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.

我们提出了基于深度学习压缩（DLC）模型 HQARF 的方法，该模型利用了学习的向量量化（VQ）来压缩由 6 个调制类组成的复杂值射频信号样本，以提高人工智能模型对射频信号调制类别推断的性能，并减少带宽和存储的使用以及实时应用的延迟。

深度学习压缩射频信号分类

Deep-Learned Compression for Radio-Frequency Signal Classification

In the decoding of linear block codes, it was shown that noticeable gains in
terms of bit error rate can be achieved by introducing learnable parameters to
the Belief Propagation (BP) decoder. Despite the success of these methods,
there are two key open problems. The first is the lack of analysis for channels
other than AWGN. The second is the interpretation of the weights learned and
their effect on the reliability of the BP decoder. In this work, we aim to
bridge this gap by looking at non-AWGN channels such as Extended Typical Urban
(ETU) channel. We study the effect of entangling the weights and how the
performance holds across different channel settings for the min-sum version of
BP decoder. We show that while entanglement has little degradation in the AWGN
channel, a significant loss is observed in more complex channels. We also
provide insights into the weights learned and their connection to the structure
of the underlying code. Finally, we evaluate our algorithm on the over-the-air
channels using Software Defined Radios.

本文介绍在研究和优化 Belief Propagation 解码器的过程中，通过研究非 AWGN 通道和权重纠缠的作用，来提高最小和版本的解码器的性能，并对所学习的权重及其对潜在代码结构的影响进行解释。最后，我们运用软件定义的无线电进行算法评估。