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

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 (
$\Sigma \Delta $
Ms). The reconfigurable nature of
$\Sigma \Delta $
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
$\Sigma \Delta $
Ms requires to adopt diverse design strategies – from the
$\Sigma \Delta \text{M}$
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)-
$\Sigma \Delta \text{M}$
, and an ANN is used to predict the occupancy of frequency bands and modify the notch frequency of the BP-
$\Sigma \Delta \text{M}$
accordingly.