发表机构
University of Illinois Urbana-Champaign; InterDigital AI Lab（伊利诺伊大学厄巴纳-香槟分校; InterDigital人工智能实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出GCNO这一基于物理的可变速率压缩器，其利用信道结构、泰勒修正和最小二乘法在无路径标签训练下实现无线信道压缩，性能优于基线且可迁移至未见过的天线数量。
AI 中文摘要
大规模天线阵列使无线系统能够服务更多用户并实现更高数据速率，但也导致信道反馈成本高昂：接收设备必须向基站反复报告大型复值信道矩阵。大多数神经压缩器将该矩阵视为图像，并用仅匹配的神经解码器可解读的固定长度编码替代，因此消息不适应信道复杂度，且改变天线数量通常需要重新训练。本文探究设备能否仅报告每个信道底层的少数主导传播路径。我们提出Gramian切比雪夫神经算子（GCNO），一种基于物理的可变速率压缩器，可识别与样本相关的路径方向集合。GCNO利用收发信道结构定位路径，采用一阶泰勒修正优化落在网格点间的方向，通过最小二乘法恢复其复强度。它无需路径标签即可训练，基站通过传输的路径元组解析重构信道，而非通过学习到的解码器。在三个射线追踪环境中，GCNO在相同有效载荷下实现更好的重构精度，或在相同精度下实现更低有效载荷，优于神经反馈基线，且无需重新训练即可迁移至未见过的天线数量。
英文摘要
Large antenna arrays allow wireless systems to serve more users and achieve higher data rates, but they also make channel feedback expensive: the receiving device must repeatedly report a large complex-valued channel matrix to the base station. Most neural compressors treat this matrix like an image and replace it with a fixed-length code that only a matched neural decoder can interpret. The message therefore does not adapt to channel complexity, and changing the antenna count typically requires retraining. We ask whether a device can instead report only the few dominant propagation paths underlying each channel. We introduce the Gramian Chebyshev Neural Operator (GCNO), a physics-based, variable-rate compressor that identifies a sample-dependent set of path directions. GCNO uses receive-transmit channel structure to locate paths, a first-order Taylor correction to refine directions that fall between grid points, and least squares to recover their complex strengths. It is trained without path labels, and the base station reconstructs the channel analytically from the transmitted path tuples rather than through a learned decoder. Across three ray-traced environments, GCNO achieves better reconstruction accuracy at the same payload - or lower payload at the same accuracy - than neural feedback baselines, and transfers to unseen antenna counts without retraining.
Comments22 pages, 6 figures, 19 tables
