Data-Driven Measurement of Receiver Sensitivity in Wireless Communication Systems

Ma, Yuan; Wang, Xu; Quan, Zhi; Poor, H. Vincent

doi:10.1109/tcomm.2019.2891708

Cited by 25 publications

(8 citation statements)

References 23 publications

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“…, M without using any information of a mathematical traffic model, as seen in ( 21)- (23). Furthermore, by representing the urban traffic dynamics of n r (k) shown by ( 4), (12), and ( 13) into the MFAPC data model (19), some imprecise problems in existing linearization methods, such as the dropout of high-order terms in Taylor's linearization [43] and the requirement of model information in piecewise linearization [44], can be avoided.…”

Section: B Mfapc Data Models Of the Regionsmentioning

confidence: 99%

“…Information exchange and negotiations among the regions are solved by the ADMM-based DMPC approach [33]. 4) A Centralized MFAPC (C-MFAPC) Strategy: In this strategy, the dynamics of shown by ( 3), (14), and ( 15) can be represented by an MFAPC data model similar to (19) and ( 21)- (23). Compared with the C-MPC strategy, the derived MFAPC data model instead of S model is utilized as the prediction model.…”

Section: ) a Centralized Mpc Controller (C-mpc) To Control Thementioning

confidence: 99%

“…MFAC was originally proposed in [17], and it has been developed to cope with control problems for a class of unknown, nonlinear, nonaffine systems based on different dynamic linearization data models using a novel concept of pseudopartial derivative, which can be estimated directly from input-output data [18]. The applications of MFAC include wireless communication systems [19], implantable heart pumps [20], nonlinear distillation columns [21], perimeter control for one-region [22] and multiregion [23], [24] urban traffic networks, and so on. MFAPC is a predictive control scheme derived by embedding the MFAC scheme in a rolling horizon framework [18].…”

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confidence: 99%

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Distributed Model-Free Adaptive Predictive Control for Urban Traffic Networks

Dai

Schutter

2022

IEEE Trans. Contr. Syst. Technol.

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Data-driven control without using mathematical models is a promising research direction for urban traffic control due to the massive amounts of traffic data generated every day. This article proposes a novel distributed model-free adaptive predictive control (D-MFAPC) approach for multiregion urban traffic networks. More specifically, the traffic dynamics of the network regions are first transformed into MFAPC data models, and then, the derived MFAPC data models instead of mathematical traffic models serve as the prediction models in the distributed control design. The formulated control problem is finally solved with an alternating direction method of multipliers (ADMM)based approach. The simulation results for the traffic network of Linfen, Shanxi, China, show the feasibility and effectiveness of the proposed method. Index Terms-Data-driven control, distributed model predictive control (DMPC), macroscopic fundamental diagram (MFD), model-free adaptive predictive control (MFAPC), urban traffic network control. I. INTRODUCTIONT RAFFIC congestion is a severe problem in urban traffic networks due to the rapid growth of vehicle numbers, and how to deal with traffic congestion problems using the existing traffic infrastructure is still a highly relevant topic. Network-wide traffic control is an excellent way to deal with urban traffic congestion.Network-wide traffic control optimizes the signal settings of all intersections simultaneously aiming to obtain the globally optimal performance of the entire network. Several approaches for network-wide urban traffic control have been proposed, in which many control and optimization theories are utilized. Some commercial traffic control systems, such as MAXBAND [1] and TRANSYT [2], have been implemented in many cities for a long time. For the traffic-responsive urban control (TUC) strategy [3] and its extended versions [4], [5], a linear quadratic regulator is utilized to balance the traffic flows within the network. To better cope with the fluctuations Manuscript

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Section: B Mfapc Data Models Of the Regionsmentioning

confidence: 99%

Section: ) a Centralized Mpc Controller (C-mpc) To Control Thementioning

confidence: 99%

mentioning

confidence: 99%

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Distributed Model-Free Adaptive Predictive Control for Urban Traffic Networks

Dai

Schutter

2022

IEEE Trans. Contr. Syst. Technol.

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“…Since the drive is the first part of instruction, the use of internal affective drivers to generate interest, increase student engagement, and generate positive self-evaluation of themselves contributes to the effectiveness of classroom instruction. As it is clear from the above that students' response to the drive material is one of the factors that influence their motivation to learn, optimization of the drive material becomes necessary, and therefore, the drive material should be optimized to stimulate students' internal affective drive [ 18 ]. Existing deep semantic relevance learning methods for cross-media data contain two main categories, namely, cross-media hashing algorithms and cross-media quantization algorithms.…”

Section: A Model For Teaching New Media In English Classrooms Based O...mentioning

confidence: 99%

A Study on Cross-Media Teaching Model for College English Classroom Based on Output-Driven Hypothetical Neural Network

Guo

2022

Computational Intelligence and Neuroscience

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In the field of education, the development of educational big data has become an important strategic choice to promote the construction of the digital campus and educational reform, and educational big data has become a new driving force in the field of education that cannot be ignored. Based on the theoretical basis of output-driven hypothesis neural network, and combining the media spanning of contemporary art and cross-media association effect, this study changes the status quo of English teaching through traditional methods such as grammar-translation method and deductive method and constructs a new cross-media university English teaching model. Based on the existing feature learning model of two-way attention, combined with existing techniques such as generative adversarial networks and semantic hashing, the semantic association between different media data is deeply mined, and feature learning is integrated with adversarial learning and hash learning to build a unified semantic space for different media data. In this paper, we focus on the structure and characteristics of convolutional neural networks through the study of deep learning theory, discuss three classical convolutional neural network models, such as AlexNet, VGG, and GoogLeNet, and propose a convolutional neural network model applicable to cross-media teaching in college English classroom and carry out experimental validation, and the results show that the proposed neural network model is based on output-driven hypothesis. The following research has been added to the abstract: to address the key problem of the semantic gap that is difficult to cross in cross-media semantic learning, a cross-media supervised adversarial hashing model based on two-way attentional features is proposed. Based on the existing two-way attention-based feature learning model, we combine existing techniques such as generative adversarial networks and semantic hashing to deeply explore the semantic association between different media data and integrate feature learning with adversarial learning and hashing to build a unified semantic space for different media data. The results show that the proposed neural network model of cross-media teaching in college English classrooms based on the output-driven hypothesis can not only promote the improvement of students’ English literacy skills but also have a certain promotion effect on their overall performance improvement.

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“…Considering different length of the past input and output data being used in equivalent model transformation, the MFAC data models can be classified into the compact form dynamic linearization (CFDL) data model, the partial form dynamic linearization (PFDL) data model, and the full form dynamic linearization (FFDL) data model. Outside of the urban traffic control, MFAC has also been applied in wireless communication systems [24], implantable heart pumps [25], nonlinear distillation columns [26], and so on.…”

Section: Introductionmentioning

confidence: 99%

Data‐driven urban traffic model‐free adaptive iterative learning control with traffic data dropout compensation

Dai

Hou

2021

IET Control Theory & Appl

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In this paper, to fully utilize the urban traffic flow characteristics of similarity and repeatability without using a mathematical traffic model, a data-driven urban traffic control strategy based on model-free adaptive iterative learning control (MFAILC) scheme is put forward. Firstly, by dynamically linearizing the urban traffic dynamics along the iteration axis, the traffic network system is transformed into a MFAILC data model with the help of repetitive pattern of urban traffic flow. Then, the traffic controller is designed based on the derived MFAILC data model only using the I/O data of the traffic network. Finally, a traffic data compensation method is proposed to deal with data dropout problem. Simulation study verifies the feasibility and effectiveness of the proposed control method.

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Data-Driven Measurement of Receiver Sensitivity in Wireless Communication Systems

Cited by 25 publications

References 23 publications

Distributed Model-Free Adaptive Predictive Control for Urban Traffic Networks

Distributed Model-Free Adaptive Predictive Control for Urban Traffic Networks

A Study on Cross-Media Teaching Model for College English Classroom Based on Output-Driven Hypothetical Neural Network

Data‐driven urban traffic model‐free adaptive iterative learning control with traffic data dropout compensation

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