# Positioning by Multicell Fingerprinting in Urban NB-IoT Networks +
## 5 Performance Evaluation
### Results for the Oslo Dataset
#### 5.2.1 Impact of Strategies and RF Parameters  
Table 3 presents the minimum average positioning error and the corresponding \(k\) value for each combination of strategy and RF parameter; the RF parameter leading to the smallest minimum average error for each strategy is highlighted in bold. All proposed strategies lead to a higher positioning accuracy than does the _single cell_ approach. The performance improvement is limited in the case of the _Net_ strategy but is high in all multicell strategies using RF information; the best performance is obtained with the _weighted coverage_ using the RSRQ, with an error of 19.5 m for \(k=3\).  
All multicell strategies using RF information lead to increased performance for an optimal \(k\leq 3\). This indicates that in most cases, the first RPs selected as nearest neighbors according to the RF-aware metric are also spatially close to the TP. This is not the case for the _Net_ strategy which does not use RF information nor for the _Single Cell_ strategy, where a single NPCI is used; for these metrics, a greater error in selecting the nearest neighbors is partially compensated for by an increased number of neighbors \(k\) in the position estimates. Results in Table 3 also show that for the _Coverage_ and _Weighted Coverage_ strategies, SINR, RSRP, and RSRQ lead to a similar performance, while RSRP leads to the best results for the _Enhanced Net_ strategy, and SINR is the best choice for the _Single Cell_ strategy. For all strategies,SINR leads to either the best or second best accuracy; given its consistent performance across strategies, it was thus selected as the reference RF parameter for further analysis.  
Figure 9 presents the average positioning error as a function of \(k\) in the range [1; 10] for all strategies, using SINR for the strategies taking advantage of RF information. The results show that the _Enhanced Net_, _Coverage_, and _Weighted Coverage_ strategies provide consistently better accuracy than did _Net_ and _Single Cell_ for all \(k\) values. In general, the combination of the information on the number of common NPCIs and on RF parameters seems to provide the best positioning performance: the _Enhanced Net_ strategy outperforms the _Net_ strategy, while the introduction of the number of common NPCIs that differentiates the _Coverage_ and _Weighted Coverage_ strategies leads to a slight performance improvement in the latter one.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Strategy** & **RF Parameter** & \(\mathbf{k}\) & **Error [m]** \\ \hline _Net_ & N/A & 8 & 54.0 \\ \hline \multirow{5}{*}{_Enhanced Net_} & RSSI & 2 & 34 \\ \cline{2-4}  & SINR & 2 & 28.1 \\ \cline{2-4}  & **RSRP** & 2 & **24.0** \\ \cline{2-4}  & RSRQ & 3 & 30.1 \\ \hline \multirow{5}{*}{_Coverage_} & RSSI & 3 & 23.9 \\ \cline{2-4}  & SINR & 2 & 20.6 \\ \cline{2-4}  & RSRP & 2 & 20.8 \\ \cline{2-4}  & **RSRQ** & 2 & **20.1** \\ \hline \multirow{5}{*}{_Weighted Coverage_} & RSSI & 3 & 22.3 \\ \cline{2-4}  & SINR & 3 & 19.9 \\ \cline{2-4}  & RSRP & 2 & 19.9 \\ \cline{2-4}  & **RSRQ** & 3 & **19.5** \\ \hline \multirow{5}{*}{_Single Cell_[7]_} & RSSI & 22 & 102.4 \\ \cline{2-4}  & **SINR** & 19 & **66.2** \\ \cline{2-4}  & RSRP & 30 & 76.9 \\ \cline{1-1} \cline{2-4}  & RSRQ & 30 & 72.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Minimum average positioning error and corresponding optimal \(k\) for the proposed strategies and the _single cell_ approach in [7] using the Oslo dataset.  
Figure 9: Average positioning error as a function of \(k\) for the proposed strategies vs. _Single Cell_[7] using SINR in all strategies but _Net_.