PHY3 – Radio based localization
Thursday, 8 June 2023, 11:00-12:30, Room G2
Session Chair: Christopher Mollén (Ericsson AB, Sweden)
A Survey of 5G-Based Positioning for Industry 4.0: State of the Art and Enhanced Techniques
Karthik Muthineni (Universitat Politècnica de Catalunya & Robert Bosch GmbH, Germany); Alexander Artemenko (Robert Bosch GmbH, Germany); Josep Vidal and Montse Nájar (Universitat Politècnica de Catalunya, Spain)
The fifth generation (5G) mobile communication technology integrates communication, positioning, and mapping functionalities as an in-built feature. This has drawn significant attention from industries owing to the capability of replacing the traditional wireless technologies used in industries with 5G infrastructure that can be used for both connectivity and positioning. To this end, we identify the Automated Guided Vehicle (AGV) as a primary use case to benet from the 5G functionalities. Given that there have been various works focusing on 5G positioning, it is necessary to analyze the existing works about their applicability with AGVs in industrial environments and provide insights to future research. In this paper, we present state of the art in 5G-based positioning, with a focus on key features, such as Millimeter Wave (mmWave) system, Massive Multiple Input Multiple Output (MIMO), Ultra-Dense Network (UDN), Device-to-Device (D2D) communication, and Recongurable Intelligent Surface (RIS). Moreover, we present the shortcomings in the current state of the art. Additionally, we propose enhanced techniques that can complement the accuracy of 5G-based positioning in controlled industrial environments.
Influence of Dataset Parameters on the Performance of Direct UE Positioning via Deep Learning
Vincent Corlay and Cristina Ciochina Duchesne (Mitsubishi Electric R&D Centre Europe, France); Julien Guillet (Mitsubishi Electric Research Centre Europe, France); Baptiste Chatelier (Rennes University, INSA Rennes, CNRS, IETR, France); Fallou Colly (Mitsubishi Electric R and D Centre Europe, France)
User equipment (UE) positioning accuracy is of paramount importance in current and future communications standard. However, traditional methods tend to perform poorly in non line of sight (NLoS) scenarios. As a result, deep learning is a candidate to enhance the UE positioning accuracy in NLoS environments. In this paper, we study the efficiency of deep learning on the 3GPP indoor factory (InF) statistical channel. More specifically, we analyse the impacts of several key elements on the positioning accuracy: the type of radio data used, the number of base stations (BS), the size of the training dataset, and the generalization ability of a trained model.
Multistatic Sensing of Passive Targets Using 6G Cellular Infrastructure
Vijaya Parampalli Yajnanarayana (Ericsson Research, India); Henk Wymeersch (Chalmers University of Technology, Sweden)
Sensing using cellular infrastructure may be one of the defining feature of sixth generation (6G) wireless systems. 6G communication channels operating at higher frequency bands (upper mmWave bands) are better modeled using clustered geometric channel models. In this paper, we propose methods for detection of passive targets and estimating their position using communication deployment without any assistance from the target. A novel AI architecture called CsiSenseNet is developed for this purpose. We analyze resolution, coverage and position uncertainty for practical indoor deployments. Using the proposed method, we show that human sized target can be sensed with high accuracy and sub-meter positioning errors in a practical indoor deployment scenario.
IQ Imbalance Compensation with a Pilot Sequence
Enrique T. R. Pinto, Markku Juntti and Visa Tapio (University of Oulu, Finland)
Signal degradation caused by receiver inphase/quadrature (IQ) processing branch imbalance (IQI) is known to increase bit error rates, and deteriorate both angle of arrival (AoA) and ranging estimation accuracies. In this paper, we present an IQI compensation procedure that leverages a pilot sequence to propose an IQI compensation method, which tolerates time synchronization errors. We then explore a single anchor positioning problem and show that the proposed procedure is effective in improving the position estimation accuracy. We evaluate its performance via computer simulations. The results show that the scheme outperforms an earlier method, which is blind in the sense that it does not capitalize the pilot sequence availability.
Joint Multistatic Sensing of Transmitter and Target in OFDM-Based JCAS System
Christopher Mollén (Ericsson AB, Sweden); Gabor Fodor (Ericsson Research & Royal Institute of Technology (KTH), Sweden); Robert Baldemair (Ericsson AB & Ericsson Research, Sweden); Jörg Huschke (Ericsson, Germany); Julia Vinogradova (Ericsson Research, Finland)
Joint communication and sensing (JCAS) systems use the same spectrum, hardware and antenna resources to jointly provide spectrally efficient communication, localization and sensing services. While previous work has analyzed the performance of communication with connected objects and localization of unconnected (passive) objects, the joint positioning of both connected and passive objects is less studied. In this paper, we consider a JCAS cellular system using orthogonal frequencydivision multiplexing, in which the uplink communication signal is scattered on a moving target towards multiple receiving base stations. In this setting, multistatic sensing by cooperating base stations makes it possible to position the moving target while also positioning the transmitting user equipment based on the received communication signal at the base stations. We propose a channel model that can characterize the propagation of both the communication and sensing signals, and algorithms that facilitate the estimation of direction of arrivals and range, which in turn enables the system to infer the positions of both the communicating user and the passive target. We also show some illustrative results from the algorithms that indicate what such joint positioning practically can look like.
