通过联邦迁移学习实现低采样率下的宽带频谱感知
Federated Transfer Learning Based Cooperative Wideband Spectrum Sensing with Model Pruning
- 用多抽样率预处理实现亚奈奎斯特采样，降低硬件成本
- 联邦迁移学习框架使模型在无本地数据时仍保持良好性能
- 选择性权重剪枝加速模型适配，适合资源受限的用户
针对超宽带高速无线通信系统中的宽带频谱感知（WSS）问题，传统方法面临高采样率导致的硬件与计算开销过大，以及场景不匹配引发的鲁棒性下降挑战。本文提出一种基于多抽样率预处理的宽带频谱感知神经网络（WSSNet），实现亚奈奎斯特采样，并采用二维卷积结构专门处理预处理后的样本。进一步构建基于联邦迁移学习（FTL）的协作框架，利用多个次级用户（SUs）协同训练，通过选择性权重剪枝实现快速模型适应与推理。仿真结果表明，所提FTL-WSSNet在不同目标场景下均表现出良好性能，即使在缺乏本地适应样本的情况下仍能保持有效感知能力。
原文摘要 · Abstract (English)
For ultra-wideband and high-rate wireless communication systems, wideband spectrum sensing (WSS) is critical, since it empowers secondary users (SUs) to capture the spectrum holes for opportunistic transmission. However, WSS encounters challenges such as excessive costs of hardware and computation due to the high sampling rate, as well as robustness issues arising from scenario mismatch. In this paper, a WSS neural network (WSSNet) is proposed by exploiting multicoset preprocessing to enable the sub-Nyquist sampling, with the two dimensional convolution design specifically tailored to work with the preprocessed samples. A federated transfer learning (FTL) based framework mobilizing multiple SUs is further developed to achieve a robust model adaptable to various scenarios, which is paved by the selective weight pruning for the fast model adaptation and inference. Simulation results demonstrate that the proposed FTL-WSSNet achieves the fairly good performance in different target scenarios even without local adaptation samples.
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