  Radar sensors are crucial for environment perception of driver assistance
systems as well as autonomous vehicles. Key performance factors are weather
resistance and the possibility to directly measure velocity. 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 in early processing stages are required to run
directly on specialized hardware, i.e. the radar sensor. 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. However, these models typically
contain millions of parameters, stored in hundreds of megabytes of memory, and
require additional memory during execution. In this paper we investigate
quantization techniques for CNN-based denoising and interference mitigation of
radar signals. We analyze the quantization potential of different CNN-based
model architectures and sizes by considering (i) quantized weights and (ii)
piecewise constant activation functions, which results in reduced memory
requirements for model storage and during the inference step respectively.
