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Multipath signal propagation is the defining characteristic of terrestrial wireless channels. Virtually all existing statistical models for wireless channels are implicitly based on the assumption of rich multipath, which can be traced back to the seminal works of Bello and Kennedy on the wide-sense stationary uncorrelated scattering model, and more recently to the i.i.d. model for multi-antenna channels...
Training-based channel estimation involves probing of the channel in time, frequency, and space by the transmitter with known signals, and estimation of channel parameters from the output signals at the receiver. Traditional training-based methods, often comprising of maximum likelihood estimators, are known to be optimal under the assumption of rich multipath channels. Numerous measurement campaigns...
Compressive sampling is an emerging theory that is based on the fact that a relatively small number of random projections of a signal can contain most of its salient information. In this paper, we introduce the concept of compressive wireless sensing for sensor networks in which a fusion center retrieves signal field information from an ensemble of spatially distributed sensor nodes. Energy and bandwidth...
The essential task in nearly all applications of sensor networks is to extract relevant information about the sensed data and deliver it to a desired destination. The overall goal in the design of sensor networks is to execute this task with least consumption of network resources. In this regard, the relevant metrics of interest are 1) the latency (bandwidth) involved in network data acquisition;...
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