Finally, it should be noted that OBOC-AW(1,1,α(4)) and OBOC-AW(1,1,α(6)) WF performances are between those of CBOC and TMBOC.
It is demonstrated that the proposed algorithm performance is slightly lower than the JSF-WF performance, while it significantly reduces the complexity of the allocation algorithm.
Also, nQ WF-MAC almost reaches the performance of WF-MAC as the number of PU increases, as expected.
The nQ WF-MAC initially experiences low integrated performance than WF-MAC, as in sparse environment QoS awareness is the only game changer, available resource is well-matched with system crowd.
Finally, the performance of WF-BEM with WF-LS at extremely high speed 480 km/h is presented in Figure 10.
From Fig. 5 d, we can see that the overhead experience does not go into vain, as WF-MAC has huge performance improvements over FMAC and random WF-MAC.
The WF yields much better performance than the LS-based estimator, especially under the low SNR scenarios.
When the power is constrained per base station the performance of WF and SWF are the same; however, the use of MWF can give a small improvement.
Figure 7 illustrates that since the wireless channel can be regarded as nearly invariant during one OFDM symbol period at 30 km/h and also because the Doppler frequency is small in this situation, the performance of WF-LS is close to that of WF-BEM, there is less than 1 dB SNR gain for WF-BEM in this situation.
Therefore we conclude that we should carefully avoid these "convergence points" when we are trying to improve the performance of WF-LS with low orders by using WF-LS with higher orders, otherwise this goodwill will prove to be in vain.
