Multi-User Detection Based on Improved Cheetah Optimization Algorithm

Targeting the issues of slow speed and inadequate precision of optimal solution calculation for multi-user detection in complex noise environments, this paper proposes a multi-user detection algorithm based on a Hybrid Cheetah Optimizer (HCO). The algorithm first optimizes the control parameters and...

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Bibliographic Details
Published in:Electronics (Basel) 2024-05, Vol.13 (10), p.1842
Main Authors: Chen, Shuang, Ji, Yuanfa, Sun, Xiyan
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Bit error rate
Cheetahs
Communication
Evolutionary algorithms
Hunting
Mathematical optimization
Optimization algorithms
Search process
Sensors
Signal processing
Solution space
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Summary:Targeting the issues of slow speed and inadequate precision of optimal solution calculation for multi-user detection in complex noise environments, this paper proposes a multi-user detection algorithm based on a Hybrid Cheetah Optimizer (HCO). The algorithm first optimizes the control parameters and individual update mechanism of the Cheetah Optimizer (CO) algorithm using a nonlinear strategy to improve the uniformity and discretization of the individual search range, and then dynamically introduces a differential evolutionary algorithm into the improved selection mechanism of the CO algorithm, which is utilized to fine-tune the solution space and maintain the local diversity during the fast search process. Simulation results demonstrate that this detection algorithm not only realizes fast convergence with a very low bit error rate (BER) at eight iterations but also has obvious advantages in terms of noise immunity, resistance to far and near effects, communication capacity, etc., which greatly improves the speed and accuracy of optimal position solving for multi-user detection and can achieve the purpose of accurate solving in complex environments.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13101842