection.

2.4.1. Interference temperature management

Interference is actually happen at receivers, it is

regulated in trans-centric way and is controlled at the

transmitter through the radiated power and location

of individual transmitters. Interference temperature

model is shown in Figure 16. The interference temp-

erature is a measure of the RF power available at a

receiving antenna to be delivered to a receiver, refle-

cting the power generated by other emitters and

noise sources [39]. specifically, it is defined as the

temperature equivalent to the RF power available at

a receiving antenna per unit bandwidth [41], i.e.,

𝑇𝐼(𝑓𝑐 , B) = 𝑃𝐼(fc ,B)

𝑘𝐵 (9)

where 𝑃𝐼(fc,B) is the average interference power in

Watts centered at fc, covering bandwidth B measured

in Hertz..

Figure 16. Interference temperature model [16]

FCC established an interference temperature limit,

which provides a maximum amount of average

tolerable intrefernce for a particular frequency band.

2.4.2. Primary receiver detection Advantage of

Local Oscillator (LO) lekage power is that all RF

recivers emit to allow cognitive radios to locate these

receivers. Block diagram of superhetrodyne receiver

is shown in Figure 17. Detection approach can detect

the LO leakage with very high probability and takes

on the order of milliseconds to make a decision. In

this architecture RF signal to be converted down to a

fixed lower intermediate frequency (IF), replacing a

low Q tunable RF filter with a low-cost high-Q IF

filter. A local oscillator (LO) is used to down-

convert an RF band to IF . This local oscillator is

tuned to a frequency such that when mixed with the

incoming RF signal, the desired RF band is down-

converted to the fixed IF band. In all of these

receivers, there is inevitable reverse leakage, and

therefore some of the local oscillator power actually

couples back through the input port and radiates out

of the antenna [42].

Figure 17. Superheterodyne receiver [42]

Figure 18. TV LO leakage versus model year [42]

Figure 18 shows the leakage power of television

receivers versus model year. Detecting this leakage

power directly with a CR would be impractical for

two reasons. First, it is difficult for the receive circuit

of the CR to detect the LO leakage over larger

distances. The second reason that it would be

impractical to detect the LO leakage directly is that

the LO leakage power is variable, depending on the

receiver model and year.

3. Conclusion The growing demand of wireless applications has put

a lot of constraints on the usage of available radio

spectrum which is limited and precious resource. In

wireless communication system spectrum is a very

important resource. In cognitive radio (CR) systems,

reliable spectrum sensing techniques are required in

order to avoid interference to the primary users. In

this paper, different spectrum sensing techniques are


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discussed that basically involves non cooperative,

cooperative and interference based detection. For cooperative detection group intelligence technique is

also discussed.

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