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We study the sensor selection problem for field estimation, where a best subset of sensors is activated to monitor a spatially correlated random field. Different from most commonly used centralized selection algorithms, we propose a decentralized architecture where sensor selection can be carried out in a distributed way and by the sensors themselves. A decentralized approach is essential since each...
We consider non-differentiable convex optimization problems that vary continuously in time and we propose algorithms that sample these problems at specific time instances and generate a sequence of converging near-optimal decision variables. This sequence converges up to a bounded error to the solution trajectory of the time-varying non-differentiable problems. We illustrate through analytical examples...
Sensor networks are used to gather information about the environment and to communicate this to the outside world. Sensor selection is an important design problem as the number of sensors is often limited by resource or economical constraints. In this work, the sensor selection problem for non-linear measurement models in additive Gaussian noise is considered. For this purpose, a greedy algorithm...
Constrained optimization problems that couple different cooperating users sharing the same communication network are often referred to as multiuser optimization programs. We are interested in convex discrete-time time-varying multiuser optimization, where the problem to be solved changes at each time step. We study a distributed algorithm to generate a sequence of approximate optimizers of these problems...
In this paper, a compressive sampling (CS) based multiple symbol differential detector is proposed, using the principle of a generalized likelihood ratio test (GLRT). The proposed detector works on the compressed samples directly, thereby avoiding the reconstruction step and thus resulting in a reduced implementation complexity along with a reduced sampling rate (much below the Nyquist rate). We also...
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