o the robustness of the UL against the channel conditions, it can be used to serve all IoT devices. Moreover, the UL capacity is lower, which also fits with the IoT devices data requirements we assumed in our usage model. On the other hand, the LL has a low capacity at lower SNR. The capacity of LL increases significantly as the SNR values improve. LL has most of the available channel capacity in good channel conditions. 9.7.2 Receiver Mobility We have simulated the user and IoT mobility based on the earlier mobility models. The LDM pair performance is evaluated for link sustainability for a different mobility model and transmission beamwidth combination. In the first case, the user is assumed to be static while the IoT device is mobile. The X position of the IoT devices is between 0 and 500 m and is randomly chosen in each iteration. The range of y position is calculated using the known transmission beamwidth, X and (9.27). In the second case, similar simulation is done for the user movement, assuming that the transmission angle is set based on the user's initial position. Moreover, we simultaneously moved both IoT and the user to determine the sustainable link time in the final setup. We have combined the random waypoint and Manhattan model to test the link sustainability of such mobility combination. These comparisons will help the system form LDM pairs more efficiently based on the mobility group of the receivers. Both models represent urban areas, whereas the freeway mobility model is for separate geographical areas. In both Figs. 9.8 and 9.9, the IoT devices are moving according to the random waypoint mobility model while we change the mobility model for users. In Fig. 9.8, both IoT and user devices are in the same mobility group. User mobility has less impact on pair sustainability as the transmission is directed toward the user. The IoT devices go out of the transmission area faster as there are cases when the IoT devices initial position can be at the network edge, which will break the LDM pair more quic