Abstract-Accurate power control is an essential requirement in the design of cellular code-division multiple-access (CDMA) systems. In this paper, we contribute three main themes to the power control problem. First, we derive an efficient algorithm for computing minimal power levels for large-scale networks within seconds. Nice and intuitive conditions for the existence of feasible power solutions follow from this approach. Second, we define the capacity region of a network by the set of effective spreading gains, or data rates, respectively, which can be supplied by the network. This is achieved by bounding the spectral radius of a certain matrix containing system parameters and mutual transmission gain information. It is shown that the capacity region is a convex set. Finally, we reveal an interesting duality between the uplink and downlink capacity region. In a clear-cut analytical way, it substantiates the fact that the uplink is the more restricting factor in cellular radio networks. The same methods carry over to certain models of soft handover. In the case that the channel gains are subject to log-normal shadowing, we introduce the concept of level-capacity regions. Despite the complicated structure, it can still be shown that this set is sandwiched by two convex sets coming arbitrarily close as variance decreases.Index Terms-Capacity region, cellular networks, code-division multiple access (CDMA), convexity, log-normal fading, soft handover.

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Slow fading or shadowing on a wireless channel is commonly modeled by stochastically independent, log-normally distributed random variables. However, as slow fading is caused by buildings and large size obstacles, spatial correlations occur. In this paper, Gaussian random fields are used as a model for correlated slow fading in urban environments. An exponential correlation function is employed. The according parameters are estimated from path gain predictions by an accurate rayoptical propagation algorithm, named CORLA. Furthermore, a multidimensional model is suggested to describe correlated shadowing of the path gains from different base stations to a single receiver.

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Clouds provide a platform for efficiently and flexibly aggregating, storing, and processing large amounts of data. Eventually, sensor networks will automatically collect such data. A particular challenge regarding sensor data in Clouds is the inherent sensitive nature of sensed information. For current Cloud platforms, the data owner loses control over her sensor data once it enters the Cloud. This imposes a major adoption barrier for bridging Cloud computing and sensor networks, which we address henceforth. After analyzing threats to sensor data in Clouds, the authors propose a Cloud architecture that enables end-to-end control over sensitive sensor data by the data owner. The authors introduce a well-defined entry point from the sensor network into the Cloud, which enforces end-to-end data protection, applies encryption and integrity protection, and grants data access. Additionally, the authors enforce strict isolation of services. The authors show the feasibility and scalability of their Cloud architecture using a prototype and measurements.