Adaptive Equalization with Variable Step Size Algorithm Based on Gradient for HF Channel

2014 ◽  
Vol 989-994 ◽  
pp. 3702-3705
Author(s):  
Fang Fang Han ◽  
Zi Hao Lin
Keyword(s):  
Channel Model ◽  
Adaptive Equalization ◽  
Variable Step Size ◽  
Step Size ◽  
Performance Simulation ◽  
Channel Condition ◽  
Variable Step ◽  
Convergence Performance ◽  
Complex Channel ◽  
Hf Channel

To further improve the performance of adaptive equalization in HF channel, a new variable step size algorithm is proposed based on the iterative gradient. The step size is set to a vector according to the weights of the equalizer, and it can vary with the iterative gradient during the equalization process. The algorithm overcomes the traditional variable step size with the constant scale for all the weights of the equalizer, thus it can obtain faster convergence rate and lower steady state error in HF channel. In the complex channel condition, the algorithm proposed in this paper can avoid the local minimum point of the objective function to obtain the global convergence performance. Simulation result shows the effectiveness in the Watterson channel model.

A Denoising Method Based on the Variable Step NLMP for the Pulsar Signal

2012 ◽  
Vol 500 ◽  
pp. 760-765
Author(s):  
Jing Rong Sun ◽  
Gui Ying Zhang
Keyword(s):  
Stable Distribution ◽  
Weight Vector ◽  
Variable Step Size ◽  
Step Size ◽  
Denoising Method ◽  
Performance Simulation ◽  
Variable Step ◽  
Convergence Performance ◽  
Simulation Results ◽  
Better Than

In order to denoise the pulsar signal, a variable step NLMP algorithm was introduced under the-stable distribution. The algorithm introduced a step update factor. By adjusting parameters and error information, the algorithm can adjust the incremental direction of the adaptive filter weight vector accurately, and improve the convergence performance. Simulation results show that the variable step-size NLMP algorithm is better than the NLMP algorithm in the denoising effect in-stable distribution noise environments.

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.

A Variable Step-Size for Sparse Nonlinear Adaptive Filters

2021 ◽  
Author(s):  
Alberto Carini ◽  
Markus V. S. Lima ◽  
Hamed Yazdanpanah ◽  
Simone Orcioni ◽  
Stefania Cecchi
Keyword(s):  
Adaptive Filters ◽  
Variable Step Size ◽  
Step Size ◽  
Variable Step

Active control for vehicle interior noise using the improved iterative variable step-size and variable tap-length LMS algorithms

2019 ◽  
Vol 67 (6) ◽  
pp. 405-414 ◽  
Author(s):  
Ningning Liu ◽  
Yuedong Sun ◽  
Yansong Wang ◽  
Hui Guo ◽  
Bin Gao ◽  
...  
Keyword(s):  
Steady State ◽  
Noise Control ◽  
Convergence Speed ◽  
Interior Noise ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Vehicle Interior ◽  
Vehicle Interior Noise ◽  
Variable Step

Active noise control (ANC) is used to reduce undesirable noise, particularly at low frequencies. There are many algorithms based on the least mean square (LMS) algorithm, such as the filtered-x LMS (FxLMS) algorithm, which have been widely used for ANC systems. However, the LMS algorithm cannot balance convergence speed and steady-state error due to the fixed step size and tap length. Accordingly, in this article, two improved LMS algorithms, namely, the iterative variable step-size LMS (IVS-LMS) and the variable tap-length LMS (VT-LMS), are proposed for active vehicle interior noise control. The interior noises of a sample vehicle are measured and thereby their frequency characteristics. Results show that the sound energy of noise is concentrated within a low-frequency range below 1000 Hz. The classical LMS, IVS-LMS and VT-LMS algorithms are applied to the measured noise signals. Results further suggest that the IVS-LMS and VT-LMS algorithms can better improve algorithmic performance for convergence speed and steady-state error compared with the classical LMS. The proposed algorithms could potentially be incorporated into other LMS-based algorithms (like the FxLMS) used in ANC systems for improving the ride comfort of a vehicle.

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