## 3 Results and Discussion
To address this issue, a possible solution is to use machine learning to mitigate the error propagation of DFCE and improve the channel equalization performance [34]. Figure 8: Comparison of CDFs for positioning errors with and without DFCE: (**a**) MISO scenario; (**b**) MIMO scenario. Figure 7: Results for mobile VLP with and without DFCE as the receiver moves along different test routes: (**a**) MISO scenario; (**b**) MIMO scenario. Next, we examine the impact of observation block length \(L\) on positioning performance. Figure 10 compares the mean positioning errors obtained in the VLCP system using different \(L\) values for the MISO and MIMO systems. In both scenarios, we generally observe improved positioning accuracy with increasing \(L\). For instance, for the MISO system in Figure 10, along Route 1, the mean positioning error with DFCE decreases from 3.4 cm to 3.1 cm when \(L\) is increased from 25 to 35. This improvement is attributed to the longer observation block length effectively equalizing the channel and averaging out noise, resulting in more accurate CSI for mobile VLP. However, further increasing \(L\) does not lead to significant enhancement in positioning accuracy, particularly when \(L\) exceeds 40 for the MISO system. Similarly, for the MIMO system in Figure 10, there is no significant enhancement when \(L\) exceeds 20. Because longer observation block lengths result in higher computing and storage requirements at the receiver, to balance system complexity and positioning accuracy, an optimal \(L\) value could be selected around 35 and 20 for MISO and MIMO systems, respectively, when DFCE is employed. This selection yields mean positioning errors of 3.1 cm for the MISO system and 2.4 cm for the MIMO system in Route 1. Figure 11 compares the positioning performance for the MISO and MIMO scenarios at different receiver moving velocities. For the MISO system in Figure 11, we observe a significant increase in mean positioning errors with increasing velocity when DFCE is not employed. For instance, in Route 1, the mean positioning error increases from 5.5 cm to 21.3 cm when the velocity increases from 1 m/s to 4 m/s. Similarly, in Route 2, the mean positioning error increases from 5.6 cm to 21.5 cm with the same change in velocity. This is due to the faster channel variations caused by higher velocities, which result in reduced  
Figure 10: Comparison of mean positioning errors with and without DFCE for different observation block lengths: (**a**) MISO scenario; (**b**) MIMO scenario. Figure 9: Eye diagrams of the real part for the received 4-QAM symbols equalized with DFCE in Route 2: (**a**) MISO scenario, \(k=1\); (**b**) MISO scenario, \(k=100\); (**c**) MIMO scenario, \(k=1\); (**d**) MIMO scenario, \(k=100\). channel estimation accuracy. However, when DFCE is employed to track and estimate the time-varying channel, the positioning errors remain relatively stable, ranging from 3.1 cm to 3.6 cm in Route 1 and from 4.0 cm to 4.3 cm in Route 2. In the MIMO system, the results exhibit a similar trend to the MISO system, with errors ranging from 2.4 cm to 2.5 cm in Route 1 and from 14.6 cm to 14.9 cm in Route 2. This indicates the effectiveness of DFCE in maintaining stable positioning performance regardless of the terminal moving speed. Next, we assess the impact of different weight coefficients on the positioning performance with DFCE. Considering various values of the forgotten factor \(\alpha\) in Equations (9) and (14), Figure 12 compares the mean positioning errors under different weight coefficients for the MISO and MIMO systems. Here, the terminal velocity is set at 4 m/s. For the MISO system in Figure 12, with equal weight coefficients, an increase in the number of subcarriers leads to higher mean positioning errors. For example, as the number of subcarriers increases from 128 to 1024, the mean positioning error increases by 1.2 cm and 1.8 cm when the receiver moves along Route 3 and Route 4, respectively.