Several existing data detection methods are introduced above. In this section, per-formances of these methods are evaluated by computer simulations. For next-generation communication systems, wireless terminals are expected to operate at high radio frequencies, at high levels of mobility, and with high bandwidth efficiency.
Thus, the targeted radio frequency, and the signal bandwidth specified in IEEE 802.20 TDD mode [12] are considered. The simulated OFDM system parameters are listed in Table 3.1. The typical two-ray equal power channel model is considered here. For the simulations, the channel power is normalized to 1. The multipath Rayleigh fading channels in the simulations are generated by the modified Jakes’ model [9] [10]. It is assumed that the perfect channel information is known at the receiver and that syn-chronization is perfect.
Table 3.1 Simulated OFDM system parameters Operating frequency 3.5GHz
Signal bandwidth 2.5MHz
FFT length 64
Number of piot subcarriers 16 Number of data subcarriers 48
Symbol duration 25.6us
Subcarrier spacing 39.1kHz
Modulation QPSK
Channel coding No
Power delay profile Two-ray equal power Normalized Doppler frequency 0.083 and 0.040
0 5 10 15 20 25 30 35 40 45
Figure 3.3 BER performances of the existing data detection methods in the two-ray equal power channel with fnd =0.040
0 5 10 15 20 25 30 35 40 45
Figure 3.4 BER performances of the existing data detection methods in the two-ray equal power channel with fnd =0.083
The BER performances of the mentioned methods in the two-ray equal power channel with different fnd’s are shown in Figure 3.3 and Figure 3.4, respectively. The term “ICI-free” indicates the theoretical BER performance of the coherent detection in the time-invariant flat Rayleigh fading channel for the reference of the ICI-free case. It is known that the BER performances of the first four mentioned detection methods are bounded by the ICI-free curve in the figure. The terms “Single tap EQ”
and “LS” indicate the single-tap equalization and LS detection, respectively. The or-dinal numbers appended to the term “ICI cancellation” indicate the numbers of the iterations of the ICI cancellation method. Jeon’s method of LS detection [8] is denoted as Jeon’s LS. The terms “q=number” appended to “Jeon’s LS” indicate the number of the dominant ICI terms adjacent to the modulated signal in each subchannel, i.e. q.
Choi’s method of successive detection with MMSE detection [7] is denoted as Choi’s SDMMSE.
Figure 3.3 shows the BER performances of the mentioned data detection methods when fnd =0.040. Due to the assumption of neglecting ICI, the single tap equaliza-tion suffers from severe ICI effects and has the worst performance of all. The LS de-tection has a better performance than single tap equalization. However, the perform-ance degradation of the LS detection increases in high SNR environments. Jeon’s method of LS detection suffers from less noise enhancement than LS detection but more ICI effect than LS detection. It also can be found that Jeon’s method of LS de-tection has worse performance than that of the ICI cancellation with 3 dede-tection itera-tions when considering the 4 most dominant ICI terms closest to the modulated signal in each subchannel, q = 4, in high SNR case. Comparatively, after 3 iterations, the performance of ICI cancellation method is better than the first four methods when SNR is lower than 40dB. However, when SNR is up to 40 dB, some ICI terms cannot
propagation dominates the performance of the ICI cancellation method and an error floor appears. Due to utilizing the time diversity provided by time-selective channels, Choi’s method of successive detection with MMSE detection outperforms the other mentioned methods and the reference of the ICI-free. This benefit is more apparent in high SNR case.
Figure 3.4 illustrates the BER performances of the mentioned data detection methods when fnd =0.083. The single tap equalization suffers from severe ICI effects and still has the worst performance of all. LS detection suffers from noise enhance-ment which increases as fnd gets higher because the minimum nonzero singular value of G becomes smaller. Jeon’s method of LS detection also suffers from severe ICI effect since too many ICI terms are ignored in the detection. Compared to the LS detection, ICI cancellation method is without the noise enhancement problem. How-ever, the error propagation becomes a serious problem for the ICI cancellation method and cannot be ignored even after six iterations. Choi’s method of successive detection with MMSE detection still outperforms the other mentioned methods. Because time selectivity of the fading channel with fnd =0.083 becomes more apparent than that of the channel with fnd =0.040, Choi’s method obtains more benefits by utilizing time diversity.
In conclusion, those ICI cancellation methods are not good enough to reduce the ICI effect under the targeted channel environments. An irreducible error floor appears in the high SNR environments regardless of the iteration numbers. Although LS de-tection outperforms the other dede-tection methods, it still suffers severe noise enhance-ment problem. The performance degradation is too large. Choi’s method is a good choice for detection of OFDM systems in the targeted time-varying fading channels.
However, the computational complexity of this method is too high.