38,884 research outputs found
Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning
This paper considers an Internet-of-Things (IoT) scenario in which devices
sporadically transmit short packets with few pilot symbols over a fading
channel. Devices are characterized by unique transmission non-idealities, such
as I/Q imbalance. The number of pilots is generally insufficient to obtain an
accurate estimate of the end-to-end channel, which includes the effects of
fading and of the transmission-side distortion. This paper proposes to tackle
this problem by using meta-learning. Accordingly, pilots from previous IoT
transmissions are used as meta-training data in order to train a demodulator
that is able to quickly adapt to new end-to-end channel conditions from few
pilots. Various state-of-the-art meta-learning schemes are adapted to the
problem at hand and evaluated, including Model-Agnostic Meta-Learning (MAML),
First-Order MAML (FOMAML), REPTILE, and fast Context Adaptation VIA
meta-learning (CAVIA). Both offline and online solutions are developed. In the
latter case, an integrated online meta-learning and adaptive pilot number
selection scheme is proposed. Numerical results validate the advantages of
meta-learning as compared to training schemes that either do not leverage prior
transmissions or apply a standard joint learning algorithms on previously
received data.Comment: journal paper to appear in IEEE Transactions on Signal Processing,
subsumes (arXiv:1903.02184
Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunities
Recently there has been a flurry of research on the use of reconfigurable
intelligent surfaces (RIS) in wireless networks to create smart radio
environments. In a smart radio environment, surfaces are capable of
manipulating the propagation of incident electromagnetic waves in a
programmable manner to actively alter the channel realization, which turns the
wireless channel into a controllable system block that can be optimized to
improve overall system performance. In this article, we provide a tutorial
overview of reconfigurable intelligent surfaces (RIS) for wireless
communications. We describe the working principles of reconfigurable
intelligent surfaces (RIS) and elaborate on different candidate implementations
using metasurfaces and reflectarrays. We discuss the channel models suitable
for both implementations and examine the feasibility of obtaining accurate
channel estimates. Furthermore, we discuss the aspects that differentiate RIS
optimization from precoding for traditional MIMO arrays highlighting both the
arising challenges and the potential opportunities associated with this
emerging technology. Finally, we present numerical results to illustrate the
power of an RIS in shaping the key properties of a MIMO channel.Comment: to appear in the IEEE Transactions on Cognitive Communications and
Networking (TCCN
Online Meta-Learning For Hybrid Model-Based Deep Receivers
Recent years have witnessed growing interest in the application of deep
neural networks (DNNs) for receiver design, which can potentially be applied in
complex environments without relying on knowledge of the channel model.
However, the dynamic nature of communication channels often leads to rapid
distribution shifts, which may require periodically retraining. This paper
formulates a data-efficient two-stage training method that facilitates rapid
online adaptation. Our training mechanism uses a predictive meta-learning
scheme to train rapidly from data corresponding to both current and past
channel realizations. Our method is applicable to any deep neural network
(DNN)-based receiver, and does not require transmission of new pilot data for
training. To illustrate the proposed approach, we study DNN-aided receivers
that utilize an interpretable model-based architecture, and introduce a modular
training strategy based on predictive meta-learning. We demonstrate our
techniques in simulations on a synthetic linear channel, a synthetic non-linear
channel, and a COST 2100 channel. Our results demonstrate that the proposed
online training scheme allows receivers to outperform previous techniques based
on self-supervision and joint-learning by a margin of up to 2.5 dB in coded bit
error rate in rapidly-varying scenarios.Comment: arXiv admin note: text overlap with arXiv:2103.1348
A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future
A High Altitude Platform Station (HAPS) is a network node that operates in
the stratosphere at an of altitude around 20 km and is instrumental for
providing communication services. Precipitated by technological innovations in
the areas of autonomous avionics, array antennas, solar panel efficiency
levels, and battery energy densities, and fueled by flourishing industry
ecosystems, the HAPS has emerged as an indispensable component of
next-generations of wireless networks. In this article, we provide a vision and
framework for the HAPS networks of the future supported by a comprehensive and
state-of-the-art literature review. We highlight the unrealized potential of
HAPS systems and elaborate on their unique ability to serve metropolitan areas.
The latest advancements and promising technologies in the HAPS energy and
payload systems are discussed. The integration of the emerging Reconfigurable
Smart Surface (RSS) technology in the communications payload of HAPS systems
for providing a cost-effective deployment is proposed. A detailed overview of
the radio resource management in HAPS systems is presented along with
synergistic physical layer techniques, including Faster-Than-Nyquist (FTN)
signaling. Numerous aspects of handoff management in HAPS systems are
described. The notable contributions of Artificial Intelligence (AI) in HAPS,
including machine learning in the design, topology management, handoff, and
resource allocation aspects are emphasized. The extensive overview of the
literature we provide is crucial for substantiating our vision that depicts the
expected deployment opportunities and challenges in the next 10 years
(next-generation networks), as well as in the subsequent 10 years
(next-next-generation networks).Comment: To appear in IEEE Communications Surveys & Tutorial
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