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。