# Multi-User Detection Based on Improved Cheetah Optimization Algorithm
###### Abstract  
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. cheetah optimization algorithm; multi-user detection; differential evolutionary algorithm; improved cheetah optimization algorithm Article  
Multi-User Detection Based on Improved Cheetah Optimization Algorithm  
2024131422  
analysis and optimization [14]. These techniques play an important role in improving system performance, reducing complexity, and enhancing real-time performance. Most of the current research on multi-user detection algorithms is based on the ideal Gaussian channel environment, where the power spectral density of Gaussian noise follows a uniform distribution. However, due to the complexity of the signal interference in the actual transmission process, especially the impact noise interference with shock characteristics, this interference can cause random changes in the signal power, posing significant challenges to the multi-user detection solution process. In this context, studying multi-user detection algorithms for impact noise environments becomes crucial. For example, the non-coherent solution proposed in the literature [15] to address the multi-user detection problem in impact noise environments proves to be an effective measure. Non-coherent detection demonstrates greater robustness in handling complex interference scenarios and can better adapt to actual transmission processes. However, coherent detection can offer higher detection accuracy and performance. Depending on practical application scenarios and requirements, various choices exist, leading to diverse outcomes. Therefore, conducting an in-depth study on multi-user detection algorithms under impact noise environments is essential for improving the actual performance of communication systems. The multi-user detection problem results in a high level of complexity and difficulty because it involves multiple users transmitting data simultaneously, the presence of multiple-access interference and proximity effects, and the influence of channel loss and noise interference. In CDMA systems, the multi-user detection problem can be regarded as a Non-deterministic Polynomial-time (NP) complete problem, so intelligent optimization algorithms can be considered to find the optimal approximation estimation of the detector data bit information. However, intelligent optimization algorithms have advantages and disadvantages in different aspects. Optimization using only a single algorithm cannot satisfy the need for accurate solutions in complex environments. Therefore, efficient algorithms need to be designed and applied to address these complexities to improve the performance and efficiency of the system. In this paper, we consider the hybrid construction of new multi-user detection algorithms, which effectively enhances the solution accuracy and reliability of the detection by dynamically fusing the advantages of multiple algorithms.