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International audience; A MIMO radar system is conveniently modeled via random matrices, and its optimal design strongly relies on spectral properties of the matrix exploited to build the model itself. We offer a way to model a High Resolution Radar (HRR) detection in the MIMO case, based on recent results on the asymptotic spectral analysis of random Vandermonde matrices with entries lying on the unit circle. Achievability of compact-form expressions for the design parameters sought for in the multiple-transmitter-multiple-receiver case is investigated, and together with the results of such a starting analysis, some open mathematical questions that arise from the new model formulation are listed and discussed.

International audience; Recently, analytical methods for finding moments of random Vandermonde matrices with entries on the unit circle have been proposed in the literature. Vandermonde matrices play an important role in signal processing and wireless applications, among which the multiple-antenna channel modeling, precoding or sparse sampling theory. Recent investigations allowed to extend the combinatorial approach usually exploited to characterize the spectral behavior of large random matrices with independent and identically distributed (i.i.d.) entries to Vandermonde structured matrices, under fairly broad assumptions on the entries distributions. While in several cases explicit expressions of the moments of the associated Gram matrix, as well as more advanced models involving the Vandermonde matrix could be provided, several issues are still open in the spectral behavior characterization, with applications either in signal processing (deconvolution, compressed sensing) and/or wireless communications (capacity estimation, topology information retrieving, etc).

In this paper we study the optimal placement and optimal number of active relay nodes through the traffic density in mobile sensor ad-hoc networks. We consider a setting in which a set of mobile sensor sources is creating data and a set of mobile sensor destinations receiving that data. We make the assumption that the network is massively dense, i.e., there are so many sources, destinations, and relay nodes, that it is best to describe the network in terms of macroscopic parameters, such as their spatial density, rather than in terms of microscopic parameters, such as their individual placements. We focus on a particular physical layer model that is characterized by the following assumptions: i) the nodes must only transport the data from the sources to the destinations, and do not need to sense the data at the sources, or deliver them at the destinations once the data arrive at their physical locations, and ii) the nodes have limited bandwidth available to them, but they use it optimally to locally achieve the network capacity. In this setting, the optimal distribution of nodes induces a traffic density that resembles the electric displacement that will be created if we substitute the sources and destinations with positive and negative charges respectively. The analogy between the two settings is very tight and have a direct interpretation in wireless sensor networks.