107 research outputs found
Convergent communication, sensing and localization in 6g systems: An overview of technologies, opportunities and challenges
Herein, we focus on convergent 6G communication, localization and sensing systems by identifying key technology enablers, discussing their underlying challenges, implementation issues, and recommending potential solutions. Moreover, we discuss exciting new opportunities for integrated localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. Regarding potential enabling technologies, 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. Besides, 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. As a result, 6G will be truly intelligent wireless systems that will provide not only 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 work concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust
Convergent Communication, Sensing and Localization in 6G Systems: An Overview of Technologies, Opportunities and Challenges
Herein, we focus on convergent 6G communication, localization and sensing systems by identifying key technology enablers, discussing their underlying challenges, implementation issues, and recommending potential solutions. Moreover, we discuss exciting new opportunities for integrated localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. Regarding potential enabling technologies, 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. Besides, 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. As a result, 6G will be truly intelligent wireless systems that will provide not only 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 work concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust
Smart Pattern V2I Handover Based on Machine Learning Vehicle Classification
The mmwave frequencies will be widely used in future vehicular communications. At these frequencies, the radio channel becomes much more vulnerable to slight changes in the environment like motions of the device, reflections or blockage. In high mobility vehicular communications the rapidly changing vehicle environments and the large overheads due to frequent beam training are the critical disadvantages in developing these systems at mmwave frequencies. Hence, smart beam management procedures are desired to establish and maintain the radio channels. In this thesis, we propose that using the positions and respective velocities of the vehicles in the dynamic selection of the beam pair, and then adapting to the changing environments using machine learning algorithms, can improve both network performance and communication stability in high mobility vehicular communications
NTN-based 6G Localization: Vision, Role of LEOs, and Open Problems
Since the introduction of 5G Release 18, non-terrestrial networks (NTNs)
based positioning has garnered significant interest due to its numerous
applications, including emergency services, lawful intercept, and charging and
tariff services. This release considers single low-earth-orbit (LEO)
positioning explicitly for purposes, which
requires a fairly coarse location estimate. To understand the future trajectory
of NTN-based localization in 6G, we first provide a comprehensive overview of
the evolution of 3rd Generation Partnership Project (3GPP) localization
techniques, with specific emphasis on the current activities in 5G related to
NTN location verification. We then delineate the suitability of LEOs for
location-based services and emphasize increased interest in LEO-based
positioning. In order to provide support for more accurate positioning in 6G
using LEOs, we identify two NTN positioning systems that are likely study items
for 6G: (i) multi-LEO positioning, and (ii) augmenting single-LEO and multi-LEO
setups with Global Navigation Satellite System (GNSS), especially when an
insufficient number of GNSS satellites (such as 2) are visible. We evaluate the
accuracy of both systems through a 3GPP-compliant simulation study using a
Cram\'{e}r-Rao lower bound (CRLB) analysis. Our findings suggest that NTN
technology has significant potential to provide accurate positioning of UEs in
scenarios where GNSS signals may be weak or unavailable, but there are
technical challenges in accommodating these solutions in 3GPP. We conclude with
a discussion on the research landscape and key open problems related to
NTN-based positioning.Comment: 7 pages, 6 figures, submitted to IEEE Wireless Communications
Magazin
A simulation study of beam management for 5G millimeter-wave cellular networks
openThis thesis aims at performing a system-level analysis of beam management protocol under different scenarios, mobility conditions and parameters configurations.This thesis aims at performing a system-level analysis of beam management protocol under different scenarios, mobility conditions and parameters configurations
Initial access with neighbor assistance in 5G mmWave cellular networks
The advent of 5G communications has already started. In order to achieve the objectives of high speed and low latency, mmWave technologies will be adopted in the near future. In this thesis we present a new cell discovery algorithm that takes advantage of context information available through legacy networks in order to achieve a faster initial access. We compute analytically the relevant probabilities and then we implement a 3GPP-compliant and spatially consistent simulation environment.openEmbargo temporaneo per motivi di segretezza e/o di proprietĂ Â dei risultati e/o informazioni sensibil
Sensing Aided Reconfigurable Intelligent Surfaces for 3GPP 5G Transparent Operation
Can reconfigurable intelligent surfaces (RISs) operate in a standalone mode
that is completely transparent to the 3GPP 5G initial access process? Realizing
that may greatly simplify the deployment and operation of these surfaces and
reduce the infrastructure control overhead. This paper investigates the
feasibility of building standalone/transparent RIS systems and shows that one
key challenge lies in determining the user equipment (UE)-side RIS beam
reflection direction. To address this challenge, we propose to equip the RISs
with multi-modal sensing capabilities (e.g., using wireless and visual sensors)
that enable them to develop some perception of the surrounding environment and
the mobile users. Based on that, we develop a machine learning framework that
leverages the wireless and visual sensors at the RIS to select the optimal
beams between the base station (BS) and users and enable 5G
standalone/transparent RIS operation. Using a high-fidelity synthetic dataset
with co-existing wireless and visual data, we extensively evaluate the
performance of the proposed framework. Experimental results demonstrate that
the proposed approach can accurately predict the BS and UE-side candidate
beams, and that the standalone RIS beam selection solution is capable of
realizing near-optimal achievable rates with significantly reduced beam
training overhead.Comment: The RIS dataset and script files will be available soon. arXiv admin
note: text overlap with arXiv:2211.0756
Towards the Next Generation of Location-Aware Communications
This thesis is motivated by the expected implementation of the
next generation mobile networks (5G) from 2020, which is being
designed with a radical paradigm shift towards millimeter-wave
technology (mmWave). Operating in 30--300 GHz frequency band
(1--10 mm wavelengths), massive antenna arrays that provide a
high angular resolution, while being packed on a small area will
be used. Moreover, since the abundant mmWave spectrum is barely
occupied, large bandwidth allocation is possible and will enable
low-error time estimation. With this high spatiotemporal
resolution, mmWave technology readily lends itself to extremely
accurate localization that can be harnessed in the network design
and optimization, as well as utilized in many modern
applications. Localization in 5G is still in early stages, and
very little is known about its performance and feasibility.
In this thesis, we contribute to the understanding of 5G mmWave
localization by focusing on challenges pertaining to this
emerging technology. Towards that, we start by considering a
conventional cellular system and propose a positioning method
under outdoor LOS/NLOS conditions that, although approaches the
Cram\'er-Rao lower bound (CRLB), provides accuracy in the order
of meters. This shows that conventional systems have limited
range of location-aware applications. Next, we focus on mmWave
localization in three stages. Firstly, we tackle the initial
access (IA) problem, whereby user equipment (UE) attempts to
establish a link with a base station (BS). The challenge in this
problem stems from the high directivity of mmWave. We investigate
two beamforming schemes: directional and random. Subsequently, we
address 3D localization beyond IA phase. Devices nowadays have
higher computational capabilities and may perform localization in
the downlink. However, beamforming on the UE side is sensitive to
the device orientation. Thus, we study localization in both the
uplink and downlink under multipath propagation and derive the
position (PEB) and orientation error bounds (OEB). We also
investigate the impact of the number of antennas and the number
of beams on these bounds. Finally, the above components assume
that the system is synchronized. However, synchronization in
communication systems is not usually tight enough for
localization. Therefore, we study two-way localization as a means
to alleviate the synchronization requirement and investigate two
protocols: distributed (DLP) and centralized (CLP).
Our results show that random-phase beamforming is more
appropriate IA approach in the studied scenarios. We also observe
that the uplink and downlink are not equivalent, in that the
error bounds scale differently with the number of antennas, and
that uplink localization is sensitive to the UE orientation,
while downlink is not. Furthermore, we find that NLOS paths
generally boost localization. The investigation of the two-way
protocols shows that CLP outperforms DLP by a significant margin.
We also observe that mmWave localization is mainly limited by
angular rather than temporal estimation.
In conclusion, we show that mmWave systems are capable of
localizing a UE with sub-meter position error, and sub-degree
orientation error, which asserts that mmWave will play a central
role in communication network optimization and unlock
opportunities that were not available in the previous generation
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