# Novel Calibration of MIESM and Reduction of CQI Feedback for Improved Fast Link Adaptation
## 5 Simulation Results
### Performance Evaluation
#### 5.2.1 Improved Calibration Method  
We present CQI feedback error rate and system throughput as measures for performance evaluation of the proposed calibration method. The common simulation parameters for evaluating the performance of the proposed calibration factors in this paper are presented in Table 1. \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Simulation type & SUSISO \\ Bandwidth & 1.4 MHz \\ Carrier frequency & 2.0 GHz \\ Subcarrier spacing & 15 kHz \\ Cyclic prefix & Normal CP \\ Channel estimation & Perfect \\ Channel model & VehA \\ Number of PDCCH & 3 \\ Velocity & 30 km/h \\ Receiver & Zero forcing \\ \hline \end{tabular}
\end{table} 
Table 1: Common simulation parameters for performance evaluation. SUSISO—single-user single-input single-output. * Comparison of CQI Feedback Error Rates  
In order to guarantee the reliability of the verification, 5000-10,000 channels were generated until the feedback error count exceeded 100 in each SNR. Noise occurred a maximum of 2000 times until the accumulated number of subframe transmission failures reached 100. Figure 3 shows the CQI feedback error rates for the calibration factors of the existing average calibration method and the calibration factors derived from the improved calibration method. Based on the verification algorithm in Section 5.1, the CQI feedback error rate was calculated by counting the number of times that the CQI\({}_{\text{real}}\) determined by the BLER\({}_{\text{real}}\) from the physical layer simulation was not equal to the CQI\({}_{\text{eff}}\) determined by SNR\({}_{\text{eff}}\). In the case of SNR = \(-\)2 dB, the CQI feedback error rate of the proposed method was larger than that of the conventional method by 10.31%, and for SNR = 8 and 22 dB, the CQI feedback error rates of the proposed method were lower than those of the conventional method by 20.38% and 48.78%, respectively. Based on Figure 3, the calibration factors derived from the proposed method show smaller CQI feedback error rates than the conventional method for all SNR values >2 dB, but they show similar to or slightly larger rates than those of the conventional method for SNR \(\leq\)2 dB. This result in low-SNR environments is due to the BLER curve slope of the AWGN channel. In the proposed method, we set the SNR corresponding to BLER 0.9 and 0.001 of each CQI as the effective interval of the effective SNR, and derived the calibration factor using the more valid channel for CQI feedback; however, the calibration factor of the existing method was derived without setting it separately. For small CQI values that occur frequently in the low-SNR range, the BLER curve of the AWGN channel is relatively flat, such that the calibration factor can be calculated by sufficiently including the valid channel near the target BLER with the conventional calibration method. On the other hand, since the BLER curve of the AWGN channel is steep for a large CQI, the conventional method calculates the calibration factor without including the valid channel well. However, the proposed calibration method shows better performance than the conventional method because the effective interval works successfully. * Comparison of System Throughputs  
The system throughput was calculated by applying calibration factors derived from the average calibration method and the improved calibration method to the actual mobile communication environment. The simulation was performed over a wide SNR interval, and the system throughput for each method is shown in Table 2.  
Figure 3: Comparison of CQI feedback error rates for the calibration factors derived from the average calibration method and the improved calibration method. The calibration factor obtained using the average method at SNR \(\leq\)\(-7\) dB had a maximum system throughput of 0.07 kbps higher than the proposed method. As described above, in an environment in which a low CQI is frequently fed back, the proposed method does not show better performance because the effective interval is not used efficiently.