用Transformer+深度可分离卷积，在城市环境下不完美信道信息下提升多用户波束成形性能。
Transformer-Driven Neural Beamforming with Imperfect CSI in Urban Macro Wireless Channels
- 结合深度可分离卷积与Transformer，实现高效波束成形权重生成。
- 在不完美信道信息下，吞吐量提升显著，块错误率降低。
- 适合高密度城市场景的5G/6G无线系统设计参考。
现有研究广泛采用Transformer架构，因其在无线信号处理中捕捉长距离依赖的能力。深度可分离卷积则提升了多输入多输出（MIMO）系统高维数据处理的参数效率。本文提出一种新型无监督深度学习框架，融合深度可分离卷积与Transformer，用于在密集城市环境下的多用户单输入多输出（MU-SIMO）系统中，基于不完美信道状态信息（CSI）生成波束成形权重。目标是通过最大化总速率来提升吞吐量，并确保通信可靠性。以频谱效率和块错误率（BLER）作为性能指标。在多种条件下对比了所提神经网络波束成形（NNBF）框架与基准方法零强迫波束成形（ZFBF）及最小均方误差波束成形（MMSE）的性能。实验结果表明，所提框架在各项指标上均优于基线方法。
原文摘要 · Abstract (English)
The literature is abundant with methodologies focusing on using transformer architectures due to their prominence in wireless signal processing and their capability to capture long-range dependencies via attention mechanisms. In particular, depthwise separable convolutions enhance parameter efficiency for the process of high-dimensional data characteristics of MIMO systems. In this work, we introduce a novel unsupervised deep learning framework that integrates depthwise separable convolutions and transformers to generate beamforming weights under imperfect channel state information (CSI) for a multi-user single-input multiple-output (MU-SIMO) system in dense urban environments. The primary goal is to enhance throughput by maximizing sum-rate while ensuring reliable communication. Spectral efficiency and block error rate (BLER) are considered as performance metrics. Experiments are carried out under various conditions to compare the performance of the proposed NNBF framework against baseline methods zero-forcing beamforming (ZFBF) and minimum mean square error (MMSE) beamforming. Experimental results demonstrate the superiority of the proposed framework over the baseline techniques.
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