Modified constant modulus algorithm based on bat algorithm

2021 ◽  
pp. 1-8
Author(s):  
Tongtong Xu ◽  
Zheng Xiang
Keyword(s):  
Communication Systems ◽  
Optimization Problems ◽  
Error Function ◽  
Bat Algorithm ◽  
Weight Vector ◽  
Wireless Channel ◽  
Constant Modulus ◽  
Constant Modulus Algorithm ◽  
Blind Channel Equalization ◽  
Almost All

In this work, modified constant modulus algorithm based on bat algorithm is proposed for wireless sensor communications systems. The bat algorithm is a swarm intelligence optimization algorithm, which mainly used to solve optimization problems. The proposed algorithm focused on modified constant modulus algorithm, which is also applicable to the constant modulus algorithm. The error function of blind equalization algorithm is used as the evaluation function of the bat algorithm, and then the initial value of the weight vector is calculated adaptively by the bat algorithm. Theoretical analysis is provided to illustrate that the proposed algorithm has a faster convergence speed than the original one and is suitable for almost all blind channel equalization algorithms. The simulation results support the newly proposed improved algorithm. The proposed algorithm could be applied to some more complex wireless channel environments to improve the reception performance of sensor communication systems.

Blind Adaptive Channel Equalization Using Modified CMA

2013 ◽  
Vol 658 ◽  
pp. 537-540
Author(s):  
Sun Shouyu
Keyword(s):  
Error Function ◽  
Channel Equalization ◽  
Convergence Time ◽  
Superior Performance ◽  
Constant Modulus ◽  
Constant Modulus Algorithm ◽  
Blind Channel Equalization ◽  
Blind Adaptive ◽  
Single User ◽  
Frequency Selective Channels

The constant modulus algorithm (CMA) equalizer is perhaps the best known and the most popular scheme for blind adaptive channel equalization. In this paper, a modified constant modulus algorithm (modified CMA or MCMA) is proposed by modifying its error function. We have discussed the MCMA to blind channel equalization for baud-rat sampling in single-user case. Computer simulations are provided for 8PSK signals in noise environments under frequency selective channels. Results demonstrate that the MCMA displays much superior performance to the CMA for both convergence-time and intersymbol interference (ISI) or mean square error (MSE).

scholarly journals Concurrent Modified Constant Modulus Algorithm and Decision Directed Scheme With Barzilai-Borwein Method

2021 ◽  
Vol 15 ◽  
Author(s):  
Tongtong Xu ◽  
Zheng Xiang ◽  
Hua Yang ◽  
Yun Chen ◽  
Jun Luo ◽  
...  
Keyword(s):  
Wireless Communication ◽  
Optimization Problems ◽  
Blind Equalization ◽  
Wireless Channel ◽  
Constant Modulus ◽  
Constant Modulus Algorithm ◽  
Step Size ◽  
Pilot Signal ◽  
Unconstrained Optimization Problems ◽  
Wireless Communications Systems

At present, in robot technology, remote control of robot is realized by wireless communication technology, and data anti-interference in wireless channel becomes a very important part. Any wireless communication system has an inherent multi-path propagation problem, which leads to the expansion of generated symbols on a time scale, resulting in symbol overlap and Inter-symbol Interference (ISI). ISI in the signal must be removed and the signal restores to its original state at the time of transmission or becomes as close to it as possible. Blind equalization is a popular equalization method for recovering transmitted symbols of superimposed noise without any pilot signal. In this work, we propose a concurrent modified constant modulus algorithm (MCMA) and the decision-directed scheme (DDS) with the Barzilai-Borwein (BB) method for the purpose of blind equalization of wireless communications systems (WCS). The BB method, which is two-step gradient method, has been widely employed to solve multidimensional unconstrained optimization problems. Considering the similarity of equalization process and optimization process, the proposed algorithm combines existing blind equalization algorithm and Barzilai-Borwein method, and concurrently operates a MCMA equalizer and a DD equalizer. After that, it modifies the DD equalizer's step size (SS) by the BB method. Theoretical investigation was involved and it demonstrated rapid convergence and improved equalization performance of the proposed algorithm compared with the original one. Additionally, the simulation results were consistent with the proposed technique.

Momentum Factor Constant Modulus Algorithm and Theory

2012 ◽  
Vol 263-266 ◽  
pp. 1058-1061
Author(s):  
Heng Yang ◽  
Jing Wang ◽  
Jing Guan ◽  
Wei Lu
Keyword(s):  
Theoretical Analysis ◽  
Blind Equalization ◽  
Weight Vector ◽  
Convergence Speed ◽  
Constant Modulus ◽  
Constant Modulus Algorithm ◽  
Slow Convergence ◽  
Iteration Formula ◽  
Equalization Algorithm ◽  
Factor Constant

The traditional constant modulus algorithm (CMA) has the disadvantage of slow convergence in blind equalization algorithm. This paper studied one improved algorithm based on momentum factor constant modulus algorithm(MCMA) to solve this problem, momentum factor was added to the weight vector iteration formula of CMA to improve the convergence speed. theoretical analysis and simulation showed that: in the case of the same equalization effect, the MCMA converges faster than the traditional constant modulus algorithm, and also different momentum factors have different convergence effects. The larger the momentum factor , the better convergence effect in the defined domain of the momentum factor.

A Modification of MOEA/D for Solving Multi-Objective Optimization Problems

2018 ◽  
Vol 22 (2) ◽  
pp. 214-223
Author(s):  
Wei Zheng ◽  
Yanyan Tan ◽  
Meng Gao ◽  
Wenzhen Jia ◽  
Qiang Wang ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Design Method ◽  
Experimental Studies ◽  
Weight Vector ◽  
The Other ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Proposed Modification ◽  
Almost All ◽  
Modified Algorithm

In this paper, a novel modified algorithm based on MOEA/D, abbreviated as mMOEA/D, is proposed for well solving the multi-objective optimization problems. Our proposed mMOEA/D inherits from MOEA/D. In mMOEA/D, a novel elastic weight vectors design method is introduced and adopted to make those weight vectors spread more widely. On the other hand, a flexible and efficient trail DE operator is designed and used in mMOEA/D for further enhancing the performance of MOEA/D. Three groups of experimental studies are carried out. Proposed mMOEA/D is compared with the four state-the-art multi-objective optimization evolutionary algorithms on solving the multi-objective optimization problems with many objectives, and the other is that mMOEA/D is compared with MOEA/D-DE, an improved version of MOEA/D, on solving the multi-objective optimization problems with complicated PS shapes. The versions of mMOEA/D with the improvement of weight vector and DE operator are compared with MOEA/D-DE to solve multi-objective optimization problems at last. The experimental results show that mMOEA/D performs the best on almost all test instances. In other words, our proposed modification of MOEA/D is effective.

scholarly journals Bat algorithm (BA): review, applications and modifications

2020 ◽  
Vol 8 (1) ◽  
pp. 1
Author(s):  
Amar Yahya Zebari ◽  
Saman M. Almufti ◽  
Chyavan Mohammed Abdulrahman
Keyword(s):  
Emission Rate ◽  
Optimization Problems ◽  
Bat Algorithm ◽  
Search Process ◽  
Metaheuristic Algorithm ◽  
Swarm Optimization ◽  
Timely Review ◽  
Wide Range ◽  
Discrete Spaces ◽  
Almost All

Generally, Metaheuristic algorithms such as ant colony optimization, Elephant herding algorithm, particle swarm optimization, bat algorithms becomes a powerful methods for solving optimization problems. This paper provides a timely review of the bat algorithm and its new variants.Bat algorithm (BA) is a Swarm based metaheuristic algorithm developed in 2010 by Xin-She Yang, BA has been inspired by the foraging behavior of micro bats, algorithm carries out the search process using artificial bats as search agents mimicking the natural pulse loudness and emission rate of real bats. It has become a powerful swarm intelligence method for solving optimization prob-lems over continuous and discrete spaces. Nowadays, it has been successfully applied to solve problems in almost all areas of opti-mization, and it found to be very efficient. As a result, the literature has expanded significantly, a wide range of diverse applications and case studies has been made base on the bat algorithm. 

Novel DFE for Underwater Acoustic Channels

2013 ◽  
Vol 760-762 ◽  
pp. 691-694
Author(s):  
Yan Liu ◽  
Yuan Min Li
Keyword(s):  
Cost Function ◽  
Communication Systems ◽  
Channel Equalization ◽  
Decision Feedback ◽  
Underwater Acoustic Communication ◽  
Constant Modulus ◽  
Constant Modulus Algorithm ◽  
Underwater Acoustic ◽  
Underwater Acoustic Channels ◽  
Receive System

In underwater acoustic communication systems, the channel equalization community has recently given much attention to decision feedback equalization (DFE). It is because that the DFE offers intersymbol interference (ISI) cancellation with reduced noise enhancement. However, its key algorithm such as constant modulus algorithm (CMA) has moderate convergence rate and steady-state mean square error (MSE), which is not sufficient for the receive system of communication. So a new cost function is defined and then a novel DFE based on such cost function is proposed. The efficiency of the proposed DFE is proved by computer simulations.

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