Increasing attention is being paid to millimeter-wave (mmWave), 30 GHz to 300
GHz, and terahertz (THz), 300 GHz to 10 THz, sensing applications including
security sensing, industrial packaging, medical imaging, and non-destructive
testing. Traditional methods for perception and imaging are challenged by novel
data-driven algorithms that offer improved resolution, localization, and
detection rates. Over the past decade, deep learning technology has garnered
substantial popularity, particularly in perception and computer vision
applications. Whereas conventional signal processing techniques are more easily
generalized to various applications, hybrid approaches where signal processing
and learning-based algorithms are interleaved pose a promising compromise
between performance and generalizability. Furthermore, such hybrid algorithms
improve model training by leveraging the known characteristics of radio
frequency (RF) waveforms, thus yielding more efficiently trained deep learning
algorithms and offering higher performance than conventional methods. This
dissertation introduces novel hybrid-learning algorithms for improved mmWave
imaging systems applicable to a host of problems in perception and sensing.
Various problem spaces are explored, including static and dynamic gesture
classification; precise hand localization for human computer interaction;
high-resolution near-field mmWave imaging using forward synthetic aperture
radar (SAR); SAR under irregular scanning geometries; mmWave image
super-resolution using deep neural network (DNN) and Vision Transformer (ViT)
architectures; and data-level multiband radar fusion using a novel
hybrid-learning architecture. Furthermore, we introduce several novel
approaches for deep learning model training and dataset synthesis.

介绍了新型混合学习算法，用于改进毫米波成像系统，应用于感知和传感的各种问题领域。

改进毫米波成像系统的新型混合学习算法

Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging  Systems

This white paper explores future localization and sensing opportunities for
beyond 5G wireless communication systems by identifying key technology enablers
and discussing their underlying challenges, implementation issues, and
identifying potential solutions. In addition, we present exciting new
opportunities for localization and sensing applications, which will disrupt
traditional design principles and revolutionize the way we live, interact with
our environment, and do business. Following the trend initiated in the 5G NR
systems, 6G will continue to develop towards even higher frequency ranges,
wider bandwidths, and massive antenna arrays. In turn, this will enable sensing
solutions with very fine range, Doppler and angular resolutions, as well as
localization to cm-level degree of accuracy. Moreover, new materials, device
types, and reconfigurable surfaces will allow network operators to reshape and
control the electromagnetic response of the environment. At the same time,
machine learning and artificial intelligence will leverage the unprecedented
availability of data and computing resources to tackle the biggest and hardest
problems in wireless communication systems. 6G will be truly intelligent
wireless systems that will not only provide ubiquitous communication but also
empower high accuracy localization and high-resolution sensing services. They
will become the catalyst for this revolution by bringing about a unique new set
of features and service capabilities, where localization and sensing will
coexist with communication, continuously sharing the available resources in
time, frequency and space. This white paper concludes by highlighting
foundational research challenges, as well as implications and opportunities
related to privacy, security, and trust. Addressing these challenges will
undoubtedly require an inter-disciplinary and concerted effort from the
research community.

本文探讨了 5G 无线通信系统和未来本地化和感知机会，在确定关键技术和讨论其基础挑战、实现问题和潜在解决方案的基础上，为本地化和感知应用程序提供了令人兴奋的新机遇，并介绍了 6G 系统的应用前景和其他相关领域的话题。

本地化和感知的 6G 白皮书

6G White Paper on Localization and Sensing

Recent advances in deep learning motivate the use of deep neutral networks in
sensing applications, but their excessive resource needs on constrained
embedded devices remain an important impediment. A recently explored solution
space lies in compressing (approximating or simplifying) deep neural networks
in some manner before use on the device. We propose a new compression solution,
called DeepIoT, that makes two key contributions in that space. First, unlike
current solutions geared for compressing specific types of neural networks,
DeepIoT presents a unified approach that compresses all commonly used deep
learning structures for sensing applications, including fully-connected,
convolutional, and recurrent neural networks, as well as their combinations.
Second, unlike solutions that either sparsify weight matrices or assume linear
structure within weight matrices, DeepIoT compresses neural network structures
into smaller dense matrices by finding the minimum number of non-redundant
hidden elements, such as filters and dimensions required by each layer, while
keeping the performance of sensing applications the same. Importantly, it does
so using an approach that obtains a global view of parameter redundancies,
which is shown to produce superior compression. We conduct experiments with
five different sensing-related tasks on Intel Edison devices. DeepIoT
outperforms all compared baseline algorithms with respect to execution time and
energy consumption by a significant margin. It reduces the size of deep neural
networks by 90% to 98.9%. It is thus able to shorten execution time by 71.4% to
94.5%, and decrease energy consumption by 72.2% to 95.7%. These improvements
are achieved without loss of accuracy. The results underscore the potential of
DeepIoT for advancing the exploitation of deep neural networks on
resource-constrained embedded devices.

DeepIoT 是一种压缩神经网络的解决方案，它通过找到每个层所需的最小数量的非冗余隐藏元素，将神经网络结构压缩成更小的密集矩阵，同时保持感知应用程序的性能。在 Intel Edison 设备上的五个不同的感知相关任务中进行实验，DeepIoT 在执行时间和能源消耗方面优于所有比较的基线算法，而不会损失准确性。