For the same long-term loss ratio, different loss patterns lead to different application-level Quality of Service (QoS) perceived by the users (short-term QoS). While basic packet loss measures like the mean loss rate are widely used in the literature, much less work has been devoted to capturing a more detailed characterization of the loss process. In this paper, we provide means for a comprehensive characterization of loss processes by employing a model that captures loss burstiness and distances between loss bursts. Model parameters can be approximated based on run-lengths of received/lost packets. We show how the model serves as a framework in which packet loss metrics existing in the literature can be described as model parameters and thus integrated into the loss process characterization. Variations of the model with different complexity are introduced, including the well-known Gilbert model as a special case. Finally we show how our loss characterization can be used by applying it to actual Internet loss traces.

Autoconfiguration of the radio parameters is a key feature for next generation mobile networks. Especially for LTE the NGMN Forum has brought it up as a major requirement. It is indispensable that algorithms used for autoconfiguration terminate quickly and do not cause infinite iterative reconfigurations within the network.Reference signal sequences are among the most important radio parameters for LTE, which are comparable to scrambling codes in 3G networks. In LTE they additionally serve as Cell Identifiers on the Physical Layer. Each cell is assigned one of the 504 available Physical Cell Identifiers. For proper operation the assignment has to be as well collision as also confusion free. Due to the high number and the layered structure of the cells within the network such an assignment is a complex task.In addition to this complexity each change of the Physical Cell ID of an operational cell causes a service interruption in the cell, which has to be avoided. The approach presented maps the ID assignment problem to the well known and well understood problem of graph coloring. It is shown that an efficient initial assignment even for complex networks is possible. Cells added during the subsequent network growth, can already be confused when inserted into the network. In this case the IDs of the operational cells causing the confusion must be changed.As a next logical step the incremental approach shows how the properties of the colored graph can be used for extending the network with new cells, with only minimal interruption while still retaining the properties of a colored graph.

We present a new error concealment technique for audio transmission over heterogeneous packet switched networks based on time-scale modification of correctly received packets. An appropriate time-scale modification algorithm, WSOLA ("Waveform Similarity OverlapAdd"), is used and its parameters are optimized for scaling short audio segments. Particular attention is paid to the additional delay introduced by the new technique. For subjective listening tests, packet loss is simulated at error rates of 20% and 33% and the new technique is compared to previous proposals by category and component judgment of speech quality. The test results show that typical disturbance components of other techniques can be avoided and overall sound quality is higher.

Abstract-The Self-Organizing Networks (SON) concept includes the functional area known as self-healing, which aims to automate the detection and diagnosis of, and recovery from, network degradations and outages. This paper focuses on the problem of cell anomaly detection, addressing partial and complete degradations in cell-service performance, and it proposes an adaptive ensemble method framework for modeling cell behavior. The framework uses Key Performance Indicators (KPIs) to determine cell-performance status and is able to cope with legitimate system changes (i.e., concept drift). The results, generated using real cellular network data, suggest that the proposed ensemble method automatically and significantly improves the detection quality over univariate and multivariate methods, while using intrinsic system knowledge to enhance performance.