Deep-Learned Compression for Radio-Frequency Signal Classification
Armani Rodriguez Yagna Kaasaragadda Silvija Kokalj-Filipovic
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 based on software defined radios (SDRs) and a
framework for the AI processing of spectrum-related data. 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 for an efficient use of the bandwidth and storage for non-real-time
analytics, and for a decreased delay in real-time applications. 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.