49 research outputs found
Learning to Select for MIMO Radar based on Hybrid Analog-Digital Beamforming
In this paper, we propose an energy-efficient radar beampattern design
framework for a Millimeter Wave (mmWave) massive multi-input multi-output
(mMIMO) system, equipped with a hybrid analog-digital (HAD) beamforming
structure. Aiming to reduce the power consumption and hardware cost of the
mMIMO system, we employ a machine learning approach to synthesize the probing
beampattern based on a small number of RF chains and antennas. By leveraging a
combination of softmax neural networks, the proposed solution is able to
achieve a desirable beampattern with high accuracy
Semi-Passive 3D Positioning of Multiple RIS-Enabled Users
Reconfigurable intelligent surfaces (RISs) are set to be a revolutionary technology in the 6th generation of wireless systems. 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. Classical approaches fail in this scenario due to lack of synchronization and lack of data association between multi-static measurements and users. To resolve this, we consider each user to be equipped with an RIS, and show that we can avoid the data association problem and estimate users\u27 3D position with submeter accuracy in a large area around the transmitter, using time-of-arrival measurements at the RXs. We develop a low-complexity estimator that attains the corresponding Cram\\u27er-Rao bound as well as a novel RIS phase profile design to remove inter-path interference
SISO RIS-Enabled Joint 3D Downlink Localization and Synchronization
We consider the problem of joint three-dimensional localization and synchronization for a single-input single-output (SISO) multi-carrier system in the presence of a reconfigurable intelligent surface (RIS), equipped with a uniform planar array. First, we derive the Cram\ue9r-Rao bounds (CRBs) on the estimation error of the channel parameters, namely, the angle-of-departure (AOD), composed of azimuth and elevation, from RIS to the user equipment (UE) and times-of-arrival (TOAs) for the path from the base station (BS) to UE and BS-RISUE reflection. In order to avoid high-dimensional search over the parameter space, we devise a low-complexity estimation algorithm that performs two 1D searches over the TOAs and one 2D search over the AODs. Simulation results demonstrate that the considered RIS-aided wireless system can provide submeter-level positioning and synchronization accuracy, materializing the positioning capability of Beyond 5G networks even with single-antenna BS and UE. Furthermore, the proposed estimator is shown to attain the CRB at a wide interval of distances between UE and RIS. Finally, we also investigate the scaling of the position error bound with the number of RIS elements
Near-field Localization with a Reconfigurable Intelligent Surface Acting as Lens
Exploiting wavefront curvature enables localization with limited infrastructure and hardware complexity. With the introduction of reconfigurable intelligent surfaces (RISs), new opportunities arise, in particular when the RIS is functioning as a lens receiver. We investigate the localization of a transmitter using a RIS-based lens in close proximity to a single receive antenna element attached to reception radio frequency chain. We perform a Fisher information analysis, evaluate the impact of different lens configurations, and propose a two-stage localization algorithm. Our results indicate that positional beamforming can lead to better performance when a priori location information is available, while random beamforming is preferred when a priori information is lacking. Our simulation results for a moderate size lens operating at 28 GHz showcased that decimeter-level accuracy can be attained within 3 meters to the lens
High-Rate Uninterrupted Internet-of-Vehicle Communications in Highways: Dynamic Blockage Avoidance and CSIT Acquisition
In future wireless networks, one of the use-cases of interest is
Internet-of-vehicles (IoV). Here, IoV refers to two different functionalities,
namely, serving the in-vehicle users and supporting the connected-vehicle
functionalities, where both can be well provided by the transceivers installed
on top of vehicles. Such dual functionality of on-vehicle transceivers implies
strict rate and reliability requirements, for which one may need to communicate
at millimeter wave (mmW) frequencies. However, IoV communication at mmW
requires up-to-date channel state information (CSI) and blockage avoidance. In
this article, we incorporate the recently proposed concept of predictor
antennas (PAs) into a large-scale cooperative PA (LSCPA) setup where both
temporal blockages and CSI out-dating are avoided via base stations
(BSs)/vehicles cooperation. Summarizing the ongoing standardization progress
enabling IoV communications, we present the potentials and challenges of the
LSCPA setup, and compare the effect of cooperative and non-cooperative schemes
on the performance of IoV links. As we show, BSs cooperation and blockage/CSI
prediction can boost the performance of IoV links remarkably.Comment: Submitted to IEEE Communications Magazin
Context-Aware Security for 6G Wireless The Role of Physical Layer Security
Sixth generation systems are expected to face new security challenges, while
opening up new frontiers towards context awareness in the wireless edge. The
workhorse behind this projected technological leap will be a whole new set of
sensing capabilities predicted for 6G devices, in addition to the ability to
achieve high precision localization. The combination of these enhanced traits
can give rise to a new breed of context-aware security protocols, following the
quality of security (QoSec) paradigm. In this framework, physical layer
security solutions emerge as competitive candidates for low complexity,
low-delay and low-footprint, adaptive, flexible and context aware security
schemes, leveraging the physical layer of the communications in genuinely
cross-layer protocols, for the first time.Comment: arXiv admin note: text overlap with arXiv:2011.0732