AirNet: Neural Network Transmission over the Air
AirNet: Neural Network Transmission over the Air
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DOI：
10.1109/isit50566.2022.9834372
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
Mikolaj Jankowski;Deniz Gündüz;K. Mikolajczyk
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文献类型：
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作者：
Mikolaj Jankowski;Deniz Gündüz;K. Mikolajczyk
许多新兴边缘应用的最先进性能是由深度神经网络（dnn）实现的。通常，所使用的深度神经网络是位置和时间相关的，并且必须将特定深度神经网络的参数从边缘服务器快速有效地传递到边缘设备以执行时间敏感的推理任务。这可以被认为是一个联合源信道编码（JSCC）问题，其中的目标不是以最小的失真恢复DNN系数，而是以在下游任务中提供最高精度的方式。为此，我们介绍了AirNet，一种新颖的训练和模拟传输方法，可以在空中传输深度神经网络。我们首先用噪声注入训练深度神经网络来对抗无线信道噪声。我们还使用剪枝来识别在可用信道带宽内可以传递的最重要的DNN参数，知识蒸馏和非线性带宽扩展，为最重要的网络参数提供更好的错误保护。我们表明，与基于分离的替代方案相比，AirNet实现了显着更高的测试精度，并且随着信道质量的下降表现出优雅的退化。
State-of-the-art performance for many emerging edge applications is achieved by deep neural networks (DNNs). Often, the employed DNNs are location- and time-dependent, and the parameters of a specific DNN must be delivered from an edge server to the edge device rapidly and efficiently to carry out time-sensitive inference tasks. This can be considered as a joint source-channel coding (JSCC) problem, in which the goal is not to recover the DNN coefficients with the minimal distortion, but in a manner that provides the highest accuracy in the downstream task. For this purpose we introduce AirNet, a novel training and analog transmission method to deliver DNNs over the air. We first train the DNN with noise injection to counter the wireless channel noise. We also employ pruning to identify the most significant DNN parameters that can be delivered within the available channel bandwidth, knowledge distillation, and nonlinear bandwidth expansion to provide better error protection for the most important network parameters. We show that AirNet achieves significantly higher test accuracy compared to the separation-based alternative, and exhibits graceful degradation with channel quality.