## 3 Proposed Solution
### Low-Intensity Reflected Signal Elimination and Noise Reduction  
With the LOS channel, the time it takes for the PD to receive the optical signal depends on the distance between the LED light and the PD. With the diffuse channel, it depends on the position of the PD in relation to the reflective point on the wall. At any particular time, the signal received by the PD may be a directed signal from an LED, or a non-directed signal from one of the four walls. The intensity of the reflected signal depends on the position of the receiver as well as the reflective rate of the wall [23]. In Reference [28], to eliminate the diffuse signals, the strongest waves collection was conducted, which can help reduce the impact of multipath reflections. However, in some cases, a combination of a very high reflective rate and other noise sources (i.e., thermal noise and shot noise) can cause the highest signals received at some locations to be diffuse signals. We describe some popular ML algorithms used for area division and location prediction in the next two Sections, while noise sensitivity is a major weakness of ML. It is clear that noise reduction and the elimination of low-intensity reflected signals are important signal pre-processing steps for improving positioning quality. To accomplish this, the following steps were taken:  
Step 1: Low Reflected Optical Power Elimination  
The received signal from the LED light can be either an LOS signal or a diffuse signal, and this happens randomly according to each sampling. As shown in Figure 7, there is a great difference between the intensity of direct signals and reflected signals at a random point on the floor. If the reflected signal has a very small power compared to direct signals, the training data is no longer uniform. Therefore, eliminating these signal types an important step. To implement this, we calculated the mean value of the RSS based on N sampling data using the following equation:  
\[\overline{RSS}=\frac{\sum_{i=1}^{N}RSS_{i}}{N}. \tag{8}\]  
Next, we used the outlier RSS filter to remove the signals whose power magnitudes are significantly different from the other signals [12]:  
\[RSS_{i}>(1+\alpha)\overline{RSS}, \tag{9}\]  
where \(\alpha\) is the outlier ratio and is set to 0.2 in our work after evaluating many different values (\(\alpha>0\)).  
Step 2: Noise Reduction with Moving Average Filter  
After removing the low power reflected signals, we continued to optimize the signals by eliminating the other noise types (i.e., thermal noise and shot noise), which are known as Gaussian noises due to the sunlight from a window or an entrance door [29]. In this Section, we proposed a very simple noise reduction technique called moving average filter [30]. In some cases, this method may not achieve high efficiency if the signal and noise distribution are related to each other [31]. However, in VLC case, the thermal noise and shot noise are signal-independent Gaussian noises [32] and the sum of these random noises is zero in any phase of signal. This filter uses the current and previous \(K\) - \(1\) samples to calculate the average RSS:  
\[RSS_{new}[n]=\frac{1}{K}\sum_{l=0}^{K-1}RSS[n-l]. \tag{10}\]  
After performing this averaging process, the results were utilized as the training dataset for the next steps. The effectiveness of this method is analyzed in detail in Section 5, by comparing cases before and after noise removal.