80 research outputs found
Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays
Massive MIMO (multiple-input multiple-output) is no longer a "wild" or
"promising" concept for future cellular networks - in 2018 it became a reality.
Base stations (BSs) with 64 fully digital transceiver chains were commercially
deployed in several countries, the key ingredients of Massive MIMO have made it
into the 5G standard, the signal processing methods required to achieve
unprecedented spectral efficiency have been developed, and the limitation due
to pilot contamination has been resolved. Even the development of fully digital
Massive MIMO arrays for mmWave frequencies - once viewed prohibitively
complicated and costly - is well underway. In a few years, Massive MIMO with
fully digital transceivers will be a mainstream feature at both sub-6 GHz and
mmWave frequencies. In this paper, we explain how the first chapter of the
Massive MIMO research saga has come to an end, while the story has just begun.
The coming wide-scale deployment of BSs with massive antenna arrays opens the
door to a brand new world where spatial processing capabilities are
omnipresent. In addition to mobile broadband services, the antennas can be used
for other communication applications, such as low-power machine-type or
ultra-reliable communications, as well as non-communication applications such
as radar, sensing and positioning. We outline five new Massive MIMO related
research directions: Extremely large aperture arrays, Holographic Massive MIMO,
Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive
MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin
On the use of programmable metasurfaces in vehicular networks
Metasurface-based intelligent reflecting surfaces constitute a revolutionary technology which can serve the purpose of alleviating the blockage problem in mmwave communication systems. In this work, we consider the hypersurface paradigm complementing the software defined metasurface with an embedded controller network in order to facilitate the dissemination of reconfiguration directives to unit cell controllers. For the first time, we describe the methodology with which to characterize the workload within this embedded network in the case of the metasurface tracking multiple users and we use a vehicular communications setting to showcase the methodology. Beyond that, we demonstrate use cases of the workload analysis. We show how the workload characterization can guide the design of information dissemination schemes achieving significant reduction in the network traffic. Moreover, we show how the workload, as a measure of the consumed power, can be used in designing energy efficient communication protocols through a multi-objective optimization problem maximizing the achieved utilization while at the same time minimizing the workload incurred.Peer ReviewedPostprint (author's final draft
Decision-making and control with metasurface-based diffractive neural networks
The ultimate goal of artificial intelligence is to mimic the human brain to
perform decision-making and control directly from high-dimensional sensory
input. All-optical diffractive neural networks provide a promising solution for
implementing artificial intelligence with high-speed and low-power consumption.
To date, most of the reported diffractive neural networks focus on single or
multiple tasks that do not involve interaction with the environment, such as
object recognition and image classification. In contrast, the networks that can
perform decision-making and control, to our knowledge, have not been developed
yet. Here, we propose using deep reinforcement learning to implement
diffractive neural networks that imitate human-level decision-making and
control capability. Such networks allow for finding optimal control policies
through interaction with the environment and can be readily realized with the
dielectric metasurfaces. The superior performances of these networks are
verified by engaging three types of classic games, Tic-Tac-Toe, Super Mario
Bros., and Car Racing, and achieving the same or even higher levels comparable
to human players. Our work represents a solid step of advancement in
diffractive neural networks, which promises a fundamental shift from the
target-driven control of a pre-designed state for simple recognition or
classification tasks to the high-level sensory capability of artificial
intelligence. It may find exciting applications in autonomous driving,
intelligent robots, and intelligent manufacturing
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
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