A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G

Sixth-generation (6G) mobile communication networks are expected to have dense infrastructures, large-dimensional channels, cost-effective hardware, diversified positioning methods, and enhanced intelligence. Such trends bring both new challenges and opportunities for the practical design of 6G. On one hand, acquiring channel state information (CSI) in real time for all wireless links becomes quite challenging in 6G. On the other hand, there would be numerous data sources in 6G containing high-quality location-tagged channel data, making it possible to better learn the local wireless environment. By exploiting such new opportunities and for tackling the CSI acquisition challenge, there is a promising paradigm shift from the conventional environment-unaware communications to the new environment-aware communications based on the novel approach of channel knowledge map (CKM). This article aims to provide a comprehensive tutorial overview on environment-aware communications enabled by CKM to fully harness its benefits for 6G. First, the basic concept of CKM is presented, and a comparison of CKM with various existing channel inference techniques is discussed. Next, the main techniques for CKM construction are discussed, including both the model-free and model-assisted approaches. Furthermore, a general framework is presented for the utilization of CKM to achieve environment-aware communications, followed by some typical CKM-aided communication scenarios. Finally, important open problems in CKM research are highlighted and potential solutions are discussed to inspire future work

Machine Learning Tools for Radio Map Estimation in Fading-Impaired Channels

In spectrum cartography, also known as radio map estimation, one constructs maps that provide the value of a given channel metric such as as the received power, power spectral density (PSD), electromagnetic absorption, or channel-gain for every spatial location in the geographic area of interest. The main idea is to deploy sensors and measure the target channel metric at a set of locations and interpolate or extrapolate the measurements. Radio maps nd a myriad of applications in wireless communications such as network planning, interference coordination, power control, spectrum management, resource allocation, handoff optimization, dynamic spectrum access, and cognitive radio. More recently, radio maps have been widely recognized as an enabling technology for unmanned aerial vehicle (UAV) communications because they allow autonomous UAVs to account for communication constraints when planning a mission. Additional use cases include radio tomography and source localization.publishedVersio

Design Framework of UAV-Based Environment Sensing, Localization, and Imaging System

In this dissertation research, we develop a framework for designing an Unmanned Aerial Vehicle or UAV-based environment sensing, localization, and imaging system for challenging environments with no GPS signals and low visibility. The UAV system relies on the various sensors that it carries to conduct accurate sensing and localization of the objects in an environment, and further to reconstruct the 3D shapes of those objects. The system can be very useful when exploring an unknown or dangerous environment, e.g., a disaster site, which is not convenient or not accessible for humans. In addition, the system can be used for monitoring and object tracking in a large scale environment, e.g., a smart manufacturing factory, for the purposes of workplace management/safety, and maintaining optimal system performance/productivity. In our framework, the UAV system is comprised of two subsystems: a sensing and localization subsystem; and a mmWave radar-based 3D object reconstruction subsystem. The first subsystem is referred to as LIDAUS (Localization of IoT Device via Anchor UAV SLAM), which is an infrastructure-free, multi-stage SLAM (Simultaneous Localization and Mapping) system that utilizes a UAV to accurately localize and track IoT devices in a space with weak or no GPS signals. The rapidly increasing deployment of Internet of Things (IoT) around the world is changing many aspects of our society. IoT devices can be deployed in various places for different purposes, e.g., in a manufacturing site or a large warehouse, and they can be displaced over time due to human activities, or manufacturing processes. Usually in an indoor environment, the lack of GPS signals and infrastructure support makes most existing indoor localization systems not practical when localizing a large number of wireless IoT devices. In addition, safety concerns, access restriction, and simply the huge amount of IoT devices make it not practical for humans to manually localize and track IoT devices. Our LIDAUS is developed to address these problems. The UAV in our LIDAUS system conducts multi-stage 3D SLAM trips to localize devices based only on Received Signal Strength Indicator (RSSI), the most widely available measurement of the signals of almost all commodity IoT devices. Our simulations and experiments of Bluetooth IoT devices demonstrate that our system LIDAUS can achieve high localization accuracy based only on RSSIs of commodity IoT devices. Build on the first subsystem, we further develop the second subsystem for environment reconstruction and imaging via mmWave radar and deep learning. This subsystem is referred to as 3DRIMR/R2P (3D Reconstruction and Imaging via mmWave Radar/Radar to Point Cloud). It enables an exploring UAV to fly within an environment and collect mmWave radar data by scanning various objects in the environment. Taking advantage of the accurate locations given by the first subsystem, the UAV can scan an object from different viewpoints. Then based on radar data only, the UAV can reconstruct the 3D shapes of the objects in the space. mmWave radar has been shown as an effective sensing technique in low visibility, smoke, dusty, and dense fog environment. However, tapping the potential of radar sensing to reconstruct 3D object shapes remains a great challenge, due to the characteristics of radar data such as sparsity, low resolution, specularity, large noise, and multi-path induced shadow reflections and artifacts. Hence, it is challenging to reconstruct 3D object shapes based on the raw sparse and low-resolution mmWave radar signals. To address the challenges, our second subsystem utilizes deep learning models to extract features from sparse raw mmWave radar intensity data, and reconstructs 3D shapes of objects in the format of dense and detailed point cloud. We first develop a deep learning model to reconstruct a single object’s 3D shape. The model first converts mmWave radar data to depth images, and then reconstructs an object’s 3D shape in point cloud format. Our experiments demonstrate the significant performance improvement of our system over the popular existing methods such as PointNet, PointNet++ and PCN. Then we further explore the feasibility of utilizing a mmWave radar sensor installed on a UAV to reconstruct the 3D shapes of multiple objects in a space. We evaluate two different models. Model 1 is 3DRIMR/R2P model, and Model 2 is formed by adding a segmentation stage in the processing pipeline of Model 1. Our experiments demonstrate that both models are promising in solving the multiple object reconstruction problem. We also show that Model 2, despite producing denser and smoother point clouds, can lead to higher reconstruction loss or even missing objects. In addition, we find that both models are robust to the highly noisy radar data obtained by unstable Synthetic Aperture Radar (SAR) operation due to the instability or vibration of a small UAV hovering at its intended scanning point. Our research shows a promising direction of applying mmWave radar sensing in 3D object reconstruction

3D Indoor Positioning in 5G networks

Over the past two decades, the challenge of accurately positioning objects or users indoors, especially in areas where Global Navigation Satellite Systems (GNSS) are not available, has been a significant focus for the research community. With the rise of 5G IoT networks, the quest for precise 3D positioning in various industries has driven researchers to explore various machine learning-based positioning techniques. Within this context, researchers are leveraging a mix of existing and emerging wireless communication technologies such as cellular, Wi-Fi, Bluetooth, Zigbee, Visible Light Communication (VLC), etc., as well as integrating any available useful data to enhance the speed and accuracy of indoor positioning. Methods for indoor positioning involve combining various parameters such as received signal strength (RSS), time of flight (TOF), time of arrival (TOA), time difference of arrival (TDOA), direction of arrival (DOA) and more. Among these, fingerprint-based positioning stands out as a popular technique in Real Time Localisation Systems (RTLS) due to its simplicity and cost-effectiveness. Positioning systems based on fingerprint maps or other relevant methods find applications in diverse scenarios, including malls for indoor navigation and geo-marketing, hospitals for monitoring patients, doctors, and critical equipment, logistics for asset tracking and optimising storage spaces, and homes for providing Ambient Assisted Living (AAL) services. A significant challenge facing all indoor positioning systems is the objective evaluation of their performance. This challenge is compounded by the coexistence of heterogeneous technologies and the rapid advancement of computation. There is a vast potential for information fusion to be explored. These observations have led to the motivation behind our work. As a result, two novel algorithms and a framework are introduced in this thesis

Indoor Positioning and Navigation

In recent years, rapid development in robotics, mobile, and communication technologies has encouraged many studies in the field of localization and navigation in indoor environments. An accurate localization system that can operate in an indoor environment has considerable practical value, because it can be built into autonomous mobile systems or a personal navigation system on a smartphone for guiding people through airports, shopping malls, museums and other public institutions, etc. Such a system would be particularly useful for blind people. Modern smartphones are equipped with numerous sensors (such as inertial sensors, cameras, and barometers) and communication modules (such as WiFi, Bluetooth, NFC, LTE/5G, and UWB capabilities), which enable the implementation of various localization algorithms, namely, visual localization, inertial navigation system, and radio localization. For the mapping of indoor environments and localization of autonomous mobile sysems, LIDAR sensors are also frequently used in addition to smartphone sensors. Visual localization and inertial navigation systems are sensitive to external disturbances; therefore, sensor fusion approaches can be used for the implementation of robust localization algorithms. These have to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone or robot

1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface

A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance

Beam scanning by liquid-crystal biasing in a modified SIW structure

A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways