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A survey of recent advances in fuzzy logic in communication systems.

Introduction

The intelligent systems require the capabilities of sensing decision making and action. The intelligence of a system emerges from the close linkage of the capabilities. The intelligence of systems evolves through the learning and adaptation to dynamic environments [1]. Latest research fields concerning intelligence include brain science, soft computing and artificial life [2-8]. The brain science aims to understand the biochemical and physical mechanism of human brain and to construct a highly interconnected neural networks like brain [2-5].Soft computing, which was proposed by Zadeh, is a new concept for information processing and its objective is to realize a new approach for analyzing and creating flexible information processing of human beings such as sensing, understanding, learning, recognizing and thinking [6, 7].

Artificial life (A- life) means life made by humans rather than nature [8]. Each field plays specific role in intelligent systems. As one of the principal constituents of soft computing, fuzzy logic is playing a key role in what might be called high MIQ (machine intelligence quotient) systems [9].

Fuzzy sets provide a robust mathematical framework for dealing with "real-world" imprecision and nonstatistical uncertainty [10-11]. Qualitative "linguistic" variables allow one to represent a range of numerical values as a single descriptive term that is described by a fuzzy set. Given that the present day complex systems are dynamic, that there is great uncertainty associated with the input [12] and other environmental parameters, that they are subject to unexpected overloads, failures and perturbations, and that they defy accurate analytical modeling, fuzzy logic appears to be a promising approach to address key aspects[13]. In a communication system, the channel situation could be variable caused by many factors, so the controller has to be capable of adapting to the changes of channel and to be more aggressive to upgrade the utilization. A detailed search coupled with a thorough review of the literature reveals that current research in fuzzy logic in communications extends from Phase Synchronization, Secure Communication, Adaptive Frequency hopping Systems, Array spectral estimation, Call Admission Control, Battlefield communication, Space applications to UPC in ISDN, Intersymbol interference in CDMA, Adaptive antennas, AGC in Radio communications, Soft handoff and Geolocation information and inter-technology handoff.

Fuzzy Logic (FL)

The concept of fuzzy logic was first introduced by Zadeh, whose classic paper has become the philosophical bible in the field [6]. The concept is simple: set membership, and indeed reasoning of any sort, carries more information when there are a continuum of grades membership. The reasoning is based on Zadeh's Principle of Incompatibility, which maintains that high precision is incompatible with high complexity. The suggestion is that the complexity of a system and the precision with which it can be analyzed bears a roughly inverse relation to each other. He asserted that since real world ideas appear to be fuzzy in nature, there is reasonable cause for adapting this approach to machines.

Controversy has surrounded fuzziness since its inception. The term fuzzy carried negative connotations in the English-speaking world that it does not in other. But in Japan, word "fuzzy" won the gold medal for a new word in 1990 meaning "intelligence". Now "fuzzy" has won the battle as its use has resulted in innumerable commercial products that work [14].

Fuzzy logic is considered as a superset of standard logic which is extended to deal with the partial truth. It has become one of the most successful technologies for developing complex control systems. Fuzzy logic is a design methodology that can be used to solve real life problems. Fuzzy set theory resembles human decision making in its use of approximate information. It was basically used to mathematically represent uncertainty and vagueness and provide tools to deal with the imprecision in many problems [15].

Fuzzy Logic in Communication

Communication systems are real-time deterministic, well defined systems that transport voice/data signals from point A to point B reliably. However the transmitted signal is subject to significant distortion by harsh environment, the medium and the system itself. As the signal departs from point A it is subject to algorithmic manipulations (equalization, Digital Signal Processing, Analog to Digital Conversions), to transformations (sound to electronic, to photonic, to electromagnetic waves), it suffers from external influences (electromagnetic, environmental) etc. The result is distorted or fuzzy signal. Hence to reproduce original transmitted signal at the receiver, fuzzy logic can be very useful.

Communications and networking has been able to use the fuzzy logic for ATM network traffic modeling, management, and rate control as well as nonlinear channel equalization, telecommunication ranking and network admission control[15]. To facilitate comprehension, the literature may be organized into following different fields:

Phase Synchronization

Phase synchronization is required at the receiver side to fully exploit the potentiality of modulation/ coding schemes without causing a bottleneck in the overall system performance. Phase synchronization means that the receiver in a coherent communication system must track the phase of the received signal. Advances in the area of channel coding and demands for higher data rates continue to force signals to noise ratios(SNR) ever lower and increase signal complexity which in turn places increased demands on synchronizer performance.

One of the fundamental function in the receiver is the phase recovery unit, which generally consists in a phase locked loop [16]. Daffara showed that fuzzy logic could be used to improve the performance of Phase-locked loop(PLL). Phase Detector derived from the Minimum Mean Square Error criterion utilized a decision directed algorithm applying Fuzzy Logic Control. He derived a fuzzy rule based PLL controller able to improve the synchronization loop performance [17].

Drake and Prasad discussed the applications where Fuzzy logic techniques might be suitable in the communication synchronization area. They concluded that soft computing shows great potential to simplify synchronizer design, increase robustness and improve performance which is critical to overall receiver performance measure [18].

Soft Handoff (SHO)

Hard handoff is a mechanism to maintain quality of service for mobile communication system. SHO is a diversity handoff scheme in which user attempts to have simultaneous traffic communication channels with more than one Base Stations (BSs). On the other hand, Mobile Stations (MSs) obviously use more resources than those of hard handoff. Some researchers applied fuzzy logic theory in handoff process.

Kinoshita et.al. applied fuzzy inference for learning cell boundary but emphasized on hard handoff in indoor area[19,20]. Homnan and Benjapolakul proposed Fuzzy Inference Scheme (FIS) by using the signal strength MS receives and the distance between MS and BS for inputs while output was the defined value for deciding handoff[21]. The work was also applied for hard handoff. Homnan and Benjapolakul proposed a new soft SHO algorithm based on FIS. The FIS and IS-95B/cdma2000 SHO algorithms were flexible because they used dynamic thresholds and had more conditions than IS-95A SHO algorithm. By investigation of all parameters among the three algorithms, the proposed FIS SHO algorithm proved to have higher performance at high traffic loads or lower thresholds [22].V. Kunsriruksakul et al extended the Soft Handoff Work of Homnan and Benjapolakul which used fixed value [23]. SHO of each MS was dynamically adjusted in the range from 1 dB to 4 dB depending on the remaining channels (representing the traffic load) of BS and the number of active set's pilots of any MS served by that BS. They showed by simulations that the proposed adaptive FIS SHO algorithms could reduce call blocking probability and handoff call blocking probability with appropriate range of universe of discourse while still keep low outage probability.

Secure Communication

Secure communications have been an important issue since the Internet and mobiles are worldwide. Chaotic systems are situated between deterministic systems and stochastic systems. The characteristics of chaotic systems include broadband spectrum and unlimited period. It is difficult to predict the future response of chaotic systems due to its property of the sensitivity to initial conditions[24]. There are lot of researches in investigating chaos based secure communications. Chaotic switching [25], chaotic modulation [26], chaotic masking [27] and chaotic synchronization [28] are proposed to make sure the data security.

Grassi and Mascolo utilized a nonlinear observer to estimate the status of the transmitter for synchronization with receiver to decrypt the signals [29]. Yu employed fuzzy logic with gray prediction to design the observer gain of the receiver for synchronization of logistic map [30]. Appending the gray predictor with the feedback loop the fuzzy controller received not only the current data but also the future information. As that control reflected both the current status and future tendency of the state, the response of the chaotic synchronization improved.

Adaptive Automatic Gain Control in radio Communication

The Automatic Gain Control (AGC) is actually a closed loop control that keeps the signal strength within predefined limits, increasing or decreasing he signal gain based on the current signal state as Attack, Stable, Hold or Decay. The algorithms traditionally used mainly consists of a method to estimate the energy level of the actual signal frame being processed, and then apply a gain factor to achieve the desired signal level.

Barajas et al described the implementation of a fuzzy logic AGC applied to amateur radio communication using ionospheric propagation mode and single side band (SSB) radio equipment. A fuzzy logic algorithm was directly substituted for the original AGC algorithm, which resulted in improved signal strength, clarity response and algorithm performance. They proved the feasibility of real time fuzzy logic based AGC in communication systems [31].

Adaptive Antenna

Adaptive antenna (smart antennas) is the promising approach to enlarge the system capacity to meet the increased demand of large capacity and higher frequency spectrum. Adaptive beam forming algorithms for adaptive antennas is the recent area of research now a day. Least Mean Square (LMS) is one of the most frequently used beam forming algorithms in adaptive antennas due to its low computational complexity. As compared to the fixed step size in conventional LMS algorithms, Variable step size methods have been reported to improve the convergence speed with a low increase of computational complexity and better tracking capacity in nonstationary environments. The existing variable step size methods adjust the step size by exploiting some linguistic rules of step size adjustment translated into numerical formulae of mathematical model. The fuzzy technique was found to be suited to work directly with the linguistic rules instead of translating those to mathematical models.

W.S.Gan introduced the application of fuzzy logic to adjust the step size of the adaptive antenna [32]. The author examined the variable step size of three (Small, Medium, Large) in the two Fuzzy Inference System model of one and two inputs. P.Van Su et al extended this work to the approach in which the definition of step size depended on the mean square error(MSE)[33].The proposed algorithm showed better performance as compared to the fixed size LMS algorithm and other variable step size in term of the convergence speed and the reduction of steady state error.

Adaptive Frequency Hopping System

A frequency hoping system spreads a transmitted signal's energy across a bandwidth larger than the minimum required for the signal. It spreads the signal when it changes or "hops" the transmission frequency many times per second. The transmitter and receiver must stay synchronized and hop to same frequency at the same time. Frequency hopping systems use a pseudorandom number generator (PN) to produce a random sequence of frequencies. If the person does not know the frequency sequence, the hopped signal looks like low intensity noise spread over the entire bandwidth.

Pacini and Kosko described a fuzzy rule based PN generator in which adaptive fuzzy rules map distributions of old output frequencies to new inputs. They proved that fuzzy system produced the sequence that was more uniform, easier to design, harder to intercept and easier to spread over any number of frequencies without changing the algorithm and the fuzzy rules [34].

Hangsheng et al opined that fuzzy theory can be used in frequency hop communications in two ways; as adaptive fuzzy frequency hopping generator and adaptive frequency hop communications with fuzzy frequency estimation. They extended the work of Pacini and Kosko. The adaptive PN sequence generator generates N frequencies which are harder to intercept. Out of these N frequencies some healthy frequencies are selected based on the channel quality and power level. Hangsheng et al used fuzzy theory to select healthy frequencies to be used for communication. The fuzzy systems gave the best frequencies by analyzing the channel quality, analyzing jamming pattern, sweeping or fixing, broadness or narrowness, pulse or continuance even some jamming behavior etc. That improved the anti-jamming ability of ordinary adaptive frequency hop communication systems [35].

Array Spectral Estimation

Perez-Neira and Lagunas solved the problem of multiple source tracking by deriving the Alternate Projection algorithm as a constrained phased array, supposed to look at one source and block others [36]. Later on Perez-Neira et al realized that the concept of global tracker includes additional processing and data fusion, which enables to cope with eventual fadings of bounded time duration as that may occur in crossing radial trajectories of two movils. They decided to use the model free function approximation capability of fuzzy logic to obtain high resolution angle estimates from the spatial spectral density. They introduced fuzzy logic in array spectral estimation for the first time. They addressed the problem of spatial reference estimation in mobile scenarios. They developed a fuzzy controller for acting as an interpolative supervisor of different trackers that apply in different operating conditions of dynamic nonlinear system. The result was a localization and tracking system that attains a resolution comparable to that of high resolution techniques as the minimum variance. The system supported the expectation of adaptive arrays for obtaining a communication front-end of affordable complexity, developing cost and good performance[37].

Intersymbol interference, Multi-access interference in CDMA

The Direct-sequence Code-division multiple-access (DSCDMA) cellular communication system is one of the favorite candidates for the third generation of radio cellular communication systems due to its high potential capacity. However, there are two main shortcomings in a CDMA system. One is multiple access interference (MAI) due to the simultaneous transmission of all users in the same band and asynchronous received signals with non orthogonal random sequences for the uplink. The other shortcoming is the near-far problem, which can be diminished by a power control scheme or even fully eliminated by a perfect power control method. Nevertheless, the goal of perfect power control is hard to reach and even though it can be fulfilled, the MAI still degrades the performance especially for a high system load. Thus the aspect of interference suppression or cancellation is an imperative perspective. Kaur and Singh investigated the fuzzy logic technique used to increase the efficiency of CDMA systems by reducing multiple access interference (MAI) by interference cancellation method [38].They concluded that the applications of fuzzy logic are wide-ranging and give the opportunity for modeling of an environment that is imprecise. Fuzzy logic has proved its great potential to solve the problems of MAI in CDMA communication systems.

Call Admission Control (CAC)

The CAC algorithm is an instrument that decides whether an incoming call is accepted or has to be rejected. The decision is based on a set of traffic descriptors that characterizes each connection. These parameters together with QoS (Quality of Service) specification are part of the traffic contract between the network and the user.

Hellendoorn used fuzzy logic in CAC for broadband ISDN networks. Fuzzy CAC was based on an integrated fuzzy system performing, first, the estimation of the effective bandwidth that has to be reserved for a single connection, then the correction of the estimated effective bandwidth by measuring the network load and by estimating the suitability of the set of existing connections with regard to statistical multiplexing and finally the comparison of the corrected effective bandwidth with the available link capacity [39].

Kaur and Singh surveyed the applications of fuzzy logic to increase the efficiency of CDMA systems by reducing multiple access interference (MAI) by CAC [40]. They opined that in CDMA systems, number of users that each cell can support is limited by the total interference received at each base station and will vary with time. When a system is congested, admitting a new call can only make the link quality worse for ongoing calls and may result in call dropping. Thus the system needs a CAC (call admission control) policy for new calls and handoff calls to maintain acceptable connections for existing users. MUD provides a method to reduce the MAI of users in the local cell, which usually dominates the communication quality and capacity. For a WCDMA cellular system using MUD, the local cell interference will be significantly decreased by MUD and then adjacent cells' MAI takes a more dominant position in communication quality. Therefore, traffic control mechanisms for WCDMA with MUD should be reconsidered, especially when adjacent cells have accommodated more users. They concluded that CAC employing fuzzy logic techniques attained better performance in keeping QoS guaranteed, blocking probability and admitted number of users. In addition FCAC was found to be more adaptive and stable than SIR-based CAC in wideband CDMA cellular systems [40].

Usage Programme Control Programs for ISDN

The usage parameter control (UPC) algorithm supervises the established connections by checking and punishing violating connections. This is necessary because users of the network may not hold the agreed upon traffic parameters and therefore the CAC is insufficient to prevent congestion. Once a new connection has been accepted, the UPC is required to ensure that traffic submitted doesn't exceed the parameters negotiated within the traffic contract. UPC has to detect a source that does not keep the negotiated parameters very quickly; otherwise other connections might be affected by delay or cell loss. Because the UPC works online, it must satisfy a strict real time demand.

Hellendoorn used fuzzy logic in UPC for broadband ISDN networks. He used Peak cell rate (PCR), Sustainable cell rate (SCR) and Maximum Burst Size (MBS) as inputs in the first rule base to derive current burstiness. The burstiness together with the cell arrival time and the state of traffic are taken to finally decide upon the cell label viz. rejected, tagged, accepted. He concluded that the flexibility and easy management of a fuzzy system allowed complex networking strategies. The use of multiple input parameters enables the UPC algorithms to consider application specific requirements [39].

Geolocation Information and Inter-Technology Handoff

Mobility between dissimilar networks is one of the future trends in network design towards fourth generation of telecommunication systems. In personal communication systems, providing mobility between dissimilar (e.g. fast indoor and slower outdoor) networks is one of the most interesting areas of research today[41-42]. Mobile access to Internet will thus be done over heterogeneous wireless networks [43].IP is seen as the interconnecting protocol and various schemes for IP mobility management has been introduced. Another trend is the integration of geolocation capabilities to the existing networks. Location Services are to be specified in the 3G and other networking related standardization. These features bring added value for the industry, network operators and mobile users.

Ylianttila et al suggested the usage of geolocation information in mobility management via distributed location databases to enable a moving host to prepare for handoff. They opined that Cellular networks can be overlaid with a spottish high data rate WLAN network in areas such as business centers, hotels and airports and hence to avoid unnecessary search of WLAN beacon the terminal must be aware of the whereabouts of the overlay system to be visited. They presented preliminary simulation results of a fuzzy logic handoff algorithm for vertical handoff[44].They left the evaluation of system performance in case of more complex cases as the future work.

Battlefield communications

A typical communication network in a battlefield consists of a moderate number of nodes that are broadcasting on a single low bandwidth radio channel. Problems arise when more than one node tries to access the channel at the same time. To ensure smooth communication and to enhance the information throughput rate, a control of network access is mandatory. A hierarchical control is not practical in a battlefield environment due to dynamic change in network status. Besides the concentration of control in a single node makes the communications network more vulnerable. In this type of environment, a distributed control is best suited. In distributed control, each node listens to the network traffic and makes independent decisions for accessing the network.

Celmins developed control procedures using fuzzy logic and tested their behaviors on a computer model of battlefield communications. He studied the inputs for distributed control in battlefield communications and found them approximate descriptors of the status of the network. Besides the control rules were found to be heuristic because the controlled process does not have a set point. These circumstances suggested him the use of fuzzy logic control procedures. He described such control procedures that had been developed at the U.S. Army Research Laboratory. He concluded from the results of those procedures that the best membership functions are robust in the sense that similar functions perform well for a wide variety of networks [45].

Space Applications

Fuzzy logic control can play an important role in development of intelligent systems for space applications [46-50]. Lee et al pointed out that the basic difficulty in design of fuzzy logic systems is the fine tuning of the membership functions of the labels used in the rules. They gave the concept of fuzzy systems that can learn from experience to improve their performance. They proposed reinforcement learning for tuning of membership functions and developed two architectures ARIC and GARIC[51-53]. GARIC is hybrid architecture for fuzzy logic control and reinforcement learning of control rules. In reinforcement learning it is not assumed that there exists a supervisor that critically judges, at each time step, the chosen control action. In these systems the learning system is told indirectly about the effect of the control action. GARIC used reinforcements from the environment to refine globally in all the rules its definitions of fuzzy labels [54].

Berenji et al employed GARIC to develop a controller for tether control on-board the space shuttle. Problem was complicated due to a number of considerations such as the need to operate in a vacuum, gravitational and magnetic forces and lack of an external gravitational field. The time varying dynamics of the long, flexible, variable-length tether and those of the orbiter and satellite made tether control an even more difficult task. GARIC learned to maintain the dead band in a small number of trials [54].

Berenji presented a report on the applications of these adaptive systems to NASA space projects such as orbital operations of the space shuttle, which include attitude control and rendezvous docking operations [55]. Using GARIC the system adjusted the membership functions automatically to keep the error within tighter dead band. He showed that GARIC could learn to perform a new task within a limited number of trials in complex environments.

Conclusion

A fuzzy logic technique provides a meaningful and valuable addition to the standard conventional Boolean logic. This paper has reviewed the current research efforts in fuzzy logic approaches to different applications in communications and highlighted the future potential and promise of fuzzy logic in communication systems. The paper also presented key research efforts in the areas of fuzzy logic-based algorithms and latest adaptations in fuzzy logic like temporal logic that are necessary both to address new challenges and to help realize the full potential of fuzzy logic in the field of communications.

References

[1] T. Fukuda and N. Kubota, "Computational Intelligence in Robotics and Mechatoronics," Proceedings of the IECON'97 23rd International Conference on Industrial Electronics, Control, and Instrumentation, pt.4, vol.4, pp1517-28, 1997.

[2] S.J. Russell, P. Norvig, "Artificial Intelligence," Prentice-Hall, Inc. 1995.

[3] J. A. Anderson, E. Reenfeld," Neurocomputing--Foundations of Research," The MIT Press, 1988.

[4] J. M. Zurada, R. J. Marks II, C. J. Robinson, "Computational Intelligence-Imitating Life," IEEE Press, 1994.

[5] M. Palaniswami, Y. Attikiouzel, R J. Marks II, O. Fogel, T. Fukuda, "Computational Intelligence A Dynamic System Perspective," IEEE Press, 1995.

[6] L. A. Zadeh, "Fuzzy Sets," Information and Control, vol.8, pp 338-353, 1965. [7] J.S.R. Jang, C.-T.Sun, E. Mizutani, "Neuro-Fuzzy and Soft Computing," New Jersey:Prentice--Hall, Inc. 1997.

[8] C.G.Langton, "Artificial Life--An Overview," The MIT Press, 1995.

[9] L.A.Zadeh, "Soft Computing and Fuzzy Logic," IEEE Trans. on Software, Nov. 1994.

[10] L. A. Zadeh, "Outline of a new approach to the analysis of complex systems," IEEE Trans. Systems, Man, Cybern., vol. 3, 1973.

[11] D. E. Thomas and B. Armstrong-Helouvry, "Fuzzy logic control--A taxonomy of demonstrated benefits," Proc. IEEE, vol. 83, no. 3, pp.407-421, Mar. 1995.

[12] J. M. Holtzman, "Coping with broadband traffic uncertainties: Statis-tical uncertainty, fuzziness, neural networks," in Proc. IEEE GLOBE-COM'90, San Diego, CA, Dec. 1990, vol. 1, pp. 7-11.

[13] S. Ghosh, Q. Razouqi, H. Jerry Schumacher, and A. Celmins, "A Survey of Recent Advances in Fuzzy Logic in Telecommunications Networks and New Challenges," IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 6, NO. 3, pp 443-447, AUGUST 1998.

[14] M. Rogers & Y Hoshuai, "The future looks fuzzy" News week, 1990.

[15] A. Gad and M. Farooq, "Applications of Fuzzy Logic in Engineering Problems," Proc of Annual Conference of IEEE Industrial Electronics Society, 2001.

[16] H. meyr and G. Ascheid, "Synchronisation in Digital Communication," Vol.1, John Wiley & Sons, New York, 1990.

[17] Flavio Daffara, " A fuzzy rule based phase error detector", Proc. of URSI Intn.Symp. on Signals, Systems, and electronics, Vol.3,no.2,pp 211-218, May 1995.

[18] Drake, J.T.; Prasad, N.R, " Current trends towards using soft computing approaches to phase synchronisation in Communication systems," IEEE 42nd Midwest Symposium on Circuits and Systems, Volume 2, pp 665-668, 8-11 Aug. 1999.

[19] B. Homnan, V. Kunsriruksakul and W. Benjapolakul.," The Evaluation of Soft Handoff Performance Between IS-95A and IS-95B/ cdma 2000," Proc. IASTED SPC2000, pp.38-42, September 2000.

[20] Y. Kinoshita and Y. Omata," Advanced Handoff Control Using Fuzzy Inference for Indoor Radio Sytems", IEEE 4th VTC, vol.2, pp. 649-653, 1992.

[21] B. Homnan and W. Benjapolakul," A handover Decision Procedure for Mobile Telephone Systems using Fuzzy Logic", IEEE APCCAS '98, pp. 503-506, November 1998.

[22] B. Homnan, V. Kunsriruksakul and W.Benjapolakul ,"Adaptation of CDMA Soft Handoff Thresholds using Fuzzy Inference System", IEEE ICPWC 2000, pp.259-263, December 2000.

[23] V.Kunsriruksakul, B. Homnan and W.Benjapolakul, "Comparative Evaluation of Fixed and Adaptive Soft Handoff Parameters using Fuzzy Inference Systems in CDMA Mobile Communication Systems," Proc. IEEE conf. VTC, 2001.

[24] S.M. Rasband," chaotic Dynamics of Nonlinear Systems", A Wiley-Interscience Publication, New York, 1989.

[25] T. Yang," Recovery of digital Signals from chaotic Switching", int. J. Circuit Theory Appl., Vol.23, No.6, pp611-615, 1995.

[26] H. Leung and J.Lam, " Design of demodulator for the Chaotic Modulation Communication System," IEEE Trans. Circuits Systems, Vol.44, 1997.

[27] C. Zhou and T. Chen, "Extracting information masked by chaos and contaminated with noise: Some Considerations on the security of communications Approaches Using Chaos", Physics Lett. A, Vol.234, 1997.

[28] T.L. Carroll and L.M. Pecora," Synchronisation in Chaotic Systems", IEEE Trans. Circuits Sys. I,Vol.38,1991.

[29] G. Grassi and S. Mascolo," Nonlinear Observer Design to synchronise Hyperchaotic Systems via a Scalar Signal," IEEE Trans. Circuits System I (Special issue on Chaos Synchronisation , Control and applications), Vol.44, Oct. 1997.

[30] Gwo-Ruey Ye,"Fuzzy synchronisation of chaos using Gray Prediction for Secure Communication", IEEE International Conference on Systems, Man and Cybernetics, Volume 4, pp 3104-3109, 5-8 Oct. 2003.

[31] Ramirez-Barajas, J.; Dieck-Assad, G.; Soto, R ," A fuzzy logic based AGC algorithm for a radio communication system", Proc. Ninth IEEE International Conference on Fuzzy Systems, FUZZ IEEE, Volume 2, pp 977-980, 7-10 May 2000 .

[32] W.S. Gan," Designing a fuzzy step size LMS algorithm", IEE Proc. Vis. Image Signal Processing,vol.144, no.5, Oct.1997.

[33] P.Van Su, L. M. Tuan, J.Kim and G. Yoon," A New Fuzzy logic Application to Variable Step Size LMS Algorithm for Adaptive Antennas in CDMA Systems," Proc. IEEE 3rd int. conf. on Microwave and Millimeter Wave Technology, 2002.

[34] P.J. Pacini and Bart Kosko," Adaptive Fuzzy Frequency Hopper", IEEE Trans. Comm. Vol.43, no.6, 1995.

[35] Zhao Hangsheng et al," Fuzzy Theory in Frequency Hop Communication", Proc. int. Conf. On Communication technology, China,1998.

[36] A. Perez-Neira and M.A.Lagunas," High Performance DOA trackers derived from parallel low resolution detectors", IEEE Workshop on SSAP, Greece, 1996.

[37] Perez-Neira, A.; Lagunas, M.A.; Bas, J.;, "Fuzzy Logic for robust detection in wireless communications", Proc. 8th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, 'Waves of the Year 2000'.PIMRC '97., Volume 3, pp 1145-1149, 1-4 Sept. 1997.

[38] Kaur, Gurmeet, Singh, M.L., "Intelligent CDMA Systems", Proc. International Conference on "Optoelectronics, Fiber Optics and Photonics", India, December 2004.

[39] Hans Hellendoorn," Fuzzy Control in TeleCommunications", Proc. Biennial Conference of the North American Fuzzy Information Processing Society,NAFIPS,pp 444-448, 19-22 June 1996.

[40] Kaur, Gurmeet and Singh, M.L., "Current Trends Towards Using Fuzzy Logic in CDMA Systems", Proceedings: 8th National Conference of Punjab Science Congress, India, Feb. 2005.

[41] M. Ylianttila, R. Pichna, J. Vallstrom, J. Makela, A. Zahedi, P. Krishnamurthy and K. Pahalvan," Handoff Procedure for Heterogeneous Wireless Networks", Proc. Globecom'99, Future Wireless Communication Systems Symposium, Brazil, 1999.

[42] A. Hatami, P. Krishnamurthy, K. Pahalvan, M. Ylianttila, J. Makela and R. Pichna," Analytical Framework for Handoff in non-heterogeneous Mobile Data Networks", Proc. PIMRC 99, Osaka, Japan,1999. [43] P. Krishnamurthy, K. K. Pahalvan, A. Zahedi, J. Vallstrom, R. Pichna and M. Ylianttila," Handoff in 3G Non-Homogeneous Mobile Data Networks", Proc. MTT-S European Wireless 98 in Amsterdam, 1998.

[44] M.Ylianttila, J. makela and K. Pahalvan,"Geolocation Information and Intertechnology Handoff", IEEE International Conference on Communications, ICC 2000, Volume 3, pp 1573-1577, 18-22 June 2000.

[45] Aivars Celmins," Distributed Fuzzy Control of Communications", Proc. ISUMA-NAFIPS, 1995.

[46] L.A. Zadeh, " A Rationale For Fuzzy Control", Trans. ASME, J. of Dynamic Systems, Measurement and Control, vol.94, pp3-4, 1972.

[47] H.R. Berenji, "On Integration of Reinforcement Learning and Approximate Reasoning for Control", 30th IEEE Conf on Decision and Control, pp 1900-1904, Bringhton, England, 1991.

[48] R. Lea, R.H.Fritz, Giarratano and Y.Jani, " Fuzzy Logic Control for Camera Tracking Systems", 8th International Congress of Cybernetics and System, New York,NY, June 1990.

[49] R. Lea, J. Hoblit and Y. Jani, " Performance Comparison of a Fuzzy Logic based Attitude Controller with the Shuttlr on-orbit Digital Auto Pilot, North American Fuzzy malformation Processing society, Columbia, Missouri, May 1991.

[50] R. Lea, J. Villarreal, Y. Jani and C. Copeland, "Learning Characteristics of a Space Time Neural Network as a Tether Skiprope Observer, North American Fuzzy Information Processing Society, pp 154-165, Pureto Vallatra, Mexico , December 1992.

[51] C.C. Lee and H.R. Berenji, " An Intelligent Controller based on Approximate Reasoning and Reinforcement Learning", Proc of IEEE Int. Symposium on Intelligent Control, Albany, NY, 1989.

[52] H.R. Berenji, " An Architecture for Designing Fuzzy Controllers using Neural Networks, Int. J. of Approximate Reasoning, vol.6, no.2, pp267-292, Feb 1992.

[53] H.R. Berenji and P. Khedkar, " Learning and Tuning Fuzzy logic Controllers through Reinforcements", IEEE Trans. on Neural tNetworks, vol. 3, no. 5, 1992.

[54] H.R. Berenji, A. Malkani and C. Copeland, "Tether control using Fuzzy Reinforcement Learning", IEEE international Conference of Fuzzy Systems, Japan, 1995.

[55] H.R. Berenji, "Computational Intelligence and Soft Computing for Space applications", IEEE AES Systems Magazine, pp 8-10, August 1996.

Gurmeet Kaur (1) and M.L. Singh (1)

(1) Reader, Dept of Electronics and Communication Engg., UCET, Punjabi University, Patiala, Punjab, INDIA. E-mail: farishta02@yahoo.co.in

(2) Reader, Dept of Electronics and Communication Engg., Guru Nanak Dev University, Amritsar, Punjab, INDIA.
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Author:Kaur, Gurmeet; Singh, M.L.
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