Energy Efficient Optimum Sampling Rate for Analogue Signals with Extremely Wide Bandwidth using Compressive Sensing

Abstract

The natural signals are mostly analogue in nature, but because of the benefits of digital processing of these signals: flexibility, accuracy, storage and low cost; processing these signals digitally is often preferred. But the existing analogues to digital converters are efficient in processing signals with small to medium bandwidths, but inefficient for signals with large bandwidths. The real-time processing of these signals with large bandwidths are done analogically or optically at the cost of the aforementioned advantages of digital processing of these signals. This paper is aimed at solving the real-time challenge of processing these extremely wide bandwidth signals digitally using a compressive sensing (CS) algorithm, with specific detail on the ways the application of CS will enhance the energy efficiency of wireless communication devices. Consequently, determine the throughput at which the use of CS is energy efficient for wireless devices using energy-efficient compressive sensing throughput (EECST) model. The simulation results show that the throughput requirements for introducing CS in any machine to machine (M2M) / internet of things (IoT) communication application to be energy efficient are minimum of 54bits per second and 317 bits per second when the required number of clock cycles for performing various device applications is 20,000 and 50000 respectively.