The penalty for the additional delay for the larger scan window is clear when examining the mean position update delay for both scan window sizes.
We propose to launch 8 parallel admission control procedures, first we update the delay value by subtracting x + del - 8y, and then we subtract x + del - 7y and so on until we find a suitable route that supports an updated delay value.
If HO d < Adi,j, MR-BS RS MR-BS RSs to find r ∈ R that fulfills the updated delay value Adi,j = Adi,j - HO d based on the QoS informationeedshanged wito the other MR-BSs and collected on the backbone.
Nevertheless, the value of x + del - y needs to be inferior to x. Regarding the exponential approach, we propose to launch 4 parallel admission control procedures, first we update the delay value by subtracting x + del - 8y, and then we subtract x+del-4y, and then we subtract x + del - 2y and finally we subtract x + del - y until we find a suitable route that supports an updated delay value.
For the real-time requirement of train regulation, a model predictive control (MPC) algorithm is particularly designed to solve the formulated optimal control model, which uses the real-time updated delay feedback information of the trains and generates the optimal train regulation strategy in real-time.
PMIPv6, on the other hand, reduces binding update delay hence ultimately reducing the handover delay.
In Figure 2, we plot prediction error ε against update delay Δ t and speed | v → |.
Table 4 Mean update delay for open and urban channel with 20 nanoseconds and 100 nanoseconds scan window.
Table 4 shows the mean and standard deviation for position update delay, which includes the effects of dropped packets, outliers, and invalid position measurement.
For the open and urban channel, the 100 nanoseconds window size has a mean position update delay that is approximately 27% larger than the 20 nanoseconds case.
