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3. Massive MIMO Evolution Towards 3GPP Release 18

Author

Jin, Huangping, Liu, Kunpeng, Lee, Gilwon, Farag, Emad J., Zhang, Min, Zhu, Dalin, Zhang, Leiming, Onggosanusi, Eko, Shafi, Mansoor, and Tataria, Harsh

Subjects
Computer Science - Information Theory, Electrical Engineering and Systems Science - Signal Processing
Abstract

Since the introduction of fifth-generation new radio (5G-NR) in Third Generation Partnership Project (3GPP) Release 15, swift progress has been made to evolve 5G with 3GPP Release 18 emerging. A critical aspect is the design of massive multiple-input multiple-output (MIMO) technology. In this line, this paper makes several important contributions: We provide a comprehensive overview of the evolution of standardized massive MIMO features from 3GPP Release 15 to 17 for both time/frequency-division duplex operation across bands FR-1 and FR-2. We analyze the progress on channel state information (CSI) frameworks, beam management frameworks and present enhancements for uplink CSI. We shed light on emerging 3GPP Release 18 problems requiring imminent attention. These include advanced codebook design and sounding reference signal design for coherent joint transmission (CJT) with multiple transmission/reception points (multi- TRPs). We discuss advancements in uplink demodulation reference signal design, enhancements for mobility to provide accurate CSI estimates, and unified transmission configuration indicator framework tailored for FR-2 bands. For each concept, we provide system level simulation results to highlight their performance benefits. Via field trials in an outdoor environment at Shanghai Jiaotong University, we demonstrate the gains of multi-TRP CJT relative to single TRP at 3.7 GHz., Comment: 23 pages, 37 Figures, one fig in the annex

Published
2022

20. Training Robust Graph Neural Networks with Topology Adaptive Edge Dropping

Author

Gao, Zhan, Bhattacharya, Subhrajit, Zhang, Leiming, Blum, Rick S., Ribeiro, Alejandro, and Sadler, Brian M.

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

Graph neural networks (GNNs) are processing architectures that exploit graph structural information to model representations from network data. Despite their success, GNNs suffer from sub-optimal generalization performance given limited training data, referred to as over-fitting. This paper proposes Topology Adaptive Edge Dropping (TADropEdge) method as an adaptive data augmentation technique to improve generalization performance and learn robust GNN models. We start by explicitly analyzing how random edge dropping increases the data diversity during training, while indicating i.i.d. edge dropping does not account for graph structural information and could result in noisy augmented data degrading performance. To overcome this issue, we consider graph connectivity as the key property that captures graph topology. TADropEdge incorporates this factor into random edge dropping such that the edge-dropped subgraphs maintain similar topology as the underlying graph, yielding more satisfactory data augmentation. In particular, TADropEdge first leverages the graph spectrum to assign proper weights to graph edges, which represent their criticality for establishing the graph connectivity. It then normalizes the edge weights and drops graph edges adaptively based on their normalized weights. Besides improving generalization performance, TADropEdge reduces variance for efficient training and can be applied as a generic method modular to different GNN models. Intensive experiments on real-life and synthetic datasets corroborate theory and verify the effectiveness of the proposed method.

Published
2021

21. Pursuer Assignment and Control Strategies in Multi-agent Pursuit-Evasion Under Uncertainties

Author

Zhang, Leiming, Prorok, Amanda, and Bhattacharya, Subhrajit

Subjects
Computer Science - Robotics
Abstract

We consider a pursuit-evasion problem with a heterogeneous team of multiple pursuers and multiple evaders. Although both the pursuers (robots) and the evaders are aware of each others' control and assignment strategies, they do not have exact information about the other type of agents' location or action. Using only noisy on-board sensors the pursuers (or evaders) make probabilistic estimation of positions of the evaders (or pursuers). Each type of agent use Markov localization to update the probability distribution of the other type. A search-based control strategy is developed for the pursuers that intrinsically takes the probability distribution of the evaders into account. Pursuers are assigned using an assignment algorithm that takes redundancy (i.e., an excess in the number of pursuers than the number of evaders) into account, such that the total or maximum estimated time to capture the evaders is minimized. In this respect we assume the pursuers to have clear advantage over the evaders. However, the objective of this work is to use assignment strategies that minimize the capture time. This assignment strategy is based on a modified Hungarian algorithm as well as a novel algorithm for determining assignment of redundant pursuers. The evaders, in order to effectively avoid the pursuers, predict the assignment based on their probabilistic knowledge of the pursuers and use a control strategy to actively move away from those pursues. Our experimental evaluation shows that the redundant assignment algorithm performs better than an alternative nearest-neighbor based assignment algorithm., Comment: 14 pages, 6 figures

Published
2021

26. Inter-cluster Transmission Control Using Graph Modal Barriers

Author

Zhang, Leiming, Sadler, Brian M., Blum, Rick S., and Bhattacharya, Subhrajit

Subjects
Computer Science - Social and Information Networks
Abstract

In this paper we consider the problem of transmission across a graph and how to effectively control/restrict it with limited resources. Transmission can represent information transfer across a social network, spread of a malicious virus across a computer network, or spread of an infectious disease across communities. The key insight is to assign proper weights to bottleneck edges of the graph based on their role in reducing the connection between two or more strongly-connected clusters within the graph. Selectively reducing the weights (implying reduced transmission rate) on the critical edges helps limit the transmission from one cluster to another. We refer to these as barrier weights and their computation is based on the eigenvectors of the graph Laplacian. Unlike other work on graph partitioning and clustering, we completely circumvent the associated computational complexities by assigning weights to edges instead of performing discrete graph cuts. This allows us to provide strong theoretical results on our proposed methods. We also develop approximations that allow low complexity distributed computation of the barrier weights using only neighborhood communication on the graph., Comment: 16 pages

Published
2020

27. Feature Interaction based Neural Network for Click-Through Rate Prediction

Author

Zou, Dafang, Zhang, Leiming, Mao, Jiafa, and Sheng, Weiguo

Subjects
Computer Science - Machine Learning, Computer Science - Information Retrieval, Statistics - Machine Learning
Abstract

Click-Through Rate (CTR) prediction is one of the most important and challenging in calculating advertisements and recommendation systems. To build a machine learning system with these data, it is important to properly model the interaction among features. However, many current works calculate the feature interactions in a simple way such as inner product and element-wise product. This paper aims to fully utilize the information between features and improve the performance of deep neural networks in the CTR prediction task. In this paper, we propose a Feature Interaction based Neural Network (FINN) which is able to model feature interaction via a 3-dimention relation tensor. FINN provides representations for the feature interactions on the the bottom layer and the non-linearity of neural network in modelling higher-order feature interactions. We evaluate our models on CTR prediction tasks compared with classical baselines and show that our deep FINN model outperforms other state-of-the-art deep models such as PNN and DeepFM. Evaluation results demonstrate that feature interaction contains significant information for better CTR prediction. It also indicates that our models can effectively learn the feature interactions, and achieve better performances in real-world datasets., Comment: 10 pages, 5 figure. arXiv admin note: text overlap with arXiv:1905.09433 by other authors

Published
2020

31. Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data.

Author

Pastorello, Gilberto, Trotta, Carlo, Canfora, Eleonora, Chu, Housen, Christianson, Danielle, Cheah, You-Wei, Poindexter, Cristina, Chen, Jiquan, Elbashandy, Abdelrahman, Humphrey, Marty, Isaac, Peter, Polidori, Diego, Reichstein, Markus, Ribeca, Alessio, van Ingen, Catharine, Vuichard, Nicolas, Zhang, Leiming, Amiro, Brian, Ammann, Christof, Arain, M Altaf, Ardö, Jonas, Arkebauer, Timothy, Arndt, Stefan K, Arriga, Nicola, Aubinet, Marc, Aurela, Mika, Baldocchi, Dennis, Barr, Alan, Beamesderfer, Eric, Marchesini, Luca Belelli, Bergeron, Onil, Beringer, Jason, Bernhofer, Christian, Berveiller, Daniel, Billesbach, Dave, Black, Thomas Andrew, Blanken, Peter D, Bohrer, Gil, Boike, Julia, Bolstad, Paul V, Bonal, Damien, Bonnefond, Jean-Marc, Bowling, David R, Bracho, Rosvel, Brodeur, Jason, Brümmer, Christian, Buchmann, Nina, Burban, Benoit, Burns, Sean P, Buysse, Pauline, Cale, Peter, Cavagna, Mauro, Cellier, Pierre, Chen, Shiping, Chini, Isaac, Christensen, Torben R, Cleverly, James, Collalti, Alessio, Consalvo, Claudia, Cook, Bruce D, Cook, David, Coursolle, Carole, Cremonese, Edoardo, Curtis, Peter S, D'Andrea, Ettore, da Rocha, Humberto, Dai, Xiaoqin, Davis, Kenneth J, De Cinti, Bruno, de Grandcourt, Agnes, De Ligne, Anne, De Oliveira, Raimundo C, Delpierre, Nicolas, Desai, Ankur R, Di Bella, Carlos Marcelo, di Tommasi, Paul, Dolman, Han, Domingo, Francisco, Dong, Gang, Dore, Sabina, Duce, Pierpaolo, Dufrêne, Eric, Dunn, Allison, Dušek, Jiří, Eamus, Derek, Eichelmann, Uwe, ElKhidir, Hatim Abdalla M, Eugster, Werner, Ewenz, Cacilia M, Ewers, Brent, Famulari, Daniela, Fares, Silvano, Feigenwinter, Iris, Feitz, Andrew, Fensholt, Rasmus, Filippa, Gianluca, Fischer, Marc, Frank, John, Galvagno, Marta, and Gharun, Mana

Abstract

The following authors were omitted from the original version of this Data Descriptor: Markus Reichstein and Nicolas Vuichard. Both contributed to the code development and N. Vuichard contributed to the processing of the ERA-Interim data downscaling. Furthermore, the contribution of the co-author Frank Tiedemann was re-evaluated relative to the colleague Corinna Rebmann, both working at the same sites, and based on this re-evaluation a substitution in the co-author list is implemented (with Rebmann replacing Tiedemann). Finally, two affiliations were listed incorrectly and are corrected here (entries 190 and 193). The author list and affiliations have been amended to address these omissions in both the HTML and PDF versions.

Published
2021

40. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data.

Author

Pastorello, Gilberto, Trotta, Carlo, Canfora, Eleonora, Chu, Housen, Christianson, Danielle, Cheah, You-Wei, Poindexter, Cristina, Chen, Jiquan, Elbashandy, Abdelrahman, Humphrey, Marty, Isaac, Peter, Polidori, Diego, Reichstein, Markus, Ribeca, Alessio, van Ingen, Catharine, Vuichard, Nicolas, Zhang, Leiming, Amiro, Brian, Ammann, Christof, Arain, M Altaf, Ardö, Jonas, Arkebauer, Timothy, Arndt, Stefan K, Arriga, Nicola, Aubinet, Marc, Aurela, Mika, Baldocchi, Dennis, Barr, Alan, Beamesderfer, Eric, Marchesini, Luca Belelli, Bergeron, Onil, Beringer, Jason, Bernhofer, Christian, Berveiller, Daniel, Billesbach, Dave, Black, Thomas Andrew, Blanken, Peter D, Bohrer, Gil, Boike, Julia, Bolstad, Paul V, Bonal, Damien, Bonnefond, Jean-Marc, Bowling, David R, Bracho, Rosvel, Brodeur, Jason, Brümmer, Christian, Buchmann, Nina, Burban, Benoit, Burns, Sean P, Buysse, Pauline, Cale, Peter, Cavagna, Mauro, Cellier, Pierre, Chen, Shiping, Chini, Isaac, Christensen, Torben R, Cleverly, James, Collalti, Alessio, Consalvo, Claudia, Cook, Bruce D, Cook, David, Coursolle, Carole, Cremonese, Edoardo, Curtis, Peter S, D'Andrea, Ettore, da Rocha, Humberto, Dai, Xiaoqin, Davis, Kenneth J, Cinti, Bruno De, Grandcourt, Agnes de, Ligne, Anne De, De Oliveira, Raimundo C, Delpierre, Nicolas, Desai, Ankur R, Di Bella, Carlos Marcelo, Tommasi, Paul di, Dolman, Han, Domingo, Francisco, Dong, Gang, Dore, Sabina, Duce, Pierpaolo, Dufrêne, Eric, Dunn, Allison, Dušek, Jiří, Eamus, Derek, Eichelmann, Uwe, ElKhidir, Hatim Abdalla M, Eugster, Werner, Ewenz, Cacilia M, Ewers, Brent, Famulari, Daniela, Fares, Silvano, Feigenwinter, Iris, Feitz, Andrew, Fensholt, Rasmus, Filippa, Gianluca, Fischer, Marc, Frank, John, Galvagno, Marta, and Gharun, Mana

Abstract

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

Published
2020

41. Mechanism of short-wall block backfill water-preserved mining based on water-conducting fractures development-heavy metal ions migration

Author

ZHANG Yun, LIU Yongzi, LAI Xingping, SONG Tianqi, ZHANG Leiming, SUN Haoqiang, WAN Peifeng, and ZHAN Rui

Subjects
water resources protection, water-conducting fractures, heavy metal ions in gangue, water-preserved mining, short-wall block backfill mining, Mining engineering. Metallurgy, TN1-997
Abstract

Short-wall block backfill mining (SBBM) technology can effectively solve the problems of coal resources waste, water resources loss and gangue waste accumulation. However, after the long-term effect of mine water, the internal heavy metal ions in the gangue backfill materials may be leached, which have a certain potential impact on the groundwater environment in the mining area. Therefore, according to the technical characteristics of SBBM technology, comprehensive prevention of water resources loss and water resources pollution during SBBM was studied systematically. First, prevention and control of water resources loss in the mining area. Based on the combination relationship of “coal pillar, backfill body-barrier layer-aquifuge”, The development of water-conducting fractures in overlying strata under SBBM were analysed. The control mechanism of water resources protection under the evolution of overlying strata structure induced during SBBM was revealed. Secondly, prevention and control of water resources pollution in mining areas. The migration model of heavy metal ions in the gob was established. Then, the pollution mechanism of gangue backfill materials to water resources was revealed, the migration law of heavy metal ions in gangue backfill materials was analyzed, the comprehensive prevention and control technology of water resources destruction in mining area was summarized, the comprehensive prevention and control technology of water resources loss-pollution in mining area was summarized, and the design method of filling ratio based on water resources loss-pollution prevention was put forward. The results show that the gangue materials were filled into the gob as the backfill body, the gangue materials would be the permanent bearing body, which bore the load of the overlying strata accompanied with the protective coal pillar among the blocks, which could greatly prevents the collapse of the low-level stratum, restrict the water-conducting fractures to penetrate the barrier layer and protect the integrity of the high-level stratum. In addition, the heavy metal ions in gangue backfill materials were subjected to the coupled action of seepage, concentration, and stress and then driven by water head pressure and gravitational potential energy to migrate along the side of the coal pillar and below the floor stratum, during which mine water served as the carrier. The migration distance of heavy metal ions increased with increasing of the permeability of floor strata, leaching concentration of heavy metal ions/pollution source intensity, depth of floor breaks and water level heights, and decreases with the increase of gangue particle size and surrounding rock stress. Based on this, the comprehensive prevention and control technology of water resources loss-pollution in mining area was summarized, it included mining parameter control technology, backfill parameter control technology, pollution source control technology, transmission route control technology and in-situ water control technology. and the design method of filling ratio based on water resource loss-pollution prevention and control was put forward, which can realize the comprehensive prevention and control of water resource loss-pollution in mining area. This study will provide scientific theoretical basis and guidance for the comprehensive prevention and control of the water resources destruction problems caused by coal mining.

Published
2023
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