  Radar sensors are crucial for environment perception of driver assistance
systems as well as autonomous vehicles. With a rising number of radar sensors
and the so far unregulated automotive radar frequency band, mutual interference
is inevitable and must be dealt with. Algorithms and models operating on radar
data are required to run the early processing steps on specialized radar sensor
hardware. This specialized hardware typically has strict resource-constraints,
i.e. a low memory capacity and low computational power. Convolutional Neural
Network (CNN)-based approaches for denoising and interference mitigation yield
promising results for radar processing in terms of performance. Regarding
resource-constraints, however, CNNs typically exceed the hardware's capacities
by far.
  In this paper we investigate quantization techniques for CNN-based denoising
and interference mitigation of radar signals. We analyze the quantization of
(i) weights and (ii) activations of different CNN-based model architectures.
This quantization results in reduced memory requirements for model storage and
during inference. We compare models with fixed and learned bit-widths and
contrast two different methodologies for training quantized CNNs, i.e. the
straight-through gradient estimator and training distributions over discrete
weights. We illustrate the importance of structurally small real-valued base
models for quantization and show that learned bit-widths yield the smallest
models. We achieve a memory reduction of around 80\% compared to the
real-valued baseline. Due to practical reasons, however, we recommend the use
of 8 bits for weights and activations, which results in models that require
only 0.2 megabytes of memory.
