## 5 Experimental Setup  
The experiment was conducted on the third floor of Block A, in the Network department at the college of computing and information science of Makerere University, as indicated in Figure 5. Five offices (rooms) were considered with over thirty one (31) access points labeled as AP1-AP31. A customized Wi-Fi analyzer application installed in a smart mobile phone was used to capture the RSSI values from APs within defined reference points seen in Figure 5.  
Fifteen (15) reference points for each room were considered by obtaining the RSSI vector at each point for room identification. The RSSI from different APs for each reference point were captured thrice at different times, each lasting for a period of 1 s. The RSSI data captured from the APs formulated a RSSI vector which was normalized and considered as a signal. This implies that each reference point had three RSSI data vectors which were captured in 3 s. In total, 45 RSSI vectors were captured for each room, since each room contains fifteen reference points. It is the 45 RSSI vectors which were processed for room identification. A CWT was performed on each RSSI vector signal and the CWT graphical image was extracted. The CWT-PSD numerical data were also extracted from each CWT graphical image of each RSSI vector signal. Since the window size was fixed to 1 s and the RSSI capture was done three times for each reference point, each reference point had three CWT graphical images. The CWT-PSD data generated a dataset size equivalent to the number of rows in the three different CWT-PSD 2-dimensional matrices attained in the 3 s. The separation distance between the reference points was one to two meters. Three (3) reference points from each of the five rooms used in the experiment were selected to collect RSSI data for specific reference point estimation. Three reference points which were close to each other were sampled out to be representative of all the reference points in a room, given that the separation distance between all the points was uniform. Again, in order to take care of the RSS variations exhibited at the various positions in a room, 200 RSSI vectors were collected at each reference point, which implies that a total time of 200 s was used at each point. The assumption was that by increasing the time-space for RSSI data acquisition, we would be able to capture the RSS diversity at the different points in the room which were represented by the three sampled reference points. Collectively, 15 reference points in all the five rooms were considered, making the total number of RSSI data vectors 3000. It was the 3000 RSSI vectors which were used to estimate each of the selected specific positions in each room. For each RSSI vector, after augmentation using additive Gaussian noise, the feature set size was doubled. A summary of the dataset sizes is presented in Table 2.  
For purposes of clarity, in the proceeding text in this paper, we have chosen to refer to the augmented CWT feature set and the augmented numerical CWT PSD feature set derived in Figure 4 as the proposed augmented CWT feature set and the CWT-PSD feature set respectively. We have chosen to follow this nomenclature in order to signify the CWT feature set as the key superior feature set formulation presented in this paper. For purposes of determining the optimal CNN and ANN configuration parameters, arbitrary values of the number of layers (10-30), learning rates (0.01-0.5), and maximum iterations (100-200) were tested. The best performing parameter values were adopted, as shown in Tables 3 and 4. \begin{table}
\begin{tabular}{c c} \hline \hline
**Description** & **Value** \\ \hline Number of hidden nodes & 2500 \\ Neural Network & FeedForward \\ Train data, Validation data and Test data samples & 75\%, 10\% and 15\% respectively \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dataset sizes.