We analyze traffic traces obtained from measurements at the edge gateway of the campus network of the University of Pavia. These traces have been obtained simply using the tcpdump utility and then analyzed and modeled using two different approaches. The first is based: on a hidden Markov model (HMM) the other on a stochastic generator based on a chaotic attractor. It is shown that the HMM is capable of capturing the general statistics of the observed streams despite poor long term correlation characteristics, while the chaotic model can achieve somewhat better results in long term characteristics and somewhat worse in short term. Moreover, the chaotic model is sensitive to the parameter extraction procedure.

The behavior of IP based network traffic has been the subject of several works. In this paper, we compare two modeling approaches and their ability to capture two basic traffic features: packet size distribution and interarrival times. Data traces were measured at the border gateway of the campus network of the University of Pavia at different times of the day. The two modeling schemes use respectively well established hidden Markov model (HMM) techniques and a stochastic engine derived from chaotic attractors.

The paper relates to some developments in modeling attenuation time series, measured on Ka band satellite links during rain events, by means of sample generators using chaotic dynamical systems. Weighted sums of Lorenz strange attractors are first used to model target sequences by themselves ("global approach"), improving procedures already introduced. Then, similar generators are used to model only low-pass versions of the same sequences, while hidden Markov models (HMM) or shaped random number series are used to mimic the complementary high-pass sequences.

Previous works have introduced models based on deterministic chaos equations, aiming to simulate transmission processes over mobile radio digital channels at the level of the post detection error stochastic process and to reproduce their renewal or nonrenewal behaviors. In particular bursts and clusters of errors are generated by these models, exploiting the correlation between successive points sampled from the trajectories of suitable strange attractors. In this paper chaos equation models are reviewed and relevant results are discussed. Different approaches have been tested, using one or more attractors, and calling for light or heavy preprocess procedures to cope with the statistical characteristics of target gap-time series. Models derived from hidden Markov chains have been implemented to provide comparison of results. Target sequences were supplied by simulation of digital enhanced cordless telecommunications (DECT) channels or derived from reception of DECT signals by a mobile unit in indoor environments.

The paper relates on experiments in deriving models for satellite link channels in the Ka band using nonlinear deterministic chaos equations. The trajectories of Lorenz attractors are sampled at suitable time rates and combined to obtain attenuation samples of the fading channel, following procedures suggested by previous experience in modeling the behavior of time error series in digital communication channels. Optimization cost function structures are discussed.

Source modelling is one of the fundamental issues in studying the performance of networks under different traffic conditions. Previously, emphasis has been put on modelling the output of digital video sources to determine the actual load and its temporal correlation. In this paper we propose a new modelling approach based on the use of chaotic equations to generate simulated MPEG sequences. The performances of an ATM node carrying video traffic are observed using both "real" and "chaotic" input video sources. We show that the proposed model is accurate enough to capture the loss-delay characteristics of the node and can thus be used as an alternative to traditional probabilistic models.

Applications of clustering and neural network techniques to channel equalization have revealed the classification nature of this problem. This paper illustrates an implementation of a global system for mobile communications (GSM) receiver in which channel equalization and demodulation are realized by means of the nearest neighbor (NN) classifier algorithm. The most important advantage in using such techniques is the significant reduction in terms of the computational complexity compared with the maximum likelihood sequence estimation (MLSE) equalizer. The proposed approach involves symbol-by-symbol interpretation and the knowledge of the channel is embedded in the mapping process of the received symbols over the symbols of the training sequence. This means that no explicit channel estimation need be carried out, either with correlative blocks or using neural networks thus speeding up the entire process. The performance of the proposed receiver, evaluated through a channel simulator for mobile radio communications, is compared with the results obtained by means of a 16-state Viterbi algorithm and other suboptimal receivers. It is shown that the presented algorithm increases the bit error rate (BER) compared with the MLSE demodulator, but the performance degradation, despite the simplicity of the receiver, is kept within the limits imposed by the GSM specifications.

The results of extensive simulations of the behavior of tandem channels show interesting trends of the statistical functions in the case of tandem-connected burst-error channels or binary symmetric channels and burst-error channels. The simulations can be divided into two different groups. In the first, the single channels are modeled by infinite Markov chains, the models are connected in tandem, and the behavior of the tandem is described in terms of significant features. In the second, the autocorrelation of the resulting channel is first calculated, and then the tandem is approximated by a renewal model, equivalent from the bit error rate and the conditional error probability points of view. The behavior of this model is discussed.>