# Multi-User Scheduling for 6G V2X Ultra-Massive MIMO System
## 4 Simulation Results and Analysis
### System Performance Comparison  
In order to verify the feasibility and effectiveness of the algorithm proposed in this paper, and to study the impact of vehicles on the algorithm performance in different mobile environments, this paper uses MATLAB simulation software to build a multi-user ultra-massive MIMO system to simulate the proposed algorithm and the comparison algorithm, and finally to compare the simulation results. In the simulation, it is assumed that the receiving end and the transmitting end can obtain perfect channel state information. There are \(M\) users, the number of each user RF chains is \(N_{t}^{RF}=1\), and the number of transmitter RF chains is \(N_{t}^{RF}=N_{t}^{RF}\times M\). Other main simulation parameters are summarized in Table 4.  
This paper mainly compares and simulates the bit error rate (BER) performance and spectrum efficiency of the proposed algorithm and the algorithm based on subspace correlation [16], the geometric angle algorithm [17], and the condition number algorithm [18]. The vehicle speed is 80 km/h, and the vehicle density is determined by the speed of the vehicle. Figure 2(a),b, respectively, show the BER and spectral efficiency comparison diagrams of the proposed algorithm and the comparison algorithm when 256 transmitting antennas are configured at the base station and two vehicle users are scheduled. We can see from Figure 3 that the BER and channel capacity of the four algorithms are relatively close at low signal-to-noise ratio. With the increase in signal-to-noise ratio, the difference of system BER and total system capacity realized by different scheduling algorithms gradually increases. In general, the proposed algorithm is better than the comparison algorithm in terms of BER and spectral efficiency. This is mainly because the conditions of the channel itself have a great impact on the information transmission effect. \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Complexity** \\ \hline SC & \(O\big{(}N_{t}N_{r}^{2}\big{)}\) \\ GA & \(O\big{(}N_{t}^{3}\big{)}\) \\ CN & \(O\big{(}N_{t}^{3}\big{)}\) \\ Proposed & \(O\big{(}\big{(}C_{k}^{M}C_{N_{t}}^{M}N_{t}^{M}\big{)}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Complexity comparison. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheduling Algorithm** & **Proposed** & **SC** & **GA** & **CN** \\ \hline Fairness factor & 0. 92392 & 0.87376 & 0.74418 & 0.63034 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of equity factors (SNR = 20 dB, \(k=10\)). There will be a better transmission effect when the conditions of the channel itself are good. The comparison algorithm only considers the correlation of the channel matrix and ignores the influence of the conditions of the channel itself. The algorithm in this paper not only considers the channel correlation, but also considers the conditions of the channel itself. Adding a scheduling factor to measure the conditions of the channel can effectively eliminate the system interference and improve the system performance. \begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **Settings** \\ \hline Carrier frequency & 28 GHz \\ Transmit antenna\(N_{t}\) & 256/512 \\ Number of single-user receiving antennas \(N_{r}\) & 8 \\ Number of single user data streams \(N_{s}\) & 2 \\ User number \(M\) & 2/4 \\ Channel model & Time-varying geometric channel \\ Number of clusters\(N_{c}\) & 3 \\ Number of paths in each cluster\(N_{avg}\) & 7 \\ Azimuth mean distribution & \([0,2\pi]\) \\ Mean elevation angle distribution & \([-2/\pi,2/\pi]\) \\ Antenna array structure & ULA \\ Antenna spacing \(d\) & 0.5\({}^{\lambda}\) \\ Vehicle antenna height & 1.5 m \\ Vehicle density & 0.0025/0.005/0.0083 vehicle/m \\ Angle expansion & 7.5\({}^{\circ}\) \\ Vehicle moving direction & Move in a straight line along the road \\ Channel estimation & Ideal \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Main parameter settings of the system simulation.