PARGAR：基于导频感知图注意力的神经 OFDM 接收机
PARGAR: Pilot-Aware Graph Attention for Sparse-Pilot Neural OFDM Receivers
把 OFDM 资源网格建成图，用图注意力做接收机，5G/6G 导频稀疏时比 DeepRx 的 CNN 方案误码率更低，做物理层的研究者可以看看。
论文提出 PARGAR（Pilot-Aware Graph Attention Receiver），将 OFDM 资源网格建模为图结构用于比特检测，在局部时频边之外引入从数据资源单元到导频资源单元的导频锚点链接。作者还设计了一种基于近似相干时间和相干带宽的信道缩放导频锚点规则。在 Sionna 生成的 OFDM 仿真中，PARGAR 与基于 CNN 的 DeepRx 接收机在密集、双符号稀疏和单符号稀疏三种 DMRS 布局下对比，覆盖信道失配场景。在 SISO 未编码设置下，信道缩放版 PARGAR 在部分稀疏导频场景取得更低 BER，密集导频下性能与 DeepRx 相当。
PARGAR: Pilot-Aware Graph Attention for Sparse-Pilot Neural OFDM Receivers
Reliable channel estimation and symbol detection in 5G New Radio (NR), as well as emerging 6G radio systems, rely on demodulation reference signals (DMRS). The density and placement of these pilots on the orthogonal frequency-division multiplexing (OFDM) time--frequency resource grid affect both receiver reliability and spectral efficiency. Sparse DMRS layouts increase the time--frequency distance between pilot observations and data resource elements (REs), which can make pilot information harder to exploit with purely local processing. This paper proposes \textbf{PARGAR (Pilot-Aware Graph Attention Receiver)}, a graph-attention neural receiver that represents the OFDM resource grid as a graph for bit detection. The graph combines local time--frequency edges with pilot-anchor links from data REs to selected pilot REs, and we further study a channel-scaled pilot-anchor rule based on approximate coherence time and coherence bandwidth. Using Sionna-generated OFDM simulations, we evaluate \textsc{PARGAR} against DeepRx, a convolutional neural network (CNN)-based receiver, across dense, two-symbol sparse, and single-symbol sparse DMRS layouts under channel-mismatch scenarios. Under the considered SISO uncoded setup, channel-scaled \textsc{PARGAR} explicitly injects pilot-aware inductive bias and yields lower BER in selected sparse-pilot scenarios while remaining competitive under dense pilots.