Deep-Learned Compression for Radio-Frequency Signal Classification

Armani Rodriguez, Yagna Kaasaragadda, Silvija Kokalj-Filipovic

Keywords: Deep learned compression, hierarchical RF compression, vector quantization, generative RF compression, AI-native air-interface, ModRec

TL;DR: proposes a hierarchical deep learned compression model of complex-valued RF data prepared for modulation classification

Abstract:

Next-generation cellular concepts rely on the processing of large quantities of radio-frequency (RF) samples.
This includes Radio Access Networks (RAN) connecting the
cellular front-end and its framework for the AI processing of
spectrum-related data, as well as the AI-native air interface.
The RF data collected by the dense RAN radio units and
spectrum sensors may need to be jointly processed for intelligent
decision making. Moving large amounts of data to AI agents may
result in significant bandwidth and latency costs. We propose
a deep learned compression (DLC) model, HQARF, based on
learned vector quantization (VQ), to compress the complex-valued samples of RF signals comprised of 6 modulation classes.
We are assessing the effects of HQARF on the performance of
an AI model trained to infer the modulation class of the RF
signal. Compression of narrow-band RF samples for the training
and off-the-site inference will allow not only for an efficient use
of the bandwidth and storage for non-real-time analytics, and a
decreased delay in real-time applications, but also for efficient AI
models in the air interface. While exploring the effectiveness of
the HQARF signal reconstructions in modulation classification
tasks, we highlight the DLC optimization space and some open
problems related to the training of the VQ embedded in HQARF.

Submission Number: 7