($\rho=0.1, \delta=0.5$). Note that for the conventional RUS, we randomly select $M$ UEs for simultaneous data transmissions. For SUS, in order to ensure fair comparisons among all the four schemes, we adopt the equal power allocation instead of the water filling allocation. Furthermore, the optimal value of $\alpha_{\text{SUS}}$, which is an important parameter in SUS (described as $\alpha$ in [@yoo_optimality_2006]), is used for the SUS scheme in our simulations[^6]. As expected, $K^*$-LUS achieves the best sum rate performance among the four schemes, and compared with RUS, $K^*$-RUS also achieves a significant sum rate improvement. In addition, due to the non-negligible channel estimation pilot overhead and the lack of multiuser diversity gain, the conventional SUS scheme achieves similar [\[]{}e.g., in sub-figures (a) and (b)[\]]{} or even worse [\[]{}e.g., in sub-figures (c) and (d)[\]]{} sum rate performance than the proposed $K^*$-RUS, even though the online computational complexity of SUS is much higher than that of $K^*$-RUS.

Furthermore, in Fig. \[fig8\], we investigate the sum rates of the four schemes for different numbers of candidate-UEs, $N$, when $P=30$ dBm and $M=32$. It is clear that with the increase of $N$, the sum rates of $K^*$-RUS and RUS remain unchanged. In contrast, $K^*$-LUS obtains 