• Nenhum resultado encontrado

Analytical Model Analysis Of Distributed Cooperative Spectrum Sensing Method

N/A
N/A
Protected

Academic year: 2017

Share "Analytical Model Analysis Of Distributed Cooperative Spectrum Sensing Method"

Copied!
4
0
0

Texto

(1)

International Journal of Electronics Communication and Computer Technology (IJECCT) Volume 2 Issue 3 (May 2012)

ISSN:2249-7838 IJ ECCT | www.ijecct.org 84

Analytical Model Analysis Of Distributed

Cooperative Spectrum Sensing Method

Ravi Prakash Shukla

PG Student, SHIATS,

Allahabad, India Ravishukla47@gmail.com

Sandeep Singh

PG Student, SHIATS,

Allahabad, India Sandeep.singhiec@gmail.com

Abstract— Spectrum sensing is a key function of cognitive radio to prevent the harmful interference with licensed users and identify the available spectrum for improving the spectrum’s utilization. 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. However, detection performance in practice is often compromised with multipath fading, shadowing and receiver uncertainty issues. To mitigate the impact of these issues, cooperative spectrum sensing has been shown to be an effective method to improve the detection performance by exploiting spatial diversity.

Keywords- cognitive radio, spectrum sensing, cooperative, distributed spectrum sensing

I. INTRODUCTION

In this paper, we focus attention on the particular task on which the very essence of cognitive radio rests: spectrum sensing, defined as the task of finding spectrum holes by sensing the radio spectrum in the local neighborhood of the cognitive radio receiver in an unsupervised manner. The term B-spectrum holes[ stands for those sub bands of the radio spectrum that are underutilized (in part or in full) at a particular instant of time and specific geographic location. To be specific, the task of spectrum sensing involves the following subtasks:

A. Detection of spectrum holes

B. Spectral resolution of each spectrum hole;

C. Estimation of the spatial directions of incoming interferes; D. Signal classification.

The subtask of spectrum-hole detection is, at its simplest form, when the focus is on a white space (i.e., a sub band that is only occupied by white noise). Specifically, the detection of a white space may be performed by using a radiometer, which is well known for its energy-detection capability [4], [5]. Alternatively, we may resort to the use of cyclostationarity, which is an inherent property of digital modulated signals that naturally occur in the transmission of communication signals

over a wireless channel [6], [7]. In both of these two approaches to spectrum sensing, the detection of a spectrum hole boils down to a binary hypothesis-testing problem. Specifically, hypothesis H1 refers to the presence of a primary user‟s signal (i.e., the sub band under test is occupied) and hypothesis H0 refers to the presence of ambient noise (i.e., the sub band is a white space). The cyclostationarity approach to detection has an advantage over the energy-detection approach in that it is also capable of signal classification and has the ability to distinguish co channel interference. The use of both of these approaches is confined to white spaces only, which limits the scope of their spectrum-sensing capabilities. In order to further refine the detection of white spaces and broaden the scope of spectrum sensing so as to also include the possible employment of gray spaces (i.e., sub bands of the spectrum that contain noise as well as interfering signals), we may have to resort to a sensing technique that includes spectrum estimation.

II. SPECTRUM SENSING:

In terms of occupancy, sub bands of the radio spectrum may be categorized as follows.

1) White spaces, which are free of RF interferers, except for noise due to natural and/or artificial sources.

2) Gray spaces, which are partially occupied by interferers as well as noise.

3) Black spaces, the contents of which are completely full due to the combined presence of communication and (possibly) interfering signals plus noise.

(2)

International Journal of Electronics Communication and Computer Technology (IJECCT) Volume 2 Issue 3 (May 2012)

ISSN:2249-7838 IJ ECCT | www.ijecct.org 85 must be taken into account [13]. Consequently, the spectrum

holes found in cellular bands may also be gray spaces.

A cognitive radio is designed to be aware of and sensitive to the changes in its surrounding. The spectrum sensing function enables the cognitive radio to adapt to its environment by detecting spectrum holes [18]. The most efficient way to detect spectrum holes is to detect the primary users that are receiving data within the communication range of an xG user. In reality, however, it is difficult for a cognitive radio to have a direct measurement of a channel between a primary receiver and a transmitter. Thus, the most recent work focuses on primary transmitter detection based on local observations of xG users.

Figure 1: Classification of spectrum sensing techniques

III. COOPERATIVE SPECTRUM SENSING

This section relates to spectrum sensing control, awareness networking and cooperative spectrum sensing in Figure 1. 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., [20] 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 Signaling. Within E3 (End-to-End Efficiency project) an awareness signaling 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 signaling 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-sub-bands. The frequency sub-band each node is sensing in each time instant is determined by a pseudorandom time-frequency code.

The hidden node problem can be alleviated if at least two nodes measure the same part of spectrum at the same time; subsets of collaborating nodes for each spectrum sub-band are formed and always changed after a certain period of time. The effects of propagation, such as fading and shadowing, are effectively mitigated through diversity because the channels the signals experience can be assumed to be uncorrelated since the secondary users are displaced from each other.

IV. SYSTEM MODEL

In this model, cognitive radio CRs operate in distributed cooperative manner; that divide CRs population into groups, each of which select the node with the best reporting channel gain as a fusion node. The CRs conduct local sensing based on maintained energy detection and forward their binary detection decision to fusion node where the processing and fusion of local spectrum observation for candidate nodes is made, the modeling flow is shown in Fig. 2. The flow chart as shown in Fig. 3 illustrate the formation of DCS network architecture and the selection of fusion nodes based on the reporting channel SNR to fusion centre.

(3)

International Journal of Electronics Communication and Computer Technology (IJECCT) Volume 2 Issue 3 (May 2012)

ISSN:2249-7838 IJ ECCT | www.ijecct.org 86 Figure 2: Modelling diagram

Figure 3: Network formation and fusion node selection

Figure 4: Schematic representation of distributed cooperation for spectrum sensing

V. DETECTION MODEL

The basic problem concerning spectrum sensing is the detection of a signal within a noisy measure. We assume that prior knowledge of the primary user signal is not known. Therefore, optimal detector based on matched filter is not an option since it would require the knowledge of the data for coherent processing. Instead a suboptimal energy detector is adopted, which can be applied to any signal type. We assume the noise is additive white Gaussian with zero mean and power spectral density. We consider a low-mobility environment, so we assume that during the course of the transmission, or for each sensing period, each user observes only one fading level towards the fusion node/fusion centre. Due to the spatial separation between users, the channels corresponding to different cognitive users are assumed to be independent. All channels are assumed to experience Rayleigh fading. Therefore, the received signal at the secondary receiver has the following simple form,

s[n] = hx[n] + w[n] (1)

Where x[n] is the signal to be detected, w[n] is the additive white Gaussian noise (AWGN), h is the channel fading coefficient, and n is the sample index. Note that x[n] = 0 when there is no transmission by a primary user. The received signal at cognitive radio has one of the following hypotheses, busy channel, H1, which indicate primary user present and White space/Spectrum hole/Idle channel, H0 , that indicate primary

user absent

H1 : s[n] = hx[n] + w[n]

H0 : s[n] = w[n] (2)

The energy detection based sensing metric can be obtained as [10],

(4)

International Journal of Electronics Communication and Computer Technology (IJECCT) Volume 2 Issue 3 (May 2012)

ISSN:2249-7838 IJ ECCT | www.ijecct.org 87 VI. ANALYSIS AND CONSIDERATION:

(i) By help of this model we investigate of ROC (receiver operating characteristics), only for group members, for the 16 user groups (=16 j G), with different SNR (5, 10 and 15) for the reporting/control channel from the fusion node to fusion centre is done. Average sensing SNR is 10 and detection threshold is set with maintained probability of false alarm.

(ii) By the impact of number of nodes within the group. We see that better performance is achieved while considering the 2 user group compared to the group with large number of cognitive radios (16 nodes). Direct reporting is plotted for each group, it can be confirmed that increasing the number of cooperative users exponentially can obtain gain in detection probability and it is clear that DCS outperforms CS corresponding to the case.

(iii) Also by this model the reporting Bit Error Rate (BER) for the proposed method calculated for different number of sensing group. The analytical result is given for 3 sensing group with 2, 4, 8 users. As more number of group incorporated in sensing the probability of error reduced, the results shown that the probability of error in the reporting stage for the same SNR is decreased when number of groups increase. This indicates that the selection of the reporting channel by the mean of fusion node (selection diversity) in the group sensing is achieved.

The number of users in these groups was varied and the effect on radio sensitivity for a 90% and 95%, probability of detection was observed.

(iv) This method creates a loss in the probability of false alarm due to the large „no decision region‟, additionally this scheme may eliminate a user with real detection information from reporting the decision which increase probability of interference to primary system.

VII. CONCLUSION

The main focus of this paper is to examine the effects of distributed decision fusion based on maintained probability of false alarm and best reporting channel selection on the cooperative spectrum sensing employed by cognitive radios. We adopted a dynamic distributed architecture for cooperative sensing based on the link quality and found condition on the channel quality for cooperation to be beneficial. Using probability of detection, and BER metrics we evaluated the performance improvement of distributed cooperation over direct cooperation and non cooperative sensing. We used analytical formulation with possible candidate selection criteria to investigate and maximize the cooperation gain. By employing such distribution and selection technique, the reporting error due to the fading channel is reduced. Results show that the method effectively improve performance of sensing, it increase the probability of detection up to 0.9 at <0.1 probability of false alarm. Sensitivity requirement is reduced with network scale and the number of nodes participate in decision fusion is reduced about 42% at probability of false alarm 0.1.

REFERENCES

[1] Federal Communications Commission, “ Spectrum Policy Task Force ,” Rep. ET Docket no. 02-135, Nov. 2002

[2] F. Digham, M. Alouini, and M. Simon, “On the energy detection of unknown signals over fading channels," in Proc. IEEE Int. Conf. Commun., vol. 5, May 2003, Seattle, Washington, pp. 3575-3579. [3] Y. Chen, Q. Zhao, S. Ananthram, Distributed Spectrum Sensing and

Access in Cognitive Radio Networks with Energy Constraint, IEEE transactions on signal processing, vol. 57, no.2, 2009, pp. 783-797. [4] R. Tandra, S. M. Mishra, and A. Sahai, BWhat is a spectrum hole and

what does it take to recognize one?[ Proc. IEEE, vol. 97, Mar. 2009. [5] S. Shellhammer, S. Shankar, R. Tandra, and J. Tomcik, BPerformance

of power detector sensors of dtv signals in ieee 802.22 WRANs,[ in Proc. 1st ACM Int. Workshop Technol. Policy Access. Spectrum (TAPAS), Aug. 2006.[Online]. Available: http://doi.acm.org/

[6] H. Chen and W. Gao, BText on cyclostationary feature detectorVInformation annex on sensing techniques,[ in IEEE 802.22 Meeting Doc., Jul. 2007. [Online]. Available: https://mentor.ieee.org/802.22/file/07/22-07-0283-00-000-text-on cyclostationaryfeature- detector-thomson.doc

[7] R. Tandra and A. Sahai, BSNR walls for signal detection,[ IEEE J. Sel. Topics Signal Process., vol. 2, pp. 4–17, Feb. 2008.

[8] G. Ganesan and Y. (G.) Li, “Cooperative Spectrum Sensing in Cognitive Radio - Part I: Two user networks,” IEEE Trans. on Wireless Commun, VOL. 6, NO. 6, June 2007.

[9] G. Ganesan and Y. (G.) Li, “Cooperative Spectrum Sensing in Cognitive Radio - Part II: Multi-users networks,” IEEE Trans. on Wireless Commun, VOL. 6, NO. 6, June 2007.

[10] I. S. Gradshteyn and I. M. Ryzhik, Table of Integrals, Series, and Products, 5th ed. Academic Press, 1994.

[11] T. S. Rappaport, Wireless Communications: Principles and Practice, Prentice-Hall, New Jersey, 2002.

[12] M. M. Buddhiko, BUnderstanding dynamic spectrum access: Models, taxonomy and challenges,[ in Proc. IEEE DySPAN 2007, Apr. 2007. [13] A. P. Subramanian, H. Gupta, S. R. Das, and M. M. Buddhiko, BFast

spectrum allocation in coordinated dynamic spectrum access based cellular networks,[ in Proc.IEEE DySPAN 2007, Apr. 2007.

[14] Xing CHEN Zhi-song BIEa and Wei-ling WU, “Detection efficiency of

cooperative spectrum sensing in cognitive radio network,” Elsevier

Journal of China Universities of Posts and Telecommunications, Volume 15, Issue 3, September 2008, pp. 1-7.

[15] C. Sun, W. Zhang, and K. Letaief , “Cluster-Based Cooperative

Spectrum Sensing in Cognitive Radio Systems,” proceedings of IEEE

ICC, 2007, pp. 2511-2515.

[16] O. Simeone, J. Gambini, Y. Bar-Ness, U. Spagnolini, Cooperation and cognitive radio, in proc. of IEEE International Conference on

Communications (ICC ‟07), June 2007, pp 6511–6515.

[17] A. H. Nuttall, “Some integrals involving the QM function,” IEEE Transactions on Information Theory, vol. 21, no. 1, January 1975, pp. 95–96.

[18] D. J. Thomson and A. D. Chave, BJackknifed error estimates for spectra, coherences, and transfer functions,[ in Advances in

[19] Spectrum Analysis and Array Processing, vol. 1, S. Haykin, Ed. Englewood Cliffs, NJ: Prentice-Hall, 1991, pp. 58–113.

[20] C. Sun, W. Zhang, and K. Letaief, “Cooperative Spectrum Sensing for Cognitive Radios under Bandwidth Constraints,” proceeding of IEEE Wireless Communications and Networking Conference (WCNC 2007), 2007, pp.1 – 5.

Referências

Documentos relacionados

social assistance. The protection of jobs within some enterprises, cooperatives, forms of economical associations, constitute an efficient social policy, totally different from

The probability of attending school four our group of interest in this region increased by 6.5 percentage points after the expansion of the Bolsa Família program in 2007 and

O relatório sobre um instrumento operacional de gestão de pessoas é um trabalho que consiste na análise crítica de um instrumento operacional de gestão de pessoas ou na concepção

[r]

Nesse cenário, profissionais de saúde devem atuar como educadores para modificar conceitos errôneos difundidos entre a população, que deve ser estimulada a participar de

Data obtained are thus only partially consistent with those reported by Quesada-Cabrera et al., who stated that K7000 had a “crystallite” size of around 15 nm, obtained using

This dissertation proposes a model for the development of participatory sensing applications, using a distributed infrastructure based on personal computing resources, providing a

Na hepatite B, as enzimas hepáticas têm valores menores tanto para quem toma quanto para os que não tomam café comparados ao vírus C, porém os dados foram estatisticamente