## 4 VLPC Network Based on SICD-OPRC Scheme
### Simulation Results  
First, in Figure 8, we compare the normalized MAI under asynchronous transmission conditions for the OPRC and OZCZ schemes, considering varying codeword lengths (CLs). Here, we assume that the adjacent bit information in user data varies. The results indicatethat normalized MAI decreases with increasing chip length, suggesting that longer codes are more effective in reducing MAI. Additionally, OPRC demonstrates a significantly lower MAI compared to OZCZ, highlighting its superior MAI suppression capability. Despite these benefits, residual interference remains a challenge for CV measurements. To address this, we introduce the SICD scheme to further mitigate the adverse effects of MAI on CV accuracy. Then, to verify the ability of SICD-OPRC to mitigate MAI on CV measurement and its robustness against AWGN, we fix the receiver at (1.25 m, 1.25 m, 0 m) and measure the NECV values for three coding strategies under varying degrees of AWGN, represented by different SNR levels. Figure 9 illustrates how the NECV values change with SNR for different coding strategies and codeword lengths. Across all test conditions, the SICD-OPRC strategy consistently exhibits the lowest NECV, demonstrating its effectiveness in mitigating the MAI in complex asynchronous transmission environments and achieving more precise CV measurements. Additionally, OPRC shows notably lower NECV values compared to OZCZ, reinforcing its superior MAI suppression capacity, as previously observed. In addition, the NECV values for all strategies increase with decreasing SNR, indicating that higher AWGN levels affect CV estimation. However, we find that longer codeword lengths typically result in lower NECV, especially in low SNR environments. This is because longer codewords are more effective at suppressing MAI and have a higher coding gain to mitigate the impact of AWGN [36]. For example, at an SNR of just 5 dB, the NECV value decreases from 0.126 for a 112-length OPRC to 0.078 for a 496-length OPRC. Therefore, in environments with poor SNR, using longer codeword lengths can improve system performance. However, increasing the codeword length also reduces the transmission rate, as more chip resources are required per bit. To balance optimal data throughput and MAI suppression, we select OPRC with a length of 112 and OZCZ with a length of 128 for subsequent system performance tests. Next, we position the receiver at various measurement points and calculate CV values according to Equation (18). Recognizing that both MAI and AWGN impact CV measurement, we quantify their individual contributions to measurement errors under differing coding mechanisms. The outcomes are depicted in color-mapped contour plots in Figure 10. Figure 10a-c clearly shows that across the entire testing area, the SICD-OPRC scheme results in substantially lower NECV due to MAI compared to the OPRC and OZCZ schemes. Interestingly, the NECV distribution pattern for SICD-OPRC differs from that of OPRC and OZCZ, with NECV values increasing as the receiver moves towards the center of the model, whereas OPRC and OZCZ exhibit a decreasing trend. This peculiarity arises because, at the center of the model, the signal strengths from the four LEDs are nearly uniform. This uniformity makes it challenging to identify the strongest VLPC signal during the SICD process, thereby reducing the scheme's effectiveness in canceling interference. Thus, the performance of SICD is more effective at the peripheries of the model compared to the  
Figure 8: Normalized MAI in the asynchronous VLPC system using: (**a**) OPRC; and (**b**) OZCZ, with varying codeword lengths. central area. Additionally, Figure 10d-f reveals a consistent NECV distribution pattern caused by AWGN: NECV values are lower near the center of the room and increase toward the edges, regardless of the coding scheme. This is because the received light intensity is higher in the center of the model, leading to a higher SNR, which mitigates the negative impact of AWGN.