In this work, we propose and develop a simple experimental testbed to study
the feasibility of a novel idea by coupling radio frequency (RF) sensing
technology with Correlated Knowledge Distillation (CKD) theory towards
designing lightweight, near real-time and precise human pose monitoring
systems. The proposed CKD framework transfers and fuses pose knowledge from a
robust "Teacher" model to a parameterized "Student" model, which can be a
promising technique for obtaining accurate yet lightweight pose estimates. To
assure its efficacy, we implemented CKD for distilling logits in our integrated
Software Defined Radio (SDR)-based experimental setup and investigated the
RF-visual signal correlation. Our CKD-RF sensing technique is characterized by
two modes -- a camera-fed Teacher Class Network (e.g., images, videos) with an
SDR-fed Student Class Network (e.g., RF signals). Specifically, our CKD model
trains a dual multi-branch teacher and student network by distilling and fusing
knowledge bases. The resulting CKD models are then subsequently used to
identify the multimodal correlation and teach the student branch in reverse.
Instead of simply aggregating their learnings, CKD training comprised multiple
parallel transformations with the two domains, i.e., visual images and RF
signals. Once trained, our CKD model can efficiently preserve privacy and
utilize the multimodal correlated logits from the two different neural networks
for estimating poses without using visual signals/video frames (by using only
the RF signals).

本文提出一个基于 Radio Frequency Sensing 技术和 Correlated Knowledge Distillation 理论的轻量级人体姿态监测系统，通过训练多支教师和学生网络以获得准确、轻量级的姿态估计，并探究 RF-visual 信号相关性。

利用相关性知识蒸馏监测人体姿态和无线电

On Correlated Knowledge Distillation for Monitoring Human Pose with  Radios

The use of unmanned aerial vehicles (UAV) as flying radio access network
(RAN) nodes offers a promising complement to traditional fixed terrestrial
deployments. More recently yet still in the context of wireless networks,
drones have also been envisioned for use as radio frequency (RF) sensing and
localization devices. In both cases, the advantage of using UAVs lies in their
ability to navigate themselves freely in 3D and in a timely manner to locations
of space where the obtained network throughput or sensing performance is
optimal. In practice, the selection of a proper location or trajectory for the
UAV very much depends on local terrain features, including the position of
surrounding radio obstacles. Hence, the robot must be able to map the features
of its radio environment as it performs its data communication or sensing
services. The challenges related to this task, referred here as radio mapping,
are discussed in this paper. Its promises related to efficient trajectory
design for autonomous radio-aware UAVs are highlighted, along with algorithm
solutions. The advantages induced by radio-mapping in terms of connectivity,
sensing, and localization performance are illustrated.

该论文讨论了使用无人机作为飞行无线接入网络（RAN）节点和射频（RF）感知定位设备的优点，以及如何将其应用于两种场景。论文讨论了无人机的合适位置选择和轨迹设计所涉及的局部地形特征和射频环境映射的挑战，并介绍了相关的算法解决方案，说明了射频映射对于连接性、感知和定位性能所带来的优势。