Location-assisted Subspace-based Beam Alignment in LOS/NLOS mm-wave V2X Communications

Brambilla, Mattia; Pardo, Daniele Filippo; Nicoli, Monica

doi:10.1109/icc40277.2020.9148587

Cited by 18 publications

(21 citation statements)

References 17 publications

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“…The second major research topic of my Ph.D. studies covers the area of communication in next-generation vehicular networks, and most of the contents have been published in [5][6][7][8][9][10]. The need of letting vehicle share massive amount of data comes from the evolution towards connected and automated mobility systems, where a set of services are going to be available for users, improving road safety and transportation efficiency [20,21].…”

Section: Vehicular Communicationmentioning

confidence: 99%

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Sensor-Assisted Cooperative Localization and Communication in Multi-agent Networks

Brambilla

2022

Special Topics in Information Technology

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This brief highlights research advances on cooperative techniques for localization and communication. These two macro trends are investigated in the general context of mobile multi-agent networks for situational awareness applications, where time-varying agents of unknown locations are asked to fulfill positioning and information sharing tasks. Cooperative localization is conceived for both active and passive agents, i.e., targets to be detected and localized, and it is analyzed in vehicular and maritime environments. Communication is investigated for vehicular scenarios, where vehicles are requested to share massive data in the perspective development of connected and automated mobility systems. Both research areas rely on the integration of heterogeneous sensors and communication. Specifically, it is studied how to improve localization by exploring communication techniques as well as how to enhance communication performances by extracting information from perception sensors. The dynamic environment of multi-agent systems calls for robust, flexible and adaptive techniques, capable of profitably fuse different types of information, and the outcomes of these researches show how a statistical approach based on cooperation guarantees higher resilience, reliability and confidence.

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Section: Vehicular Communicationmentioning

confidence: 99%

“…This chapter summarizes the main research works characterizing my Ph.D. studies at Politecnico di Milano from Nov. 2017 until Oct. 2020, under the supervision of Professor Monica Nicoli, which lead to the doctoral dissertation in [1] and the scientific publications in [2][3][4][5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Sensor-Assisted Cooperative Localization and Communication in Multi-agent Networks

Brambilla

2022

Special Topics in Information Technology

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“…LR are algebraic-based methods that leverage on the sparsity of the MIMO channel, as opposite to CS. Originally proposed in [30]- [33] for lower-spectrum systems, where the channel is not sufficiently sparse to boost the LR application to practical systems, the LR has recently been resumed for mmW/sub-THz systems, for Fully Digital (FD) systems only [28], [34]. In particular, the work in [28] demonstrates that LR methods attain similar performances to CS with lower sensitivity to hardware impairments.…”

Section: Related Workmentioning

confidence: 99%

“…In mobility, AoAs and AoDs describe an algebraic span of MIMO channel that has a LR, with a set of subspaces, and for mobile-to-fixed links, both approaches are location dependent. Differently from [34], we adapt this concept to Multi-Vehicular (MV) LR and we specialize the channel estimation to high-mobility hybrid massive MIMO systems in mmW/sub-THz bands, e.g., Vehicle-to-Infrastructure (V2I), considering both FC-HBF and SC-HBF architectures. In this context, this paper proposes a training process for an algebraic estimation of the single-user spatial hybrid MIMO channel in a mobile scenario.…”

Section: Contributionmentioning

confidence: 99%

Channel Estimation for 6G V2X Hybrid Systems Using Multi-Vehicular Learning

Mizmizi

Tagliaferri

Badini³

et al. 2021

IEEE Access

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Channel estimation for hybrid Multiple Input Multiple Output (MIMO) systems at Millimeter-Waves/sub-THz is a fundamental, despite challenging, prerequisite for an efficient design of hybrid MIMO precoding/combining. Most works propose sequential search algorithms, e.g., Compressive Sensing (CS), that are most suited to static channels and consequently cannot apply to highly dynamic scenarios such as Vehicle-to-Everything (V2X). To address the latter ones, we leverage recurrent vehicle passages to design a novel Multi Vehicular (MV) hybrid MIMO channel estimation suited for Vehicle-to-Infrastructure (V2I) and Vehicle-to-Network (V2N) systems. Our approach derives the analog precoder/combiner through a MV beam alignment procedure. For the digital precoder/combiner, we adapt the Low-Rank (LR) channel estimation method to learn the position-dependent eigenmodes of the received digital signal (after beamforming), which is used to estimate the compressed channel in the communication phase. Extensive numerical simulations, obtained with ray-tracing channel data and realistic vehicle trajectories, demonstrate the benefits of our solution in terms of both achievable spectral efficiency and mean square error compared to the unconstrained maximum likelihood estimate of the compressed digital channel, making it suitable for both 5G and future 6G systems. Most notably, in some scenarios, we obtain the performance of the optimal fully digital systems.

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“…Conventional BA solutions that rely on an exhaustive search of the optimal Tx/Rx beam pair are too time demanding for vehicular scenarios due to the latency constraints. To speed up the BA procedure, different solutions have been proposed in the literature [22][23][24][25][26][27][28]. The authors of [29] propose to explore the channel and queue state information to optimize both transmission and reception beamwidths.…”

Section: Millimeter-wave V2x Communicationmentioning

confidence: 99%

Sensor-Aided V2X Beam Tracking for Connected Automated Driving: Distributed Architecture and Processing Algorithms

Brambilla

Combi

Matera

et al. 2020

Sensors

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This paper focuses on ultra-reliable low-latency Vehicle-to-Anything (V2X) communications able to meet the extreme requirements of high Levels of Automation (LoA) use cases. We introduce a system architecture and processing algorithms for the alignment of highly collimated V2X beams based either on millimeter-Wave (mmW) or Free-Space Optics (FSO). Beam-based V2X communications mainly suffer from blockage and pointing misalignment issues. This work focuses on the latter case, which is addressed by proposing a V2X architecture that enables a sensor-aided beam-tracking strategy to counteract the detrimental effect of vibrations and tilting dynamics. A parallel low-rate, low-latency, and reliable control link, in fact, is used to exchange data on vehicle kinematics (i.e., position and orientation) that assists the beam-pointing along the line-of-sight between V2X transceivers (i.e., the dominant multipath component for mmW, or the direct link for FSO). This link can be based on sub-6 GHz V2X communication, as in 5G frequency range 1 (FR1). Performance assessments are carried out to validate the robustness of the proposed methodology in coping with misalignment induced by vehicle dynamics. Numerical results show that highly directional mmW and/or FSO communications are promising candidates for massive data-rate vehicular communications even in high mobility scenarios.

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Location-assisted Subspace-based Beam Alignment in LOS/NLOS mm-wave V2X Communications

Cited by 18 publications

References 17 publications

Sensor-Assisted Cooperative Localization and Communication in Multi-agent Networks

Sensor-Assisted Cooperative Localization and Communication in Multi-agent Networks

Channel Estimation for 6G V2X Hybrid Systems Using Multi-Vehicular Learning

Sensor-Aided V2X Beam Tracking for Connected Automated Driving: Distributed Architecture and Processing Algorithms

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