# Multi-User Detection Based on Improved Cheetah Optimization Algorithm
## 3 Results and Discussion
### Performance Testing of the Algorithm  
1. Investigate the correlation between the algorithm's BER and the iteration count in an \(\alpha\)-stable noise environment. To validate the advantages of the proposed HCO algorithm, the simulation experiment introduces the Genetic Algorithm (GA) [26], the classical evolutionary algorithm of DE [22], and the more advanced Grey Wolf Optimization algorithm (GWO) [27], Grasshopper Optimization Algorithm (GOA) [28], Whale Optimization Algorithm (WOA) [29], and CO [16] group intelligence optimization algorithms for performance comparison. Based on the analytical setup of the previous paper, the algorithms are set up to establish a multi-user detection study. According to the literature [13] on fusion strategy, the cheetah optimization algorithm introduced in the DE is replaced with JADE [30] (HCO-jade) to participate in the simulation experiments in the search process part, to compare and verify the fusion advantage of this paper's algorithm. Under the aforementioned experimental conditions, the initialization parameters are set based on the optimal values obtained from the above experiments. Figure 5a illustrates the correlation between the algorithm's iteration count and the BER. Figure 5a demonstrates that the cheetah optimization algorithm, due to its homecoming characteristics, enriches the diversity of the population. However, the return home greatly affects the hunting efficiency. The hybrid HCO algorithm solves this problem well, and the HCO algorithm has converged at a very low BER when the number of algorithm iterations reaches eight. In contrast, the other optimization algorithms require around 25 iterations before convergence. As can be seen from Figure 5, according to the literature [13], the HCO-jade algorithm fusion strategy exhibits superior performance compared  
Figure 4: The system BER as affected by initialization parameters. (a) mod and T-parameter simulation; (b) Scaling factor parameter simulation. to other algorithms. However, its convergence performance is significantly lower than that of the HCO algorithm proposed in this paper. Analyze the relationship between the algorithm's BER and the generalized SNR in an \(\alpha\)-stable noise environment. Similarly, other basic experimental conditions are set as above. The range of generalized SNR is 0-10, and the relationship between the SNR and BER of each algorithm is shown in Figure 5b. Figure 5b demonstrates that with the continuous deterioration of the complex channel environment, the BER of each experimental algorithm is greatly affected. However, according to the figure, the HCO algorithm proposed in this paper has an obvious advantage over other algorithms in terms of noise immunity. Analyze the relationship between the BER of the algorithms and the near-far ratio under the \(\alpha\)-stable noise environment. Other basic experimental conditions are the same as above. Here, the signal-to-noise ratio (SNR) of the first user is set to 5 dB, and the SNR of the other users is the same and changes at the same time. Figure 5c illustrates how the Bit Error Rate (BER) relates to the Nearness/Farness Ratio. Figure 5c demonstrates that as the proximity effect increases gradually, the BER of each algorithm in the complex channel environment also rises gradually. However, the comparison shows that the performance of the HCO algorithm surpasses that of the other algorithms, being least affected by the proximity effect and the most stable. Analyze the relationship between the BER of the algorithms and the number of users in the \(\alpha\)-stable noise environment. Figure 5: Performance test results of the HCO algorithm in \(\alpha\)-stable noise. (**a**) Effect of number of iterations on BER; (**b**) Effect of SNR on BER; (**c**) Effect of near-far ratio on BER; (**d**) Effect of Number of Users on BER.