We propose a network topology estimation strategy using unicast end-to-end packet pair delay measurements that is based on mixture models for the delay covariances. An unsupervised learning algorithm is applied to estimate the number of mixture components and delay covariances. The leaf pairs are clustered by a MAP criterion and passed to a hierarchical topology construction algorithm to rebuild the tree. Results from an ns simulation show that our algorithm can identify a network tree with 8 leaf nodes.

As telecommunication networks grow larger and more complex, it is important to monitor internal link characteristics for operation, monitoring, and diagnosis purposes. Since router link monitoring is not practical due to high communication overhead, there has been considerable interest in monitoring from edge (end-to-end) observations. This paper focuses on the estimation of internal link delay distributions from edge measurements. Discrete and continuous delay models are introduced and we propose a new mixed finite mixture model for link delay probability density functions (p.d.f.). When collecting end-to-end unicast packet delays from edge nodes, we are able to estimate internal link delay distributions using the EM algorithm. Simulation results are given to illustrate our method.

Inference of network internal link characteristics has become an increasingly important issue for operating and evaluating large telecommunication networks. Since it is usually impractical to directly monitor each link along a specific path, end-to-end probes are sometimes used to collect link characteristic information at edge nodes of the network. This paper deals with unicast probing methods for estimation of link delay characteristics. Unicast traffic is easy to generate and is supported by almost every network currently in operation. Under the assumptions that link delays are spatially and temporally independent, we propose a bias corrected estimator for the internal link delay cumulant generating function (CGF) based on unicast probe end-to-end delay measurements. Through simulation we show that the proposed estimator attains a level of mean squared error comparable to link delay CGF estimates obtained from directly measured link delay statistics. We can use these CGF estimates to estimate delay mean, variance and level exceedance probabilities for each link.

Performance of fractionally spaced equalizers using polyperiodic linear filtering is depicted. The fractionally sampled version of an arbitrary stable channel's outputs with stationary random inputs exhibit cyclostationarity with cycle frequencies being integer multiples of the reciprocal of the oversampling factor. By optimally polyperiodic linear filtering the oversampled outputs, we can fully utilize the cyclostationary property. With some traditional Bussgang techniques, such as stop-and-go decision-directed method, to adaptively update the filter coefficients, the convergence rate of the fractionally spaced equalizers proposed is improved, while the complexity retains in the same order as original Bussgang ones. Some computer simulation results are shown to demonstrate the improvement we can achieve.