Wireless Compressive Sensing for Energy Harvesting Sensor Nodes Over Fading Channels
The authors consider the scenario in which multiple sensors send spatially correlated data to the Fusion Center (FC) via independent Rayleigh-fading channels with additive noise. Assuming that the sensor data is sparse in some basis, they show that the recovery of the signal can be formulated as a Compressive Sensing (CS) problem. To model the scenario where sensors operate with intermittently available energy that is harvested from the environment, they propose that each sensor transmits independently with some probability, and adapts the transmit power to its harvested energy. Due to probabilistic transmissions, the elements of the equivalent sensing matrix are not Gaussian.