This project will devise an adaptive framework for efficient data acquisition of spectrally sparse signals to enhance the sampling accuracy while reducing the energy consumption demands of future sensing and communication systems that are ubiquitous and vital to our modern and connected society. Novel adaptive sampling and reconstruction techniques will be designed concomitantly with a spin-based hardware approach to minimize the overall cost of data acquisition and transmission for applicability in a wide-range of communication systems and a large and important class of spectrally sparse signals, which arise in many applications such as cognitive radio networks, radar, and emerging spectrum-aware communication systems. Thus, this project will serve national interests of advancing vital technologies of communication systems with improved energy-efficiency and increased circuit density. Educational materials for undergraduate and graduate course modules will be created and disseminated, as well as an interactive website to engage and attract high school students to studies and careers in the field, including a diverse cohort of underrepresented and women learners. Broad dissemination through nano-device library webpages will be used to increase the impact while supplementing the publication of research outcomes via high-quality scholarly journals and conferences, and websites.
A multi-disciplinary effort will be used to develop a systematic approach that bridges the gap between advanced theoretical research in signal processing/compressive sensing and innovative circuit designs that leverage the signal processing, memory, and thresholding capabilities inherent in emerging spin-based devices. The first research thrust focuses on investigating the tradeoffs between Sampling Rate (SR) and Quantization Resolution (QR) in the context of quantized compressive sensing (CS), under power and bandwidth constraints using dynamic optimization of SR and QR in an online manner. The energy consumption, hardware limitations, and specifics of the underlying sampler and quantizer will be optimized. Computationally-efficient signal reconstruction algorithms are investigated to reconstruct the original signal back from its non-uniform (in terms of sampling rate and quantization depth) quantized CS measurements. In the second research thrust, the investigators will research and design an Intermittent Spin-based Adaptive Quantizer which utilizes Voltage-Controlled Magnetic Anisotropy Magnetic Tunnel Junction (VCMA-MTJ) devices to provide fast SR and adaptive QR in a novel energy-efficient fashion.
Expected contributions include:
A novel framework for efficient and intelligent sensing through integration of resource allocation, quantized CS, and adaptable spin-based devices will be developed
SR and QR trade-offs under resource constraints are utilized to attain an energy-aware adaptive SR/QR optimization framework which is integrated with VCMA-MTJ devices
Novel sampling and reconstruction algorithms will be developed in context of adaptive quantized CS
VCMA-MTJ circuits will be designed to realize faster and more energy-efficient sampling and signal processing
Spin-based lookup table and encoder circuits using new switching strategies will be designed
The energy consumption of VCMA-MTJs will be analyzed and the derived energy equation will be utilized for SR/QR optimization.