针对6G多用户通信，提出自适应语义传输框架，有效抑制干扰并提升低信噪比性能。
Scenario-Adaptive MU-MIMO OFDM Semantic Communication With Asymmetric Neural Network
- 根据信道状态动态调整语义编码，利用神经预编码降低多用户干扰
- 在3GPP信道下，低信噪比时PSNR与分类准确率显著优于传统方法
- 轻量级接收机结合导频注意力机制，适合边缘设备实时部署
语义通信（SemCom）作为6G网络的潜在范式，旨在传输任务相关的信息而非最小化比特错误。然而，将语义通信应用于实际的下行链路多用户多输入多输出正交频分复用（MU-MIMO OFDM）系统仍面临严重多用户干扰（MUI）和频率选择性衰落的挑战。现有深度联合源信道编码（DJSCC）方案主要面向点对点链路，在多用户场景中性能趋于饱和。为此，我们提出一种面向下行传输的场景自适应MU-MIMO语义通信框架，采用不对称架构。发射端引入场景感知语义编码器，根据信道状态信息（CSI）和信噪比（SNR）动态调整特征提取，并设计神经预编码网络以在语义域缓解多用户干扰。接收端采用轻量化解码器，结合新型导频引导注意力机制，利用参考导频符号隐式完成信道均衡与特征校准。基于3GPP信道模型的大量仿真结果表明，该框架在峰值信噪比（PSNR）和分类准确率上均显著优于DJSCC与传统分离源信道编码（SSCC）方案，尤其在低信噪比条件下表现突出，同时保持边缘设备上的低延迟与低计算开销。
原文摘要 · Abstract (English)
Semantic Communication (SemCom) has emerged as a promising paradigm for 6G networks, aiming to extract and transmit task-relevant information rather than minimizing bit errors. However, applying SemCom to realistic downlink Multi-User Multi-Input Multi-Output (MU-MIMO) Orthogonal Frequency Division Multiplexing (OFDM) systems remains challenging due to severe Multi-User Interference (MUI) and frequency-selective fading. Existing Deep Joint Source-Channel Coding (DJSCC) schemes, primarily designed for point-to-point links, suffer from performance saturation in multi-user scenarios. To address these issues, we propose a scenario-adaptive MU-MIMO SemCom framework featuring an asymmetric architecture tailored for downlink transmission. At the transmitter, we introduce a scenario-aware semantic encoder that dynamically adjusts feature extraction based on Channel State Information (CSI) and Signal-to-Noise Ratio (SNR), followed by a neural precoding network designed to mitigate MUI in the semantic domain. At the receiver, a lightweight decoder equipped with a novel pilot-guided attention mechanism is employed to implicitly perform channel equalization and feature calibration using reference pilot symbols. Extensive simulation results over 3GPP channel models demonstrate that the proposed framework significantly outperforms DJSCC and traditional Separate Source-Channel Coding (SSCC) schemes in terms of Peak Signal-to-Noise Ratio (PSNR) and classification accuracy, particularly in low-SNR regimes, while maintaining low latency and computational cost on edge devices.
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