AI-Assisted Sigma-Delta Converters. Application to Cognitive Radio

Abstract

This brief discusses the use of Artificial Intelligence (AI) to manage the operation and improve the performance of Analog-to-Digital Converters (ADCs) based on Sigma-Delta Modulators (Ms). The reconfigurable nature of Ms can be enhanced by AI algorithms in order to adapt the specifications of ADCs to diverse input signal requirements, environment interferences, noise levels, battery status, etc. A high degree of programmability is required, which demands for scaling-friendly, mostly-digital analog circuit techniques as well as suitable topologies of Artificial Neural Networks (ANNs) to implement the AI engine. Moreover, the practical implementation of AI-assisted Ms requires to adopt diverse design strategies – from the M architecture itself to AI modules and circuit building blocks – which are overviewed in this brief. As an application and case study, an ANN-assisted ADC for Software-Defined Radio (SDR) and Cognitive Radio (CR) is considered. The system is based on the use of a widely-tunable Band-Pass (BP)-M, and an ANN is used to predict the occupancy of frequency bands and modify the notch frequency of the BP-M accordingly.