## 5 Experiments with Short-Term SNR  
In Section 2, we delineated two distinct measurements of SNR: ensemble-average SNR and short-term SNR. The ensemble-average SNR is predominantly utilized in simulations, as it reflects the average performance across numerous channel realizations. However, in practical scenarios, the instantaneous SNR over subcarriers significantly influences the BER at the receiver. Consequently, this instantaneous SNR information, once converted, can be transmitted to the base station to facilitate the adaptation of the modulation and coding scheme (MCS) [17; 18]. It is important to note that the short-term SNR discussed in this paper is intrinsically linked to the instantaneous SNR over subcarriers. We now elucidate the procedure for conducting simulations with a specified short-term SNR value. Again, an experiment for a given SNR value encompasses a substantial number of trials. In each trial, a new realization of the designated channel model is generated, followed by the calculation of noise power based on the received signal power as described in (9) and the given SNR value. The BER is then computed by averaging the BER across all trials. Using the aforementioned settings and experiments, we obtained the demodulated BER in relation to short-term SNR, as shown in Figure 9. It is observed that the EPA model yields a lower demodulated BER compared to the other two channel models. This is attributed to the EPA model's shorter delay spread, resulting in a smoother channel frequency response. In the EPA model, the value \(|H^{(i)}[n,k_{0}]|^{2}\) in trial \(i\) consistently approximates the average value \(\left|\overline{H^{(i)}[n,\cdot]}\right|^{2}\) of the same trial. Conversely, the EVA and ETU channel models occasionally produce significantly lower \(\left|H^{(i)}[n,k_{0}]\right|^{2}\) values compared to \(\left|\overline{H^{(i)}[n,\cdot]}\right|^{2}\)  
Figure 8: Examples of \(|H[n,k]|\) for three trials, each having the duration of one frame. leading to an increased BER upon demodulation. To justify this claim, Figure 10 illustrates the pdfs of the magnitude of the normalized channel frequency response  
\[\mathbf{H}^{\prime}[n,k]=\frac{\mathbf{H}[n,k]}{\sqrt{\left|\mathbf{H}[n,\,\cdot\right|]^{2}}} \tag{17}\]  
for the three channel models, corroborating our hypothesis. It is observed from Figure 10 that, when compared with the Rayleigh distribution, the pdf corresponding to the EPA channel has a smaller variation (or standard deviation). Therefore, there is a lower probability of encountering significantly lower \(|H^{(i)}[n,k_{0}]|^{2}\) values. In terms of decoded BER (after six decoding iterations), a comparison between Figures 6 and 11 reveals that the BER results derived from short-term SNR are signifi  
Figure 10: The pdfs of the magnitude of normalized channel frequency response. Figure 9: Demodulated BERs using short-term SNR as a basis of comparison. cantly lower than those based on ensemble-average SNR. This discrepancy arises because, in the short-term SNR scenario, no single codeword is transmitted through a channel realization with substantially lower instantaneous (effective) SNR. Furthermore, given that source data are transmitted at a very low code rate, the empirical distribution of \(|H^{(i)}[n,k]|^{2}\) has only a minimal impact on the decoded BER, provided that \(\left|H^{(i)}[n,\cdot]\right|^{2}\) remains constant. Consequently, the decoded BER performances of the three channel models depicted in Figure 11 are relatively similar. However, among these models, the BER performance of the EPA model most closely approximates that of an AWGN channel due to its lower variance, as illustrated in Figure 10. In contrast, the EPA channel exhibits significantly worse BER performance in Figure 6. This disparity is solely due to the use of different SNR calculations. In Figure 11, the convergence threshold of the same turbo code with a code rate of 1/9 for the AWGN channel is also given for comparison.