Radio communication is among the most energy consuming tasks in wireless sensor nodes. Reducing the amount of data to be transmitted holds a large power saving potential. The combination of compressed sensing (CS) and local signal parameter estimation can achieve a massive data rate reduction in applications where the primary interest is in the acquisition of a scalar feature of the signal rather than the reconstruction of the entire waveform. In this paper, We propose a compressed estimator, building upon an enhancement of the typical CS signal-modulation scheme via punctured sampling. Specifically, a subset of signal samples and associated weighting coefficients are chosen so as to minimize node power consumption while achieving a given estimation performance. We detail a corresponding puncturing algorithm and present the design of an integrated digital compressed estimation unit in 28nm FDSOI CMOS. In a concrete case study, local estimation combined with subsampling is shown to result in a power reduction of up to an order of magnitude with respect to the standard solution of sampling and transmitting samples for off-board processing.

An architecture for low-power compressed sensing and estimation in wireless sensor nodes / Bellasi, David; Rovatti, Riccardo; Benini, Luca; Setti, Gianluca. - STAMPA. - (2014), pp. 1732-1735. ((Intervento presentato al convegno 2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014 tenutosi a Melbourne, VIC, aus nel 2014 [10.1109/ISCAS.2014.6865489].

An architecture for low-power compressed sensing and estimation in wireless sensor nodes

Setti Gianluca
2014

Abstract

Radio communication is among the most energy consuming tasks in wireless sensor nodes. Reducing the amount of data to be transmitted holds a large power saving potential. The combination of compressed sensing (CS) and local signal parameter estimation can achieve a massive data rate reduction in applications where the primary interest is in the acquisition of a scalar feature of the signal rather than the reconstruction of the entire waveform. In this paper, We propose a compressed estimator, building upon an enhancement of the typical CS signal-modulation scheme via punctured sampling. Specifically, a subset of signal samples and associated weighting coefficients are chosen so as to minimize node power consumption while achieving a given estimation performance. We detail a corresponding puncturing algorithm and present the design of an integrated digital compressed estimation unit in 28nm FDSOI CMOS. In a concrete case study, local estimation combined with subsampling is shown to result in a power reduction of up to an order of magnitude with respect to the standard solution of sampling and transmitting samples for off-board processing.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2696786
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