The MIX technique forms the basis of many popular services that offer anonymity of communication in open and shared networks such as the Internet. In this paper, fundamental limits on the anonymity provided by the MIX technique are found by considering two different settings. First, we consider

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Cognitive radio refers to wireless architectures in which a communication system does not operate in a fixed assigned band, but rather searches and finds an appropriate band in which to operate. In this paper we explore, from first principles, the fundamental requirements for such system that tries

increasingly heroic efforts, stimulating research in alternative hardware and stirring controversy. To help evaluate emerging technologies and enrich our understanding of integrated-circuit scaling, we review fundamental limits to computation: in manufacturing, energy, physical space, design and verification

. One contribution of this paper is to present fundamental limitations of this general local to global approach. We show that based only on local information, the normalized cut functional is not a suitable measure for the quality of clustering. Further, even with a suitable similarity measure, we show

The fundamental limits of performance for a general model of information retrieval from databases are studied. In the scenarios considered a large quantity of information is to be stored on some physical storage device. Requests for information are modeled as a randomly generated sequence with a

Cooperation is viewed as a key ingredient for interference management in wireless systems. This paper shows that cooperation has fundamental limitations. The main result is that even full cooperation between transmitters cannot in general change an interference-limited network to a noise-limited

reasonable approximate metrics that capture the two desirable features of a spectrum sensor: safety to primary users and performance for the cognitive radios. It is the tradeoff between these two that is fundamental. Single-user sensing turns out to have fundamental limits that require access to more

the mathematical content and meaning of the two most important limit theorems in the modern theory of probability: the central limit theorem 1 and the recently discovered precise form of what was generally

Suppose we can sequentially acquire arbitrary linear measurements of an n-dimensional vector x resulting in the linear model y = Ax + z, where z represents measurement noise. If the signal is known to be sparse, one would expect the following folk theorem to be true: choosing an adaptive strategy which cleverly selects the next row of A based on what has been previously observed should do far better than a nonadaptive strategy which sets the rows of A ahead of time, thus not trying to learn anything about the signal in between observations. This paper shows that the folk theorem is false. We prove that the advantages offered by clever adaptive strategies and sophisticated estimation procedures—no matter how intractable—over classical compressed acquisition/recovery schemes are, in general, minimal.