Learning task-based analog-to-digital conversion for MIMO receivers

Analog-to-digital conversion allows physical signals to be
processed using digital hardware. This conversion consists of two stages:
Sampling, which maps a continuous-time signal into discrete-time, and
quantization, i.e., representing the continuous-amplitude quantities using
a finite number of bits. This conversion is typically carried out using
generic uniform mappings that are ignorant of the task for which the
signal is acquired, and can be costly when operating in high rates and
fine resolutions. In this work we design task-oriented analog-to-digital
converters (ADCs) which operate in a data-driven manner, namely they
learn how to map an analog signal into a sampled digital representation
such that the system task can be efficiently carried out. We propose
a model for sampling and quantization which both faithfully represents
these operations while allowing the system to learn non-uniform mappings
from training data. We focus on the task of symbol detection in multipleinput multiple-output (MIMO) digital receivers, where multiple analog
signals are simultaneously acquired in order to recover a set of discrete
information symbols. Our numerical results demonstrate that the proposed approach achieves performance which is comparable to operating
without quantization constraints, while achieving more accurate digital
representation compared to utilizing conventional uniform ADCs.
Index terms— Analog-to-digital conversion, deep learning.