## 4 Simulation Results and Analysis
### Computational Complexity Analysis  
We compare the proposed JAS-OUS algorithm with JAS-CPUS, JAS-SUS, and the exhaustive search method. The algorithms JAS-OUS, JAS-CPUS, and JAS-SUS belong to the same class of methods, where the antenna selection part is implemented using the successive elimination method, and the difference in complexity lies only in the user scheduling algorithm employed. Figure 8: Average weighted sum rates comparison between JAS-PFUS and JAS-CPUS. For the JAS-OUS algorithm, a total of \(N_{t}-N_{RF}\) iterations are required, where in each iteration one antenna is removed until only \(N_{RF}\) antennas remain. As shown in Algorithm 1, the main computational burden lies in step 3. In the \(i\)th iteration, \(i-1\) antennas have already been removed, so the channel matrix \(\overset{\sim}{\mathbf{H}}\) in step 3 has a dimension of \(U\times(N_{t}-i)\). The OUS algorithm (Algorithm 2) is applied to \(\overset{\sim}{\mathbf{H}}\), where in each iteration, one user is added to the scheduling set. The complexity of Algorithm 2 is \(\mathcal{O}(\sum\limits_{n=1}^{K-1}(U-n)(N_{t}-i)n)\). Subsequently, step 3 in Algorithm 1 computes the system sum rate \(R_{sum}(\mathcal{A}\backslash\{i\},\mathcal{S})\) for the selected users. This involves ZF precoding and water-filling power allocation, where the complexity of ZF precoding is \(\mathcal{O}(K^{2}(N_{t}-i)+K^{3})\), and the complexity of water-filling is \(\mathcal{O}(K\log K)\). Therefore, the total complexity of the JAS-OUS algorithm is \(\mathcal{O}(\sum\limits_{i=1}^{N_{t}-N_{RF}}(\sum\limits_{n=1}^{K-1}(U-n)(N_ {t}-i)n+K^{2}(N_{t}-i)+K^{3}+K\log K))\). After removing lower-order terms and constant factors, the simplified complexity is \(\mathcal{O}(\sum\limits_{i=1}^{N_{t}-N_{RF}}(UK^{2}-K^{3})(N_{t}-i))\). Similarly, the computational complexity of JAS-CPUS is \(\mathcal{O}(\sum\limits_{i=1}^{N_{t}-N_{RF}}((U+K^{2})(N_{t}-i)+K^{3}))\), and that of JAS-SUS is \(\mathcal{O}(\sum\limits_{i=1}^{N_{t}-N_{RF}}(UK^{2}-K^{3})(N_{t}-i))\). In the exhaustive search method, all possible combinations of \(N_{RF}\) antennas from \(N_{t}\) candidates and \(K\) users from \(U\) total users are enumerated. There is a total of \(C_{N_{t}}^{N_{RF}}C_{U}^{K}\) combinations. For each combination, ZF precoding and water-filling power allocation are performed to compute the system sum rate. Therefore, the overall complexity of the exhaustive search is \(C_{N_{t}}^{N_{RF}}C_{U}^{K}(K^{2}N_{RF}+K^{3})\). It can be observed that JAS-OUS and JAS-SUS have similar computational complexity, while JAS-CPUS has a relatively lower complexity. This is because CPUS directly compares the channel norms of all users and selects the top \(K\) users with the largest norms, without any iterative process. In contrast, JAS-OUS iteratively selects users by evaluating the channel orthogonality between each remaining user and the set of already selected users, choosing the one with the highest orthogonality at each step. This process iterates \(K-1\) times, which contributes to the higher complexity. The exhaustive search clearly has the highest complexity but guarantees the optimal solution. The computational complexities of different algorithms are summarized in Table 3.  
In addition, we compare the execution time required by the above algorithms under a single channel realization. Since the simulation time is influenced by both the software version and computer hardware configuration, we provide the corresponding details in Table 4. All simulations are conducted on a personal computer using MATLAB R2024b. Table 5 compares the simulation time required by different user scheduling algorithms in a single channel realization. It can be seen that JAS-CPUS has the smallest time overhead.