Emerging mobile virtual reality (VR) systems will require to continuously
perform complex computer vision tasks on ultra-high-resolution video frames
through the execution of deep neural networks (DNNs)-based algorithms. Since
state-of-the-art DNNs require computational power that is excessive for mobile
devices, techniques based on wireless edge computing (WEC) have been recently
proposed. However, existing WEC methods require the transmission and processing
of a high amount of video data which may ultimately saturate the wireless link.
In this paper, we propose a novel Sensing-Assisted Wireless Edge Computing
(SAWEC) paradigm to address this issue. SAWEC leverages knowledge about the
physical environment to reduce the end-to-end latency and overall computational
burden by transmitting to the edge server only the relevant data for the
delivery of the service. Our intuition is that the transmission of the portion
of the video frames where there are no changes with respect to previous frames
can be avoided. Specifically, we leverage wireless sensing techniques to
estimate the location of objects in the environment and obtain insights about
the environment dynamics. Hence, only the part of the frames where any
environmental change is detected is transmitted and processed. We evaluated
SAWEC by using a 10K 360$^{\circ}$ camera with a Wi-Fi 6 sensing system
operating at 160 MHz and performing localization and tracking. We perform
experiments in an anechoic chamber and a hall room with two human subjects in
six different setups. Experimental results show that SAWEC reduces the channel
occupation, and end-to-end latency by 93.81%, and 96.19% respectively while
improving the instance segmentation performance by 46.98% with respect to
state-of-the-art WEC approaches. For reproducibility purposes, we pledge to
share our whole dataset and code repository.

提出了一种新颖的感知辅助无线边缘计算 (SAWEC) 范式来解决基于深度神经网络 (DNNs) 的算法在移动虚拟现实 (VR) 系统中进行复杂计算机视觉任务的问题，SAWEC 通过利用对物理环境的认知来减少端到端延迟和整体计算负担，通过仅传输和处理为服务提供方交付相关数据的视频帧的部分来实现，实验证明 SAWEC 相对于现有的无线边缘计算方法，在减少信道占用和端到端延迟方面分别提高了 93.81% 和 96.19%，同时相对于现有技术的无线边缘计算方法，在实例分割性能方面提高了 46.98%。

SAWEC: 传感辅助无线边缘计算

SAWEC: Sensing-Assisted Wireless Edge Computing

We study federated machine learning (ML) at the wireless edge, where power-
and bandwidth-limited wireless devices with local datasets carry out
distributed stochastic gradient descent (DSGD) with the help of a remote
parameter server (PS). Standard approaches assume separate computation and
communication, where local gradient estimates are compressed and transmitted to
the PS over orthogonal links. Following this digital approach, we introduce
D-DSGD, in which the wireless devices employ gradient quantization and error
accumulation, and transmit their gradient estimates to the PS over a multiple
access channel (MAC). We then introduce a novel analog scheme, called A-DSGD,
which exploits the additive nature of the wireless MAC for over-the-air
gradient computation, and provide convergence analysis for this approach. In
A-DSGD, the devices first sparsify their gradient estimates, and then project
them to a lower dimensional space imposed by the available channel bandwidth.
These projections are sent directly over the MAC without employing any digital
code. Numerical results show that A-DSGD converges faster than D-DSGD thanks to
its more efficient use of the limited bandwidth and the natural alignment of
the gradient estimates over the channel. The improvement is particularly
compelling at low power and low bandwidth regimes. We also illustrate for a
classification problem that, A-DSGD is more robust to bias in data distribution
across devices, while D-DSGD significantly outperforms other digital schemes in
the literature. We also observe that both D-DSGD and A-DSGD perform better by
increasing the number of devices (while keeping the total dataset size
constant), showing their ability in harnessing the computation power of edge
devices.

本研究在有限的功率和带宽条件下，探索了运用多个边缘设备进行分布式随机梯度下降算法的联邦机器学习，提出了 D-DSGD 和 A-DSGD 两个方法，其中 A-DSGD 采用了一项新颖的模拟方案，比 D-DSGD 更快地收敛，并表现出更好的鲁棒性和可扩展性。