RESEARCH OVERVIEWMy research falls in the area of communications and signal processing. Currently, I am interested in various problems in wireless communications. See my publications for details. Here is a brief summary of my research.
COOPERATIVE COMMUNICATION
A fundamental problem in wireless networks is determining the broadcast capacity, i.e. the maximum data transfer rate from a given
node to every other node in a
relay network. In this work, we study the scaling of the broadcast capacity
for a network with a single random networks under different node configurations and channel fading models.
Cooperative broadcast aims to deliver a
source message to a locally connected network by means of collaborating nodes
at the physical layer. In this work, we studied the effect of network parameters such as source/relay transmission powers and the decoding threshold on the number of nodes reached by cooperative broadcast. Our approach is based on the idea of continuum approximation, which yields closed-form expressions accurate when the network density is high. ·
Decentralized
diversity schemes for cooperative communication In ad-hoc network, utilizing multiple-antenna systems is impractical for certain network applications. Cooperation among the nodes can provide spatial diversity gains without utilizing multiple transmit antennas. We introduced randomized strategies that decentralize the transmission of a space time code from a set of distributed relays. Furthermore, we provided design criteria and analyzed the performance of different strategies. ·
Power
efficiency of cooperative transmission in dense wireless networks In this work, we studied the optimal power allocation problem for cooperative broadcast in dense large-scale networks. We proposed practical distributed schemes that are almost optimal in terms of the scaling of power consumption and also compared their power consumption with those of conventional non-cooperative multi-hop broadcast. CONSTRAINED TOTAL LEAST SQUARES WITH APPLICATION TO HYPERSPECTRAL IMAGERYSpectral unmixing is a pixel-by-pixel approach to the detection and localization of features by spectral analysis techniques. Usually, partial knowledge about the feature, noise, and clutter spectra are provided, and the problem is to ``unmix'' each pixel, or to estimate the relative concentrations of the reference spectra on a per pixel basis. We developed total least squares (TLS) based algorithms for spectral unmixing of hyperspectral images in case of dominant modelling errors. We implemented the developed techniques to medical diagnosis and remote sensing problems. RECENT TALKSSep 21, 2007 SJSU/EE297 Seminar Talk
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