50 research outputs found
Time-Domain Channel Estimation for Extremely Large MIMO THz Communications with Beam Squint
In this paper, we study the problem of extremely large (XL) multiple-input
multiple-output (MIMO) channel estimation in the Terahertz (THz) frequency
band, considering the presence of propagation delays across the entire array
apertures, which leads to frequency selectivity, a problem known as beam
squint. Multi-carrier transmission schemes which are usually deployed to
address this problem, suffer from high peak-to-average power ratio, which is
specifically dominant in THz communications where low transmit power is
realized. Diverging from the usual approach, we devise a novel channel
estimation problem formulation in the time domain for single-carrier (SC)
modulation, which favors transmissions in THz, and incorporate the beam-squint
effect in a sparse vector recovery problem that is solved via sparse
optimization tools. In particular, the beam squint and the sparse MIMO channel
are jointly tracked by using an alternating minimization approach that
decomposes the two estimation problems. The presented performance evaluation
results validate that the proposed SC technique exhibits superior performance
than the conventional one as well as than state-of-the-art multi-carrier
approaches
D4.2 Intelligent D-Band wireless systems and networks initial designs
This deliverable gives the results of the ARIADNE project's Task 4.2: Machine Learning based network intelligence. It presents the work conducted on various aspects of network management to deliver system level, qualitative solutions that leverage diverse machine learning techniques. The different chapters present system level, simulation and algorithmic models based on multi-agent reinforcement learning, deep reinforcement learning, learning automata for complex event forecasting, system level model for proactive handovers and resource allocation, model-driven deep learning-based channel estimation and feedbacks as well as strategies for deployment of machine learning based solutions. In short, the D4.2 provides results on promising AI and ML based methods along with their limitations and potentials that have been investigated in the ARIADNE project
1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface
A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool
Accelerated by the increasing attention drawn by 5G, 6G, and Internet of
Things applications, communication and sensing technologies have rapidly
evolved from millimeter-wave (mmWave) to terahertz (THz) in recent years.
Enabled by significant advancements in electromagnetic (EM) hardware, mmWave
and THz frequency regimes spanning 30 GHz to 300 GHz and 300 GHz to 3000 GHz,
respectively, can be employed for a host of applications. The main feature of
THz systems is high-bandwidth transmission, enabling ultra-high-resolution
imaging and high-throughput communications; however, challenges in both the
hardware and algorithmic arenas remain for the ubiquitous adoption of THz
technology. Spectra comprising mmWave and THz frequencies are well-suited for
synthetic aperture radar (SAR) imaging at sub-millimeter resolutions for a wide
spectrum of tasks like material characterization and nondestructive testing
(NDT). This article provides a tutorial review of systems and algorithms for
THz SAR in the near-field with an emphasis on emerging algorithms that combine
signal processing and machine learning techniques. As part of this study, an
overview of classical and data-driven THz SAR algorithms is provided, focusing
on object detection for security applications and SAR image super-resolution.
We also discuss relevant issues, challenges, and future research directions for
emerging algorithms and THz SAR, including standardization of system and
algorithm benchmarking, adoption of state-of-the-art deep learning techniques,
signal processing-optimized machine learning, and hybrid data-driven signal
processing algorithms...Comment: Submitted to Proceedings of IEE
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