# Channel Characteristics and Link Adaption for Visible Light Communication in an Industrial Scenario
## 4 Link Adaption Method to Optimize the Multipath Interference  
As observed from Figures 3-5, the channel characteristics vary significantly based on the user location. Furthermore, the large time-domain dispersion (RMS DS) in the IIoT scenario will lead to severe ICI. To solve this problem, a two-step approach to optimizing the multipath interference with distance variation is further proposed. The algorithm details are shown in the Algorithm 1. Firstly, the greedy algorithm is used to select the suitable LED index to maximize the SNR. The received power from each TX to the RX is first calculated. Then, the received power matrix is sorted in descending order (\(P_{sort}\)). Furthermore, the sequence is accumulated (\(P_{cum}\)). Then, filter out the LED index that contributes 95 percent of the total received power (\(P_{LED}\)). The optimal index and the maximum received power (\(P_{r}\)) are further selected to meet the maximum SNR. The maximum SNR can be calculated by Equation (11) according to the noise power and index of active LEDs. Moreover, compared with the brute-force search algorithm (\(O(N! )\)) in the [13], the greedy algorithm in this paper has a lower algorithmic complexity (\(O(N\log N)\)). \[SNR=\frac{P}{|\phi|\sigma_{N}^{2}}|\sum_{i\in\phi}H_{i}|^{2}, \tag{11}\]  
Figure 5: **(a)** The distance dependence of ASA samples under single TX case; **(b)** the CDF of ASA under the multiple TXs’ case in Road 1.  
where P is the total information power, \(H_{i}\)=\(\int_{0}^{\infty}h(t)d(t)\), \(\phi\) is a vector including the index of active LEDs, \(|\phi|\) denotes the number of active LEDs, and \(\sigma_{N}^{2}\) is the noise power. Although the suitable LED index is selected to maximize the SNR, the number of LEDs has not been significantly reduced, i.e., CCI problems should be solved. In this paper, the delay adaptation method in the [19] is used. This method can control the time of transmitting the effective signal so that the LOS components from variable transmitters arrive at the receiver side at the same time. The specific method is listed in the Algorithm 1 Step 2. After the data processor based on the two-step approach to optimizing the multipath interference, four parameters, including the SNR, the RMS DS, the CIRs, and the BER of the DCO-OFDM system, are simulated to verify the performance of the link adaption method. The results are drawn in Figures 6-8. Figure 6a shows the RMS DS of the user walking from the wall side to the center in Road 1, and the Figure 6b gives the SNR before and after optimization. The difference for RMS DS before and after optimization is 2.41 ns in the \(D_{10}\), and the difference for SNR is 1.22 dB. Furthermore, the closer the user is to the center of the room, the smaller range can be optimized. This is mainly because as the distance increases, the interval of LEDs that can be selected for optimization increases, i.e., the number of optimized LEDs decreases, resulting in a larger RMS DS. Figure 7 gives the CIR superimposed by the LOS and NLOS components before and after optimization in Road 1 under multiple TXs' cases. It can be found that the received power at \(D_{10}\) decreases from \(1.72\times 10^{-4}\) before optimization to \(1.37\times 10^{-4}\) after optimization. Furthermore, after optimization, the number of the main multipath in the CIR decreased from 9 to only 1. This indicates that the system can significantly reduce the impact of multipath fading based on the reduced receiving power by using the multipath optimization method in the paper.