École Polytechnique Fédérale de Lausanne
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Efficient routing with small buffers in dense networks
No full textThe analysis and design of routing algorithms for finite buffer networks requires solving the associated queue network problem which is known to be hard. We propose alternative and more accurate approximation models to the usual Jackson’s Theorem that give more insight into the effect of routing algorithms on the queue size distribu- tions. Using the proposed approximation models, we analyze and design routing algorithms that minimize overflow losses in grid networks with finite buffers and different communication patterns, namely uniform communication and data gathering. We show that the buffer size required to achieve the maximum possible rate decreases as the network size increases. Motivated by the insight gained in grid networks, we apply the same principles to the design of routing algorithms for random networks with finite buffers that minimize overflow losses. We show that this requires adequately combining shortest path tree routing and traveling salesman routing. Our results show that such specially designed routing algorithms increase the transmitted rate for a given loss probability up to almost three times, on average, with respect to the usual shortest path tree routing.LCA
On Compression Using the Distributed Karhunen-Loeve Transform
No full textIn this paper, we discuss a framework for the distributed compression of vector sources, based on our previous work on distributed transform coding. In particular, our goal is to develop a strategy of first applying a suitable distributed Karhunen-Loeve transform, whereafter each component can be andled by standard distributed compression techniques. In the present paper, we first study the scenario where all but one terminal furnish a noisy approximation of their observation. For the case where the underlying vector is Gaussian, and the added noise is also Gaussian, we establish that indeed, it is optimal for the last terminal to apply a (local) transform to its observations, and to separately compress each component in the transform domain. Then, we outline how this leads to a general simple distributed compression strategy for Gaussian vector sources: Each terminal applies a suitable local transform to its observations, and encodes the resulting components separately in a Wyner-Ziv fashion, i.e., treating the compressed descriptions of all other terminals as side information available to the decoder. This achieves the best known performance. The optimum performance in unknown to date.LINXLCA
Separation of Harmonic Instruments with Overlapping Partials in Multi-Channel Mixtures
No full textLCA
The Plenacoustic Function, Sampling and Reconstruction
No full textIn the present paper, we study the spatialization of the soundfield in a room, in particular the evolution of room impulse responses as function of their spatial positions. The presented technique allows us to completely characterize the sound field in any arbitrary location if the sound field is known in a certain finite number of positions. The existing techniques make usually use of room models to recreate the sound field present at some point in the space. Our technique simply starts from the measurements of impulse responses in a finite number of positions and with this information the total sound field can be recreated. An analytical solution of the problem is given for different cases of spaces. Further, we determine the number and the spacing between the microphones needed to perfectly reconstruct the sound field up to a certain temporal frequency. The optimal sampling pattern for the microphone positions is given. Applications are also discussed.LCA
Low-Complexity Subspace Methods for Channel Estimation and Synchronization in Ultra-Wideband Systems
No full textWe consider the problem of low-complexity channel estimation in digital ultra-wideband receivers. We extend some of our recent sampling results for certain classes of parametric non-bandlimited signals and develop several methods that take advantage of transform techniques to estimate channel parameters from a low-dimensional subspace of a received signal, that is, by sampling the signal below the Nyquist rate. By lowering the sampling rate we reduce computational requirements compared to current digital solutions, allow for slower A/D converters and potentially significantly reduce power consumption of digital receivers. Our approach is particularly suitable for indoor wireless sensor networks, where low rates and low power consumption are required. One application of our framework to high-resolution acquisition in ultra-wideband localizers is also presented.LCA
