Multi-Gigabit/s OFDM real-time based transceiver engine for emerging 5G MIMO systems

2020 ◽  
Vol 38 ◽  
pp. 100957 ◽  
Author(s):  
Carlos Ribeiro ◽  
Rodolfo Gomes ◽  
Luís Duarte ◽  
Akram Hammoudeh ◽  
Rafael F.S. Caldeirinha
Keyword(s):  
Real Time ◽  
Mimo Systems

scholarly journals Real-time Distributed MIMO Systems

2016 ◽  
Author(s):  
Ezzeldin Hamed ◽  
Hariharan Rahul ◽  
Mohammed A. Abdelghany ◽  
Dina Katabi
Keyword(s):  
Real Time ◽  
Mimo Systems

Implementation and Evaluation of Real-Time Distributed Zero-Forcing Beamforming for Downlink Multi-User MIMO Systems

2013 ◽  
Vol E96.B (10) ◽  
pp. 2521-2529 ◽  
Author(s):  
Tomoki MURAKAMI ◽  
Koichi ISHIHARA ◽  
Riichi KUDO ◽  
Yusuke ASAI ◽  
Takeo ICHIKAWA ◽  
...  
Keyword(s):  
Real Time ◽  
Mimo Systems ◽  
Zero Forcing

Real time input subset selection for linear time-variant MIMO systems

2007 ◽  
Vol 22 (1) ◽  
pp. 83-98 ◽  
Author(s):  
S.-P. Kim ◽  
J. C. Sanchez ◽  
J. C. Principe
Keyword(s):  
Real Time ◽  
Mimo Systems ◽  
Linear Time ◽  
Subset Selection ◽  
Selection For

Real-time dynamic simulation of non-linear mimo systems

1993 ◽  
Vol 17 ◽  
pp. S343-S348 ◽  
Author(s):  
Pedro C.C. Pimenta ◽  
Sebastião Feyo de Azevedo
Keyword(s):  
Real Time ◽  
Dynamic Simulation ◽  
Mimo Systems ◽  
Time Dynamic ◽  
Non Linear

scholarly journals Loss Visibility Optimized Real-Time Video Transmission Over MIMO Systems

2015 ◽  
Vol 17 (10) ◽  
pp. 1802-1817 ◽  
Author(s):  
Amin Abdel Khalek ◽  
Constantine Caramanis ◽  
Robert W. Heath
Keyword(s):  
Real Time ◽  
Video Transmission ◽  
Mimo Systems

scholarly journals Evaluation of a Combined MHE-NMPC Approach to Handle Plant-Model Mismatch in a Rotary Tablet Press

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1612
Author(s):  
Yan-Shu Huang ◽  
M. Ziyan Sheriff ◽  
Sunidhi Bachawala ◽  
Marcial Gonzalez ◽  
Zoltan K. Nagy ◽  
...  
Keyword(s):  
Product Quality ◽  
Real Time ◽  
Manufacturing Industry ◽  
Time Window ◽  
Mimo Systems ◽  
Mechanistic Model ◽  
Model Parameters ◽  
Plant Model ◽  
Model Mismatch ◽  
Highly Sensitive

The transition from batch to continuous processes in the pharmaceutical industry has been driven by the potential improvement in process controllability, product quality homogeneity, and reduction of material inventory. A quality-by-control (QbC) approach has been implemented in a variety of pharmaceutical product manufacturing modalities to increase product quality through a three-level hierarchical control structure. In the implementation of the QbC approach it is common practice to simplify control algorithms by utilizing linearized models with constant model parameters. Nonlinear model predictive control (NMPC) can effectively deliver control functionality for highly sensitive variations and nonlinear multiple-input-multiple-output (MIMO) systems, which is essential for the highly regulated pharmaceutical manufacturing industry. This work focuses on developing and implementing NMPC in continuous manufacturing of solid dosage forms. To mitigate control degradation caused by plant-model mismatch, careful monitoring and continuous improvement strategies are studied. When moving horizon estimation (MHE) is integrated with NMPC, historical data in the past time window together with real-time data from the sensor network enable state estimation and accurate tracking of the highly sensitive model parameters. The adaptive model used in the NMPC strategy can compensate for process uncertainties, further reducing plant-model mismatch effects. The nonlinear mechanistic model used in both MHE and NMPC can predict the essential but complex powder properties and provide physical interpretation of abnormal events. The adaptive NMPC implementation and its real-time control performance analysis and practical applicability are demonstrated through a series of illustrative examples that highlight the effectiveness of the proposed approach for different scenarios of plant-model mismatch, while also incorporating glidant effects.

Designing a Knowledge base for Intelligent Information System of MIMO Control

2018 ◽  
pp. 24-30
Author(s):  
D. Yu. Muromtsev ◽  
A. N. Gribkov ◽  
I. V. Tyurin ◽  
V. N. Shamkin
Keyword(s):  
Control System ◽  
Knowledge Base ◽  
Real Time ◽  
Energy Efficient ◽  
Mimo Systems ◽  
Control Object ◽  
Information Control ◽  
Control Actions ◽  
Efficient Control ◽  
Intelligent Information

Introduction: The problem of designing information control systems for MIMO systems requires a comprehensive analysis of their operational and technological regimes. Artificial intelligence methods can be used to solve problems related to building models and their optimization in conditions of uncertainty when it is necessary to make prompt decisions.Purpose:Developing a methodology for designing an intelligent information control system which would be invariant to various MIMO systems and could promptly synthesize energy-efficient control actions in real time, taking into account the features of these objects.Results:A static model has been developed for a frame-based knowledge base of an information-control system for energy-intensive process plants in dynamic operation modes. It allows you to take into account the number of states of the operating capability of the control object, many states of its operation, and destabilizing factors of various types. An integrated graph is proposed for generalized intellectualization technology of synthesizing energy-saving control actions for MIMO thermal facilities in warm-up mode.Practical relevance: The created knowledge base structure allows you to promptly provide information for modules realizing algorithmic support of an intelligent information and control system, which in turn makes it possible to synthesize energy-efficient control of a MIMO thermal facility in real time. In addition, energysaving control is characterized by a smooth flow of thermal processes, and this leads to increased durability and safety of the equipment operation.

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