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6G Wireless communications: From Far-field Beam Steering to Near-field Beam Focusing

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arXiv 2022年

作者： Zhang, Haiyang Shlezinger, Nir Guidi, Francesco Dardari, Davide Eldar, Yonina C. The School of Communication and Information Engineering Nanjing University of Posts and Telecommunications China The School of Electrical and Computer Engineering Ben-Gurion University Israel Electronics Telecommunications and Information Technologies from Ecole Polytechnique Paris-Tech France Computer Science Specialty The University of Bologna Italy IEIIT-CNR Italy The University of Bologna WiLAB-CNIT Italy The Department of Math and Computer Science Weizmann Institute of Science Israel The Center for Biomedical Engineering and Signal Processing The Israel Academy of Sciences and Humanities IEEE Fellow EURASIP Fellow

6G networks will be required to support higher data rates, improved energy efficiency, lower latency, and more diverse users compared with 5G systems. To meet these requirements, electrically extremely large-scale antenna arrays are envisioned to be key physical-layer technologies. As a consequence, it is expected that some portion of future 6G wireless communications may take place in the radiating near-field (Fresnel) region, in addition to the far-field operation as in current wireless technologies. In this article, we discuss the opportunities and challenges that arise in radiating near-field communications. We begin by discussing the key physical characteristics of near-field communications, where the standard plane-wave propagation assumption no longer holds, and clarifying its implication on the modelling of wireless channels. Then, we elaborate on the ability to leverage spherical wavefronts via beam focusing, highlighting its advantages for 6G systems. We point out several appealing application scenarios which, with proper design, can benefit from near-field operation, including interference mitigation in multi-user communications, accurate localization and focused sensing, as well as wireless power transfer with minimal energy pollution. We conclude by discussing some of the design challenges and research directions that are yet to be explored to fully harness the potential of near-field operation. Copyright © 2022, The Authors. All rights reserved.

关键词： Antenna arrays

Coded Data Rebalancing: Fundamental Limits and Constructions

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Coded Data Rebalancing: Fundamental Limits and Constructions

IEEE International Symposium on Information Theory

作者： Prasad Krishnan V. Lalitha Lakshmi Natarajan Signal Processing & Communications Research Center International Institute of Information Technology Hyderabad India Department of Electrical Engineering Indian Institute of Technology Hyderabad

ISBN: (数字)9781728164328

ISBN: (纸本)9781728164335

Distributed databases often suffer unequal distribution of data among storage nodes, which is known as `data skew'. Data skew arises from a number of causes such as removal of existing storage nodes and addition of new empty nodes to the database. Data skew leads to performance degradations and thus necessitates `rebalancing' at regular intervals to reduce the amount of skew. We define an r-balanced distributed database as a distributed database in which the storage across the nodes has uniform size, and each bit of the data is replicated in r distinct storage nodes. We consider the problem of designing such balanced databases along with associated rebalancing schemes which maintain the r-balanced property under node removal and addition operations. We present a class of r-balanced databases (parameterized by the number of storage nodes) which have the property of structural invariance, i.e., the databases designed for different number of storage nodes have the same structure. For this class of r-balanced databases, we present rebalancing schemes which use coded transmissions between storage nodes, and characterize their communication loads under node addition and removal. We show that the communication cost incurred to rebalance our distributed database for node addition and removal is optimal, i.e., it achieves the minimum possible cost among all possible balanced distributed databases and rebalancing schemes.

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Faster convergence in deep-predictive-coding networks to learn deeper representations

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arXiv 2021年

作者： Sledge, Isaac J. Príncipe, José C. The Advanced Signal Processing and Automated Target Recognition Branch Naval Surface Warfare Center Panama CityFL United States The Department of Electrical and Computer Engineering University of Florida GainesvilleFL United States The Department of Biomedical Engineering University of Florida GainesvilleFL United States

Deep-predictive-coding networks (DPCNs) are hierarchical, generative models. They rely on feed-forward and feed-back connections to modulate latent feature representations of stimuli in a dynamic and context-sensitive manner. A crucial element of DPCNs is a forward-backward inference procedure to uncover sparse, invariant features. However, this inference is a major computational bottleneck. It severely limits the network depth due to learning stagnation. Here, we prove why this bottleneck occurs. We then propose a new forward-inference strategy based on accelerated proximal gradients. This strategy has faster theoretical convergence guarantees than the one used for DPCNs. It overcomes learning stagnation. We also demonstrate that it permits constructing deep and wide predictive-coding networks. Such convolutional networks implement receptive fields that capture well the entire classes of objects on which the networks are trained. This improves the feature representations compared with our lab's previous non-convolutional and convolutional DPCNs. It yields unsupervised object recognition that surpass convolutional autoencoders and are on par with convolutional networks trained in a supervised manner. © 2021, CC BY.

关键词： Convolution

Coded Data Rebalancing: Fundamental Limits and Constructions

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arXiv 2020年

作者： Krishnan, Prasad Lalitha, V. Natarajan, Lakshmi Signal Processing & Communications Research Center International Institute of Information Technology Hyderabad India Department of Electrical Engineering Indian Institute of Technology Hyderabad

Distributed databases often suffer unequal distribution of data among storage nodes, which is known as ‘data skew’. Data skew arises from a number of causes such as removal of existing storage nodes and addition of new empty nodes to the database. Data skew leads to performance degradations and necessitates ‘rebalancing’ at regular intervals to reduce the amount of skew. We define an r-balanced distributed database as a distributed database in which the storage across the nodes has uniform size, and each bit of the data is replicated in r distinct storage nodes. We consider the problem of designing such balanced databases along with associated rebalancing schemes which maintain the r-balanced property under node removal and addition operations. We present a class of r-balanced databases (parameterized by the number of storage nodes) which have the property of structural invariance, i.e., the databases designed for different number of storage nodes have the same structure. For this class of r-balanced databases, we present rebalancing schemes which use coded transmissions between storage nodes, and characterize their communication loads under node addition and removal. We show that the communication cost incurred to rebalance our distributed database for node addition and removal is optimal, i.e., it achieves the minimum possible cost among all possible balanced distributed databases and rebalancing schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Digital storage

Annotating motion primitives for simplifying action search in reinforcement learning

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arXiv 2021年

作者： Sledge, Isaac J. Bryner, Darshan W. Príncipe, José C. The Advanced Signal Processing and Automated Target Recognition Branch Naval Surface Warfare Center Panama CityFL United States The Branch Head of the Advanced Signal Processing and Automated Target Recognition Branch Naval Surface Warfare Center Panama CityFL32407 United States The Department of Electrical and Computer Engineering The Department of Biomedical Engineering University of Florida GainesvilleFL32611 United States The Computational NeuroEngineering Laboratory University of Florida United States

Reinforcement learning in large-scale environments is challenging due to the many possible actions that can be taken in specific situations. We have previously developed a means of constraining, and hence speeding up, the search process through the use of motion primitives;motion primitives are sequences of pre-specified actions taken across a state series. As a byproduct of this work, we have found that if the motion primitives' motions and actions are labeled, then the search can be sped up further. Since motion primitives may initially lack such details, we propose a theoretically viewpoint-insensitive and speed-insensitive means of automatically annotating the underlying motions and actions. We do this through a differential-geometric, spatio-temporal kinematics descriptor, which analyzes how the poses of entities in two motion sequences change over time. We use this descriptor in conjunction with a weighted-nearest-neighbor classifier to label the primitives using a limited set of training examples. In our experiments, we achieve high motion and action annotation rates for human-action-derived primitives with as few as one training sample. We also demonstrate that reinforcement learning using accurately labeled trajectories leads to high-performing policies more quickly than standard reinforcement learning techniques. This is partly because motion primitives encode prior domain knowledge and preempt the need to re-discover that knowledge during training. It is also because agents can leverage the labels to systematically ignore action classes that do not facilitate task objectives, thereby reducing the action space. © 2021, CC BY.

关键词： Reinforcement learning

Terahertz communications and Sensing for 6G and Beyond: A Comprehensive View

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TechRxiv

TechRxiv 2023年

作者： Jiang, Wei Zhou, Qiuheng He, Jiguang Habibi, Mohammad Asif Melnyk, Sergiy El-Absi, Mohammed Han, Bin Di Renzo, Marco Schotten, Hans Dieter Luo, Fa-Long El-Bawab, Tarek S. Juntti, Markku Debbah, Mérouane Leung, Victor C.M. Kaiserslautern Germany The Technology Innovation Institute Abu Dhabi United Arab Emirates Centre for Wireless Communications University of Oulu Oulu90014 Finland The Institue of Digital Signal Processing University of Duisburg-Essen Duisburg Germany Germany Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et Systèmes 3 Rue Joliot-Curie Gif-sur-Yvette91192 France The Electrical and Computer Engineering Department University of Washington SeattleWA98195 United States Nigeria The University of Oulu Finland Khalifa University of Science and Technology P O Box 127788 Abu Dhabi United Arab Emirates The College of Computer Science and Software Engineering Shenzhen University China Canada Germany

The next-generation wireless technologies, commonly referred to as the sixth generation (6G), are envisioned to support extreme communications capacity and in particular disruption in the network sensing capabilities. The terahertz (THz) band is one potential enabler for those due to the enormous unused frequency bands and the high spatial resolution enabled by both short wavelengths and bandwidths. Different from earlier surveys, this paper presents a comprehensive treatment and technology survey on THz communications and sensing in terms of the advantages, applications, propagation characterization, channel modeling, measurement campaigns, antennas, transceiver devices, beamforming, networking, the integration of communications and sensing, and experimental testbeds. Starting from the motivation and use cases, we survey the development and historical perspective of THz communications and sensing with the anticipated 6G requirements. We explore the radio propagation, channel modeling, and measurements for THz band. The transceiver requirements, architectures, technological challenges, and approaches together with means to compensate for the high propagation losses by appropriate antenna and beamforming solutions. We survey also several system technologies required by or beneficial for THz systems. The synergistic design of sensing and communications is explored with depth. Practical trials, demonstrations, and experiments are also summarized. The paper gives a holistic view of the current state of the art and highlights the issues and challenges that are open for further © 2023, CC BY-NC-SA.

关键词： Beamforming

An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plants

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arXiv 2021年

作者： Marcelino, Carolina Gil Leite, G.M.C. Delgado, C.A.D.M. De Oliveira, L.B. Wanner, E.F. Jiménez-Fernández, S. Salcedo-Sanz, S. Department of Signal Processing and Communications Universidad de Alcalá Spain Institute of Computing Federal University of Rio de Janeiro Brazil Post-Graduate Program in Systems Engineering and Computer Science Federal University of Rio de Janeiro Brazil Computing Department Federal Center of Technology Education of Minas Gerais Brazil

This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system - a cascade-based operation scenario. For this, we propose a new mathematical modelling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multiobjective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out. © 2021, CC BY-NC-ND.

关键词： Multiobjective optimization

Estimating Canopy Interception Water Storage with GNSS-Transmissometry

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Estimating Canopy Interception Water Storage with GNSS-Trans...

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Konstantin Schellenberg Thomas Jagdhuber David Chaparro Oliver Binks Florian Hellwig Clémence Dubois Mehmet Kurum Adriano Camps Henrik Hartmann Christiane Schmullius Department for Earth Observation Friedrich Schiller University Jena Jena Germany Department Biogeochemical Processes Max Planck Institute for Biogeochemistry Jena Germany German Aerospace Center (DLR) Microwaves and Radar Institute (HR) Wessling Germany Institute of Geography University of Augsburg Augsburg Germany Centre for Ecological and Forestry Applications (CREAF) Cerdanyola del Vallès Spain German Aerospace Center (DLR) Institute of Data Science Jena Germany School of Electrical and Computer Engineering University of Georgia Athens GA USA Department of Signal Theory and Communications CommSensLab and Institut d’Estudis Espacials de Catalunya Universitat Politècnica de Catalunya Barcelona Spain Julius Kühn-Institute for Forest Protection Federal Research Institute for Cultivated Plants Quedlinburg Germany

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Storage of interception water in the canopy (S c ) heavily affects measurements of vegetation optical depth (VOD) from rain, dew and fog, impeding the direct retrieval of tree physiological parameters such as biomass and plant water content. This study presents a time series decomposition of VOD from Global Navigation Satellite System-Transmissometry (GNSS-T) into biomass, plant moisture content (M g ) and S c . The experiment was conducted at eddy covariance (EC) towers in two temperate forest types in Germany, over the entire vegetation period of 2023 and under fairly wet conditions. S c -values were 1.5 times (needleleaf) to two times (broadleaf) higher than the average diurnal M g cycle, allowing partitioning of interception water storage from plant water. Furthermore, we found indications that S c maxima did not linearly increase with precipitation, suggesting sensitivity of VOD to saturation effects when canopy interception storage reaches a maximum during strong precipitation events. Results indicate the sensitivity of VOD from GNSS-T to canopy wetness. This allows partitioning of canopy water storage from other VOD components and improves the usefulness of VOD as a remote sensing metric for forest canopy water relations. Moreover, it opens pathways to quantify S c and evaporation fluxes independently from EC measurements and field experiments.

关键词： Water storage Sensitivity Biomedical optical imaging Vegetation mapping Forestry Vegetation Optical variables measurement

Regularized Training of Convolutional Autoencoders using the Rényi-Stratonovich Value of Information

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Regularized Training of Convolutional Autoencoders using the...

International Joint Conference on Neural Networks (IJCNN)

作者： Isaac J. Sledge José C. Príncipe Advanced Signal Processing and Automated Target Recognition Branch Naval Surface Warfare Center Department of Electrical and Computer Engineering University of Florida Department of Biomedical Engineering University of Florida

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

We propose an information-theoretic cost function or the regularized training of convolutional auto encoders that imposes an organization on the bottleneck-layer-projected samples so as to facilitate discrimination. This function is based on a continuous-space, Rényi-mutual-information version of Stratonovich's value of information. It quantifies the maximum benefit that can be obtained for a given bottleneck-layer representation compression amount. The compression amount is controlled by a single hyperparameter that trades off between the autoencoder reconstruction quality and the hidden-layer representation uncertainty.

关键词： Random variables Training Encoding Uncertainty Entropy Mutual information Organizations