## 10 Simulation Results
Furthermore, the curves of OTMBMF-SIC may not be shown in some following figures, because there is no significant difference between OGMBMF-SIC and OTMBMF-SIC in the BER performance. In what follows, the RSL is not plotted, due to its close to AMFB performance. Unlike the RSL, the BP-MAP may outperform other ICI cancellation methods. This is because the ICI cancelers mitigate the uncoded interference from neighbor subcarriers, but in the BP-MAP scheme, more reliable extrinsic information is obtained from the channel decoder in an iterative detection and decoding system. Hence, its performance will be improved by the additional weights when the LLR value is updated. However, the BP-MAP performs a little worse than the proposed MBMF-SIC with SINR ordering, which introduces additional precoding gain and removes the error floor. The BER performance of the fourth iteration against normalized Doppler frequencies \(f_{d}T_{\text{OFDM}}\) from \(0.25\) to \(0.65\) has been illustrated in Figure 6, which validates the statements that the proposed OGMBMF-SIC and OTMBMF-SIC can work in a wide range of high Doppler frequencies \(f_{d}T_{\text{OFDM}}\). We can observe that the power of ICI inside the band is reduced at high Doppler frequencies. Additionally, the MF-SIC and MF-PIC can only be used at low Doppler frequencies. Figure 7 compares the BER performance against SNR (dB) with a normalized Doppler frequency \(f_{d}T_{\text{OFDM}}=0.65\) in the fourth iteration. The MSCE is iteratively performed in every iteration, once the new symbol estimates become available from the channel decoder. The BER performance of these receivers in the first iteration is very poor, with \(f_{d}T_{\text{OFDM}}=0.65\), which may not be useful for comparison. Matched filter-based receivers (MF-SIC, OGMBMF-SIC) are less sensitive to channel estimation errors, because they do not make use of channel coefficients as much as band  
MMSE-SIC, the autocorrelation matrix must be used, which may amplify the channel estimation errors. In Figure 5, the banded MMSE-SIC outperforms MF-SIC with perfect channel knowledge in the BER performance. However, their performance at \(\text{SNR}=20\) dB has been degraded to the same level of BER (less than \(1\) dB performance loss) in the fourth iteration with MSCE. MF-PIC is not very close to banded MMSE-SIC with channel estimation, because the autocorrelation matrix is also required for MF-PIC for ICI cancellation. Intuitively, the proposed OGMBMF-SIC has the same advantages as MF-SIC. With MSCE, OGMBMF-SIC can still achieve an acceptable BER performance in such a high mobility scenario (\(f_{d}T_{\text{OFDM}}=0.65\)), which makes OGMBMF-SIC more practical. Figure 6: BER performance against \(f_{d}T_{\text{OFDM}}\) of MF-PIC, OGMBMF-SIC, OTMBMF-SIC,  
MF-SIC and banded MMSE-SIC in the fourth iteration at SNR =12 dB. Figure 7: BER performance against SNR (dB) of MF-PIC, GMBMF-SIC, OGMBMF-SIC,  
MF-SIC, banded MMSE-SIC and AMFB using MSCE in the fourth iteration with \(f_{d}T_{\text{OFDM}}=0.65\). To show the robustness to the high normalized Doppler frequencies, we compared the SBCE, PA-LS and MSCE in terms of BER performance in the fourth iteration. In Figure 8, the MF-PIC and OGMBMF-SIC with SBCE cannot work at normalized Doppler frequencies over \(0.4\). Additionally, the curves for MSCE are almost identical to those for PA-LS, only slightly poorer at high normalized Doppler frequencies, which implies that the MSCE performed in an iterative manner and with the aid of data symbols can approach the performance of PA-LS, which uses the discrete Karhunen-Loeve (DKL)-basis expansion model (BEM) to approximate the channels with five expansion coefficients. To determine an appropriate number of multi-feedback candidates (\(B\)) of MBMF-SIC for a given normalized Doppler frequency, the BER performance against the number of feedback elements has been plotted in Figure 9 for OGMBMF-SIC and OTMBMF-SIC. The BER performance improves with an increasing number of feedback elements.