# A Simplistic Downlink Channel Estimation Method for NB-IoT
## 5 Measurement Results
### Comparison of SNR Performance with the Existing Method  
Once it was verified that the frequency averaging approach presents acceptable performance, TIFA was compared with the channel estimation method implemented in MATLAB R2020b LTE Toolbox(TM). To be precise, the _lteDLChannelEstimate_ function is used to estimate the channel response. According to the documentation, this function implements the method described in 3 GPP TS 36.104/TS 36.141 Annex E/F for the purposes of transmitter EVM testing for LTE downlink. However, the above method is intended for LTE, assuming the usage of multiple adjacent PRBs. For an NB-IoT case [33], the function provides a different processing scheme, consisting of three stages: least squares pilot estimation, pilot averaging and interpolation of channel coefficients. The first step is common for both compared methods, as mentioned in Section 2. Pilot averaging is an optional step which is intended to reduce the adverse effect of noise on pilot estimates in low SNR conditions. By default, it is enabled and configured to use a frequency-time window the size of 13 by 9 resource elements. The final step performs 2D cubic interpolation independently for each subframe, preceded by generation of virtual pilots outside the subframe boundary.  
Figures 9 and 10 show the results of SNR performance comparison between MLT and TIFA. As expected, both methods perform better in the AWGN scenario than in the fading channel scenario. It may be observed that, for all the considered SNR values and channel profiles, TIFA provides lower phase RMS error than the estimation method from MATLAB LTE Toolbox(TM). Error reduction is approximately 3% on average. The advantage of TIFA over MLT is especially visible for low SNR values. The same applies to symbol correlation represented by \(\rho_{IQ}\). In the AWGN scenario, as SNR increases \(\rho_{IQ}\) approaches  
Figure 8: Symbol correlation coefficient for (**a**) AWGN channel, (**b**) fading channel (EPA 5 Hz).  
Figure 7: Phase RMS error for (**a**) AWGN channel, (**b**) fading channel (EPA 5 Hz).  
1.0 value, which corresponds to a perfect match between equalized and original symbols. The average difference in \(\rho_{IQ}\) between TIFA and MLT is approximately 5-6%.  
Figure 10: Symbol correlation coefficient for MLT and TIFA channel estimation methods in AWGN channel and fading (EPA5) channel.  
Figure 9: Phase RMS error for MLT and TIFA channel estimation methods in AWGN channel and fading (EPA5) channel.  
In addition to channel estimation quality indicators, the percentage of successfully decoded information blocks, MIB-NB and SIB1-NB, was evaluated. The results are presented in Figure 11. For MIB-NB, the results for MLT and TIFA are very similar, except the case of the EPA profile with \(-5\) dB SNR, where TIFA outperforms MLT by 4%. The difference between the two methods is more distinct in the case of SIB1-NB decoding. Especially for low SNR, a significant advantage of TIFA is observed. The number of decoded SIBs doubled when TIFA was applied to the EPA channel at \(-5\) dB SNR. In general, the percentage of successfully decoded MIBs is higher than that of SIBs due to smaller transmission redundancy of the latter. For MIB-NB in standalone operation mode, 128 OFDM resource elements are used per one bit of transport block, while for SIB1-NB this ratio is approximately 99 resource elements per bit in the analyzed transmission scheme.