Neural Networks Intelligent Tools For ... - CiteSeerX

Buletinul Ştiinţific al Universităţii "Politehnica" din Timişoara Seria ELECTRONICĂ şi TELECOMUNICAŢII TRANSACTIONS on ELECTRONICS and COMMUNICATIONS

Tom 48(62), Fascicola 1, 2003

Neural Networks Intelligent Tools For Telecommunications Problems Corina Botoca1, Georgeta Budura

Abstract: Neural Networks are nonlinear dynamic systems able to offer a solution almost anywhere classic methods have failed. This paper gives an overview of the applications of neural networks in telecommunications. There are presented the relevant characteristics that recommend neural networks as elegant and reliable tools for complex telecommunications problems. There are exposed some of the training algorithms. The conclusions highlight the difficulties that may arise in using neural networks. Key words: neural networks, training algorithm, neuron, telecommunication applications

I.INTRODUCTION Intelligence is defined as the ability to adapt to new situations, to solve ongoing problems on the basis of acquired experience (dictionary). In computer engineering the significance of the term intelligence is still of actual debate. One thing is clear: intelligence implies the capability of deriving useful data from the stored information, to change the behavior without the intervention of a user. There are two distinctive directions of developing artificial intelligence: • On the basis of efficient soft programs using traditional computers; • On the basis of a brand new architecture, trying to model the human brains processing; In the first direction it is important to achieve the proposed task in the most secure and efficient way, without trying to model the human methods and techniques of knowledge processing. It includes all rule-based methods such as artificial intelligence, fuzzy logic and genetic algorithms. Anywhere the conventional formalism has failed due to the combinatorial explosion and the long processing time caused by the sequentiallity of operation, neural networks offer a new frame for knowledge processing. A NN is a computer architecture soft , hard or both models, that works on the way that human brains process sensory stimuli. NN is a non rule-based technique and can be made stochastic, so the same action does not necessary take place each time for the same input. A stochastic behavior allows to a NN to fully explore its environment and potentially to find a better solution than all other methods. 1

Facultatea de Electronică şi Telecomunicaţii, Departamentul Comunicaţii Bd. V. Pârvan Timişoara 1900 e-mail [email protected]

There have been conceived many definitions for NN, one of them of Haykin [17], a personality in this field, is following: "A neural network is a massively parallel distributed processor that has a natural propensity for storing experiential knowledge and making it available for use. It resembles the brain in two respects: 1.Knowledge is acquired by the network through a learning process. 2.Interneuron connection strengths known as synaptic weights are used to store the knowledge" NN take a great amount of information and draw all at once a conclusion, not as a traditional computer in deductive consecutive steps. Some training data are fed into the NN and it learns by modifying its parameters in order to find the correct answer. The more data fed into a NN the better the results (it’s not the case of a conventional computer). II THE ARGUMENTS OF NEURAL NETWORKS USE The characteristics of NN are also argu-ments of using them in complex problems of telecommunications: Learning from examples Learning is the ability of NN to modify their parameters, improving their performance, in order to minimize a cost function. In general learning is stopped according to given criteria (e.g. when the obtained weights produce a sufficiently small error performance). Adaptability is closely related with learning and it is indispensable when the NN operates in a nonstationary environment. From this point of view there are two types of NN: • with fixed weights , when the weights are modified during a first phase of processing (named learning or training) and than "frizzed" at the final values obtained (that are used during operation); • with adaptable weights, when learning continues also during the phase of functioning (" on line" learning) ;

Nonlinearity NN are multiple inputs –multiple outputs systems that can learn a nonlinear input-tooutput relation. NN don’t need a model of the problem to be solved They can learn from examples to solve problems without explicit knowledge or apriori experience. So it isn’t necessary a model, only a good representation of the problem. Generalization NN can handle noisy or missing data. To generalize means to derive principles and conclusions through experience. If a NN is trained for a specific problem it will be able to find the correct solution even if the problem to be solved is not exactly the same as the already learned. For example let’s suppose that a NN was trained to recognize human speech. During the learning process a certain person has to pronounce some words which are learned by the NN. Than the NN will be able to recognize also those words spoken by another person too. Flexibility Each basic element (named neuron or perceptron) is a processor performing a very simple function independently from all the others in the NN. To solve more difficult problems a NN can extend in a modular manner, without a new design. Tolerance to deterioration The performance of a NN degrades gradually with deterioration of weights or neurons functioning similarly to human nervous system. Due to their distribute and parallel processing the NN will continue to work even in deterioration conditions untill the stage of major damage. Processing speed NN have an extraordinary processing speed caused by their parallel and distributed structure. It has been reported a speed of Terra operations per second for a chip of 1 cm2 [9]. The processing potential NN have an extraordinary processing applicability potential. They have applications in almost all the fields of human activity and every day appear other new. III LEARNING ALGORITHMS There are three major types of training a NN: 1.Supervised learning A set of pairs input-desired output models are provided to the network. The NN modifies its parameters in order to minimize the difference e[n] between the desired response d[n] and its current output y[n], according to an error criterion:

Some algorithms of this type are least mean square algorithm (LMS), backpropagation (BKP), BKP through–time, real time recurrent learning; 2.Unsupervised learning In this case the desired response is not available and the NN answer is based on its ability to self organize (Fig.2). This means that the NN extracts itself the relevant characteristics of the input models and realizes internal distinctive representations of them. Between the NN neurons takes place a kind of competition having as effect adaptation of the parameters. Typical algorithms of this type the are : self-organizing maps (SOM), learning vector quantization (LVQ), principal component analysis (PCA). They can also incorporate control mechanisms for further refinement of NN parameters. x(n)

Neural Network W

y(n)

Learning Algorithm Fig.2 Unsupervised learning 3.Reinforcement learning (also named learning with "reward" and "punishment") In this case the desired response isn’t available, only a signal (named reinforcement signal) that evaluates how well the systems is performing. The algorithms of this type have a biological inspiration and operate on the following principle: if the action of a learning system provided benefit this action would be encouraged, in other cases this action would be inhibited. Adaptive Resonance Theory Networks use with success this type of learning . Action a

State x Environement Reinforcement Signal r Learning element

e[n] = d[n] − y[n]

d(n) x(n)

Neural Network W

+

y(n) + e(n)

Learning Algorithm

Fig.1 Supervised learning

Fig.3 Reinforcement learnig IV THE NN BASIC ELEMENT AND A TYPICAL STRUCTURE The NN basic element, the neuron (Fig. 4) receives weighted inputs xi from the other processors, sums them with a bias term b (positive or negative) and through activation function provides an output x=f(y):

N

y=

∑w x + b i i

i =1

where wi is the interconnection weight from input xi.

x1

w1

x2

y w2

Σ

f(y) Output o

xN

wN Bias b Fig.4 The neuron

The activation function may be linear, non linear or probabilistic. The choise of the activation function depend on the application. The most used function is a sigmoide, for example: f (y ) =

1 1 + e −β .y

One of the most popular NN architecture (Fig.5) is the multi-layer feed forward neural network (MLNN), called also multi-layer perceptron (MLP) .The input information is processed from input layer to the output layer, successively from layer to layer,through the hidden layers. The interconnection weight wji links the output of neuron ui to the input of neuron j. x1 · · ui ·

xi

wji xN

· · ·

· uj ·

· · · wkj

uk Output layer

Input layer

Hidden layer

Fig.5 An example of a multilayer neural network V TELECOMUNNICATIONS APPLICATIONS The limited bandwidth resources have been restricted the enormous growth of telecommunications systems. The transmitter and receiver as well as transmission channel introduce many kinds of disturbances on the useful signal. Due to more effective use of the bandwidth new intelligent solutions are needed in telecommunications

systems. Intelligent structures would be able to compensate the channel distortions, interference, other disturbances and offer an efficient control. Nonlinear channel modeling and identification The application of NN to system modeling and identification is motivated by the ability of NN to represent any real valued function (linear or nonlinear) with necessary precision [16]. This is known under the name of universal approximation property. This property can be used for several purposes such as channel design, transmitter and receiver design (beamforming and adaptive antennas) , computer simulation and performance evaluation of communications channels, diagnoses, fault detection. A nonlinear time varying fading channel was modeled with a NN in [39]. A NN approach has been used for satellite channels identification [19] Research in antenna array signal processing (AASP) has been mainly focused on the directionof-arrival (DoA) estimation and beamforming. The DoA problem is considered as a mapping from the space of the sensor output to the space of DoA, while the beamforming function is an inversion of the DoA estimation function [29]. The purpose of beamforming is to minimize interference either is caused by fading, reflections or the effects of multiuser interference. Conventional methods are typically linear algebra-based methods, requiring computationally intensive matrix inversion, and cannot meet realtime requirements. They also require calibrated antennas with uniform features and are sensitive to the manufacturing fault and other physical uncertainties. Due to their properties NN overcome all these mentioned problems offering robust, accurate and real time solutions [12], [25] If the communication channel is unknown or changing, an adaptive antenna system can offer capacity enhancement or allow higher bit rates to be used [29]. NN have been used in the design of neural network receivers for code division multiple access (CDMA) signals [21]. To demodulate the received CDMA signal, it is necessary to overcome the inherent channel noise, which is in general assumed to be Gaussian, and the multiple access interference The conventional receivers have failed to recover the information when the number of simultaneous transmissions is high. The near-optimal receivers proposed in literature (e.g. [48]) require the knowledge of the spreading codes of all users. The adaptive NN receivers do not require the knowledge of the spreading codes of the interfering users [35]. In [26] the objective function of the optimal multi-user detector was translated into an energy function of a Hopfield NN. The structure was evaluated through several simulations and at a much lower computational cost. The NN receiver can be implemented in analog VLSI, which makes

it well suited for high-speed communication applications. A NN-based predictor was proposed for power level prediction in CDMA receivers [13]. The predictor was composed of an. adaptive tapped delay line followed by a MLP. An information criterion-based model selection principle, the predictive minimum description length method, was applied to select optimal neural network structure. The NN was applied to very noisy Rayleigh fading channels with 1.8 GHz carrier frequency for predictive filtering of the noisy power signal. The NN predictor offered higher noise attenuation and wider prediction bandwidth than the linear predictor. NN have been used to model nonlinear microwave memoryless amplifiers such as traveling wave tube amplifier (TWT) [20] TWT is a nonlinear device that exhibits nonlinear phase and amplitude conversion. NN have outperformed classical TWT analytical models due to their adaptability, potential to approximate complicated nonlinearities with a reduced number of parameters. Many NN recurrent and FIR networks have been proposed for time-series modeling [49]. Solid state power amplifiers (SSPA) are examples of nonlinear dynamic system successfully modeled with complex valued NN [19]. A SSPA is composed of adaptation circuits and a transistor. Channel equalization Adaptive channel equalization is used to eliminate channel disturbances such as noise, nonlinear distortions, fading, time varying characteristics, intersymbol interference, co-channel and adjacent channel interference. The principle structure of an adaptive equalizer is presented in Fig.6. Numerous adaptive NN based equalizers have been reported in literature to overcome the problems of communications channels.

Input signal

Training Sequence

Channel

NN Equaliser W

+

y(n) + +

e(n) Learning Fig.6 The bloc scheme of an adaptive equalizer For example, in [3] is showed that MLP equalizers are able to generate complex nonlinear separation curves and thus equalize highly nonlinear channels. Reference [6] presents a programmable VLSI neural network processor configured as a four-layer perceptron for a communication receiver. This processor implements a powerful channel equalizer. Paper [22] introduces a functional-link neural-network-based decision feedback equalizer

(DFE) in order to overcome intersymbol interference, additive noise, and co-channel interference. The structure was shown to provides significantly superior bit error rate (BER) performance compared to the conventional equalizers The MLP-based receivers proposed in [10] provided a significant performance improvement in a variety of communication channels. Complex-valued neural networks have been used for channel equalization with two-dimensional signaling, for instance quadrature amplitude modulation (QAM) and phase shift keying (PSK) modulation channels. In [34] is studied the performance of conventional and complex perceptron-based equalizers of linear channels in the presence of intersymbol interference, additive noise and co-channel interference. A complex-valued version of radial basis function networks (RBF) has been introduced in[4], [5]. It has been shown that this structure is able to generate complicated nonlinear decision surfaces and to approximate an arbitrary nonlinear function in complex multi-dimensional space. The complex RBF network implemented a 4-QAM digital communication channel equalization. A comparative study between a recurrent NN equalizer and conventional methods is presented in [51] Another approach to recurrent NN [39], that exploits the principle of discriminative learning , minimizes an error function that is a direct measure of the classification error. The proposed equalizer was able to provide higher convergence speed with respect to gradient-based methods. The self-organizing maps have been combined with conventional equalizers, such as linear transversal equalizer (LTE) and decision feedback equalizer (DFE) [21]. LTE or DFE overcomes the dynamic linear distortions, while the SOM adaptively compensates the non linear distortions. The SOM-based equalizer outperformed conventional equalizers for different nonlinearity types and SNR levels. Neural networks have been applied to satellite channel equalization [15]. Several combinations of NN with classical techniques have been implemented, which outperformed conventional equalizers. Cellular neural networks, a particular class of NN with local interconnections, can implement a nonlinear equalizer with a remarkable speed performance and bit error rate [2]. Echo cancellation Acoustic echo is created by sound waves originating in the receiver (earpiece of a handset or speaker phone) that enters the microphone (mouth-piece) via reflections bouncing off solid objects in the sound path. It can be present in both wireline and wireless applications. The acoustic echo problem is more pronounced in the case of digital wireless applications because of long processing delay times (>200ms round trip delay) introduced by speech compression techniques and their non-linearity. In plus signal quality is degraded also by background noise and

reverberation. While for each individual problem of those above mentioned several solutions have been developed the combined problem has remained largely unsolved by traditional methods. NN seem to be a very good alternative solution for this problem [36]. It has been developed also a multichannel echo canceller [37]. Coding, decoding and error correcting codes This domain requires high speed and power of computation. The adaptability and flexibility of NN allow them to efficiently operate in complex situations, where some of the simplifying assumptions of standard coding and decoding techniques are not fulfilled. NN have been applied as block-parallel decoders for convolutional codes in [21]. The NN structure is based on hetero-associations and modified Hamming network concepts. These NN decoders allow noise-free data to be decoded without adding processing noise. The study shows that NN can reduce the computational complexity of the Viterbi decoding by means of a block-parallel implementation, producing similar error correcting capabilities. Image processing Image compression is an important tool for several applications in telecommunications such as satellite remote sensing, multimedia communications, broadcast television, Internet. A very efficient compression method for image coding systems is vector quantization [17]. Neural networks are interesting alternatives to classical image processing and compression techniques due to their quality image reproduction, computational capability and adaptation. Robust vector quantization. consists of optimizing a vector quantizer so that the overall distortion, due to quantization and to the channel noise, is minimized.[43] • Due to their extraordinary processing speed (trilions of operations per 1 cm2) cellular neural networks (CNN) are a natural frame for static as well as dynamic image processing, such as: feature extraction, filtering, halftoning skeletonization, detecting area with gradients that exceed a given threshold, classification by dimension or orientation, objects counting and size estimation, minim and maxim detection, contrast enhancement, growing or shrinking of images, pattern extraction, approximation, interpolation and three-dimensional object reconstruction , object rotation, character recognition, fingerprint enhancement, microscopic and neurology image enhancement , image compression and decompression , image segmentation, default detection on texture and on printed circuit boards. CNN are also able to perform complex tasks on video images: multi-target and path tracking detection, stereo vision, traffic monitoring and collision avoidance, navigation in an unknown environement. Network design, management, routing and control Modern communications networks have thousands of nodes, deal with very different traffic types and deserve a huge number of users. Most

traffic parameters are variable in time, for instance the number of users, the network topology, the data transfer rate, the necessary frequency band. As modern services became more bandwidth-hungry communications are demanding higher spectrum efficiency. Radio resource management is essential and requires dynamic channel assignment, interference avoidance, propagation prediction and automated planning techniques. There are also a lot of problems related to optimal traffic shaping, optimal billing, optimal data deployment. In this difficult traffic context the limits of conventional algorithms became evident. They are determined especially by the necessity of exact modeling of the traffic, almost impossible to be achieved in complex conditions of real communications. Finding a real time mechanism for network management and control without degradation of quality of services parameters is vital. In these conditions neural networks (NN) came to offer promising, elegant, intelligent solutions due to their capability to assure an adaptive, flexible, optimal control and an extraordinary processing speed. A NN is capable of learning the density probability function of traffic and to estimate its statistical predictable parameters. Characterization, classification and prediction of traffic is a direct application of a NN. Usual applications are implemented with MLP trained with BKP [31], [47]. There are many scheduling methods presented in literature , most of them based on Hopfield networks [38], multilayer supervised networks [24], [38] and competitive algorithms [41]. All these models are capable to deal with bursty traffic in any conditions. Fraud detection Fraudulent use of cellular telephones is a huge problem, representing for example for US more than 3% of revenue [8] The records of phone calls represent an enormous database within which anomalous use must be detected. The type of problem is unusual and difficult, as it mixes both statistic classification and temporal prediction Anomalous use has to be classified as such, but only in relation with an emerging temporal pattern. Over a period of time individual phone will generate a macroscopic pattern of use, in which intercontinental calls may be rare. Within this overall pattern there will be inevitably violations because one day, for example, the phone may be used for intercontinental calls. A full-scale fraud detector, product of NORTEL is already under a successful use. Software analysis Maintenance of large software systems evolved over years is a difficult problem that involves the complexity analysis and clone detection. The possibility of using NN to classify units of software into natural clusters, when represented by a set of complexity measures was investigated. [8]. Clone detection means identification of copied and modified software units when it is necessary to change them. It has been developed a clone detection tool, a NORTEL

product, which proved to be very useful in examination of the PROTEL soft system. Consumer products These products have already the capability of high-speed communications. This requires low cost and low power electronics. However, the domestic environment may not be radio frequency friendly so intelligent and adaptive receiver may improve the throughput without requiring an increase in transmitter power. VI DESADVANTAGES Although NN have proved the capability to solve many difficult problems, a critical analyses reveals some drawbacks: • NN is a "black box" – it doesn’t explain its decisions. Deriving behavior rules seems inherent difficult. • It’s a fact that NN can handle large numbers of variables and parameters. But data acquisition, relevant selection of variables is a difficult and time-consuming task. Data are normally preprocessed or parameterized before being presented to a NN. In this designing phase expertise in the field of the problem to be solved is necessary and invaluable. • There are not clear construction rules concerning the best NN architecture for a given application (e.g. number of layers, number of neurons on a layer, type of network, with or without feedback) .The most used method in NN design is "trial and error". • There is an art in choosing also the best method of learning, since if an inappropriate method is chosen the networks’ weights might not converge at all during training. • The obtained NN configuration is appropriate only to a particular application, adaptation to another application is very difficult. • Usually the NN configuration results in a massive structure, with many interconnections, of higher order, that can create difficulties in hard implementation. Repairing is practically impossible, because the operation is distributed in the whole NN structure and identification of broken component is not possible. • It has become apparent that the task of moving from a successful, NN based prototype to a full applicable system is quite difficult. VII CONCLUSIONS This paper gives an overview of the applications of NN in telecommunications. The exponentially growing number of papers in recent years show the great interest of researchers to neural based communications systems. A lot of successful products already developed show that NN can provide a powerful tool for telecommunications industry. As one of the experts in NN concluded [50]: "NN can determine relationships between things you didn’t even know existed and will reveal hidden truths about your business".

Modern communications with their complex problems are quite the field recommended for NN applications. As conclusions, NN may offer solutions: • where a conventional process is not suitable, can’t be easy defined or cannot fully capture the complexity in the data; • where stochastic behavior is important, where an explanation of the NN decision is not required; NN will certainly be one of the key technologies in the 21st century, so it is expected to concentrate a special effort of world research community. A rapid evolution has had recently the merging research field between all intelligent techniques : NN, genetic algorithms, fuzzy and expert systems. Hybrid intelligent systems will offer better, robust solutions implying a greater intelligence coefficient.

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