1:00, SPCOM-L4.1
ESTIMATION OF NETWORK LINK LOSS RATES VIA CHAINING IN MULTICAST TREES
A. ZIOTOPOULOS, A. HERO, K. WASSERMAN
Of increasing importance is estimation of internal link parameters in communications networks. Multicast probes are a way to gather statistics about internal links from edge node measurements. The problem of estimating link loss probabilities for a multicast distribution tree is examined here. Our model assumes loss statistics are distributed to session participants by a network protocol such as RTCP. We propose a decentralized algorithm for ML estimation of the link loss probabilities in a chain of nodes rooted at the source node of the multicast distribution tree and terminating at a given leaf. An expression for the Cramer-Rao bound and an approximate form for the probability distribution function of the estimator are given. The performance of the algorithm is evaluated using computer simulations for a bottleneck detection application.

1:20, SPCOM-L4.2
PERFORMANCE ANALYSIS OF MOMENT-BASED ESTIMATORS FOR THE K PARAMETER OF THE RICE FADING DISTRIBUTION
C. TEPEDELENLIOGLU, A. ABDI, G. GIANNAKIS, M. KAVEH
In mobile communications the strength of a line of sight component measured by the $K$ factor of the Ricean received envelope distribution has significant impact on system performance analysis and link budget calculations. In this paper, we study the performance of moment-based estimators for the Ricean $K$-factor as less complex alternatives to the maximum likelihood estimator. Our asymptotic analysis reveals that the estimators that rely on lower-order moments have a better asymptotic performance for moderate/large values of $K$. We also illustrate, by Monte Carlo simulations, that the fading correlation among the envelope samples deteriorates the estimator performance. The simplest estimator, which can be expressed in closed form in terms of the second- and fourth-order sample moments offers a good compromise between statistical performance and computational simplicity.

1:40, SPCOM-L4.3
MARKOVIAN MODEL OF THE ERROR PROBABILITY DENSITY AND APPLICATION TO THE ERROR PROPAGATION PROBABILITY COMPUTATION OF THE WEIGTHED DECISION FEEDBACK EQUALIZER
J. PALICOT, A. GOUPIL
A Markovien model of the error probability density for Decision Feedback Equalizer is propoed and its application to the error propagation probability computation is derived. The model is a generalization of the Luckemeyer and Noll model proposed in [1].It is obtained by the analysis of the Gaussian mixture distribution of the errors which follows a Markov Process. The analysis of this process shows that the error propagation probability of the Weighted DFE[2] is less than the one of the classical DFE.

2:00, SPCOM-L4.4
CONVERGENCE ANALYSIS OF A NEW BLIND EQUALIZATION ALGORITHM WITH M-ARY PSK CHANNEL INPUTS
P. LIU, Z. XU
In our previously proposed equalization criterion with M-PSK modulated channel inputs [10], perfect equalization was guaranteed in principle under constrained optimization. The algorithm was implemented based on stochastic gradient method. In this paper, we investigate the convergence property of that algorithm. By carefully examining all stationary points of the objective function, we prove that all other points are unstable except the desired solution. The effects of the ary number M and the background noise on the performance of the proposed equalizer are studied in detail in our simulations. Satisfactory bit error rate performance and output constellation are observed.

2:20, SPCOM-L4.5
EXPRESSION OF THE CAPACITY FOR THE GILBERT CHANNEL IN PRESENCE OF INTERLEAVING
T. RANCUREL, D. ROVIRAS, F. CASTANIE
Many models based on Hidden Markov Models were developped to model errors bursts in communication channels. These models allow the computation of errors distributions as well as the capacity expression of a channel. In this paper, we evaluate the effect of interleaving on the capacity of the widely used Gilbert model. An expression of this capacity is derived and is illustrated by simulation. This allows, when having an identified error model, to simulate in an easy way the effects of any interleaving depth on the capacity of the channel.

2:40, SPCOM-L4.6
IMPORTANCE SAMPLING EVALUATION OF DIGITAL PHASE DETECTORS BASED ON EXTENDED KALMAN-BUCY FILTERS
F. SILVA, J. LEITAO
This paper proposes an importance sampling methodology for the performance evaluation of a class of open-loop receivers with random carrier phase tracking in additive white Gaussian noise channels. The receivers, consisting of a bank of extended Kalman-Bucy filters and a decision algorithm based on the filters' innovations processes, perform symbol-by-symbol phase detection while kepping track of the random phase process within the symbol interval. We use a large deviations approach to start a stochastic importance sampling optimization, both for the irreducible error floor and for the general noisy operation of the receiver. Our simulations show a practical coincidence with conventional Monte Carlo results, with considerable simulation time gains.