For integrated sensing and communications, an intriguing question is whether information-bearing channel-coded signals can be reused for sensing - specifically ranging. This question forces the hitherto non-overlapping fields of channel coding (communications) and sequence design (sensing) to intersect by motivating the design of error-correcting codes that have good autocorrelation properties. In this letter, we demonstrate how machine learning (ML) is well-suited for designing such codes, especially for short block lengths. As an example, for rate 1/2 and block length 32, we show that even an unsophisticated ML code has a bit-error rate performance similar to a Polar code with the same parameters, but with autocorrelation sidelobes 24dB lower. While a length-32 Zadoff-Chu (ZC) sequence has zero autocorrelation sidelobes, there are only 16 such sequences and hence, a 1/2 code rate cannot be realized by using ZC sequences as codewords. Hence, ML bridges channel coding and sequence design by trading off an ideal autocorrelation function for a large (i.e., rate-dependent) codebook size.

翻译：在一体化感知与通信的背景下，一个引人深思的问题是：承载信息的信道编码信号能否被复用进行感知——特别是测距应用。这一问题促使原本互不重叠的信道编码（通信领域）与序列设计（感知领域）产生交集，从而推动设计兼具良好自相关特性的纠错码。本文论证了机器学习方法特别适用于设计此类编码，尤其在短码长场景中。以码率1/2、码长32为例，我们证明即使采用简易的机器学习编码方案，其误码率性能仍可与同等参数的Polar码相媲美，同时自相关旁瓣降低24dB。虽然长度为32的Zadoff-Chu序列具有零自相关旁瓣，但此类序列仅存在16种，因此无法通过直接采用ZC序列作为码字实现1/2码率。由此可见，机器学习通过以理想自相关特性换取大规模（即码率相关的）码本容量，成功构建了信道编码与序列设计之间的桥梁。