e3 white paper sensing

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End to End Efficiency (E3) White Paper November 2009 1/18

E3 White Paper

Spectrum Sensing

1. ABSTRACT

This white paper presents an overview, and the most importantconclusions, of the work related to spectrum sensing performed within

the End-to-End Efficiency project (E3) [1]. Spectrum sensing could be

an important enabling technology for future opportunistic spectrum

sharing scenarios. One of the principal aims of spectrum sensing is

transmitter detection, which is the main focus of this white paper.

Various methods for spectrum sensing control, such as deciding which

sensors should perform sensing simultaneously and finding the

appropriate trade-off between probability of misdetection and false

alarm rate, are described. Also, spectrum sensing and data fusion

algorithms and their performances under realistic conditions areinvestigated. The findings are summarized in a concluding section.

2. INTRODUCTION

Spectrum sensing is the art of performing measurements on a part of the spectrum and

forming a decision related to spectrum usage based upon the measured data. In recent years,

the service providers are faced with a situation where they require a larger amount of

spectrum to satisfy the increasing quality of service (QoS) requirements of the users. This has

raised the interest in unlicensed spectrum access, and spectrum sensing is seen as an

important enabler for this. In a scenario in which there exist a licensed user (primary user),any unlicensed (secondary) user needs to ensure that the primary user is protected, i.e., that

no secondary user is harmfully interfering any primary user operation. Spectrum sensing can

be used to detect the presence (or absence) of a primary user. Recently, FCC regulations [2]

have paved way for utilizing spectrum obtained from unused TV channels, the so-called TV

white spaces. In these regulations, spectrum sensing plays a major role.

There are some other solutions that can be thought of as alternatives, or complements, to

spectrum sensing; such as using a database of (licensed) spectrum usage, which can be

queried for spectrum opportunities, or advertising spectrum opportunities over a Cognition

enabling Pilot Channel (CPC) as developed in the E2R and E3 projects [3] and in ETSI RRS [4].

The database solution requires a connection to that database, e.g., over the Internet, and also

it requires that at least all primary users report any usage of the spectrum to the database

owner continuously. Similarly, CPC-based solutions may require additional infrastructure.

Spectrum sensing seems to be an attractive distributed approach for finding unused spectrum

opportunities, although it should be noted that reliable spectrum sensing is sometimes a

challenging task; see below. Additionally, spectrum sensing can provide valuable information

on the spectrum situation to a database or CPC-based solution. It is worth noting that the

main goal of all the possible solutions, and in particular the one based on sensing, is the

reliability of the obtained information on the status of the spectrum.


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measurements from several sensors are combined in a fusion center to obtain a more reliable

decision. In this manner cooperative spectrum sensing offers increased detection performance

by spatial diversity of the sensors.

The least demanding approach, from an a prioriinformation point of view, is energy detection.

An energy detector measures the energy in a radio resource and compares the value against a

threshold. Generally, if and only if the measured energy is below the threshold, the radio

resource is declared as not occupied, i.e., it is available for opportunistic use. Energy detection

is a non-specific detection method in the sense that no particular knowledge of the signal

properties is used. In this sense, energy detection can be used for declaring whether a

resource is occupied or not, but it can not be used to identify the type of system or user (e.g.,

primary or secondary) that is occupying the channel. Also, an energy detector needs to have

an idea of the noise level to adjust the detection threshold.

A cyclostationaryprocess has statistical properties which vary periodically over time. A wide-

sense cyclostationary process (the analogue of a wide sense stationary process) has an

autocorrelation function which is cyclic with a certain periodicity T, i.e., R(t, s) = R(t+T, s) for

all time indices s and t. Communication signals are typically cyclostationary with multiple

periodicities, e.g., the symbol frequency. Other periodicities may be related to coding andguard periods [5]. Cyclostationary detection is typically a statistical test based on the

estimated autocorrelation function of one or several known cyclic frequencies.

Cyclostationary detection exploits more knowledge (i.e., the cyclic frequencies) about the

process one wishes to detect than energy detection does. Hence, cyclostationary detection will

only be able to detect a limited amount of systems for which the communication signals

possess known cyclostationary properties, but, on the other hand, these systems can be

explicitly identified by the cyclostationary detector.

Sometimes some parts of the signal one wishes to detect can be known; examples of such

signals occurring in communication applications are synchronization words for GSM, preambles

for WiMAX, Pseudo-Noise (PN) sequences in Advanced Television Systems Committee (ATSC),

and spreading sequences for UMTS. In this case one can utilize an MF detector. The MF

detector can be shown to be optimal in the sense that it maximizes the Signal-to-Noise Ratio

(SNR) of a received single path signal in additive white Gaussian noise. An MF detector works

by correlating the received signal with the pattern one wishes to detect. Thus, the amplitude

and the phase of the signal are extracted. If this magnitude is above a threshold value, a

detection decision is made. Generally, MF detection has very good detection capabilities.

However, it requires a prioriinformation which may not be available for all applications.

When spectrum sensing is performed using a single sensor, that sensor may be in a deep fade,

e.g., it may be shadowed, relative to a transmitter one wishes to detect. This is known as thehidden node problem. Because of this possibility a secondary unit basing its decisions on single

sensor sensing may not engage in a secondary transmission unless it is highly confident in its

detection of a spectrum opportunity, i.e., it must be able to detect a transmitter even as it

experiences deep fading. To this end, the sensing node must use conservative detection

thresholds and/or highly sensitive receivers, which cause high false alarm probability (the

probability of reaching a detect decision when there was nothing there) and high cost

devices, respectively.

An approach that does not posses the above disadvantages, but requires some coordination, is

cooperative sensing in which multiple sensors are utilized. If the sensor measurements are

independent and identically distributed the probability that a collaborative sensing detectsother spectrum usage becomes PCD = 1-(1-PD)

N, provided a one out ofN detection approach


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10-6

10-4

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100

0.97

0.98

0.99

1

Probability of false alarm PF

Prob

abilityofdetection

PD

Selection algorithmRandom selection

10-6

10-4

10-2

100

0.85

0.9

0.95

1

Probability of false alarm PF

Probability

ofdetectionPD

Selection algorithmRandom selection

Figure 2: Probability of detection vs. probability of false alarm for a sensor selection algorithm [6] compared to

random sensor selection for two sensor position distributions: uniform (left figure), and clustered in one part of the

fading map (right figure).

5. COOPERATIVE SPECTRUM SENSING MANAGEMENT

This section relates to spectrum sensing control, awareness networking and cooperativespectrum sensing in Figure 1. As discussed in Section 3, cooperative spectrum sensing is a

powerful concept to leverage the spatial separation of multiple spectrum sensing nodes in a

wireless network. The optimal fusion of sensing results, acquired by distributed network nodes,

allows to alleviate the hidden node problem and/or to share the sensing load between network

nodes. The optimal fusion of decentralized observations has been studied since a long time,

see e.g., [7]and the references therein. It has been shown already in [8], [9] that the optimal

fusion rule is to compute the joint likelihood ratio of the distributed observations.

Cooperative spectrum sensing requires a networking solution to

communicate sensing results (sensing messages) between nodes.

Using spectrum sensing individual network nodes, as well as the

whole network by virtue of collaboration, becomes aware of the

local radio spectrum situation. Consequently the distribution of

spectrum sensing results can be understood as Awareness

Signalling. Within E3 an awareness signalling solution, namely

Cognitive Control Radio (CCR) has been developed [10], [11].

The CCR is targeted for sharing spectrum sensing and use related information between

Cognitive Radio networks. The CCR network provides information mainly for the secondary

users, which form local wireless networks. Thus, it can be seen to complement CPC, which is

mainly targeted for providing information to primary users. CCR is an awareness signalling

solution that supports the exchange of Information, Query, and Negotiation messages, needed

for general collaborative information sharing.

CCR is also a means to coordinate collaborative spectrum sensing

between network nodes. Here coordination covers functions like:

sharing of sensing effort, requests for spectrum sensing, or

coordination of quiet periods for spectrum sensing. To share the

spectrum sensing load between network nodes, the frequency

band allowed for cognitive use is divided into sub-bands.

Different nodes sense different frequency sub-bands. The

frequency sub-band each node is sensing in each time instant is

determined by a pseudorandom time-frequency code.

Consequently, sensing utilizes frequency hopping. The division of the spectrum sensing task

Cooperative spectrum

sensing is a powerful

concept to leverage the

spatial separation of

multiple sensing nodes

in wireless networks.

CCR is a means to

distribute awareness

information in wireless

networks. It can also be

used to coordinate

cooperative spectrum

sensing.


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S1 S2

Pseudorandomdelay

f1 (cat 1)

f2 (cat 2)

f3 (cat 2)

f4 (cat 3)

f5 (cat 3)

f6 (cat 3)

Frequencyhoppingsequence

Cat 1 timerCat 2 timerCat 3 timer

S3 S4 S5 S6

Synchronizing time instantQuiet period

Time

Frequency

Active node

Inactive node

Node 1

Node 2

Node 1

Node 1

Node 1

Node 1

Node 1

Node 2

Node 2

Node 2

Node 2

Node 2

Node 3

Node 3

Node 3

Node 3

Node 3

Node 3

Node 4

Node 4

Node 4

Node 4

Node 4

Node 4

Node 5

Node 5

Node 5

Node 5

Node 5

Node 5

Node 6

Node 6

Node 6

Node 6

Node 6

Node 6

Figure 3: Frequency sub-band monitoring is divided in frequency, time, and space among the cognitive radio nodes. A quiet

period which is at a pseudorandom position inside a synchronizing time interval is reserved for spectrum sensing. The

different frequency sub-bands are divided into categories (cat1, cat2, cat3) for which the sensing is performed at different time

cycles.

6. TRIGGERING OF COOPERATIVE SENSING IN AD HOC NETWORKS

This section relates to spectrum sensing control, spectrum sensing and cooperative spectrum

sensing in Figure 1. Often the detection reliability of a single sensor is not sufficient as fast

fading and shadowing may render a primary system signal very difficult to detect at a given

location, while it may still be easily detectable at a nearby, but different, location. When thesituation is such that a single sensor is certain that there is an active primary user transmitting

on the frequency intended for secondary usage, the sensing task is complete and the

secondary system may be informed of the detection. The sensing task is likewise complete

when a single sensor is sure, to a certain level, that the intended band is free for secondary

usage.

However, for the case when neither of the two above cases is reached the sensor needs

support to reach a reliable decision on the availability of the channel. This occurs when the

confidence probability, defined as the probability of the presence of the primary signal in a

channel kgiven the measurement y as experienced by sensor u:

( )( ) ( )

( ) ( ) ( ) ( )110011

1,PrPrPrPr

PrPrPr

HHyYHHyY

HHyYyYHP ku

=+=

====

(6.1)

where Pr(H0) and Pr(H1) are estimated from the statistics of previous detected usage of the

channel under consideration, falls within a predetermined interval, e.g. between 0.1 and 0.9.

The blue curve in Figure 4 (left) shows the probability of this event as a function of assumed

SNR in an energy detection context when there is no primary transmitter present. The blue

curve in Figure 4 (right) shows the corresponding probability for the case when there is a

primary transmitter present. In a suggested implementation [10] the sensor then transmits a

request for assistance through cooperative sensing to nearby sensors in its ad hoc network.The request may be transmitted over several hops until a specified maximal range has been


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reached; this to ensure that the sensors in the ad hoc network are not unnecessarily burdened

with sensing actions.

-30 -25 -20 -15 -10 -5 00

0.2

0.4

0.6

0.8

1No signal present

Assumed SNR

Pr(certain of H1)

Pr(uncertain)

Pr(certain of H0)

-30 -25 -20 -15 -10 -5 00

0.2

0.4

0.6

0.8

1Signal is present

SNR

Pr(certain of H1)

Pr(uncertain)

Pr(certain of H0)

Figure 4: Probabilities of the various decisions based on the confidence probability for variable assumed SNR using

ideal energy detection for the two cases when there is not (left) and is (right) a primary signal present. The green

curves show the probability of reaching the decision that the channel is free for secondary usage, the blue curves

show the probability that the sensor is uncertain and needs assistance via cooperation, and the red curves show the

probability that the sensor reaches the decision that a primary signal is present.

Once the request for assistance is received by the nearby sensors they will perform spectrum

sensing and report their respective confidence probabilities back to the initializing sensor. The

final decision on channel access is then based on the fused confidence probability:

( )

=

V

kkD PP ,, 11 (6.2)

Here,

V is the set of sensors that has provided sensing reports. If this probability exceeds a

certain predetermined threshold, the channel is determined to be free for secondary usage,

otherwise, the initializing node will block access to the channel by distributing a blocking

message to some or all of the nodes in the system.

7. DATA AIDED SPECTRUM SENSING

This section relates to spectrum sensing block of Figure 1. Stand-alone spectrum sensing

techniques dealing with MF detection achieve high processing gain in a relatively short time.

The hereafter-proposed spectrum sensing technique makes use of extended correlation with

reference sequences such as synchronization midamble for TDMA waveforms (GSM, DECT,

TETRA etc.), scrambling and spreading codes for CDMA waveforms (UMTS, HSDPA, HSUPA,

CDMA2000, several WiMAX standards), preamble symbols and/or pilot sub-carriers for OFDM

and OFDMA (DVB-T, DVB-H, WiFi, most of the WiMAX standards, LTE). The method is

recommended in [12], [13] and [14].

Figure 5 illustrates the general architecture of the proposed spectrum sensing technique

dealing with most of todays standards. The process is implemented on the cognitive device,

either a mobile terminal station or a base station (BS), and tests the presence of reference

sequences in the received signal. Tested waveforms can be GSM, DVB-T/H, UMTS, WiFi,

WiMAX signal or any other known standard working with reference sequences. When available,

the CPC [3] can be of great help to indicate which standards are present in the environment.

The proposed spectrum sensing technique aims at detecting the most significant base stations

and can provide relevant radio measurements.


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Cognitivedevice

Detection/Synchronizationprocessing

Terminal/BS Testedwaveforms :- UMTS- GSM- DVB-T- WiMAX-

Characterization

- Number of detected BS- Type of detected RATs(UMTS, GSM, )- jamming level- SNR, SINR- channel impluse response-

Receivedsignal

Figure 5: General architecture of the proposed data-aided spectrum sensing method

Figure 6 shows examples of real field GSM and UMTS signals being processed by the method.

The process results in the detection and recognition of a FCCH sequence and of a SCH

sequence for GSM, and the synchronization on the SCH sequence leads to the detection of 9

different base stations for the UMTS case.

Intercorrelation

results

Recognition

of GSM/FCCH

sequence

Recognition

of GSM/SCH

sequence

I/Q signal &

Instantaneous

One GSM frame

Decisio

n

SCH synchro. Criteria (5 antennas)

Detection of 9 P- SCHRelatedto 9 different BTS

Timesignal

Signal spectrum

Imp response computation at P- CPICHscramblingCode 122

Yellow : antenna1Green: averaged over 5antennas

Synthesis of detection and P-CPICH

Dominant scramblingCode 122

Figure 6: application to real-field GSM (left) and UMTS (right) signals

Figure 7 presents detection curves for real WiMAX waveforms in a simulated environment. 3WiMAX BSs are present with preamble index 1, 40, 100. Signals from BS2 and BS3 have SNR= 10 dB and 20 dB, propagation channels are mono path. N=1 to 5 sensors are considered forthe processing.

-30 -25 -20 -150

0.1

0.2

0.3

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0.5

0.6

0.70.8

0.9

1

SNR dB

Probability

ofdetectio

n

N=1N=2N=3N=4N=5

Figure 7: application to real WIMAX signals in a simulated environment BS1 detection curves


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8. COOPERATIVE DETECTION OF PMSEDEVICES IN TVWHITE SPACE

The current section relates to cooperative spectrum sensing in Figure 1. In recent FCC

regulations related to utilization of TV white spaces for opportunistic spectrum usage [2] it is

understood that additional spectrum can be acquired by ensuring that a TV channel is not in

use at a certain location neither by a licensed TV transmitter nor by a licensed Programme

Making Special Event (PMSE) device, such as a wireless microphone. The FCC regulations

require protection of PMSE devices for any opportunistic usage of TV white space. Generally,detecting a TV signal is simpler compared to detecting the presence of a PMSE device since TV

signals are relatively static in time and have high transmission power. In comparison, PMSE

devices are mobile and use much lower transmission power levels. In addition there are no

standards related to PMSE devices, so a method for detecting the presence of a PMSE device

must not rely on very specific features of the signal.

As mentioned in Section 3, cooperative sensing makes use of multiple sensors to detect the

presence of primary signals. In the TV spectrum, both TV signals and PMSE-type signals are

primary signals. The problem treated in this section is to find out the constraints and

limitations that PMSE devices impose on usage of TV white space channels.

In the results presented here the TV signal is an out-of-band interferer and the goal is to

detect PMSE devices with unknown signal structures operating on 200 kHz channels. Energy

detection is a suitable solution for such scenario since it does not rely on specific signal

features. In the scenario there is a node that desires to transmit but first has to make sure

that no PMSE device is placed within a distance of 1000 meters, corresponding to the

contamination distance of the intended transmission. To this end, the node requires

assistance from a number of sensors which are placed uniformly inside a circle extending 1100

m (i.e., 100 m additional protection distance) from the node. A DVB transmitter transmitting at

50 kW over 8 MHz is placed 2.5 km from the node with 13 MHz carrier separation from the

PMSE channel where the sensing is performed. Each sensor takes a hard sensing decision andsends the result to the node which employs a 1-out-of-Ndecision fusion (see Section 3). The

sensors are subject to an indoor-to-outdoor loss (15 dB) with probability 0.4 and have a noise

floor of -110 + n dBm where n is zero mean normally distributed with standard deviation 0.5

and represents the sensor noise uncertainty. The interference from the TV transmitter in the

PMSE channel is defined by path loss, shadowing (log normal with standard deviation 5.5 dB)

(both described in ITU-R P1546), and TX and RX filter leakages. The signal from the PMSE

device, when present, is subject to pathloss (a 2-ray urban street model [15]), Rayleigh fading

and log normal shadow fading with a standard deviation of 7 dB. The sensing bandwidth is 200

kHz and the wireless microphone transmits at 10 dBm and is subject to a bodyloss of 2 dB.

The results in Figure 8 show that, even for a high number of sensors, the probability of falsealarm is quite high for reasonable detection probabilities. This is undesirable since it means

that many spectrum opportunities will be missed. The results indicate that the potential

presence of wireless microphones may impose significant constraints and restrictions on the

whitespace usage. Furthermore, for an indoor wireless microphone, with additional associated

indoor-to-outdoor loss, the detection performance becomes even worse.

The poor performance herein can likely be attributed to the inability of the method of energy

detection to differ between the signal of interest (the wireless microphone) and other

phenomena (the out-of-band DVB transmitter and the noise uncertainty). Possibly, other

detection methods may perform better. However, the choice of methods is quite limited due to

the fact that the PMSE devices are non-standardized and hence have few signal properties toexploit when trying to detect them.


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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10.9

0.92

0.94

0.96

0.98

1

PFA

PD

10 sensors

20 sensors

50 sensors

100 sensors

160 sensors200 sensors

Figure 8: Probabilities of detection vs probabilities of false alarm for cooperative sensing in the described scenario.

9. OPERATING POINT SELECTION FORSPECTRUM SHARING IN

SENSING-BASED COGNITIVE ACCESS NETWORKS

In the context of opportunistic spectrum access in cognitive radio networks, spectrum sensing

techniques and methodologies (see Spectrum Sensing block in Figure 1) may be considered by

secondary users to detect spectrum holes that can be accessed in a non-interfering manner

[1]. As stated in Section 3, spectrum sensing is known to be affected by errors in the form of

false-alarm and misdetection. False-alarm causes spectrum under-use while misdetection leads

to spectrum interference between primary and secondary users. Unfortunately, these two

magnitudes pose a trade-off on the sensing mechanism, i.e. low misdetection is achieved at

the expense of high false-alarm and vice versa. Consequently, an adequate Operating Point

(OP) for the sensing mechanism should be determined such that some QoS is attained by both

primary and secondary users.

The trade-off between the false-alarm probability () and the misdetection probability () can

be observed by representing the so-called Receiver Operating Characteristic (ROC) curves

where is plotted against for some given average signal-to-noise ratio () and time-

bandwidth product (m), see Figure 9. Accordingly, the feasible OPs are those pairs (,) that

lie on the ROC curve. By appropriately selecting a specific decision threshold value in the

energy detector a particular value for the OP is obtained. For ease of representation, a set of

curves traversing the origin of ordinates with slope , where 01, is defined [16][17].Parameter defines the operating-point mixwhich represents a normalized parameterization

of the feasible OPs. In Figure 9, the set of curves is represented by the dotted lines which

intersect with the ROC curve at specific OP values represented by the red circles.

In order to evaluate the impact of and on the performance of a spectrum sensing scenario

we use a Discrete Time Markov Chain (DTMC) model [18], and we determine the suitable OP

for the sensing mechanism under different traffic load conditions. As a QoS metric we adopt

the classical Grade-of-Service (GoS) definition and adapt it to the primary-secondary spectrum

access scenario by defining the aggregate GoS (GoSA) [16], [17], which is a weighted

contribution of both primary GoS (GoSP) and secondary GoS (GoSS). In turn, the primary GoS

is a weighted metric that considers primary blocking probability ( PBP ) along with interference

probability (PI). Primary weight factor P is adjusted so that interference involves higher

penalization than blocking. Similarly, secondary GoS is defined as a weighted contribution of

the secondary blocking probability ( SBP ) along with the secondary interruption probability ( S

DP ).

In the same way, secondary weight factor S penalizes interruption further than blocking.


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Some illustrative results are shown in the following. In Figure 10, the aggregate GoS is plotted

against a range of operating point values for different offered secondary traffic loads.

Performance results reveal that by effectively choosing the OP bearing in mind the traffic load

levels will lead to enhanced perceived GoS. The suitable values for the OP correspond to those

marked as red stars in Figure 10. In addition, and not shown here for space reasons, the

sensitivity of the OP with respect to the time-bandwidth product (m), the experienced signal-

to-noise ratio (), and the willingness towards secondary operation (given by the weight factor

A) has also been evaluated, see [16], [17]. Results indicate that improved operation of both

PUs and SUs can be achieved by suitably selecting the OP which, in turn, enables to identify

some general rules. The OP values should be increased, so that primary usage is protected,

when secondary traffic increases (see Figure 10). In addition, the OP should also be increased

whenever the signal-to-noise ratio and/or the time-bandwidth product increases (see [17]).

Finally, restrictions on the willingness towards secondary spectrum usage (i.e. high values of

A) also imply a higher OP value to be selected.

10-4

10-3

10-2

10-1

100

10-4

10-3

10-2

10-1

100

False-Alarm Probabil ity ()

Missed-DetectionProbability(

)

ROC CurveOperating Point

1

0

=0.5

m=100= 10dB

Figure 9: Tradeoff between false-alarm () and

misdetection probability ().

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.90

0.02

0.04

0.06

0.08

0.1

0.12

0.14

Operating Poi nt ()

GradeofService(GoS)

Minimum

GoS

Increasing

secondary

Traffic

Figure 10: Aggregate GoS against OP for primary offered

traffic of 2 Erlangs and varying secondary traffic.

10. CENSORING IN COOPERATIVE SPECTRUM SENSING

This section relates to awareness networking (e.g. CCR in E3)

block as shown in Figure 1. As stated in Sections 3 and 5,

collaborative spectrum sensing is a way to improve spectrum

sensing performance by combining the sensing results (log-

likelihood ratios) of multiple spectrum sensors. The spectrum

sensing results which consist of local decision statistics of the

nodes are communicated to other nodes using for example the

awareness networking solution (CCR). The amount of information

that is transmitted in the collaborative sensing can be reduced

when a censoring scheme for spectrum sensing is utilized [19].

When censoring is used, only informative detection results are

taken into account and transmitted in the collaborative sensing.

The amount of spectrum

sensing messagesdistributed in a network

can be reduced by

sending only test

statistics which are

informative. The

decision, which test

statistic is informative,

is called censoring.


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In censoring, a communication rate constraint is defined which

limits the number of detection results transmitted under the

noise only (H0) hypothesis. Spectrum sensing performance when

censoring is used is shown in Figure 11. The communication rate

constraint is denoted by and it defines the number of sensing

results transmitted per sub-band per sensing time slot. For

example, 10% of the sensing nodes will transmit the sensing

result when = 0.1 or 0.1% of the sensing nodes will transmit the sensing result when =

0.001. The number of sensing results transmitted is shown in Figure 12. The communication

rate constraint applies only under the noise only hypothesis, i.e., low SNR region. Always, if a

primary user is detected, the sensing result is transmitted to other nodes.

Figure 11: Probability of detection for spectrum sensing with censoring as a function of the signal-to-noise ratio of

the primary signal to be detected. The primary user is an OFDM-modulated WLAN signal. Detection time is 0.8 ms

and 10 users are sensing the same frequency sub-band. The false alarm rate was 0.01. The communication rate

constraint is denoted by .

Figure 12: The number of sensing results sent in the collaborative sensing scheme, when censoring is applied. At

the low SNR region the communication rate constraint defines how many results are sent. When the signal is

detected, all results are sent regardless of.

Censoring is a powerful

solution to reduce the

communication

overhead in cooperative

spectrum sensing.


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12. SELECTION OF HARD DECISION FUSION RULES IN ENERGY

DETECTION BASED COOPERATIVE SPECTRUM SENSING

In this section a cooperative spectrum sensing scheme is proposed which takes into account

the channel conditions, user correlation and mean SNR of each cooperative cognitive user. It

has been demonstrated that local mean SNR and spatially correlated shadowing has direct

impact on optimal decision fusion at the fusion centre and must be taken into account [22].

Energy detection is chosen as an underlying scheme for local spectrum sensing because of its

simplicity and ease of implementation. In this section we are trying to answer the following

questions: (1) Is the OR fusion rule the best fusion rule in all cases? (2) What is the impact of

users local SNR on the global primary user signal detection performance? (3) Do channel

conditions and spatially correlated shadowing have any impact on the performance of global

spectrum sensing?

Extensive simulations have been conducted when all cooperating users are not far away from

the primary transmitter and have different mean SNR values. Three different cases are

considered which describe three different scenarios depending on the location of the primary

user and secondary users. Case 1 refers to a scenario in which all the secondary users arerelatively close to each other and hence experience similar mean SNRs. Case 2 depicts the

situation when half of the collaborating users have high mean SNRs while in Case 3 only one

user has a high mean SNR as compared to other collaborating secondary users. The effect of

spatially correlated shadowing on the selection of optimal decision fusion is also investigated.

According to Figure 15, for optimal global sensing performance in AWGN channel, the band

manager must know the mean SNR of each user along with its 1-bit decision.

Similarly, in correlated shadow fading, for lower values of the dB-spread of the correlated log

normal shadowing (the standard deviation of which is denoted by dB) voting based fusion rule

gives superior performance while in heavily correlated shadowing (higher values of dB) the

performance of all fusion rules are similar. Simulation results for Case 3 are shown in Figure 16and Figure 17. Similar results can be obtained for other two cases [22]. Hence it is suggested

that users estimate their local mean SNR values and send this information along with their

decision to the fusion centre.


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OR

AND

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1Detection Performance in AWGN Channel

Pf

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OR

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AND

Case 1

Case 2

Case 3

Figure 15: ROC curves of cooperating users in AWGN channel

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0.7

0.8

0.9

1

Pf

(With User Correlation)

Pd

10

-210

00

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Pf

(Without user correlation - i.i.d.)

Pd

OR

AND

1 user

Majority

OR

AND

1 user

Majority

Figure 16: ROC curves in urban environment with

shadowing, dB = 4 (Case 3)

10-2

100

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Pf

(With User Correlation)

Pd

10

-210

00

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Pf

(Without user correlation - i.i.d.)

Pd

OR

AND

1 user

Majority

OR

AND

1 user

Majority

Figure 17: ROC curves in urban environment with

shadowing, dB = 12 (Case 3)

13. CONCLUSIONS AND OUTLOOK

In this white paper, various spectrum sensing methods are evaluated and presented. It is

important to note that suitable detection thresholds need to be found for satisfactory sensing

performance. The confidence probability concept described in Section 6 provides a means of

performing an informed selection of a suitable detection threshold.

It is further shown that cooperation among sensing nodes may provide significant gains in

detection performance in some scenarios; hence cooperation can be crucial for protection of

primary transmissions. When cooperative sensing is used, the number of sensors involved in

the cooperation will determine the system performance and complexity, but also the

correlation of the sensors has a large impact on the performance, and hence proper sensor

selection is very important. This is investigated in Section 5, where a method for controlling

cooperative sensing in a decentralized manner by utilizing pseudorandom time-frequency

hopping codes is described. The sensing policy in the network is reduced to design and

assignment of pseudorandom hopping codes that ensure a certain number of sensors are

sensing the same frequency range. It is found that the proposed scheme outperforms a

random hopping scheme. In another study, described in Section 4, it is found that, if

information on location of the sensors is available, much can be gained by utilizing this


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information with the goal of reducing the correlation of the shadow fading experienced by the

sensors.

In this white paper, an approach for energy detection based cooperative sensing scheme that

takes into account the channel conditions and average SNR of each sensor, aiming to find the

best decision fusion, is also presented. It is concluded that cooperative spectrum sensing does

not always outperform single user sensing in highly correlated shadowing scenarios when

using energy detection.

An interesting scenario in terms of collaborative spectrum sensing is detection of primary users

and protection of wireless microphones (i.e. PMSE devices) in TV white space. Reliable

detection of PMSE devices by means of sensing seems difficult to achieve in realistic fading

environments, at least with energy detection and 1-out-of-Nfusion rule.

In general terms, proper fusion techniques of sensing information from collaborating sensors

need to be studied with realistic systems parameters before making the best use of

collaborative sensing mechanisms. Moreover, complementary and/or alternative solutions have

also been investigated, e.g. CPC, as reported in [3].

14. AUTHORS

Arshad, Kamran: [email protected]

Chantaraskul, Soamsiri: [email protected]

Gelabert, Xavier: [email protected]

Germond, Ccile: [email protected]

Kronander, Jonas:[email protected]

Rahman, Muhammad Imadur: [email protected]

Richter, Andreas: [email protected]

Sallent, Oriol: [email protected]

Seln, Yngve: [email protected]

15. ACKNOWLEDGMENTS

This work has been performed in the framework of the EU funded project E3. The authors

would like to acknowledge the contributions of their other colleagues from the E 3 consortium.

The views expressed herein are under development within E3 and therefore are subject to

change and do not necessarily reflect the views of each partner of the consortium.

16. REFERENCES

[1] IST-2007-216248 E3 Project, http://www.ict-e3.eu/.

[2] FCC 08-260, Second Report and Order, November 2008,

http://hraunfoss.fcc.gov/edocs_public/attachmatch/FCC-08-260A1.pdf

[3] E3 White Paper, Support for heterogeneous standards using CPC, June 2009,

https://www.ict-e3.eu/project/white_papers/CPC_white_paper_FINAL.pdf.

[4] ETSI TR 102 683 V1.1.1, Reconfigurable Radio Systems (RRS), Cognitive Pilot Channel (CPC)

September 2009, http://pda.etsi.org/pda/home.asp?wki_id=o-Vw,d7_@VqsyyrqcdIy5

[5] A. Huttunen, J. Pihlaja, V. Koivunen, J. Junell, and K. Kalliojrvi, Collaborative, distributed

spectrum sensing for cognitive radio, in Wireless World Research Forum Meeting 20, Ottawa,

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[17]X. Gelabert, O. Sallent, J. Prez-Romero and R. Agust Exploiting the Operating Point in

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e3 white paper sensing

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