Low Complexity Radar Gesture Recognition Using Synthetic Training Data

Developments in radio detection and ranging (radar) technology have made hand gesture recognition feasible. In heat map-based gesture recognition, feature images have a large size and require complex neural networks to extract information. Machine learning methods typically require large amounts of data and collecting hand gestures with radar is time- and energy-consuming. Therefore, a low computational complexity algorithm for hand gesture recognition based on a frequency-modulated continuous-wave (FMCW) radar and a synthetic hand gesture feature generator are proposed. In the low computational complexity algorithm, two-dimensional Fast Fourier Transform is implemented on the radar raw data to generate a range-Doppler matrix. After that, background modelling is applied to separate the dynamic object and the static background. Then a bin with the highest magnitude in the range-Doppler matrix is selected to locate the target and obtain its range and velocity. The bins at this location along the dimension of the antenna can be utilised to calculate the angle of the target using Fourier beam steering. In the synthetic generator, the Blender software is used to generate different hand gestures and trajectories and then the range, velocity and angle of targets are extracted directly from the trajectory. The experimental results demonstrate that the average recognition accuracy of the model on the test set can reach 89.13% when the synthetic data are used as the training set and the real data are used as the test set. This indicates that the generation of synthetic data can make a meaningful contribution in the pre-training phase

European H2020 Project WORTECS Wireless Mixed Reality Prototyping

This paper presents European collaborative project WORTECS objectives and reports on the development of several radio and optical wireless prototypes and a demonstrator targeting mixed reality (MR) application. The aim is to achieve a net throughput of up to Tbps in an indoor heterogeneous network for the MR use case, which seems to be a high throughput "killer application" beyond 5G. A special routing device is associated with the demonstrator to select the most suitable wireless access technology. Post introduction to the project, an overview of the demonstrator is presented with details of the current progress of the prototypes

5G-CLARITY: 5G-Advanced Private Networks Integrating 5GNR, WiFi, and LiFi

The future of the manufacturing industry highly depends on digital systems that transform existing production and monitoring systems into autonomous systems fulfilling stringent requirements in terms of availability, reliability, security, low latency, and positioning with high accuracy. In order to meet such requirements, private 5G networks are considered as a key enabling technology. In this article, we introduce the 5G-CLARITY system that integrates 5GNR, WiFi, and LiFi access networks, and develops novel management enablers to operate 5G-Advanced private networks. We describe three core features of 5G-CLARITY, including a multi-connectivity framework, a high-precision positioning server, and a management system to orchestrate private network slices. These features are evaluated by means of packet-level simulations and an experimental testbed demonstrating the ability of 5G-CLARITY to police access network traffic, to achieve centimeter-level positioning accuracy, and to provision private network slices in less than one minuteThis work is supported by the European Commission’s Horizon 2020 research and innovation program under grant agreement No 871428, 5G-CLARITY project

Towards joint communication and sensing (Chapter 4)

Localization of user equipment (UE) in mobile communication networks has been supported from the early stages of 3rd generation partnership project (3GPP). With 5th Generation (5G) and its target use cases, localization is increasingly gaining importance. Integrated sensing and localization in 6th Generation (6G) networks promise the introduction of more efficient networks and compelling applications to be developed

Radio frequency ranging for precise indoor localization

In den letzten Jahrzehnten sind Satellitennavigationssysteme zu einem unverzichtbaren Teil des modernen Lebens geworden. Viele innovative Anwendungen bieten ortsabhängige Dienste an, welche auf diesen Navigationssystemen aufbauen. Allerdings sind diese Dienste in Innenräumen nicht verfügbar. Daher werden seit einigen Jahren alternative Lokalisierungsmethoden für Innenräume aktiv erforscht und entwickelt. Der Schwerpunkt dieser Arbeit liegt darauf, die Genauigkeit von Lokalisationsmethoden in Innenräumen zu erhöhen, sowie auf der effektiven Integration der entsprechenden Verfahren in drahtlose Kommunikationssysteme. Es werden zwei Ansätze vorgeschlagen und untersucht, welche die Präzision von ToF-basierten Methoden erhöhen. Zum einen wird im „Modified Equivalent Time Sampling“ (METS) Verfahren eine überabgetastete Version der vom Radioempfänger gelieferten Wellenform erzeugt und zur ToF Bestimmung verwendet. Der zweite erforschte Ansatz hat zum Ziel, Fehler auf Grund von Taktfrequenz-Abweichungen zu kompensieren. Dieses ist für kooperative Lokalisationsmethoden (N-Way ranging) von Bedeutung. Das in der Arbeit entwickelte Verfahren führt zu einer erheblichen Reduzierung der Fehler in der Abstandsmessung und damit der Positionsbestimmung. Darüber hinaus wurde eine neue Methode untersucht, um Lokalisationsverfahren in Funksysteme für die ISM Bänder bei 2,4 GHz und 5 GHz zu integrieren. Die Methode wurde auf einer Software Defined Radio (SDR) Plattform implementiert und bewertet. Es konnte eine Genauigkeit bis zu einem Meter in der Positionsbestimmung demonstriert werden. Schließlich wurde ein Verfahren vorgeschlagen und untersucht, mit welchem Lokalisationsfähigkeit in bestehende Funksysteme integriert werden kann. Die betrachtete Methode wurde in einem 60 GHz Funksystem mit hoher Datenrate implementiert. Die Untersuchungen zeigten eine Positionsgenauigkeit von 1 cm bei einer gleichzeitig hohen Datenrate für die Übertragung von Nutzdaten.In the last couple of decades the Global Navigation Satellite Systems (GNSS) have become a very important part of our everyday life. A huge number of applications offer location based services and navigation functions which rely on these systems. Nevertheless, the offered localization services are not available indoors and their performance is significantly affected in urban areas. Therefore, in the recent years, a large number of wireless indoor localization systems are being actively investigated and developed. The main focus of this work is on improving precision and accuracy of indoor localization systems, as well as on the implementation and integration of localization functionality in wireless data transmission systems. Two approaches for improving the localization precision and accuracy of ToF based methods are proposed. The first approach, referred to as modified equivalent time sampling (METS) is used to reconstruct an oversampled versions of the waveforms acquired at the radio receiver and used for ToF based localization. The second proposed approach is used to compensate the ranging error due to clock frequency offset in cooperative localization schemes like N-Way ranging. This approach significantly reduces the ranging and, therefore, localization errors and has much better performance compared to the existing solutions. An approach for implementation of localization system in the 2.4/5 GHz ISM band is further proposed in this work. This approach is implemented and tested on a software defined radio platform. A ranging precision of better than one meter is demonstrated. Finally, an approach for integrating localization functionality into an arbitrary wireless data transmission system is proposed. This approach is implemented in a 60 GHz wireless system. A ranging precision of one centimeter is demonstrated

Low Complexity Radar Gesture Recognition Using Synthetic Training Data

Developments in radio detection and ranging (radar) technology have made hand gesture recognition feasible. In heat map-based gesture recognition, feature images have a large size and require complex neural networks to extract information. Machine learning methods typically require large amounts of data and collecting hand gestures with radar is time- and energy-consuming. Therefore, a low computational complexity algorithm for hand gesture recognition based on a frequency-modulated continuous-wave (FMCW) radar and a synthetic hand gesture feature generator are proposed. In the low computational complexity algorithm, two-dimensional Fast Fourier Transform is implemented on the radar raw data to generate a range-Doppler matrix. After that, background modelling is applied to separate the dynamic object and the static background. Then a bin with the highest magnitude in the range-Doppler matrix is selected to locate the target and obtain its range and velocity. The bins at this location along the dimension of the antenna can be utilised to calculate the angle of the target using Fourier beam steering. In the synthetic generator, the Blender software is used to generate different hand gestures and trajectories and then the range, velocity and angle of targets are extracted directly from the trajectory. The experimental results demonstrate that the average recognition accuracy of the model on the test set can reach 89.13% when the synthetic data are used as the training set and the real data are used as the test set. This indicates that the generation of synthetic data can make a meaningful contribution in the pre-training phase

Präzise Lokalisierung und Breitband-Kommunikation im 60-GHz-Band - PreLocate, Teilvorhaben: HF-Chips und Basisbandverarbeitung für Lokalisierung und Kommunikation im 60- GHz-Band (PreLocate-IHP) : Schlussbericht ; Projektlaufzeit: 01.10.2011 bis 31.07.2014

Für zahlreiche Anwendungen im Bereich mobile drahtlose Kommunikation besteht der Bedarf nach einer genauen Lokalisierung mobiler Terminals. Dabei ist oft eine zentimetergenaue Lokalisierung bei gleichzeitiger Datenübertagung mit Raten von > 1 Gb/s notwendig. Auf Grund der hohen verfügbaren Bandbreite eignen sich dafür Funksysteme im mm-Wellen Bereich und insbesondere im 60-GHz-Band hervorragend. Am IHP wurde ein HF-Chip-Satz für 60 GHz Übertragung mit Beamsteering-Funktionalität entwickelt. Des Weiteren wurde ein OFDM Basisband-Prozessor für Übertragungsraten bis zu 3,6 Gb/s entwickelt. Dieser wurde mit einem Verfahren zur Entfernungsmessung erweitert. Die Steuerung von Datenübertragung und Entfernungsmessung erfolgt über einen speziellen MAC Prozessor. Beide Funktionen laufen im zeitmultiplex quasi-gleichzeitig ab. Zum Nachweis der Funktionalität der gleichzeitigen Datenübertragung und Lokalisierung wurde ein FPGA-basierter Demonstrator entwickelt und mehrfach ausgestellt.For many applications using mobile wireless communications, precise localization of mobile terminals is required or advantageous. Often the precision of localization has to be in the cm-range and data rates are required to exceed 1 Gb/s. Due to the large available bandwidth, mmWave radio systems operating in the 60 GHz band are particularly well suited. At IHP a RF-chip set for data transmission with beamsteering functionality was developed. Furthermore, an OFDM baseband processor for data rates up to 3.6 Gbit/s was developed. This baseband processor was extended with a function for round-trip time-of-flight (RTToF) based ranging. The control of data transmission and ranging is done by a special MAC Processor. Both functions are performed in time-multiplex quasi-simultaneously. To show and verify the simultaneous data transmission and ranging functionality, an FPGA-based demonstrator was developed. This demonstrator was successfully presented at several events

5G-CLARITY Deliverable D3.2 Design Refinements and Initial Evaluation of the Coexistence, Multi-Connectivity, Resource Management and Positioning Frameworks

This document, 5G-CLARITY D3.2, aims to provide evaluation results and refinements on the initially designed 5G-CLARITY user and control plane architecture that is introduced in 5G-CLARITY D3.1 [1]. This document is also aligned with the "network function and application stratum" that covers not only user- and controlplane but also application plane functionality as presented in 5G-CLARITY D2.2 [2]. In essence, 5G-CLARITY D3.2 provides the performance evaluations and refinements for: Multi-WAT aggregation: Including 5GNR CU/DU/RU integration, integration of Wi-Fi and LiFi networks as a single non-3GPP network, integration of 3GPP and non-3GPP wireless access technologies (WATs) and assignment of traffic flows via MPTCP; 5G-CLARITY eAT3S framework: Including operational flows, initial enhanced access traffic steering, switching and splitting (enhanced AT3S / eAT3S) algorithm design and control plane aspects of the custom MPTCP scheduler; Scheduling and resource management: Including Wi-Fi and LiFi airtime-based schedulers and utilitybased scheduler to manage different service types; Positioning: Including WAT-specific positioning scheme and its performance evaluations, as well as the fusion approach; Integrated 5G/Wi-Fi/LiFi network performance evaluation: Including possible access point (AP)/gNB deployment options, achievable communication bandwidths, technology-specific areacapacity achievements and integrated network area-capacity performance. Details for the 5G-CLARITY multi-connectivity framework evaluation are presented in Section 2. The 5GCLARITY multi-connectivity design includes, i) the multi-WAT aggregation, integrating 3GPP (5GNR) and non3GPP (Wi-Fi and LiFi) access networks, and ii) an enhancement on the AT3S scheme to improve the (multiaccess based) multi-connectivity functionalities. The details of design and validation of these features for the 5G-CLARITY user- and control-plane are provided. Section 3 delivers discussions on AP level and service level (traffic routing) resource scheduling techniques. Primarily, the corresponding telemetry and performance measurements are used to route the traffic across 3GPP/non-3GPP networks in near real-time (near-RT) using 5G-CLARITY eAT3S introduced to ensure qualityof-service (QoS), and as a following step, the AP level resource scheduling is performed by the gNB and/or Wi-Fi/LiFi AP. In this respect, a Linux-kernel based airtime management evaluation framework is discussed which can be used to segregate multi-WAT resources for a given 5G-CLARITY slice. Due to LiFi’s different channel and link reliability characteristics, the airtime scheduling for the LiFi technology is specifically discussed and slicing the attocellular network resources is researched. Section 4 is focused on 5G-CLARITY multi-WAT positioning solution. The associated technologies are 60 GHz mWave, sub-6 GHz, LiFi and Optical Camera Communications (OCC) based positioning. A localisation server obtains the position information from these WATs, and provides the position estimate, by fusing all the relevant data, to the entities requiring position services. Details of the overall architecture, each technology ranging/positioning scheme, and the fusion approach are provided. The simulation architecture to evaluate the integration and performance of 5G-CLARITY multi-WAT scheme, including the corresponding user- and control-plane functionalities are presented in Section 5. Results for a dense deployment of multi-WAT AP/gNB in contrast to the generic scenario, using both conservative (based on the available technologies) and opportunistic (assuming greedy usage of available bandwidth), are presented. The achievable system area capacity in each scenario is discussed and the limiting factors are introduced. Overall, this document, 5G-CLARITY D3.2, presents the achievable KPIs of the main components of the 5GCLARITY integrated 5G/Wi-Fi/LiFi network user- and control-plane architecture. The integration of these components and the evaluation of the overall 5G-CLARITY user- and control-plane will be reported in 5GCLARITY D3.3