# DARPA A2I Receiver Program

The power, stability, and low cost of digital signal processing (DSP) has pushed the analog-to-digital converter (ADC) increasingly close to the front-end of many important sensing, imaging, and communication systems. Unfortunately, many systems operating in the radio frequency bands severely stress current ADC technologies. In these settings, current ADC technologies cannot perform at the bit rate and depth required for faithful detection and reconstruction. Even worse, the current pace of ADC development is incremental and slow. It will be decades before ADCs based on current technology will be fast and precise enough for many pressing applications.

Fortunately, recent developments in mathematics and signal processing have uncovered a possible solution to the ADC bottleneck. An exciting new field, known as compressive sensing, establishes mathematically that a relatively small number of non-adaptive, linear measurements can harvest all of the information necessary to faithfully reconstruct sparse or compressible signals. A closely related field in computer science, known as streaming algorithms, adds the capability to recover and characterize sparse signals in real-time with limited computational resources.

In this project, we are exploring the new framework of direct analog-to-information conversion (AIC) as an alternative to conventional ADCs. For sparse and compressible signals, AIC promises greatly reduced digital data rates (matching the sparsity level of the signal and below the Nyquist rate); it also offers the ability to focus only on the relevant information in the signal. This project involves an interdisciplinary team of mathematicians, electrical engineers, and computer scientists from Rice University, Applied Signal Technology, Caltech, and the University of Michigan.

**Other Research at Rice**