The presence of "unwanted" (or background) traffic in the Internet is a well-known fact. In principle any network that has been engineered without taking into account the presence of unwanted traffic might experience troubles during periods of massive exposure to it, e.g. large-scale infections. A concrete example was provided by the spreading of Code-Red-II in 2001, which caused several routers crashes worldwide. Similar events might take place in 3G networks as well, with further potential complications due to the functional complexity and the scarcity of radio resources.In this explorative paper we show that unwanted traffic is present also in GPRS/UMTS, mainly due to the widespread use of 3G connect cards for laptops. Based on a mixture of real-world measurements and theoretical speculations, we investigate the potential impact of such traffic onto the underlying network. We show that under certain hypothetical network configuration settings unwanted traffic, and specifically scanning traffic from infected Mobile Stations, can cause large-scale wastage of logical resources, and in extreme cases starvation.We urge the research community and network operators to consider the issue of 3G robustness to unwanted traffic as a prominent research area. The goal of this paper is to trigger interest and at the same time move a first pioneering step in this direction.

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Abstract. Users expect mobile Internet access via 3G technologies to be comparable to wired access in terms of throughput and latency. HSPA achieves this for throughput, whereas delay is significantly higher.In this paper we measure the overall latency introduced by HSUPA and accurately dissect it into contributions of USB-modem (UE), base station (NodeB) and network controller (RNC). We achieve this by combining traces recorded at each interface along the data-path of a public operational UMTS network. The actively generated sample traffic covers real-time applications.Results show the delay to be strongly dependent on the packet size, with random components depending on synchronization issues. We provide models for latency of single network entities as well as accumulated delay. These findings allow to identify optimum settings in terms of low latency, both for application and network parameters.

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