563 research outputs found
Hybrid Supervised-Unsupervised Channel Estimation Scheme with Dynamic Transmission of Pilots
The final publication is available at Springer via http://dx.doi.org/10.1007/s11063-010-9161-x[Abstract] Multiple-Input Multiple-Output (MIMO) digital communications standards typically include pilot symbols in the definition of the transmit signals with the purpose of acquiring the Channel State Information (CSI) using supervised algorithms at the receiver side. Such pilot symbols convey no information and, therefore, system throughput, spectral efficiency and transmit energy consumption are all penalized. In this article, we propose to acquire the CSI combining supervised and unsupervised algorithms. Our strategy avoids the periodical transmission of unnecessary pilots by using a simple decision criterion to determine the time instants when the performance obtained with an unsupervised algorithm degrades or, equivalently, the time instants when pilots are required. We show the performance of this scheme for MIMO systems with Decision-feedback equalizers at the receiver.Galicia. ConsellerĂa de EconomĂa e Industria; 09TIC008105PRMinisterio de Ciencia e InnovaciĂłn; TEC2007-68020-C04-01Ministerio de Ciencia e InnovaciĂłn; TIN2009-0573Ministerio de Ciencia e InnovaciĂłn; CSD2008-0001
Detection of channel variations to improve channel estimation methods
“The final publication is available at Springer via http://dx.doi.org/[10.1007/s00034-014-9767-8]”[Abstract] In current digital communication systems, channel information is typically
acquired by supervised approaches that use pilot symbols included in the transmit
frames. Given that pilot symbols do not convey user data, they penalize throughput
spectral efficiency, and transmit energy consumption of the system. Unsupervised
channel estimation algorithms could be used to mitigate the aforementioned drawbacks
although they present higher computational complexity than that offered by
supervised ones. This paper proposes a simple decision method suitable for slowly
varying channels to determine whether the channel has suffered a significant variation,
which requires to estimate the matrix of the recently changed channel. Otherwise, a
previous estimate is used to recover the transmitted symbols. The main advantage of
this method is that the decision criterion is only based on information acquired during
the time frame synchronization, which is carried out at the receiver. We show that the
proposed criterion provides a considerable improvement of computational complexity
for both supervised and unsupervised methods, without incurring in a penalization in
terms of symbol error ratio. Specifically, we consider systems that make use of the popular
Alamouti code. Performance evaluation is accomplished by means of simulated
channels as well as making use of indoor wireless channels measured using a testbed
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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
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
Deep Learning Aided Parametric Channel Covariance Matrix Estimation for Millimeter Wave Hybrid Massive MIMO
Millimeter-wave (mmWave) channels, which occupy frequency ranges much higher
than those being used in previous wireless communications systems, are utilized
to meet the increased throughput requirements that come with 5G communications.
The high levels of attenuation experienced by electromagnetic waves in these
frequencies causes MIMO channels to have high spatial correlation. To attain
desirable error performances, systems require knowledge about the channel
correlations. In this thesis, a deep neural network aided method is proposed
for the parametric estimation of the channel covariance matrix (CCM), which
contains information regarding the channel correlations. When compared to some
methods found in the literature, the proposed method yields satisfactory
performance in terms of both computational complexity and channel estimation
errors.Comment: M.Sc. Thesis, published at:
https://open.metu.edu.tr/handle/11511/9319
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