# Multi-User Detection Based on Improved Cheetah Optimization Algorithm
## 4 Conclusions  
Aiming to address the problems of poor anti-noise performance and low solving performance associated with traditional multi-user detectors under complex channel environments, this paper presents a new multi-user detection algorithm method based on the cheetah optimization algorithm, which firstly improves the control parameter and individual updating strategy of the CO algorithm according to the shortcomings of the CO algorithm's adaptability. After that, dynamically introducing a differential evolutionary algorithm into the improved CO algorithm and leveraging the strengths of both algorithms to find the optimal solution not only improves the optimal solution ability of the multi-user detection algorithm under the complex channel environment but also greatly ensures population diversity throughout the algorithmic solution process. Through comparison simulations in a noisy environment, it is evident that our algorithm achieves fast convergence, with a BER lower than 0.02 after eight iterations. In contrast, most other algorithms converge only after 25 iterations, with a BER above 0.03. This demonstrates that our algorithm outperforms others in terms of resistance to noise and convergence. As the signal-to-noise ratio increases, the BER of other algorithms typically fluctuates between 0.12 and 0.04, while our algorithm's BER fluctuates between 0.11 and 0.02. This consistently lower BER, coupled with a more significant decrease trend, demonstrates our algorithm's clear advantage in noise resistance over others. As the near-to-far ratio increases, all algorithms are affected. Our algorithm's BER fluctuates between 0.04 and 0.06, consistently lower than the range of 0.07 to 0.10 observed in other algorithms. This indicates our algorithm's superior resistance to near-far effects and its stability. As the number of users increases, the BER of all algorithms rises. However, our algorithm exhibits a narrower BER fluctuation range of 0.035 to 0.063, compared to the range of 0.05 to 0.14 observed in other algorithms. This demonstrates not only our algorithm's significantly lower BER but also its greater stability and larger capacity. The simulation demonstrates that our algorithm is least affected by user growth and has the largest capacity. In summary, our algorithm exhibits superior advantages over others in noise resistance, convergence, shock resistance, near and far resistance, and high throughput. It can not only improve the speed and accuracy of optimal position solving for multi-user detection but also realize the purpose of accurate solving in complex environments. The next work plan is to optimize the multi-user detector in a more realistic simulation, placing the model into a problem closer to a real application scenario for testing and optimization. We aim to explore and address the system-influencing factors one by one. User mobility may affect the channel state and interference situation, which in turn affects the effectiveness of the optimization algorithm. Current experimental tests only consider the use of cooperative optimization to reduce the impact of user mobility, which is far from sufficient. User mobility as a significant influence factor in mobile communication systems deserves our focused research. Therefore, the next step is that we intend to take this part as the base point for large-scale optimization of the system model as well as the experiments and try to adapt to the changes in user mobility by searching for and trying to introduce the dynamic adaptive strategy and real-time updating of the model parameters, etc., and try to analyze the laws of user mobility in fixed scenarios and introduce the prediction mechanism to make optimization adjustments in advance, to improve the algorithms' performance and stability in the complicated environments. **Author Contributions:** Conceptualization, Y.J., S.C. and X.S. ; methodology, Y.J. ; software, Y.J. and S.C.; validation, Y.J., S.C. and X.S. ; investigation, X.S. ; data curation, S.C. and X.S.