几何归纳偏置用于半监督均衡：星座感知Transformer
Geometric Inductive Biases for Semi-Supervised Equalization: The Constellation-Aware Transformer
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中文总结 AI 辅助
针对未知信道解码中导频开销大的问题，提出星座感知Transformer（CAT），通过注入几何归纳偏置，在半监督均衡中减少导频需求并降低符号错误率。
中文摘要 AI 辅助
在下一代通信中，以最小的导频开销对未知信道上的信号进行解码是一项关键挑战。现有的深度学习方法通常依赖通用编码器，这些编码器难以建模长距离时间依赖，或难以从稀缺数据中有效捕获信道的物理特性。我们认为，标准架构存在不可知的估计差距，因为它们必须隐式学习已知的星座几何。我们引入了星座感知Transformer（CAT），一种新颖的架构，明确地将几何归纳偏置注入均衡过程。CAT由一系列定制的TransFIRmer块组成，这些块采用“早期交互”范式，共同处理接收信号和理想星座符号。每个块包含一个分裂的前馈网络，该网络应用基于有限脉冲响应（FIR）的滤波器进行解卷积，以及一个并行的多层感知机（MLP）进行几何细化。我们表明，这种设计与最优线性（MIMO维纳）接收机在结构上对齐：其注意力可以实现匹配滤波器组，其双向FIR分支提供了块MMSE均衡所需的非因果滤波。在半监督设置中，CAT所需的导频比VAE和标准Transformer基线更少：在我们的三个ISI信道中的两个上，它使用64个导频即可达到比它们使用128个导频更低的符号错误率（SER）。
英文摘要
Decoding signals over unknown channels with minimal pilot overhead is a critical challenge in next-generation communications. Existing deep learning approaches typically rely on generic encoders that struggle to model long-range temporal dependencies or efficiently capture the channel's physical properties from scarce data. We argue that standard architectures suffer from agnostic estimation gaps, as they must implicitly learn the constellation geometry that is already known. We introduce the Constellation-Aware Transformer (CAT), a novel architecture that explicitly injects geometric inductive biases into the equalization process. CAT is composed of a stack of custom TransFIRmer blocks, which use an "early interaction" paradigm to co-process received signals and ideal constellation symbols. Each block features a split Feed-Forward Network that applies a Finite Impulse Response (FIR)-inspired filter for deconvolution and a parallel MLP for geometric refinement. We show that this design is structurally aligned with the optimal linear (MIMO Wiener) receiver: its attention can implement a matched-filter bank, and its bidirectional FIR branch provides the non-causal filtering that block MMSE equalization requires. In the semi-supervised setting, CAT needs fewer pilots than VAE and standard Transformer baselines: on two of our three ISI channels, it reaches a lower SER with 64 pilots than they do with 128.
