We distinguish the different types of sensing problems and then focus on mapping and SLAM as running examples.
Phase noise (PN) can become a major bottleneck for integrated sensing and communications (ISAC) systems towards 6G wireless networks.
Specifically, we observe that the state of the user with a high speed (42 m/s) can be estimated virtually with the same accuracy as a static user.
Bayesian receiver autonomous integrity monitoring (RAIM) algorithms are developed for the snapshot cellular positioning problem in a simplified one-dimensional (1D) linear Gaussian setting.
In 5G/6G wireless systems, reconfigurable intelligent surfaces (RIS) can play a role as a passive anchor to enable and enhance localization in various scenarios.
no code implementations • 2 Nov 2022 • Ali Behravan, Vijaya Yajnanarayana, Musa Furkan Keskin, Hui Chen, Deep Shrestha, Traian E. Abrudan, Tommy Svensson, Kim Schindhelm, Andreas Wolfgang, Simon Lindberg, Henk Wymeersch
Among the key differentiators of 6G compared to 5G will be the increased emphasis on radio based positioning and sensing.
In this paper, the programmable signal propagation paradigm, enabled by Reconfigurable Intelligent Surfaces (RISs), is exploited for high accuracy $3$-Dimensional (3D) user localization with a single multi-antenna base station.
Radio positioning is an important part of joint communication and sensing in beyond 5G communication systems.
The recent research in the emerging technology of reconfigurable intelligent surfaces (RISs) has identified its high potential for localization and sensing.
This letter considers the problem of end-to-end learning for joint optimization of transmitter precoding and receiver processing for mmWave downlink positioning.
This paper considers the problem of estimating the position and orientation of a user equipped with a three-dimensional (3D) array receiving downlink far-field THz signals from multiple base stations with known positions and orientations.
Spatial wideband effects are known to affect channel estimation and localization performance in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems.
Reconfigurable intelligent surfaces (RISs) have tremendous potential to boost communication performance, especially when the line-of-sight (LOS) path between the user equipment (UE) and base station (BS) is blocked.
This letter is part of a two-letter tutorial on radio localization and sensing, with a focus on mobile radio systems, i. e., 5G and beyond.
Networks in 5G and beyond utilize millimeter wave (mmWave) radio signals, large bandwidths, and large antenna arrays, which bring opportunities in jointly localizing the user equipment and mapping the propagation environment, termed as simultaneous localization and mapping (SLAM).
In this work, we first devise a mathematically tractable analytical model for a vehicle with arbitrary shape, modeled as an extended target parameterized by the center position, the orientation (heading) and the perimeter contour.
Due to the deployment of RISs with a large dimension, near-field (NF) scenarios are likely to occur, especially for indoor applications, and are the focus of this work.
With the help of multipath components (MPCs), localization and mapping tasks can be done with a single base station (BS) and single unsynchronized user equipment (UE) if both of them are equipped with an antenna array.
Radio localization is applied in high-frequency (e. g., mmWave and THz) systems to support communication and to provide location-based services without extra infrastructure.
We investigate a reconfigurable intelligent surface (RIS)-aided near-field localization system with single-antenna user equipment (UE) and base station (BS) under hardware impairments by considering a practical phase-dependent RIS amplitude variations model.
no code implementations • 22 May 2022 • Henk Wymeersch, Aarno Pärssinen, Traian E. Abrudan, Andreas Wolfgang, Katsuyuki Haneda, Muris Sarajlic, Marko E. Leinonen, Musa Furkan Keskin, Hui Chen, Simon Lindberg, Pekka Kyösti, Tommy Svensson, Xinxin Yang
6G will be characterized by extreme use cases, not only for communication, but also for localization, and sensing.
We consider the problem of monostatic radar sensing with orthogonal frequency-division multiplexing (OFDM) joint radar-communications (JRC) systems in the presence of phase noise (PN) caused by oscillator imperfections.
In this paper, we propose a convolutional neural network (CNN)-based DPD in the frequency domain, taking place before the precoding, where the dimensionality of the signal space depends on the number of users, instead of the number of BS antennas.
Secondly, the Poisson multi-Bernoulli (PMB) SLAM filter is based on the standard reduction from PMBM to PMB, but involves a novel interpretation based on auxiliary variables and a relation to Bethe free energy.
In this paper, a novel low-complexity method for joint localization and synchronization based on an optimized design of the base station (BS) active precoding and RIS passive phase profiles is proposed, for the challenging case of a single-antenna receiver.
The proposed method can successfully recover the performance loss of the AMML under a wide range of RIS parameters and effectively calibrate the RIS amplitude model online with the help of a user that has an a-priori unknown location.
Location information is expected to be the key to meeting the needs of communication and context-aware services in 6G systems.
Reconfigurable intelligent surfaces (RISs) have tremendous potential for both communication and localization.
We consider the single base station localization problem and extend it to 3D position and 3D orientation estimation of an unsynchronized multi-antenna user, using downlink MIMO-OFDM signals.
Smart radio environments (SREs) are seen as a key rising concept of next generation wireless networks, where propagation channels between transmitters and receivers are purposely controlled.
The rising concept of reconfigurable intelligent surface (RIS) has promising potential for Beyond 5G localization applications.
In this paper, we introduce a novel use-case of the RIS technology in radio localization, which is enabling the user to estimate its own position via transmitting orthogonal frequency-division multiplexing (OFDM) pilots and processing the signal reflected from the RIS.
Multi-object tracking (MOT) is the problem of tracking the state of an unknown and time-varying number of objects using noisy measurements, with important applications such as autonomous driving, tracking animal behavior, defense systems, and others.
In this paper, we study the linearization of the measurement function with respect to the posterior PDF, and implement the iterated posterior linearization filter into the Poisson multi-Bernoulli SLAM filter.
Simulation results show that the reinforcement-learning-based algorithm achieves similar performance to the standard supervised end-to-end learning approach assuming perfect channel knowledge.
We propose an over-the-air digital predistortion optimization algorithm using reinforcement learning.
The proposed method is compared to the tensor ESPRIT method, in terms of channel estimation, communication, localization, and sensing performance, further validating the perturbation analysis.
The approach includes the proposal of the AE architecture, a novel ISAC loss function, and the training procedure.
Terahertz (THz) communications are celebrated as key enablers for converged localization and sensing in future sixth-generation (6G) wireless communication systems and beyond.
Reconfigurable intelligent surfaces (RISs) have the potential to enable user localization in scenarios where traditional approaches fail.
Upcoming beyond fifth generation (5G) communications systems aim at further enhancing key performance indicators and fully supporting brand new use cases by embracing emerging techniques, e. g., reconfigurable intelligent surface (RIS), integrated communication, localization, and sensing, and mmWave/THz communications.
Millimeter wave (mmWave) signals are useful for simultaneous localization and mapping (SLAM), due to their inherent geometric connection to the propagation environment and the propagation channel.
Evaluating the performance of multi-object tracking (MOT) methods is not straightforward, and existing performance measures fail to consider all the available uncertainty information in the MOT context.
Future cellular networks that utilize millimeter wave signals provide new opportunities in positioning and situational awareness.
Inspired by the remarkable learning and prediction performance of deep neural networks (DNNs), we apply one special type of DNN framework, known as model-driven deep unfolding neural network, to reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) single-input multiple-output (SIMO) systems.
By comparing the results with the SLAM based on the Rao-Blackwellized probability hypothesis density filter, we confirm a slight drop in SLAM performance, but as a result, we validate that it has a significant gain in computational complexity.
no code implementations • 24 Jun 2021 • Henk Wymeersch, Deep Shrestha, Carlos Morais de Lima, Vijaya Yajnanarayana, Björn Richerzhagen, Musa Furkan Keskin, Kim Schindhelm, Alejandro Ramirez, Andreas Wolfgang, Mar Francis de Guzman, Katsuyuki Haneda, Tommy Svensson, Robert Baldemair, Stefan Parkvall
6G will likely be the first generation of mobile communication that will feature tight integration of localization and sensing with communication functionalities.
Using the multiple-model (MM) probability hypothesis density (PHD) filter, millimeter wave (mmWave) radio simultaneous localization and mapping (SLAM) in vehicular scenarios is susceptible to movements of objects, in particular vehicles driving in parallel with the ego vehicle.
We take into consideration the limited resolution of the RIS phase shifters and show that in the presence of this practical limitation, orthogonal phase profiles can be designed based on Butson-type Hadamard matrices.
We demonstrate, for the first time, experimental over-the-fiber training of transmitter neural networks (NNs) using reinforcement learning.
We consider the problem of spatial signal design for multipath-assisted mmWave positioning under limited prior knowledge on the user's location and clock bias.
In this work, we study the application of RIS in a multi-user passive localization scenario, where we have one transmitter (Tx) and multiple asynchronous receivers (Rxs) with known locations.
Neural networks (NNs) for multiple hardware impairments mitigation of a realistic direct conversion transmitter are impractical due to high computational complexity.
In this paper, we provide an overview of a hybrid relay-reflecting intelligent surface (HR-RIS) architecture, in which only a few elements are active and connected to power amplifiers and radio frequency chains.
We show that the proposed model outperforms state-of-the-art Bayesian filters in complex scenarios, while matching their performance in simpler cases, which validates the applicability of deep-learning also in the model-based regime.
Orthogonal time frequency space (OTFS) is a promising alternative to orthogonal frequency division multiplexing (OFDM) in high-mobility beyond 5G communications.
For each estimated angle, we next formulate the radar delay-Doppler estimation as a joint carrier frequency offset (CFO) and channel estimation problem via an APES (amplitude and phase estimation) spatial filtering approach by transforming the delay-Doppler parameterized radar channel into an unstructured form.
We propose an end-to-end learning-based approach for superchannel systems impaired by non-ideal hardware component.
This property is then exploited for developing our distributed deterministic channel access scheme.
Integrating efficient connectivity, positioning and sensing functionalities into 5G New Radio (NR) and beyond mobile cellular systems is one timely research paradigm, especially at mm-wave and sub-THz bands.
We illustrate how chance-constrained ActInf weights all imposed (prior) constraints on the generative model, allowing e. g., for a trade-off between robust control and empirical chance constraint violation.
We propose a novel three-stage delay-Doppler-angle estimation algorithm for a MIMO-OFDM radar in the presence of inter-carrier interference (ICI).
We will provide the formulas and derivations that are required to understand and analyze RIS-aided systems using signal processing, and exemplify how they can be utilized for improved communication, localization, and sensing.
We show that the received signals from several base stations, having known positions, can be used to estimate the unknown orientation of the user.
We consider the problem of joint three-dimensional localization and synchronization for a single-input single-output (SISO) system in the presence of a reconfigurable intelligent surface (RIS), equipped with a uniform planar array.
Signal Processing Information Theory Information Theory
However, for MISO systems synchronization cannot be performed jointly with user localization unless two-way transmissions are used.
Location information offered by external positioning systems, e. g., satellite navigation, can be used as prior information in the process of beam alignment and channel parameter estimation for reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) multiple-input multiple-output networks.
Exploiting wavefront curvature enables localization with limited infrastructure and hardware complexity.
Signal Processing Information Theory Information Theory
5G mmWave MIMO systems enable accurate estimation of the user position and mapping of the radio environment using a single snapshot when both the base station (BS) and user are equipped with large antenna arrays.
A reconfigurable intelligent surface (RIS) can shape the radio propagation environment by virtue of changing the impinging electromagnetic waves towards any desired directions, thus, breaking the general Snell's reflection law.
Signal Processing Information Theory Information Theory
5G millimeter wave (mmWave) signals can be used to jointly localize the receiver and map the propagation environment in vehicular networks, which is a typical simultaneous localization and mapping (SLAM) problem.
no code implementations • 2 Jun 2020 • Andre Bourdoux, Andre Noll Barreto, Barend van Liempd, Carlos de Lima, Davide Dardari, Didier Belot, Elana-Simona Lohan, Gonzalo Seco-Granados, Hadi Sarieddeen, Henk Wymeersch, Jaakko Suutala, Jani Saloranta, Maxime Guillaud, Minna Isomursu, Mikko Valkama, Muhammad Reza Kahar Aziz, Rafael Berkvens, Tachporn Sanguanpuak, Tommy Svensson, Yang Miao
This white paper concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust.
End-to-end data-driven machine learning (ML) of multiple-input multiple-output (MIMO) systems has been shown to have the potential of exceeding the performance of engineered MIMO transceivers, without any a priori knowledge of communication-theoretic principles.
5G radio at millimeter wave (mmWave) and beyond 5G concepts at 0. 1-1 THz can exploit angle and delay measurements for localization, by the virtue of increased bandwidth and large antenna arrays but are limited in terms of blockage caused by obstacles.
A simple approach to obtaining uncertainty-aware neural networks for regression is to do Bayesian linear regression (BLR) on the representation from the last hidden layer.
5G millimeter wave (mmWave) signals can enable accurate positioning in vehicular networks when the base station (BS) and vehicles are equipped with large antenna arrays.
A novel quantization method is proposed, which exploits the specific properties of the feedback signal and is suitable for non-stationary signal distributions.
In 5G mmWave, joint positioning and synchronization can be achieved even when the line-of-sight path is blocked.
Machine learning is used to compute achievable information rates (AIRs) for a simplified fiber channel.