scholarly journals A Novel Adaptive Channel Equalization Method Using Variable Step Size Partial Rank Algorithm

2010 ◽  
Author(s):  
Sayed A. Hadei ◽  
Paeiz Azmi
Keyword(s):  
Channel Equalization ◽  
Variable Step Size ◽  
Step Size ◽  
Variable Step ◽  
Equalization Method

scholarly journals An Active Equalization Method for Lithium-ion Batteries Based on Flyback Transformer and Variable Step Size Generalized Predictive Control

Energies ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 207
Author(s):  
Jianwen Cao ◽  
Bizhong Xia ◽  
Jie Zhou
Keyword(s):  
Energy Consumption ◽  
Lithium Ion Batteries ◽  
Predictive Control ◽  
Lithium Ion ◽  
Generalized Predictive Control ◽  
Variable Step Size ◽  
Size Factor ◽  
Step Size ◽  
Variable Step ◽  
Equalization Method

The inconsistency in large-scale battery pack significantly degrades the performance of electric vehicles. In order to diminish the inconsistency, the study designs an active equalization method comprising of equalizer and equalization strategy for lithium-ion batteries. A bidirectional flyback transformer equalizer (BFTE) is designed and analyzed. The BFTE is controlled by a pulse width modulation (PWM) controller to output designated balancing currents. Under the purpose of shortening equalization time and reducing energy consumption during the equalization process, this paper proposes an equalization strategy based on variable step size generalized predictive control (VSSGPC). The VSSGPC is improved on the generalized predictive control (GPC) by introducing the Step Size Factor. The VSSGPC surmounts the local limitation of GPC by expanding the control and output horizons to the global equalization process without increasing computation owing to the Step Size Factor. The experiment results in static operating condition indicate that the equalization time and energy consumption are reduced by 8.3% and 16.5%, respectively. Further validation in CC-CV and EUDC operating conditions verifies the performance of the equalizer and rationality of the VSSGPC strategy.

Network Intrusion Feature Map Node Equalization Algorithm Based on Modified Variable Step-Size Constant Modulus

2019 ◽  
Vol 33 (14) ◽  
pp. 1955015 ◽  
Author(s):  
Jiazhong Lu ◽  
Xiaolei Liu ◽  
Teng Hu ◽  
Jianwei Zhang ◽  
Xiaosong Zhang
Keyword(s):  
Channel Model ◽  
Channel Equalization ◽  
Variable Step Size ◽  
Transmission Channel ◽  
Constant Modulus ◽  
Step Size ◽  
Feature Map ◽  
Network Intrusion ◽  
Equalization Algorithm ◽  
Variable Step

When the network is subject to intrusion and attack, the node output channel equalization will be affected, resulting in bit error and distortion in the output of network transmission symbols. In order to improve the anti-attack ability and equalization of network node, a network intrusion feature map node equalization algorithm based on modified variable step-size constant modulus blind equalization algorithm (MISO-VSS-MCMA) is proposed. In this algorithm, the node transmission channel model after network intrusion is constructed, and sequential processing is performed to intruded nodes with the variable structure feedback link control method. With diversity spread spectrum technology, the channel loss after network intrusion is compensated and the network intrusion map feature is extracted. According to the extracted feature amount, channel equalization processing is performed for the cost function with the MISO-VSS-MCMA method to reduce the damage of network intrusion to the channel. Simulation results show that in node transmission channel equalization after network intrusion, this algorithm can reduce the error bit rate of signal transmission in network, and provide a good ability of correcting phase deflection in the output constellation, thus avoiding the error bit distortion and channel damage caused by network intrusion to the signal with a good equalization effect. This algorithm provides stronger convergence and map concentration, which demonstrates that its anti-interference and signal recovery capabilities are better, so it improves the anti-attack ability of the network.

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