Architecture Designfor Compressed Sensing-Based Low Power Systems

Abstract: In the Internet of Things scenario, a desirable feature of wireless sensors is the energy autonomy. However, the transmitter stage needs a great amount of energy to modulate and transmit the interested information. The power consumption can be improved using data compression algorithms: thereby, the total amount of the transmitted data is reduced, at expense of computational complexity. Alternatively, the recent compressed sensing technique can be applied on sparse signal instances, that is when most of the entries of the signal are zero or negligible in a fixed representation. Compressed sensing acquires directly the compressed information using nonadaptive measurements and it reconstructs the signal using non-linear algorithms. Each measurement contains information of the whole signal within a frame having a fixed length. As a result, the front-end architecture complexity is decreased at expense of the reconstruction. Moreover, thanks to the compressed sensing schemes, the sampling frequency can be far lower than the conventional Nyquist rate.This thesis investigates the applicability and the advantages of this technique for the electrocardiogram signal acquisition and for the ultra-wideband receiver; indeed, both signals can be considered sparse. The focus is on the reduction of the CS measurements for the impact on the hardware requirements. The optimal design parameters are defined for the CS-based systems, leading to a reduction of power consumption.During the simulations, a noisy electrocardiogram signal is acquired and reconstructed using different setups. More interest is made on the sparsity representation, the sampling frequency, and the compressed sensing frame length. The results show clearly that the signal is more sparse when it is represented using a Biorsplines wavelet function. Moreover, a trade-off between the sampling frequency and the signal length is needed and the performance is strongly influenced by the heart rate. It is convenient to use low sampling frequency, and high frame length: a valid performance is obtained using 350 Hz as sampling rate and frames of 1024 samples.Ultra-wideband technology is suitable for Internet of Things applications because the transmitter is easier to implement and it consumes less power if compared to the traditional narrow band transmitters. However, in fully digital receivers, the required Nyquist rate is high. In the ultra-wideband scenario, compressed sensing is an attractive solution in the receiver side for the capability of recovering the signal from a small number of measurements using sub-Nyquist sampling rate: using a parallel receiver the sampling rate can be one hundred times lower than the Nyquist one. Models of the ultra-wideband transmitter and receiver are developed and simulations are performed in different noisy scenarios. Therefore, practical design parameters are investigated, including the pulse bandwidth and the compressed sensing frame length. From the results, the 3.1 10.6 GHz band allows better performance. Moreover, this thesis suggests using short compressed sensing frame length in order to reduce the total amount of needed measurements.The thesis has been developed during an European exchange program in Stockholm, Sweden, at KTH, Royal Institute of Technology.