# A Simplistic Downlink Channel Estimation Method for NB-IoT
## 6 Discussion  
The results presented in the previous section show that the proposed downlink channel estimation method is well suited for application in the NB-IoT UE which requires low complexity due to its limitations on cost and power consumption. The initially considered method using linear interpolation in both time and frequency domains was further simplified so that frequency interpolation was substituted with frequency averaging. The modified method turned out to provide considerably higher estimation accuracy than the original one, especially for the AWGN channel at low SNR. When compared to the existing method, implemented in MATLAB LTE ToolboxTM, the proposed solution offers several percent of improvement in terms of SNR performance. However, the major advantage of the described method is a several-fold reduction in computation time required for the interpolation of channel coefficients. Although NB-IoT UEs have been available in the market for a few years, there is still a demand to reduce their power consumption in order to prolong their operational time. Along with power-saving features such as Discontinuous Reception (DRX), energy efficiency can be improved by developing less computationally intensive algorithms for physical layer processing, particularly in the receive chain. The method proposed in this paper provides a good balance between complexity and SNR performance. The combination of least squares estimation with linear interpolation and averaging results \begin{table}
\begin{tabular}{c c c} \hline \hline
**SNR** & **MLT** & **TIFA** \\ \hline \(-5\) dB & 2.13 & 1.98 \\ \(0\) dB & 0.71 & 0.63 \\ \(5\) dB & 0.24 & 0.21 \\ \(10\) dB & 0.07 & 0.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Channel estimation MSE estimated for fading (EPA 5) channel. in a very low computational burden while still providing comparable quality (MSE vs. SNR) compared to other LS-based methods. It should be noted that the complexity of the proposed solution is currently assessed solely by comparing processing times. The complete assessment of its advantages necessitates actual hardware implementation and a long-term analysis of power consumption. It should be noted that the performance of any channel estimation method is dependent on the properties of the propagation environment, especially on the power delay profile which determines whether the channel is frequency-selective or not. 3 GPP technical documents do not specify a typical profile for NB-IoT radio access channel. However, it may be assumed that for fixed UE located indoors, the channel response is generally short and changes less rapidly than in the vehicular case. Thus, assuming the pedestrian propagation model with low Doppler frequency shift is justified in the author's opinion. The same model was used to simulate the NB-IoT channel in [35]. It was proven that the proposed method is well suited for such a model. However, due to the variety of IoT applications, more challenging propagation conditions may occur, which requires further investigation. This research received no external funding. Not applicable. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy. The author declares no conflict of interest. The following abbreviations are used in this manuscript: