Research Challenges of Cognitive Radio

Research Challenges of Cognitive Radio

Ashfaque Ahmed Khan

Islamic university of Technology

S.M. Imrat Rahman

Green University of Bangladesh

Mohiuddin Ahmed

Green University of Bangladesh

Abstract

A cognitive radio is an adaptive, multi- dimensionally aware, autonomous radio system that learns from its experiences to reason, plan, and decide future actions to meet user needs. Due to the technological advancement in last few decades, we have a magnificent communication setup now-a-days. Cognitive radio is considered to be one of the k ey issues to give such break -through in communication process. In this paper, we are providing a simple yet efficient overview on cognitive radio and the existing research challenges, which will help the researchers around the globe to grab the concept of cognitive radio fast enough and work on it.

1. Introduction

   It has been observed that, in some locations or at some time period of the day, 70 percent of the allocated spectrum may be sitting idle. FCC [1] has recommended that significantly greater efficiency could be realized be developing wire less devices that can coexist with the prima ry users, generating minimal interfe rence while taking advantage of the available resources. A novel class of radio, that is able to reliably sense the spectral environment over a wide bandwidth, detects the presence/absence of prima ry users and use the spectrum only if the communicat ion does not interfere with prima ry users is defined by the term cognitive radio. Cognitive rad ios integrate radio technology and

   networking technology to provide efficient use of radio spectrum. Cognitive rad io network is a comple x mu ltiuser wireless communicat ion system capable of eme rgent behavior [2]. Cognitive rad io wireless network is considered as an advanced technology integration environment with focus on building adaptive, spectrum-effic ient systems with eme rging programmable rad io. The idea of cognitive radio e xtends the concepts of a hardware radio and a software defined radio fro m a simp le, single function device to a radio that senses and reacts to its operating environment. The ma in feature of cognitive radio is their ability to recognize their co mmunicat ion environment and independently adapt the parameters of their communicat ion scheme to ma ximize the quality of service for the secondary users [3].

   In cognitive radio cycle a cognitive radio monitors spectrum bands, captures their informat ion and then detects the spectrum spaces. The characteristics of the spectrum spaces that are detected through spectrum sensing are estimated. Then, the appropriate spectrum band is chosen according to the characteristics and user require ments. Once the operating spectrum band is determined, the communicat ion can be performed over this spectrum band.

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   Figure 1: Cognitive Radio Cycle

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2. Cognitive Radio Functions & Primary Objectives

   Cognitive rad io e mbodies the follo wing functions:

   1. It perceives the radio environment by empowe ring each users receiver to sense the environment on a continuous time basis

   2. It lea rns fro m the environ ment and adapts the performance of each transceiver to statistical variat ions in the incoming RFstimu li.

   3. Facilitates communication between mu ltip le users through cooperating in a self-organized manner.

   4. To control the communicat ion process among co mpeting users through the proper allocation of ava ilab le resources.

   5. To create the experience of intention and self-awa reness.

      Primary object ives of cognitive radio networks:

      1. Facilitate effic ient utilizat ion of the radio spectrum in a fair- minded way.

      2. To provide highly reliable co mmun ication for a ll users of the network.

3. General Operations

   There are three basic areas of radio operation where cognitive radio can ma ke an immediate impact are human-machine interface, rad io-centric operations and network-centric operations.

   • In HMI a rea, cognitive radio technology can provide a level of automation that can simp lify the user interface to a comp le x device.

   • For radio-centric operations, the adaptive RF signal-in-space formation and adaptive modulation provide adaptation capabilities under cognitive control that could improve system performance based on observed conditions.

   • Network-centric applications of cognitive radio could include the autonomous selection of network me mbe rship (e.g., 3G/Wi-Fi hotspot/WiMax) where the cognitive device anticipates the need to hand-off based on prior e xperience rather than simp ly by following predefined algorith ms based solely on signal level. That implies, the device recognizes that it regularly traverses the same path and over time, lea rns when it is going to enter a bad spot and reasons to hand off to a diffe rent system before the outage occurs.

4. Research Challenges

   In this section, there will be an e xtensive discussion on the research challenges of cognitive radio. To simp lify this concept, we point out the core challenges of cognitive radio.

   1. Spectrum Sensing

      Spectrum sensing has been identified as a key enabling cognitive radio to not interfere with prima ry users, by reliability detecting primary user signals. So, sensing require ments are based on prima ry user modulation type, power, frequency and temporal parameters. It is often considered as a detection problem. Many techniques were developed in order to detect the holes in the spectrum band. Focusing on each narrow band, e xisting spectrum sensing techniques are wide ly categorized into energy detection[4] and feature detection[5]. However, the performance of the energy detector is susceptible to unknown or changing noise levels and interference. In addition, the energy detector does not differentiate between modulated signals, noise and interference but can only determine the presence of the signal. It does not work if the signal is direct-sequence or frequency hopping signal, or any time varying signal. On the other hand cyclostationary models have been shown in recent years to offer many advantages over stationary models. Thus, cyclostationary feature detection performs better than energy detector. But, it is computationally comple x and requires significantly long observation time[6]. The biggest challenge regarding sensing is in developing sensing techniques which hare able to detect every weak prima ry signal while being suffic iently fast and low cost to imple ment.

   2. Advance Spectrum Management

      Cognitive radios have a great potential to improve spectrum utilizat ion by enabling users to access the spectrum dynamically without disturbing licensed prima ry radios. A key challenge in operating these

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      radios as a network is how to imple ment an efficient mediu m access control mechanism that can adaptively and effic iently allocate transmission powers and spectrum among Cognitive rad ios according to the surrounding environment. Most e xisting works address this issue via suboptimal heuristic approaches or centralized solutions [7].

   3. Unlicensed Spe ctrum Usage

      It is this discrepancy between FCC a llocations and actual usage, which indicates that a new approach to spectrum licensing is needed [8].What is clearly needed is an approach, which provides the incentives and efficiency of unlcensed usage to other spectral bands, while accommodating the present users who have higher priority (prima ry users) and enabling future systems a more fle xible spectrum access.

   4. Spectrum sharing strate gies

      Spectrum sharing is allocation of an unprecedented amount of spectrum that could be used for unlicensed or shared services. Opportunistic communicat ion with interfe rence avoidance faces a mu ltitude of challenges in the detection of sharing in mu lti-user cognitive radio systems. Because of the presence of user priority (primary and secondary), they pose unique design challenges that are not faced in conventional wireless systems. A ma jor issue in a multip le secondary user environment is sharing, a topic that has generated a lot of research interest in the recent past [9] [10]

   5. Hidden node and sharing issues

      Cognitive radio sensitivity should outperform prima ry user receivers by a large margin in order to prevent what is essentially a hidden node problem of Secondary User Prima ry User Unused band cognitive radios to ensure cognitive radios do not interfere with each other [11].

   6. Trusted access and security

      With increased focus over the past few years on system security and survivability, it is important to note that distributed intelligent systems, such as cognitive radio, offer benefit in the event of attacks. Intelligence and military application require application-specific secure wire less systems [12] [13].

   7. Cross-layer design

      The fle xib ility of cognitive radios has significant implications for the design cross layer algorithms which adapt to changes in physical link quality, radio interference, rad io node density, network topology or traffic de mand may be e xpected to require an advanced control and management fra me work with support for cross -layer information [14][15]. Spectru m handoff and mobility manage ment will face some new challenges which are required to do a cross -layer design, especially when required providing the necessary capabilities in terms of quality of service at the same t ime.

   8. Hardware and So ftware archite cture

   The potential for Cognitive radio is a novel efficient methodology, extension of software- defined radio, to transmit and rece ive information over various wireless communicat ion devices [16]. According to the existing operators in the environment, Cognitive rad io chooses the best available option based on performance for each application. The diffe rent performance measuring parameters include frequency, power, antenna, transmitter bandwidth, modulation and coding schemes etc. This means that the radio has to deal with different rad io frequencies spectrum and baseband varieties at the same time, thus requiring a mo re robust, effic ient and reconfigurable hardware and software architecture.

5. Conclusion

   Cognitive radio is already being considered as the candidate for the 5th generation of wireless communicat ions. The study of the cognitive radio will be one of the most influential scientific endeavors in the 21st century. This paper presents some of the cognitive radio research ch allenges which are crucia l while applying the cognitive radios in order to determine the effectiveness and reliability of wire less networks. Existing methods in a wide ranging wire less environment are not considered to be the most reliable methods and so more research is needed to overcome the challenges specified in this paper.

6. References

1. Federa l Co mmun ications Co mmission, Cognitive Radio Technologies Proceeding (CRTP)

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2. Cognitive Radio : Research Cha llenges, Simon Haykin, McMaster University, Hamilton, Ontario, Canada.

3. Hsiao-Hwa Chen, Dr. Mohsen Gu izani, Ne xt Generation Wire less Systems and Networks, 2006 John Wiley & Sons, Ltd

4. H.Urkowitz, Energy detection of unknown deterministic signals, Proceeding of the IEEE, Vo l.55, No.4, pp.523-531, Apr.1967.

5. A.V. Dandwat and G.B. Giannakis, Statistical tests for presence of cyclostationarity, IEEE Transactions on Signal Processing, Vol.42,Issue 9, Sept. 1994, pp.2355-2369.

6. W.A. Ga rdner and G. Zivanovic, Degrees of cyclostationary and their applicat ion to signal detection and estimation, Signal Processing, Vo l.22, No.3, march 1991.

7. G.Dimit rakopoulos, P.De mestichas, D.Grandblaise, K. J.Hoffmeyer, J.Luo, Cognitive Radio, Spectru m and Radio Resource Management, Wire less World Research Foru m, 2004.

8. R.W. Brodersen, A. Wolisz, D. Cabric, S.M. Mishra, D. W illko mm, Corvus: a cognitive radio approach for usage of virtual unlicensed spectrum, Berke ley Wire less Research Center (BWRC) White paper, 2004.

9. Nie Nie, Cristina Co manic iu,Adaptive Channel Allocation Spectru m Et iquette for Cognitive Rad io Networks Springer Sc ience Business Media, 2006

10. Raul Etkin, Abhay K. Parekh, David Tse, Spectrum Sharing for Unlicensed Bands, IEEE Journal on Selected Areas in Co mmunications, vol. 25, pp. 517528, April 2007.

11. A.Shu kla, P.Hall, J.Bradford, D.Chandler, M.Kennett, P.Lev ine, A.Alptekin, Cognitive Radio, QINETIQ/ 06/ 00420 Issue 1.1, November 2006.

12. Kwang-Cheng Chen, Irv ing T. Ho, Cognitive Radio Networks, CTiF Workshop 2007.

13. Willia m Kren ik, Anuj Batra, Cognitive Rad io Techniques for Wide Area Networks, ACM, Anaheim, Ca liforn ia, USA, June 2005.

14. Dipankar Raychaudhuri , Narayan B. Mandayam, Joseph B. Evans, Benja min J. Ewy, Srinivasan Seshan, Peter Steenkiste ,CogNet An Architectural Foundation for Experimental

    Cognitive Radio Net works within the Future Internet, MobiArch06, San Francisco, CA, USA.

    Dece mber 1, 2006

15. IM EC research group, Cross -layer performance-energy modeling and optimization for wireless mu ltimed ia systems, scientific report 2006

16. Mark Scoville, Stephen Berger, Richard C. Re inhart, Jeffrey E. Smith, The Software -Defined Radio and Cognitive Radio Inter-Consortia Affiliation, Military Co mmunications Conference (MILCOM ), Washington, USA, 2006.

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