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Detection of Primary Radio Signals in Cognitive Radio Environment

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Detection of Primary Radio Signals in Cognitive Radio Environment Powered By Docstoc
					              Detection of Primary Radio Signals in Cognitive Radio
                                  Environment
                                                 Jide Julius Popoola and Rex Van Olst
                                         Centre for Telecommunications Access and Services,
                                          School of Electrical and Information Engineering,
                                             University of Witwatersrand, Johannesburg,
                                                              South Africa.
                                               Email: Jide.Popoola@students.wits.ac.za



Abstract - This paper presents a work in progress on                promote sustainable and efficient information transfer coupled
adaptive cognitive radio system. The paper discusses an             with availability and efficient spectrum usage, recent efforts
approach towards the sensing and detection of primary               have centred on flexibility in spectrum assignment polices and
radio signals using modulation identification technique.            optimal spectrum usage.
Cognitive radio is visualized and realized as an intelligent
software package simply referred to as a cognitive engine.          One of the ways of achieving these laudable goals is by the
In carrying out this work, cognitive radio engine will be           opportunistic utilization of licensed spectrum by unlicensed
developed using universal software radio peripheral                 users. In this new policy, the unlicensed or secondary user can
(USRP), GNU radio and artificial neural network (ANN).              opportunistically operate in idle licensed bands as long as it
The USRP and GNU radio will be used to develop the                  does not interfere with the licensed or primary user. This
software defined radio (SDR) that will be coupled with              could be achieved by a radio that can make autonomous and
ANN for the development of the desired cognitive radio              rapid decisions about how it accesses spectrum. Cognitive
engine that can sense the presence of primary radio signal          radios have the potential to do this [1,4].
through its ability to classify different modulation
techniques.                                                         Cognitive radio has been defined in various ways [1,4,5]. In
                                                                    relation to this study, bearing in mind that its application is to
                                                                    improve the utilization of the radio spectrum, it will therefore
Keywords – Cognitive engine, cognitive radio, modulation            be defined according to [4], as “an intelligent wireless
techniques, spectrum sensing.                                       communication system that is aware of its surrounding
                                                                    environment, and uses the methodology of understanding-by-
                                                                    building to learn from the environment and adapt its internal
                  I. INTRODUCTION                                   states to statistical variations in the incoming RF stimuli by
As society becomes increasingly mobile and more dependent           making corresponding changes in certain operating
on information technologies, there has been a dramatic              parameters (e.g., transmit-power, carrier-frequency, and
increase in the overall demand for information and                  modulation strategy) in real-time, with two primary objectives
communication technology (ICT) services requiring the use of        in mind:
the radio spectrum. This is true for both services in the           • highly reliable communications whenever and wherever
licensed and unlicensed frequency bands. The increased                   needed
demand is propelled by a host of factors such as the use of         • efficient utilization of the radio spectrum
ICT in socio-economic activities; the increased mobility of
the workforce, and consumers embracing the convenience and          As a new technology for overcoming the apparent spectrum
cost efficiency of the multitude of wireless devices available      scarcity problem [6-8], the fundamental requirement of
today.                                                              cognitive radio is to ensure no or little interference to the
                                                                    licensed or primary spectrum owner [3,4,9]. Spectrum sensing
With this increase in wireless services and devices, it has been    has been identified as a key enabling functionality to ensure
observed that the radio spectrum that can be assigned for their     that cognitive radio would not interfere with the primary users
usage is limited. This scarcity of the radio spectrum has been      [3,4,10,11]. There are numbers of spectrum sensing and
traced to its under-utilization as a result of the current fixed    detection techniques proposed in the literature. With these
spectrum allocated policies [1]. Recent measurements have           various sensing and detection methods in place, the
proven that most of the radio frequency spectrums allocated to      fundamental problem is still how to detect the presence of a
licensed users is vastly under-utilized [2,3]. Hence, in order to   weak primary user’s signal [11]. The solution to this problem
is the motivation behind this research. This is because being     [5] J. Mitola, “Cognitive radio: An integrated Agent
able to reliably detect all forms of primary radio signals        Architecture for Software Defined Radio” PhD Dissertation,
presence (weak or strong; pre-known or not) in the                KTH Royal Institute Technology, Stockholm, Sweden, May
environment will enhance the general acceptability of             2000.
cognitive radio technology.                                        [6] C.J. Rieser “Biologically Inspired Cognitive Radio
                                                                  Engine Model utilizing distributed Genetic Algorithms for
                                                                  Secured and Robust Wireless Communications and
                   II. OUR APPROACH                               Networking” PhD Dissertation, Virgina Polytechnic Institute
In carrying out this research, modulation identification          and State University, April 2004.
techniques will be used. This approach was adopted with the        [7] S.M. Mishra, D. Cabric and C. Chang, “A real time
view that it will enable the cognitive engine developed to        cognitive radio testbed for physical and link layer
sense and detect all forms of primary radio signals (weak or      experiments”, in Proc. of IEEE International Symposium on
strong; pre-known or not) in the cognitive environment. This      New Frontiers in Dynamic Spectrum Access, 2005, pp. 562 –
is because it is believed that every device transmitting in the   567.
radio environment will be using one form of modulation            [8] T.R. Newman, R. Rajbanshi, A.M. Wyglinski, J.B. Evans
technique or another. These modulation techniques can use         and G.J. Minden, “Population Adaptation for Genetic
either analog modulation (e.g. amplitude modulation (AM),         Algorithm-based Cognitive Radios” Mobile Networks and
single-sideband modulation (SSB), etc) or digital modulation      Applications, Vol.13 No. 5, October 2008, pp. 442 – 451.
(e.g. amplitude shift keying (ASK), Quadrature Amplitude          [9] D. Cabric, A. Tkachenko and R.W. Brodersen, “Spectrum
modulation (QAM), etc). The execution of the research work        Sensing Measurements of Pilot, Energy, and Collaborative
will be in two phases. The first phase will involve simulation    Detection” in Proc. of Military Communications Conference
of the overall system using MATLAB while the second phase         (MILCOM), October 2006, pp. 1 – 7.
will be an experimental development of the cognitive radio        [10] M. Gandetto and C. Regazzoni, “Spectrum Sensing: A
engine using a software defined radio coupled with ANN.           Distributed Approach for Cognitive Terminals” IEEE Journal
The diagrammatic representation of the radio environment          on Selected Areas in Communications, Vol. 25, No. 3, April
model for the study is as shown in Fig. 1.                        2007, pp. 546 – 557.
                                                                  [11] E.G. Larson and G. Regnoli, “Primary System Detection
                  III. CONCLUSION                                 for Cognitive Radio: Does Small Scale Fading Help?” IEEE
Since this work will introduce another approach to primary        Communication Letters, Vol. 11, No. 10, October 2007, pp.
radio signal sensing and detection in cognitive radio             799 – 801.
environment, it is expected that the result of this work will
                                                                                       Licensed Spectrum
significantly enhance the detection of all forms of primary
radio signal in cognitive radio environment. Likewise, it is
expected that the result of this work will indeed reduce the
probability of interference in cognitive radio environment.
This is because once any modulation type is identified on a
channel; the secondary or cognitive device will immediately                                                      10 Rx
                                                                           0
stop transmission or not transmitting on such channel as the              1 Tx
case may be. Hence, the probability of interference in the
cognitive environment is expected to be low if not completely
eliminated.

                     REFERENCES
[1] I.F. Akyildiz, W.Y. Lee, M.C. Vuran and S. Mohanty,
“NeXt generation/dynamic spectrum access/cognitive radio                   20 TRx                                20 TRx
wireless networks: A survey,” International J. of Computer          KEYS:
and Telecom. Networking, Vol. 50, No. 13, pp. 2127 – 2159,          10 Tx - Primary Transmitter, 10 Rx – Primary Receiver
September 2006.                                                     20 TRx – Secondary Transceiver.
[2] M.A. McHenry, “National Science Foundation (NSF)
Spectrum Occupancy Measurements Project Summary,”                      Fig.1. The Cognitive Radio Environment Model
Shared Spectrum Company Report, August, 2005. Online
[Available]: http://www.sharedspectrum.com.
[3] Federal Communications Commission (FCC), “Spectrum            Jide     Julius     Popoola        obtained     his    B.Eng.[Hons]      and
                                                                  M.Eng.[Communication.] degrees from Department of Electrical and
Policy Task Force,” REPORT ET Docket No. 02 – 135,                Electronic Engineering, Federal University of Technology Akure (FUTA),
November             2002.        Online        [Available]:      Nigeria in 1999 and 2003 respectively. He is currently pursing his PhD
http://www.pulse.tiaonline.org/uploads/SPTFreport.pdf             degree at the School of Electrical and Information Engineering, University of
                                                                  the Witwatersrand, Johannesburg under the supervision of Prof. Rex Van Olst.
[4] S. Haykin, “Cognitive Radio: Brain-Empowered Wireless
Communications,” IEEE Journal on Select. Areas in                 Rex van Olst is an Associate Professor and Head of Telecommunication
Commun., Vol. 23, No. 2, pp. 201 – 220, February 2005.            Engineering research at the School of Electrical and Information Engineering,
                                                                  University of the Witwatersrand, Johannesburg.

				
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posted:3/29/2011
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