Spectrum Sensing for Cognitive Radio Stateoftheart and Recent Advances

Recent Advances in Cognitive Radios

Abstruse

Recent advances in the field of wireless have pb to an increase in flexibility of spectrum usage. With the fixed spectrum assignment policy much of the spectrum remains unused most of the time and is wasted. This led to the technological evolution of cognitive radios which optimize the spectrum usage. This led to the process of monitoring the spectrum called spectrum sensing. Spectrum sensing forms the base of cerebral radios and is 1 of the most important techniques that enable the cerebral radios to optimize the spectrum usage. The newspaper covers a detailed survey of the background of Cognitive radios: characteristics, functions and architecture. Nosotros besides discuss different spectrum sensing techniques along with some of the recent advances in spectrum sensing methods. The paper further looks at the recent applications in use in the field of cognitive radios.

Keywords- Cerebral radios, spectrum sensing, main users, secondary users, primary signal, secondary point, energy detector, cyclostationary feature detector, matched filter, cooperative spectrum sensing.

Table of contents

• 1. Introduction to Cerebral Radios
• two. Background
  • two.1 Cognitive Radio Characteristics
  • 2.2 Cognitive Radio Functions
  • 2.3 Cognitive Radio Architectures
• 3. Spectrum Sensing
  • 3.1 Local Spectrum Sensing
  • 3.ii Cooperative Spectrum Sensing
• 4. Applications
  • 4.ane Authentication Application
  • 4.2 Wireless Medical Networks
• 5. Decision
• six. References
• vii. Acronyms

ane. Introduction to Cognitive Radios

With the rapid growth of wireless communication, the terminal decade has seen an all-encompassing amount of growth in demand for wireless radio spectrum. Promoting competitions, innovations, investment and regulations in radio spectrum is handled by The Federal Communications Commission (FCC). The use of cognitive radio (CR) engineering has led the FCC to consider more flexibility in the usage of available spectrum. In the current spectrum framework, the spectrum bands are allocated to licensed holders, likewise known every bit main users (PUs), for large demographical regions, on a long term basis. However there is partial utilization of the allocated spectrum. This inefficient utilization of spectrum necessitates evolution of dynamic spectrum access techniques (DSA). The DSA allows users with no spectrum license, chosen secondary users (SUs), to temporally use the unused licensed spectrum.

The priority users have priority in using the spectrum; SUs need to constantly perform existent fourth dimension monitoring of the licensed spectrum which tin can be used. In doing so the SU should not violate the interference temperature. The SUs should be aware of the PUs reappearance. The technique used for sensing the PUs presence is called spectrum sensing. In that location are various sensing techniques such as free energy detection, cyclostationary characteristic detection, matched filter, cardinal cooperative sensing and distributive cooperative sensing. In spectrum sensing the SU constantly senses/checks the transmission channel for the presence of the primary signals in the channel. Later on sensing the spectrum the CRs allocate the spectrum to the SUs and the SUs need to reconfigure themselves in order to apply the newly allocated spectrum. The block diagram of CR wheel is shown in figure ane.

Figure one: Cognitive Radio Cycle

In the past few years there have been pregnant developments in CRs. Here in department 2 we discuss some background topics such as CR characteristics, CR functions and CR architectures. In section 3 we discuss spectrum sensing and various spectrum sensing techniques such as energy detector, cyclostationary feature detector, matched filter detector, centralized cooperative sensing and distributed cooperative sensing. In section iv nosotros discuss two recent applications of CRs and in section v we conclude this paper.

two. Background

In this department nosotros discuss some of the CR characteristics. We so discuss some CR functions which are necessary for a SU to efficiently manage the spectrum and at concluding we discuss the CR architecture in brief.

two.1 Cognitive Radio Characteristics

Some CR network characteristics are as follows:

• Operating environment sensing: CRs operate in a multi-dimensional surroundings which can include cooperative or non-cooperative emitters that can toggle on and off, adapting to local changes equally well as traffic loads which vary rapidly. In order to perform its job properly, a CR must change in accordance to the changing surround and information technology should be able to notify other devices in the network regarding the inverse configuration.
• Operational state languages: Operational state languages are used for data sharing in a CR network. As mentioned above CR should inform its states and observations to other nodes in its network. The linguistic communication that CRs utilize for this purpose is called operational state linguistic communication. The information that a CR sends might be a list of all emitters that it recently sensed.
• Distributed Resource Direction: The radio spectrum is a distributed resource. Therefore utilize of a spectrum band at one location makes information technology unavailable elsewhere. Therefore the allocation of spectrum resources must be done in a counterbalanced fashion. Various algorithms have been developed to handle the allocation and managing the distributed spectrum and resource based on traffic loads.

2.2 Cognitive Radio Functions

• Spectrum Sensing: In order to avert interference the spectrum holes (bands not being used by the PUs) need to be sensed. PU detection technique is the most efficient way in this respect. The spectrum sensing techniques are basically divided into three categories, which are transmitter detection, co operative detection and interference based detection.
• Spectrum Management: There is a need to capture the best available spectrum to come across the user communication requirements. CRs should make up one's mind on the best spectrum band to meet quality of service requirements over all spectrum bands. The direction function is classified as spectrum analysis and spectrum detection.
• Spectrum Mobility: Information technology is the process where a CR user exchanges the frequency of operation. They target to use the spectrum in a dynamic fashion past assuasive the radio terminals to operate in the best bachelor frequency band. The shift to a better spectrum must be seamless.
• Spectrum Sharing: Information technology is of utmost importance to provide a fair spectrum scheduling policy. It is also one of the near important challenges in open spectrum usage. In the existing systems it corresponds to the existing MAC problems.

2.3 Cognitive Radio Architecture

A cognitive radio network consists of primary networks as well every bit secondary networks. A master network comprises of one or more than PUs and one or more primary base stations. The PUs are licensed to use the spectrum and are coordinated past the primary base of operations stations. PUs communicate among each other through the base of operations station but. Generally the PUs as well as the master base stations practise non accept CR properties.

On the other hand, a secondary network comprises of one or more SUs and may or may not contain a secondary base station. For SUs, the spectrum access is managed and handled by the secondary base station which acts as a hub/admission point for the SU network. The SUs under the range of the same base station communicate with each other through the base station. If more than one secondary base of operations station shares a single spectrum band so their spectrum usage and coordination is done past a fundamental spectrum broker. A set up of SUs can besides connect to each other and communicate amid themselves without the presence of the secondary base station. This kind of network is called an ad-hoc network. Cyberspace of things (IoT) besides equally vehicular advertizing-hoc network are some of the examples.

As the SUs should not cause interference with the PUs transmissions, all the SUs along with the secondary base stations are equipped with the CR properties. And so whenever SUs detect the presence of a PU in a spectrum ring they should immediately stop using that band and should movement to some other available ring to avoid interference with the PU transmission.

As shown in the figure 2, spectrum band consists of licensed as well equally unlicensed bands. PUs are authorized to utilise the licensed bands while the SUs can only use the licensed bands when the licensed bands are idle and are non being used past the PU. If a PU starts using the licensed ring on which a SU is transmitting, the SU should immediately discover PU's presence and should end transmitting on that band and should move to some other available ring. The information regarding the bachelor bands as well equally the occupied bands is provided to the SUs by the secondary base station. The secondary base of operations station is supposed to handle the ring allocation and maintain coordination among all the SUs within that network. Whenever a SU detects the presence of a PU, information technology sends this information to the secondary base station and the secondary base of operations station and so informs all other SUs regarding the presence of PU on that band and asks all the SUs to give up that detail band. If SUs are using an unlicensed band then they tin form an ad-hoc network and tin can coordinate among themselves without the secondary base station.

Effigy ii: Cerebral Radio Architecture

Ref - [Beibei11]

iii. Spectrum Sensing

Spectrum sensing refers to the task of estimating the radio channel parameters such as transmission channel characteristics, interference level, racket level, spectrum availability, power availability, etc. Spectrum sensing is mainly done in the frequency and time domain. Even so information technology can also be washed in code and stage domains besides.

The unlicensed users or SUs need to continuously monitor the spectrum for the presence of the licensed users or PUs. If the PU is absent for a particular fourth dimension, the SU can utilize that spectrum for transmission till the PU reappears. Once the PU reappears, the SU should yield that spectrum for the PU and should shift to some other unused spectrum. This implies that the SUs should continuously monitor the entire spectrum for an opportunity to apply a aqueduct that is non being used by the PU. This technique of continuously monitoring the spectrum is called spectrum sensing. Optimizing the spectrum usage existence the main aim of CRs, makes spectrum sensing the most bones and important process for CRs. The unused spectrums may be available in ii cases either a temporal unused spectrum or a spatial unused spectrum. A temporal unused spectrum appears when a PU does not transmit during a certain amount of time period and the SUs can employ the spectrum for that time. A spatial unused spectrum appears when the PU transmits within an area and the SUs can and so apply that spectrum outside that area. The spectrum sensing performance still is affected past noise uncertainty, shadowing and multipath fading. The major spectrum sensing techniques that take been adult in the past decade are discussed in this section. The sensing techniques can be classified into two major types: Local spectrum sensing in which SU makes an contained decision regarding the presence of the PU and the cooperative spectrum sensing in which a group of SUs decide on the presence of the PU.

Before diving into the spectrum sensing techniques we introduce the hypothesis test, based on which the performances of the techniques are tested. The hypothesis model is as follows:

H0: y(t) = n(t),
H1: y(t) = h*x(t) + n(t)

Where y(t) is the received signal, x(t) is the primary user signal, due north(t) is condiment white Gaussian dissonance and h is the aqueduct gain of the primary user. The hypothesis H0 is a null hypothesis which ways that there is no primary indicate present whereas H1 indicates the presence of the principal signal. The summary of all the spectrum sensing techniques discussed in the post-obit sections is given in table 1 at the end of the department.

three.i Local Spectrum Sensing

• Energy Detection

  This is one of the nearly primal and easy to implement method for spectrum sensing. This method is highly used due to its simplicity. We practise non require any prior data regarding the PU's signal while using this method. In this method the free energy of the received point is compared to a threshold value. If the energy is more than the threshold and so we conclude that the PU is present and if the received energy is less than the threshold we conclude that the PU is absent.

  
  The determination value for free energy detector is measured by averaging N observed samples and is given by
  

  ---- (1)

  
  If the conclusion value T is greater than a threshold λ then the primary signal is present otherwise information technology is non nowadays. The performance of the energy detector is measured by two probabilities:
  P^f - probability of false alert (i.e. detection of primary signal when it is non present)
  P^d - probability of detection (i.e. detection of primary signal when information technology is really nowadays)
  These two probabilities are written as:
  

  Pf = Pr(T > λ | H0)

  ---- (ii)

  
  

  Pd = Pr(T > λ | H1)

  ---- (3)

  From the above equations nosotros can say that an platonic detector will take a high Pd and a very low Pf. From the hypothesis equations we see that the energy detector depends on the dissonance. And so when deciding the threshold nosotros demand to consider the noise. In the above hypothesis we assume the racket blazon and its construction to be white Gaussian. But by and large this is not the case because the noise keeps on varying and it is not possible to model the noise every time. So it becomes difficult for energy detector to requite good results in such cases where dissonance structure is not known, but past increasing the sensing time it might exist possible to become proper results in some cases. However if the SNR is depression and then it becomes very difficult to sense a weak chief signal even by increasing the sensing time. This SNR threshold, below which the detection becomes impossible, is called the SNR wall. However with the assistance of the PU's signal information the SNR wall tin be lessen but it cannot be eliminated. Ane other trouble with the energy detector is that it is not able to differentiate between the primary indicate or the noise or any other bespeak from some SU and this leads to a high false alarm charge per unit.

  Many techniques have been used to overcome the limitations of the energy detector and boost its performance likewise as optimizing the threshold. A technique discussed in [Nair10] is changing the threshold based on the number of input samples to optimize the faux warning charge per unit and detection probability. It also discusses using of a command parameter to vary the threshold such that information technology can adapt to the noise to give optimum output. The paper [Song12] proposes an enhanced free energy detector (EED) which performs meliorate in case of depression SNR assuming that the dissonance is additive white Gaussian noise (AWGN). The method proposes a detector in which received input signal aamplitude is operated by an arbitrary positive power instead of power of 2 as in the case of traditional energy detectors. Another arroyo proposed in [Bagwari13] describes an arrangement in which each node has multiple energy detectors organized based on the square constabulary combining receive diversity scheme.

• Feature Detection

  Usually primary signals contain some features along with the information that they behave, such as the periodicity of the statistics of the signal. These features are because of the inherent periodicity from the carrier waves, pulse trains or hopping sequences and are associated with the primary signal due to modulation rate, carrier frequency and many such functions. Such periodic features are called cyclostationary features and contain dissimilar signatures for all the signals. These features can be used to distinguish the main signal from racket (which is normally stationary). Moreover as each primary betoken will take a dissimilar signature for its cyclostationary features, they can also exist used to distinguish among dissimilar types of signals and chief signals.

  Feature detection identifies the main betoken past applying the above defined hypothesis in the frequency domain instead of the time domain. Suppose we define the auto correlation role of the transmitted signal every bit:

  
  

  Rα y = E[y (t+τ) y*(t-τ)ej2παt]

  ---- (iv)

  
  where East is the expectation operation, * is the circuitous conjugation and α is the cyclic frequency. One of the techniques used to observe the primary bespeak is cyclic spectrum density (CSD) which can exist obtained past the Fourier serial expansion of the machine correlation function mentioned higher up as:
  

  ---- (v)

  Equations 1, 2, 3, 4 and 5 reference - [Beibei11]

  The CSD function will accept some non zero values when the cyclic frequency α equals the frequency of the transmitted signal x(t). Under the hypothesis H1 the CSD function will have some not zero values as the signal contains some cyclostationary feature. So a acme detector or a likelihood ratio exam can be used to check between the two hypotheses. Moreover primary signals using different modulation, multiplexing, coding, etc tin also exist distinguished from each other comparing their CSDs. As compared to free energy detector, the feature detectors are more efficient in distinguishing primary signal from noise and tin can perform well under low SNR where the energy detector might neglect. However, if the main signal uses modulation technique which does not take cyclostationary property, this method will fail. Moreover, the complexity cost of cyclostationary feature detection (CFD) is very high because of the FFT and cyclic calculations. Apart from this nosotros also require some prior cognition of the main bespeak for this method.

  One of the about popular methods for CFD is to judge the second society statistics of the received point which suggests choosing of lags from statistical testing. An efficient method for CFD is presented in [Juei-Chin13]. This letter discusses an idea for lags prepare selection which is an improvement over the traditional 2nd gild statistics interpretation of the primary indicate and suggests an idea for efficiently choosing multiple lags for 2nd guild statistics in the low SNR. [Yingpei08] presents an algorithm for reducing the computational complication for CFD by dividing the input serial into subseries, calculating second social club statistics for each subseries and then taking the mean of all the subseries' second lodge statistics. Another method for overcoming the complication upshot of CFD, discussed in [Spooner13], uses under sampling of the signal using tunneling to yield much smaller bandwidth and then using this under samples for further detection which results in depression complexity.

• Matched Filter

  If nosotros take the information of the primary signal such as the modulation rate, coding technique, etc and then matched filter is the ideal method for detecting the presence of the principal signal and for reducing the SNR at the receiver. The matched filter correlates the received signal with the already known principal signal and thus reduces the SNR every bit well every bit provides robust detection functionality. In other words the matched filter convolves the received point with the mirror and fourth dimension shifted reference signal. One of the other advantages of matched filter is that it requires very less sensing fourth dimension to achieve a certain detection rate because information technology needs less received samples due to its a priori noesis of the primary bespeak.

  In that location are many limitations of the matched filter however. For using matched filter we must accept information of the chief signal at the receiver end which is not e'er possible. Moreover the complexity and power consumption is also an issue as we need different receivers for all types of primary signals forth with the different receiving algorithms for each receiver. If sufficient information almost the primary indicate is not available the performance of the matched filter degrades rapidly. A method called coherent detection has been proposed which can use sure patterns from the received signals to brand decisions regarding the presence of primary betoken and doesn't require exact information about the primary signal.

Table 1: Summary of spectrum sensing techniques

Sensing Method	Decision Parameter	Advantages	Disadvantages	Suggested Improvements
Energy Detector	Comparing free energy of received point to a threshold	Simple to implement, requires no prior information near the primary indicate	Cannot part in low SNR surroundings, has a loftier false alarm rate	Enhanced Energy detector, noise adaptive energy detector, adaptive threshold energy detector
Cyclostationary Feature Detector	Comparing the non zippo values obtained by CSD for the cyclostationary backdrop of the primary signal	Robust to noise, Tin can differentiate among different types of primary transmissions	Volition fail if the principal bespeak does not have cyclostationary belongings, has a high computational complexity, loftier cost, requires some prior knowledge of the principal indicate	Reducing the complication- by dividing the input into sub series, by nether sampling the signal using tunneling
Matched Filter Detector	Correlating the received signal with the already know chief signal	Requires less sensing time, optimum if the principal point is known	High complexity as requires divide receiver for every primary user, requires a priori knowledge about the primary signal	Coherent detection using fewer details regarding the master point

iii.2 Cooperative Spectrum Sensing

At that place are few limitations of the local spectrum sensing technique discussed in section [3.1]. Some of the factors that may brand local spectrum sensing ineffective are uncertainty of noise, shadowing, multi-path event and hidden primary user problem. To overcome these issues cooperative spectrum sensing (CSS) has been suggested. In CSS, multiple SUs send their local observations to a primal controller and the controller decides the available channel based on a conclusion function and informs all the secondary stations regarding the decision of availability of channels. This approach is chosen centralized cooperative sensing. In the other method, called the distributive cooperative sensing, the secondary users exchange the information among themselves without the back up of the cardinal controller. In distributed arroyo, one of the SUs can human activity equally a relay and help other users to improve sensing performance. When a SU detects presence of the primary signal it tin can utilise amplify and relay to assistance other users. However at that place are challenges with the CSS approach.

1 of the challenges is the power utilization. If the SUs are low power devices then it becomes difficult for them to sense the channel for presence of the primary indicate as it is a complex process and requires high computation as described in the previous section. Even afterward sensing the channel information technology takes a fair corporeality of power to transmit the sensing result to the central controller or to any other SU. Moreover information technology has likewise been observed that cooperating of all SUs is not optimal considering of the different location and channels for each SU. To address this low power result user selection method has been proposed. To obtain optimum detection rate, just a selected group of SUs, having higher SNR, cooperate to reach to a decision. Another technique called data fusion has likewise been proposed. In this method many different fusion rules are used to combine the decisions of the SUs at the central controller and a decision is then made based on the issue obtained by the fusion of all the individual decisions. This method has ii main sub types. In the first 1, called the soft combination method, the SUs ship their sensed or candy data to the central controller. The controller then uses different techniques such as energy detection or likelihood ratio test to brand a decision and the decision is so broadcasted to all the SUs. The problem with the soft combination is the high feedback overhead. To overcome this problem hard combination has been proposed. In this method, the SUs make their own binary decision so transport this binary decision to the central controller. At the central controller a fusion scheme such every bit OR-scheme, AND-scheme or bulk scheme to brand the conclusion. Under the OR scheme, if whatsoever of the SU detects the PU's presence the central controller decides that the PU is present. Under the AND scheme, the PU presence is declared only if all the SUs detect it and under the bulk scheme, if more than than half of the SUs detect the presence of the PU, the controller declares the presence of the PU. Many other techniques accept been proposed to reduce the overhead in centralized cooperative sensing such equally Sequential centralized cooperative sensing, Compressive sensing and Efficient Information Sharing.

four. Applications

The ability of the CRs to monitor the Radio Frequency (RF) in the environment and their ability to adapt to the changes in the environment by irresolute their configurations run time make them suitable for many useful applications. Two of such applications are briefly discussed below.

four.1 Authentication Applications

A CR tin can larn the identity of its user(s). Authentication applications can prevent unauthorized users from using the CR. Since a radio is usually used for voice communications, at that place is a microphone in the system. The captured indicate is encoded with a VoCoder and transmitted. The source radio tin cosign the user and add the known identity to the data stream. At the destination end, decoded vocalisation can be analyzed for the purposes of authentication. Recently cell phones have been equipped with digital cameras. This sensor coupled with facial recognition software may be used to authenticate a user. Other biometric sensors may exist used for hallmark and admission control.

4.two Wireless Medical Networks

CRs can also show helpful in establishing Medical Body Area Networks (MBAN). MBANs are generally used for implementing ubiquitous patient monitoring in hospitals. Ubiquitous monitoring can assistance to instantly notify the doctors regarding the vital data of patients such as claret pressure level, carbohydrate level, claret oxygen and electrocardiogram (ECG), etc. MBANs using the CR technology can aid provide such information through wireless networks and thus eliminate the use of wires and tubes for monitoring the patients. MBANs help in gathering the vital information well-nigh a patient and collectively send information technology to the doctors which enables the doctors to act instantly and thus a patient's condition can be recognized at an early stage and enables the doctors to take advisable action. Moreover the replacing of wires and tubes for monitoring the patient's status with the wireless networks and sensors reduces the adventure of infections and increases the patient's mobility.

v. Conclusion

Spectrum utilization has been a major topic of research in the past decade because of the increasing need of spectrum usage due to huge amount of increase in the wireless networks. CRs accept emerged every bit a promising applied science for the optimum utilization of the available spectrum. CRs enable a SU (unlicensed user) to apply a licensed spectrum whenever it is idle and a PU (licensed user) is not using the spectrum. For doing and then the CRs need to adapt to operating parameters of the environment while shifting form one band to the other by tuning the frequency to the unused bands. For efficiently using the spectrum the SUs demand to continuously monitor the spectrum to sense the presence or absence of PU. This technique of monitoring the spectrum, called spectrum sensing, thus forms the base of a CR system.

In this paper, we discuss the CR characteristics, CR functions and CR architectures. We likewise discuss 2 main spectrum sensing techniques namely, local spectrum sensing - each SU makes an independent determination; and cooperative spectrum sensing - a grouping of SUs make a collective decision. Main types of local spectrum sensing techniques such as energy detector, cyclostationary feature detector and matched filter detector are discussed in item forth with their advantages, disadvantages and a few methods to improve each of their performances. The centralized and distributed cooperative sensing techniques have also been discussed briefly. Two of the few interesting applications of the CRs - Hallmark Application and Wireless Medical networks - are also discussed briefly.

vi. References

1. [Beibei11] Beibei Wang; Liu, K.J.R., Advances in cognitive radio networks: A survey, Selected Topics in Signal Processing, IEEE Periodical of (Volume:5 , Issue: i ), Feb. 2011, Pages 5 - 23, http://ieeexplore.ieee.org/document/5639025/
2. [Axell12] Axell, E. ; Leus, G. ; Larsson, E.Grand. ; Poor, H.5., Spectrum Sensing for Cerebral Radio : State-of-the-Fine art and Contempo Advances, Signal Processing Mag, IEEE (Volume:29 , Issue: 3 ), May 2012, Pages 101 - 106, http://ieeexplore.ieee.org/certificate/6179814/
3. [Xiangwei12] Xiangwei Zhou; Lu Lu*; Uzoma Onunkwo; Geoffrey Ye Li, X years of inquiry in spectrum sensing and sharing in cerebral radio, Lu et al. EURASIP Journal on Wireless Communications and Networking 2012, 2012:28, http://jwcn.eurasipjournals.com/content/2012/1/28
4. [stanford] Spectrum sensing in Cognitive Radios, http://www.stanford.edu/~nayaks/reportsFolder/cognitiveRadio.pdf
5. [Nair10] Nair, P.R.; Vinod, A.P. ; Krishna, A.K., An Adaptive Threshold Based Energy Detector for Spectrum Sensing in Cognitive Radios at Low SNR, Communication Systems (ICCS), 2010 IEEE International Conference, 17-nineteen Nov. 2010, Pages 574 - 578, http://ieeexplore.ieee.org/document/5686712/
6. [Ben12] Ben Jemaa, A.; Turki, M. ; Guibene, Due west., Enhanced energy detector via algebraic approach for spectrum sensing in cerebral radio networks, Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM), 2012 7th International ICST Conference, eighteen-20 June 2012 , Pages 113 - 117, http://ieeexplore.ieee.org/document/6333724/
7. [Sadhukhan13] Sadhukhan, P.; Kumar, N. ; Bhatnagar, M.R., Improved energy detector based spectrum sensing for cognitive radio: An experimental report, India Conference (INDICON), 2013 Annual IEEE, thirteen-15 Dec. 2013, Pages 1 - 5, http://ieeexplore.ieee.org/document/6725925/
8. [Bagwari13] Bagwari, A.; Tomar, Thousand.Due south., Multiple Energy Detectors Based Spectrum Sensing for Cognitive Radio Networks, Communication Systems and Network Technologies (CSNT), 2013 International Briefing, half-dozen-8 April 2013, Pages 303 - 308, http://ieeexplore.ieee.org/document/6524408/
9. [Song12] Song, J.; Feng, Z. ; Zhang, P. ; Liu, Z, Spectrum sensing in cognitive radios based on enhanced energy detector, Communications, IET (Volume:6 , Outcome: 8 ), May 22 2012, Pages 805 - 809, http://ieeexplore.ieee.org/document/6231123/
10. [Hwang09] Hwang, S.-H.; Baek, J.-H. ; Dobre, O.A., Spectrum sensing using multiple antenna-aided energy detectors for cognitive radio, Electrical and Computer Engineering, 2009. CCECE '09. Canadian Conference, 3-six May 2009, Pages 209 - 212, http://ieeexplore.ieee.org/document/5090122/
11. [Juei-Chin13] Juei-Chin Shen; Alsusa, E., An Efficient Multiple Lags Selection Method for Cyclostationary Feature Based Spectrum-Sensing, Signal Processing Letters, IEEE (Volume:20 , Upshot: ii ), Feb 2013, Pages 113 - 136, http://ieeexplore.ieee.org/document/6378397/
12. [Yingpei08] Yingpei Lin; Chen He, Subsection-average cyclostationary feature detection in cognitive radio, Neural Networks and Signal Processing, 2008 International Conference, 7-11 June 2008, Pages 604 - 608, http://ieeexplore.ieee.org/document/4590421/
13. [Spooner13] Spooner, C.M. ; Mody, A.N. ; Chuang, J. ; Anthony, Chiliad.P., Tunnelized Cyclostationary Bespeak Processing: A Novel Arroyo to Depression-Free energy Spectrum Sensing, Military Communications Briefing, MILCOM 2013 - 2013 IEEE, 18-20 Nov. 2013, Pages 811 - 816, http://ieeexplore.ieee.org/document/6735724/
14. [Hussain09] Hussain, Due south.; Fernando, X., Spectrum sensing in cognitive radio networks: Upwardly-to-date techniques and futurity challenges, Science and Technology for Humanity (TIC-STH), 2009 IEEE Toronto International Conference, 26-27 Sept. 2009, Pages 736 - 741, http://ieeexplore.ieee.org/document/5444402/
15. [CompSS] Comparison of Spectrum Sensing Techniques in Cognitive Radio Networks, http://www.researchgate.net/publication/236846573_Comparison_of_spectrum_sensing_techniques_used_in_Cognitive_radio
16. [cmu] Futurity Directions in Cognitive Radio Network Research, https://www.cs.cmu.edu/~prs/NSF_CRN_Report_Final.pdf

7. Acronyms

• FCC - Federal Communications Commission
• CR - Cerebral Radio
• PU - Chief User
• SU - Secondary User
• DSA - Dynamic Spectrum Access
• MAC - Media Access Command
• SNR - Signal to Noise Ratio
• CSD - Cyclic Spectrum Density
• CFD - Cyclostationary Characteristic Detection
• FFT - Fast Fourier Transform
• CSS - Cooperative Spectrum Sensing

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