A New Approach for Spectrum Sensing in

Wideband

Liudas Stasionis #1, Arturas Serackis #2

# Electronic Systems Department, Vilnius Gediminas Technical University

Naugarduko str., 41-413, Vilnius, LITHUANIA1liudas.stasionis@dok.vgtu.lt2arturas.serackis@vgtu.lt

Abstract—A new algorithm of the spectrum sensing is proposedin this paper. The spectrum sensing methods presented in thispaper are optimised to implement these in FPGA based embed-ded systems. The low power and highly parallelised architectureof FPGA requires low complexity in implementation of separateprocessing units – spectrum sensors (SpS). The widely used de-tection methods, based on analysis of signal spectrum energy andstandard deviation are integrated in proposed spectrum analysisalgorithm for cognitive radio spectrum sensing applications. Theexperimental investigation of proposed algorithm is performed insimulated and real RF environments. It is showed that proposedalgorithm increase the spectrum sensing efficiency and minimisethe miss-detection of licensed users to 12 % using only the energydetector in proposed algorithm and 5 % additionally addingthe standard deviation based spectrum sensing. Three channelverification techniques with proposed spectrum sensors weretested during our investigation. The Forward Consecutive MeanExcision (FCME) based technique showed the highest accuracyin channel verification task.

Index Terms—spectrum sensing, cognitive radio (CR), parallelcomputing

I. INTRODUCTION

The increasing number of communication devices, which

requires high data rates, makes current static frequency alloca-

tion schemes ineffective. The use of cognitive radio increase

the effectiveness of the use of various frequency bands not

occupied by licensed users [1].

The most challenging task for establishing the cognitive

radio is the determination of the frequency bands not occupied

by primary user. A number of spectrum sensing methods

are proposed to detect the absence of primary users in anal-

ysed frequency bands [2]. The most known spectrum sensing

methods are based on: energy detector [1], [3], [4], [5], [6],

[7], signal waveform analysis [8], detection of cyclostation-

arity features [9], transmission technology detection [10]. All

spectrum sensing methods are highly computational intensive.

Most of the proposed methods are not even tested in real-time

environment.

In this paper authors propose a new parallelized structure

of spectrum sensing algorithm based on energy detector and

estimation of modified standard deviation. The estimation of

signal spectrum energy and modified standard deviation are

made in parallel. This is a continuation of previous authors

work, related to the spectrum sensors implementation for

different RF environment [11]. The proposed spectrum sensing

algorithm is additionally tested in simulated and real RF

environments to investigate the performance of proposed mod-

ifications in evaluation of standard deviation, parallelisation of

energy and standard deviation calculations and modifications,

made for FCME algorithm implementation. Additionally pro-

posed spectrum sensor has been tested in three new RF envi-

ronments: one test signal with multiple carriers, one wideband

signal with dynamically changing carrier frequency and one

with combination of two burst signals and one narrowband

signal.

The use of energy detector-based spectrum sensing tech-

nique is reasoned by low complexity of this approach. The

parallelisation of spectrum processing threads is specially

designed for implementation of the algorithm in Field Pro-

grammable Gate Array (FPGA). The use of FPGA makes

possible to perform spectrum sensing in real-time. However

the resources on particular FPGA are limited by a number

of hardware multiply/divide units. Thus some additional spec-

trum analysis simplifications are introduced in the proposed

approach.

Three channel verification techniques are investigated in this

paper to compare the performance of direct, partial estimation

of signal spectrum parameters with adaptive FCME-based

threshold setting algorithm proposed by Lehtomaki et al. [12].

The paper is organized as follows: In Section II the structure

of the spectrum sensors is introduced ant estimated statistical

parameters are explained. Three channel verification tech-

niques are presented Section III. The parallelisation of spec-

trum sensing related calculations is introduced in Section IV. In

Section V the simulation results are discussed, which reflects

the processing speed and accuracy of proposed algorithms. The

results of experimental investigation in real RF environment

are presented and followed by conclusions in Section VII.

II. SPECTRUM SENSOR

Using ordinary averaging and squaring procedures for the

analysed signal may limit the SpS capabilities to detect

only wideband signals. The averaging of the signal spec-

trum estimates makes SpS unable to detect a low magnitude

narrowband signals. The signal standard deviation estimation

could solve this challenging task. The high values of standard

deviation for a narrowband signal makes able to create a

detector for this type of primary users.

The structure of SpS, optimised for FPGA implementation,

is shown in figure 1. Two statistical parameters are calculated

in this SpS: an average of signal spectrum estimates in selected

frequency range:

AV GH =1

N

N−1∑

k=0

|H (jω) |2, (1)

and signal modified standard deviation:

σ =

√

√

√

√

1

N

N−1∑

k=0

(AV GH − |H (jωk) |)2. (2)

The decision is made accordingly to both estimated statis-

tical parameters.

The main stages of the proposed SpS are given in Algo-

rithm 1. The structure diagram is shown in Figure 1. An

average of the spectrum estimates is calculated in upper part of

structure. Squaring is implemented by the use of multiplying

element, which multiples two same spectrum samples. To

multiply two signal samples we must use two multiplication

units: one for imaginary and one for real part of the complex

signal, received at the output of FFT block.

H (jω) = ℜ (jω) + jℑ (jω) . (3)

To estimate (|H (jω) |2), the following expression is used:

|H (jω) |2 = (ℜ (jω))2+ (ℑ (jω))

2. (4)

These operations can‘t be calculated directly, because sev-

eral bit manipulations should be performed. Xilinx FFT ip core

supports only signed 2‘s complement output. If imaginary and

real parts of this signal was multiplied from itself, then results

would be unpredictable. Not to distort the negative part of the

signal it must be converted to positive, therefore additional

signal sample preparations should be performed:

Bit manipulation operation reacts to signed bit, which is

most significant bit. If most significant bit bit is logical 1, then

all bits must be inverted, otherwise bits is left unchanged. This

operation must be done before squaring to real and imaginary

parts.

The result of the first operation (|H (jω) |2) is transferred

to accumulation part. The accumulation operation is controlled

by control signal C1. This signal appears when amount k of

accumulated samples reaches quantity N (see (1) equation).

Accumulation result SUMH is passed to multiplier element,

which is also used for standard deviation calculations. The load

of this multiplier element is low comparing with accumulation

and multiplication operations performed in previous steps.

To increase the efficiency of the algorithm several parallel

threads are sent to the multiplexer, implemented just before

the multiplier element.

Algorithm 1 The SpS algorithm

A. Average calculation of the spectrum estimates |H (jω) |.

if the number k of processed |H (jω) | values is below Nthen

set control signal C1 = 0;calculate the square of the spectrum value |H (jωk) |

2;

accumulate the calculated result: SUMH (k + 1) =SUMH (k) + |H (jωk) |

2;

increase counter value: k = k + 1;else

set control signal C1 = 1;reset counter: k = 1;

end if

calculate the multiplicative inverse: N−1;

multiply: AV GH = SUMH ·N−1.

B. Generation of the hypothesis

if AV GH > THD1 then

set hypothesis: HY Pwideband1 = 1;

else

set hypothesis: HY Pwideband0 = 1.

end if

C. Standard deviation estimation

take a negative value of |H (jω) |;calculate sum: g (n) = AV GH (n− 1)− |H (jω) |;take an absolute value |g (n) |;calculate the square of the difference: |g (n) |

2;

if the number k of processed |g (n) |2

values is below Nthen

set control signal C2 = 0;accumulate the calculated result: SUMg (k + 1) =

SUMg (k) + |g (n) |2;

root calculation: σ =√

SUMg;

increase counter value: k = k + 1;

else

set control signal C2 = 1;

reset counter: k = 1;

end if

D. Generation of the hypothesis

if σ > THD2 then

set hypothesis: HY P narrowband1 = 1;

else

set hypothesis: HY P narrowband0 = 1.

end if

Algorithm 2 Bit reconstruction

if most significant bit is 1 then

then invert all bits

else

then do nothing

end if

The multiplier element multiplies multiplexed threads of

SUMH, calculated for each sub-band, and N−1. To speedup

C1

Z-1

0

C2

1/N

SQRT

C3

|H(jω)|2 SUMH

TMPH(k)

AVGH(n)

AVGH(n-1)

-g(n) |g(n)| |g(n)|2

TMPg(k)

2(n) (n)

HYP1

HYP0THD1

THD2

MUX

MUX

MUX MUX

σ σ

∑ ∑

∑

|H(jω)|

Fig. 1. Spectrum sensor

the calculations and save system resources, the size of the sub-

band window could be selected equal to 2x samples. Then this

multiplication element can be replaced by shift operation to

the right side by x positions.

Received average AV GH of the sub-band spectrum es-

timates is compared to the threshold value THD1. Two

hypothesis could be generated accordingly made comparison:

• HY PAVG1 channel is occupied by wideband signal;

• HY PAVG0 channel is free.

The accuracy of primary user detection relies on the proper

selection of THD1 value. A simple strategy for selecting the

threshold THD1 is to set it higher, than receiver noise floor

[5]. This boundary can be found by estimating average of

input signal spectrum in analyzed bandwidth. The analysis

sub-band should be chosen wide enough to incorporate all

active noise components. The optimal sub-band width for

noise boundary estimation is whole operational bandwidth.

However the selection of the wider band for signal analysis

increases the computational load of the system.

Another strategy that could be applied for setting THD1

value is based on noise variance estimation [13]:

THD1 = σ2noise

(

1 +Q−1 (Pfa) /√

N/2)

, (5)

here Pfa is the probability of false alarm, N is the total

number of spectrum samples in the channel. Pfa value in

(5) equation must be chosen considering risk, because the

estimated threshold THD1 must guarantee non-interference

communications with primary user and utilization of unused

spectrum.

Together with signal energy estimation a standard deviation

σ is calculated for narrowband primary user signal detection.

To estimate σ, the average of the channel spectrum samples

AV GH should be calculated. The implementation of sepa-

rate AV GH estimation algorithm requires additional hardware

resources. Because the average of the spectrum samples is

already being calculated in Algorithm 1 stage A, this result

can be used to calculate the standard deviation σ. In order to

calculate the standard deviation, the AV GH value is compared

to signal spectrum estimates.

To speedup the calculation of σ, this process is implemented

in hardware as a thread, parallel to calculation of AV GH. In

order to parallelise the σ calculation, the energy average is

taken from previous spectrum analysis window. The use of

neighbouring spectrum estimates AV GH (n− 1) is possible

taking assumption that the changes of environmental noise is

not essential in small shift of spectrum analysis window. On

the other hand, the AV GH (n), which should be used for σ (n)calculation is different comparing to AV GH (n− 1), that

is used in proposed parallelised architecture. The difference

Err = AV GH (n) − AV GH (n− 1) ; increases the actual

calculated σ value. Therefore the selection of threshold THD2

should be adjusted for modified standard deviation value:

σ(n) =

√

√

√

√

1

N

N−1∑

k=0

(AV G(n− 1)− |H (jω) |)2. (6)

The procedure of σ estimation (C part of the Algorithm 1)

is shown in lower part of the diagram shown in Figure 1. The

standard deviation σ (n) part in hardware implementation is

inactive until AV G (n− 1) is calculated. These two threads

should work in parallel, but not sequential, so further modifi-

cation could be done.

After performed modifications the σ (n) is estimated by sub-

tracting signal spectrum components |H (jω) | from delayed

average AV G (n− 1). The sign of the result is changed to

positive, by changing the most significant bit into 0. The fol-

lowing operations are performed like in signal averaging part.

Before the decision is made, the square of the intermediate

estimate |g (n) | is calculated. The decision about presence of

the narrowband primary user signal is made by comparison of

(6) equation result with THD2.

The modification of standard deviation frees up: 7 slices, 26slice registers and 1 dsp48e1s. If there are 20 signal spectrum

components in analysed channel, modified calculation requires

164 less cycles and 84 cycles less if there are 10 spectrum

components.

From SpS structure (Figure 1) it is obvious, that the average

thread AV GH delay should be selected according to the

σ (n) estimation time. Therefore, if accordingly to the energy

detector estimate the hypothesis HY Pwideband1 is set to 1,

then standard deviation estimation is meaningless and it can

be interrupted.

III. CHANNEL VERIFICATION TECHNIQUES

Several techniques could be used for channel verification,

based on:

• direct estimation of statistical parameters;

• partial estimation of statistical parameters;

• FCME algorithm [12].

In the first channel verification technique statistical parame-

ters are calculated for all channel spectrum components. This

is an ordinary way to estimate system parameters, but it takes

more time to detect a wideband signal. This is because all

samples should be accumulated before the decision could be

made.

Accordingly to second channel verification technique (par-

tial estimation of statistical parameters) the signal spectrum

analysis is made in 2 stages (see Figure 2). A part of

the spectrum section is analysed in the first stage and the

remaining part in the second. This kind of channel processing

is chosen due to increase processing speed of the sections.

Fig. 2. Illustration of partial estimation of statistical parameters using onesub-band processing

First stage of this channel verification technique is used

to calculate the statistical parameters only in narrow the part

of the analysed channel. If it is decided after this step, that

the channel is occupied, then the second stage of the channel

processing is skipped and algorithm proceeds with another

channel. If it is decided, that the channel is free, the second

stage of analysis is initiated. At the second stage of channel

verification technique the statistical parameters for the rest part

of section are calculated.

If channels are occupied by the wideband signals, then it

is a big probability that the primary user will be detected in

the first stage. Second step is used mostly for detection of

narrowband signals.

In the suggested channel analysis scheme SpS sub-band

processing speed depends on the quantity of narrowband and

wideband signals. If in the analysed RF spectrum part are

many wideband signals, then this segment will be processed

sufficiently faster than using direct analysis scheme.

The third technique is based on FCME algorithm. An

assumption is made, that the first sample of the spectrum

in the channel is caused by noise. Accordingly the following

comparison is used:

H (ωk+1) > Tk

N−1∑

k=0

H (ωk), (7)

here Tk is scaling factor, given in (8) equation, which defines

coefficients used by selected method, N is the number of

channel spectrum samples.

Tk = FINV (1− Pfa, 2N, 2Nk) /k. (8)

If the comparison given in equation (7) is confirmed, then

it is decided that the channel has components of the primary

user signal. Otherwise the comparison is repeated for the rest

channel samples until the boundary is reached.

Algorithm 3 FCME algorithm

A. It is decided, that the first sample of channel is caused by

noise.

B. The scaling factor for kth sample is calculated (see (7)

equation).

C. The comparison of signal spectrum samples is performed:

if k = 0 then

if H (ω1) > T0H (ω0)) then

primary signal is detected.

set hypothesis: HY P1 = 1;else

continue primary signal search. Go to part B.

end if

else if k ≤ N then

if H (ωk+1) > Tk

N−1∑

k=0

H (ωk) then

if sum of previous samples is below signal spectrum

(i. e. for k = 10, H (ω11) > T10

10∑

k=0

H (ω10))

primary signal is detected.

set hypothesis: HY P1 = 1;else

continue primary signal search. Go to part B.

end if

else

all samples are tested.

set hypothesis: HY P0 = 1;end if

The implementation of this algorithm in embedded system

is complicated by scaling factor estimation procedure. It

uses F inverse cumulative function and implementation of

this function requires a lot of computational resources. The

structure proposed in Figure 3 makes possible to efficiently

implement FCME algorithm using FPGA.

T0

Tn-1

Tn

T1

T2

ctrl

Z-1 ACC

MUX

Hk+1

>_

Fig. 3. Proposed structure of FCME algorithm implementation in FPGA

The scaling factor used in FCME algorithm depends on: Pfa

false alarm ratio, N channel capacity (number of spectrum

samples in the channel) and k number of current spectrum

sample in the channel (see (8) equation). All these parameters

can be predefined before applying equations, so all Tk values

can be calculated in advance. The FCME algorithm can use

these pre-estimated scaling factor values from lookup table.

All other operations listed in this channel verification

strategy can be implemented by using simple elements like:

summation by ACC (accumulation element), comparison by

comparator, etc.

The weak part of this algorithm is the assumption, which is

made in the first stage. The probability, that the first spectrum

sample in the channel can be caused by noise or primary user,

is near in the densely used RF spectrum parts. Therefore for

the first algorithm stage it is better to use a small value, which

is near to noise level, instead of first sample of the channel.

IV. WIDEBAND SPECTRUM DIVISION TO PARALLEL

SENSORS

Energy detector based spectrum sensing methods requires

performing a certain arithmetic operations for analysis of the

selected signal frequency rage. Main arithmetic operations

should be parallelized in order to process a wideband spectrum

signal in real-time. In this situation, while one thread is used

for low frequency range spectrum processing, system is able

to analyse the high frequencies simultaneously.

In the proposed sensing method the wideband spectrum is

divided into equal width sub-bands with no overlap. For energy

detection based spectrum sensing methods the overlapping of

analysed sub-bands is inessential. Each sub-band (processing

thread) is analysed separately in parallel (diagram shown in

Figure 4).

Results combination

1 2 3 n-1 n

FFT

SPS SPS SPS SPS SPS

ADC

Fig. 4. Spectrum processing parallelization diagram

The ability to implement several parallel SpS defines poten-

tial band width that could be analysed by the system in real-

time. For example: if there are 10 parallel signal processing

threads, the sub-band dedicated for one spectrum sensor (SpS)

is 20 MHz, the relevant band of analysed signal is 200

MHz. To increase the whole wideband spectrum processing

speed, embedded system must have resources for more parallel

SpS. The spectrum analysis simplifications introduced in this

paper are applicable to different FPGA devices with various

parallelisation abilities.

Each SpS in dedicated sub-band search for free channels.

The selection of channel width should be carefully performed.

The minimum width of the sub-band should be selected

according to the possible bandwidth of the primary user signal.

The signal spectrum analysis algorithm is summarised in

Algorithm 4.

Algorithm 4 The parallelisation of the spectrum for SpS

A. Estimation of the signal spectrum.

1) Analog-to-Digital conversion.

2) Calculation of the Fast Fourier Transform.

B. Wideband spectrum division into n number of sub-bands.

C. Each sub-band is analysed using SpS.

D. The decision on the presence of primary user is made.

V. SIMULATION RESULTS

Two simulations were performed in this experimental inves-

tigation. The aim of the first simulation is to investigate the

spectrum processing speed by using 2 stages and FCME algo-

rithm for channel verification. Second simulation is performed

to investigate the efficiency of proposed SpS for narrowband

signal detection.

For the first simulation the 10 MHz spectrum band was

occupied by different wideband signals from 0.05 % to 50 %of analysed band width. The relative duration of spectrum

processing (see Figure 5) was registered and results compared

to ordinary calculation (channels calculated by using first ver-

ification strategy), by using different 1 stage channel windows

(0.2, 0.25, 0.4, 0.5, 0.6 of channel width). Best results for

Fig. 5. Processing time ratio dependency from 10 MHz spectrum occupancyby wideband signals

2 stage channel verification strategy were achieved by using

0.2 window. It was most efficient than regular processing,

when spectrum occupancy by wideband signals was 5 %. The

algorithm was most inefficient using 0.6 window, and it has

reached ”Efficiency boundary“ just when occupancy was 25 %.

Better results were achieved by using FCME algorithm

based technique (see Figure 5). The received efficiency ra-

tio was close to ordinary estimation result, when channel

occupancy was just 0, 05 %. FCME algorithm has showed

excellent performance, when channel occupancy was 50 %,

and it performed more than 2 times faster than first channel

verification technique. Third analysis method has showed

∼ 20 % better results than 2 stage algorithm best scenario,

in all occupancy situations.

Fig. 6. Mismatch ratio dependency from SNR

The second simulation were performed for generated fre-

quency hopping examples in 1 MHz spectrum band. Hoppers

band various from 30 kHz to 50 kHz. One hop length was fixed

at 500 µs, to model GSM signal hoping [14]. This simulation

was made because GSM band is occupied just by 50 % even in

densely populated areas. Channels who are not being used by

cellular phones, can be exploited by other services. Therefore

the capability of detecting this kind of signals must be tested.

Five detectors (energy, standard deviation, modified stan-

dard deviation with direct verification strategy and energy,

standard deviation based on FCME algorithm) were tested on

various SNR, changed from 40 dB to 0 dB (see Figure 6). An

additive white Gaussian noise (AWGN) was used as a noise

source.

Worst results were achieved by using simple energy detector

at 0 dB SNR. Using this detector acceptable results were

achieved, when SNR was 10 dB and there error ratio was

below 5 %.

The use of other 4 detectors showed similar results, but

these detectors are significantly more efficient comparing to

simple energy detector. The acceptable performance of SpS

was reached with SNR equal to 5 dB and error ratio was less

than 3 %. Worst results were achieved at 0 dB SNR with error

range from 10 % to 14 %.

VI. EXPERIMENTAL RESULTS

Five SpS (energy, standard deviation, modified standard

deviation detectors with direct channel verification technique

and energy, standard deviation detectors based on FCME al-

gorithm) were tested in this section using real RF environment

data.

Fig. 7. Spectrograms of four recorded test signals of different type

For this purpose four RF environment records were recorded

with low cost SDR (see Figure 7). These records were used

to test the primary user detection capabilities of different

signals types, which are common for RF environment [14].

First record was taken with center frequency FC = 430MHz and bandwidth BW = 0.3 MHz. This record has

two narrowband signals with constant parameters (like carrier

frequency, band) and one transmitter, which makes long bursts

(with approximate duration of 0.5 s). Second record param-

eters were equal to FC = 479 MHz, BW = 1.3 MHz, it

was one wide-band signal (approximate 0.4 MHz) with fixed

carrier frequency. Third record was made with FC = 947MHz and BW = 1.8 MHz. In this record there are several

wide-band signals (in the range from 0.35 to 0.5 MHz) with

changing carrier frequency (frequency hopping, burst time few

milliseconds). Fourth record was taken with FC = 1095 MHz,

BW = 0.2 MHz, one narrow band signal and two similar band

burst signals (the duration of burst is several milliseconds).

These four signals reflects four main types of signals met

in real environment: the narrow-band, wide-band, frequency

hoping and burst signals. The detection capabilities of pro-

posed SpS algorithm were experimentally tested using these

recorded signals to estimate the performance of SpS in various

situations.

Fig. 8. Test signals processed by energy detector

Energy detector with direct channel verification strategy

showed good performance for wide-band signal detection with

constant carrier frequency (see Figure 8). The received Pfa is

just 2 %. Narrowband signals were detected without mistakes,

but the Pfa was received at 4 % measurements and it is higher

than result, received for the wideband signal. All bursts of

signals with changing and constant carrier frequency were

detected, but Pfa was equal to 2 % and 5 % accordingly.

For all these records separate threshold THD1 was selected

manually.

Fig. 9. Test signals processed by standard deviation detector

Excellent performance standard deviation detector have

achieved for narrowband signals detection – only 1 % of Pfa

(see Figure 9). Similar to energy detector results were achieved

for wideband signal with fixed carrier frequency. False alarm

ratio was less than 2 %. For wideband and narrowband burst

signals the received Pfa was just 2 %. All bursts were detected

for signal with frequency hoping and fixed carrier frequency.

For both ordinary and modified standard deviation detectors

the threshold THD2 was selected manually too.

Fig. 10. Test signals processed by modified standard deviation detector

For all test records best results were achieved by using

modified standard deviation detector (see Figure 10). False

alarm ratio for narrowband and wideband signals was received

near to 0 %. For wideband signal with changing carrier

frequency the Pfa was about 1 %.

Fig. 11. Test signals processed by energy detector with FCME channelverification strategy

Significantly worse results were achieved by using energy

detector with FCME channel verification strategy (see Fig-

ure 11). Narrowband signals were detected with 5 % false

alarm ratio. Wideband signals were detected with 12 % of

miss-detection ratio and the Pfa reached 14 %. Signal with

frequency hopping detection was inefficient (only 65 % of

bursts were detected). Worst results were achieved in burst

detection of narrowband signals (only few burst detected). For

both detectors (energy and standard deviation) with FCME

channel verification algorithms the threshold was selected

automatically.

Fig. 12. Test signals processed by standard deviation detector with FCMEchannel verification strategy

Better results were achieved by using standard deviation

detector with FCME algorithm (see Figure 12). Narrow band

signals were detected with 3 % false alarm ratio. Miss-

detection ratio for the wideband signal was less than 5 %and Pfa = 6 %. All bursts of wideband signal with changing

carrier frequency were detected with achieved 3 % false alarm

ratio. Excellent performance for this detector was received for

narrowband burst signals. These signals were detected with

false alarm ratio near to 0 %.

VII. CONCLUSION

This article discusses spectrum parallelization importance

for wide-band spectrum sensing in real time. System which

uses just one SpS requires high clock rate. This problem can

be solved by using several parallel SpS.

For SpS implementation the energy, standard deviation and

modified standard deviation based detectors were selected.

Better results in simulation and experimental investigation

were achieved by using ordinary and modified versions of

standard deviation detectors. During the simulation the energy

detector achieved acceptable results (with Pfa ≤ 5 %) with

10 dB S/N ratio and 5 dB for standard deviation detector.

The experimental investigation has showed that the standard

deviation detectors may achieve lower false alarm ratio by few

percents.

The shortest processing time for channel verification were

achieved by using FCME algorithm. This algorithm is 2 times

faster comparing to standard channel verification technique

with 50 % spectrum occupancy by wideband signals. This ver-

ification technique has shown good detection performance in

simulation results where it perform effectively (Pfa ≤ 3 % for

5 dB SNR) comparing to standard energy detector (Pfa ≤ 5 %for 10 dB SNR). Unfortunately with real RF spectrum data

FCME algorithm based channel verification technique was less

efficient. The miss-detection ratio for wideband signals using

energy detector was 12 % and 5 % using standard deviation

detector.

REFERENCES

[1] S. ElRamly, F. Newagy, H. Yousry, and A. Elezabi, “Novel modifiedenergy detection spectrum sensing technique for fm wireless microphonesignals,” in Communication Software and Networks ICCSN, 2011 IEEE

3rd International Conference on, may 2011, pp. 59–63.[2] T. Yucek and H. Arslan, “A survey of spectrum sensing algorithms for

cognitive radio applications,” Communications Surveys Tutorials, IEEE,vol. 11, no. 1, pp. 116–130, quarter 2009.

[3] F. Penna, C. Pastrone, M. Spirito, and R. Garello, “Energy detectionspectrum sensing with discontinuous primary user signal,” in Communi-

cations, 2009. ICC ’09. IEEE International Conference on, june 2009,pp. 1–5.

[4] B. Shent, L. Huang, C. Zhao, Z. Zhou, and K. Kwak, “Energy detectionbased spectrum sensing for cognitive radios in noise of uncertain power,”in Communications and Information Technologies, 2008. ISCIT 2008.

International Symposium on, oct. 2008, pp. 628–633.[5] S. Imani, A. Dehkordi, and M. Kamarei, “Adaptive sub-optimal energy

detection based wideband spectrum sensing for cognitive radios,” inElectrical, Control and Computer Engineering (INECCE), 2011 Inter-

national Conference on, june 2011, pp. 22–26.[6] H. Rasheed, N. Rajatheva, and F. Haroon, “Spectrum sensing with

energy detection under shadow-fading condition,” in Wireless Pervasive

Computing (ISWPC), 2010 5th IEEE International Symposium on, may2010, pp. 104–109.

[7] S. Srinu, S. Sabat, and S. Udgata, “Wideband spectrum sensing basedon energy detection for cognitive radio network,” in Information and

Communication Technologies (WICT), 2011 World Congress on, dec.2011, pp. 651–656.

[8] S. Mishra, S. ten Brink, R. Mahadevappa, and R. Brodersen, “Cognitivetechnology for ultra-wideband/wimax coexistence,” in New Frontiers in

Dynamic Spectrum Access Networks, 2007. DySPAN 2007. 2nd IEEE

International Symposium on, april 2007, pp. 179–186.[9] M. Oner and F. Jondral, “Cyclostationarity based air interface recog-

nition for software radio systems,” in Radio and Wireless Conference,

2004 IEEE, sept. 2004, pp. 263–266.[10] M. Gandetto, M. Guainazzo, and C. S. Regazzoni, “Use of time-

frequency analysis and neural networks for mode identification in awireless software-defined radio approach,” EURASIP J. Appl. Signal

Process., vol. 2004, pp. 1778–1790, Jan. 2004. [Online]. Available:http://dx.doi.org/10.1155/S1110865704407057

[11] L. Stasionis and A. Serackis, “Experimental study of spectrum sensingalgorithm with low cost sdr,” in Proceedings of the 22nd International

Conference, September 20-21 2012, R. Rinkeviciene, Ed., VGTU. Vil-nius: Vilnius Gediminas Technical University Press Technika, September2012, pp. 117–120.

[12] J. J. Lehtomaki, J. Vartiainen, M. Juntti, and H. Saarnisaari, “Spectrumsensing with forward methods,” in Proceedings of the 2006 IEEE

conference on Military communications, ser. MILCOM’06. Piscataway,NJ, USA: IEEE Press, 2006, pp. 2217–2223. [Online]. Available:http://dl.acm.org/citation.cfm?id=1896579.1896914

[13] S. Ramly, F. Newagy, H. Yousry, and A. Elezabi, “Novel modified energydetection spectrum sensing technique for fm wireless microphone sig-nals,” in Commun. Software and Networks (ICCSN), 2011 International

Conference on, may. 2011, pp. 59–63.[14] M. A. McHenry, P. A. Tenhula, D. McCloskey, D. A. Roberson,

and C. S. Hood, “Chicago spectrum occupancy measurements &analysis and a long-term studies proposal,” in Proceedings of the

first international workshop on Technology and policy for accessing

spectrum, ser. TAPAS ’06. New York, NY, USA: ACM, 2006.[Online]. Available: http://doi.acm.org/10.1145/1234388.1234389