AirNet: Neural Network Transmission over the Air

State-of-the-art performance for many edge applications is achieved by deep neural networks (DNNs). Often, these DNNs are location-and time-sensitive, and must be delivered over a wireless channel rapidly and efficiently. In this paper, we introduce AirNet, a family of novel training and transmission methods that allow DNNs to be efficiently delivered over wireless channels under stringent transmit power and latency constraints. This corresponds to a new class of joint source-channel coding problems, aimed at delivering DNNs with the goal of maximizing their accuracy at the receiver, rather than recovering them with high fidelity. In AirNet, we propose the direct mapping of the DNN parameters to transmitted channel symbols, while the network is trained to meet the channel constraints, and exhibit robustness against channel noise. AirNet achieves higher accuracy compared to separation-based alternatives. We further improve the performance of AirNet by pruning the network below the available bandwidth, and expanding it for improved robustness. We also benefit from unequal error protection by selectively expanding important layers of the network. Finally, we develop an approach, which simultaneously trains a spectrum of DNNs, each targeting a different channel condition, resolving the impractical memory requirements of training distinct networks for different channel conditions. The results in this paper were presented in part at the 2022 IEEE International Symposium on Information Theory (ISIT) [1]. Developments within the area of DL have been made possible mainly thanks to the rapid growth of the computational power and memory available for both the researchers and the potential users of various DL-based algorithms. This resulted in the development of increasingly complex deep neural networks (DNNs) with millions and even billions of parameters trained on massive datasets, achieving impressive accuracy and performance in a wide variety of applications. On the other hand, the memory required to store a single modern DNN model can easily go from a few megabytes up to hundreds of gigabytes.