提出一种无需类别统计的神经接收机分布外检测方法
Out-of-Distribution Detection via Channelwise Feature Aggregation in Neural Network-Based Receivers
- 基于通道特征聚合的分层后处理框架，避免依赖类别统计
- 早期网络层检测效果优于后期，高延迟场景检测可靠
- 适合无线通信中多标签软比特输出的分布外检测场景
基于神经网络的无线接收机将在未来通信系统中发挥关键作用，因此可靠的分布外（OOD）检测至关重要。本文提出一种后处理、分层的OOD检测框架，基于通道特征聚合，避免依赖类别统计——这对具有天文数字级类别的多标签软比特输出尤为关键。接收机激活值不呈现离散聚类，而是随信噪比（SNR）平滑分布，符合经典接收机行为，支持基于流形感知的OOD检测。我们评估了多种特征类型、距离度量和方法在不同层次的表现。以均值激活为基础的高斯马氏距离表现最优，早期层优于后期层，而基于SNR或分类器的融合仅带来微小且不一致的AUROC提升。高延迟的分布外样本可被可靠检测，但高速场景仍具挑战。
原文摘要 · Abstract (English)
Neural network-based radio receivers are expected to play a key role in future wireless systems, making reliable Out-Of-Distribution (OOD) detection essential. We propose a post-hoc, layerwise OOD framework based on channelwise feature aggregation that avoids classwise statistics--critical for multi-label soft-bit outputs with astronomically many classes. Receiver activations exhibit no discrete clusters but a smooth Signal-to-Noise-Ratio (SNR)-aligned manifold, consistent with classical receiver behavior and motivating manifold-aware OOD detection. We evaluate multiple OOD feature types, distance metrics, and methods across layers. Gaussian Mahalanobis with mean activations is the strongest single detector, earlier layers outperform later, and SNR/classifier fusions offer small, inconsistent AUROC gains. High-delay OOD is detected reliably, while high-speed remains challenging.
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