Matthias Kaschub

A Cell Outage Detection Algorithm Using Neighbor Cell List Reports

Base stations experiencing hardware or software failures have negative impact on network performance and customer satisfaction. The timely detection of such so-called outage or sleeping cells can be a difficult and costly task, depending on the type of the error. As a first step towards self-healing capabilities of mobile communication networks, operators have formulated a need for an automated cell outage detection. This paper presents and evaluates a novel cell outage detection algorithm, which is based on the neighbor cell list reporting of mobile terminals. Using statistical classification techniques as well as a manually designed heuristic, the algorithm is able to detect most of the outage situations in our simulations.

Context-aware resource allocation for cellular wireless networks

Current cellular networks are often overloaded by Smartphone traffic, while the users’ Quality of Service (QoS) demands are not met. To cope with this problem, we demonstrate a new radio resource management approach. With Context-Aware Resource Allocation, the base station’s scheduler (i) observes Context Information (CI) from the user’s environment and (ii) utilizes this knowledge for an efficient throughput-delay tradeoff. After introducing our framework for accessing CI from the handheld’s applications and operating system, we use time-utility functions to develop a practical scheduling algorithm. Studying this heuristic under various traffic assumptions shows that our context-aware scheduler can support three times the load of proportional fair scheduling, at equal capacity and utility. Thus, even a small degree of CI increases the wireless links’ efficiency without sacrificing the users’ QoS.

A Novel Architecture using NVIDIA CUDA to Speed Up Simulation of Multi-Path Fast Fading Channels

The complexity of mobile communication systems is permanently increasing. This is due to the introduction and refinement of sophisticated communication mechanisms such as multiple input multiple output (MIMO), hybrid ARQ, channel-aware scheduling, or node cooperation. Many of these mechanisms require detailed multi-cell simulation models for their performance evaluation. This involves computationally extensive models of the wireless multi-path fading channel, which quickly becomes a bottleneck with respect to simulation time. It is therefore of great interest to reduce the amount of computation time spent in the channel calculation. In this paper, we present an attractive approach that off loads the channel computation to a massively parallel but inexpensive NVIDIA graphics card. We discuss the parallel architecture of the resulting simulation system, and study the involved synchronization and communication aspects. We show that the developed system achieves a speed-up factor of about 30 compared to an implementation on regular PC hardware.

Interference mitigation by distributed beam forming optimization

Inter-cell interference is a major issue in OFDMA networks. One approach to reduce the amount of interference is to use beam forming antennas. Further interference mitigation is achieved if neighbor base stations coordinate the directions of their beams. This paper presents a novel distributed interference coordination algorithm using main-lobe steering beam formers. Our algorithm does not require any explicit signaling between base stations. Also, it does not require any additional channel measurements to be signaled to the base stations besides those already performed for basic operation. Our evaluations show that significant performance gains can be achieved even with non-greedy traffic. Index Terms – Interference coordination, beam forming antennas, COMP

Improving Anomaly Detection for Text-Based Protocols by Exploiting Message Structures

Service platforms using text-based protocols need to be protected against attacks. Machine-learning algorithms with pattern matching can be used to detect even previously unknown attacks. In this paper, we present an extension to known Support Vector Machine (SVM) based anomaly detection algorithms for the Session Initiation Protocol (SIP). Our contribution is to extend the amount of different features used for classification (feature space) by exploiting the structure of SIP messages, which reduces the false positive rate. Additionally, we show how combining our approach with attribute reduction significantly improves throughput.