Signalling Storms in 3G Mobile Networks

We review the characteristics of signalling storms that have been caused by certain common apps and recently observed in cellular networks, leading to system outages. We then develop a mathematical model of a mobile user's signalling behaviour which focuses on the potential of causing such storms, and represent it by a large Markov chain. The analysis of this model allows us to determine the key parameters of mobile user device behaviour that can lead to signalling storms. We then identify the parameter values that will lead to worst case load for the network itself in the presence of such storms. This leads to explicit results regarding the manner in which individual mobile behaviour can cause overload conditions on the network and its signalling servers, and provides insight into how this may be avoided.Comment: IEEE ICC 2014 - Communications and Information Systems Security Symposiu

Characterizing Multi-radio Energy Consumption in Cellular/Wi-Fi Smartphones

Cellular networks evolved to meet the ever increasing traffic demand by way of offloading mobile traffic to Wi-Fi network elements. Exploiting multi-radio interfaces on a smartphone has recently been examined with regards to heterogeneous bandwidth aggregation and radio switching. However, how a smartphone consumes its energy in driving cellular and Wi-Fi multi-radio interfaces, is not well understood. In this paper, we revealed the energy consumption behavior of 3G cellular and Wi-Fi multi-radio operations of a smartphone. We modified smartphone’s firmware to enable multi-radios operations simultaneously and we performed extensive measurements of multi-radio energy consumption in a real commercial network. From the measured data set, we established a realistic multi-radio energy consumption model and it gave 98% stability from the derived coefficients

TCP over CDMA2000 Networks: A Cross-Layer Measurement Study

Modern cellular channels in 3G networks incorporate sophisticated power control and dynamic rate adaptation which can have significant impact on adaptive transport layer protocols, such as TCP. Though there exists studies that have evaluated the performance of TCP over such networks, they are based solely on observations at the transport layer and hence have no visibility into the impact of lower layer dynamics, which are a key characteristic of these networks. In this work, we present a detailed characterization of TCP behavior based on cross-layer measurement of transport layer, as well as RF and MAC layer parameters. In particular, through a series of active TCP/UDP experiments and measurement of the relevant variables at all three layers, we characterize both, the wireless scheduler and the radio link protocol in a commercial CDMA2000 network and assess their impact on TCP dynamics. Somewhat surprisingly, our findings indicate that the wireless scheduler is mostly insensitive to channel quality and sector load over short timescales and is mainly affected by the transport layer data rate. Furthermore, with the help of a robust correlation measure, Normalized Mutual Information, we were able to quantify the impact of the wireless scheduler and the radio link protocol on various TCP parameters such as the round trip time, throughput and packet loss rate

On the Interaction between TCP and the Wireless Channel in CDMA2000 Networks

In this work, we conducted extensive active measurements on a large nationwide CDMA2000 1xRTT network in order to characterize the impact of both the Radio Link Protocol and more importantly, the wireless scheduler, on TCP. Our measurements include standard TCP/UDP logs, as well as detailed RF layer statistics that allow observability into RF dynamics. With the help of a robust correlation measure, normalized mutual information, we were able to quantify the impact of these two RF factors on TCP performance metrics such as the round trip time, packet loss rate, instantaneous throughput etc. We show that the variable channel rate has the larger impact on TCP behavior when compared to the Radio Link Protocol. Furthermore, we expose and rank the factors that influence the assigned channel rate itself and in particular, demonstrate the sensitivity of the wireless scheduler to the data sending rate. Thus, TCP is adapting its rate to match the available network capacity, while the rate allocated by the wireless scheduler is influenced by the sender's behavior. Such a system is best described as a closed loop system with two feedback controllers, the TCP controller and the wireless scheduler, each one affecting the other's decisions. In this work, we take the first steps in characterizing such a system in a realistic environment