Research on Modeling and Application of Downhole Visible Light Channel

Objective Underground mine environments are intricate and complex, and maintaining smooth and stable communication in addition to the real-time positioning of miners is beneficial for the safety of mining operations. Visible light communication has several advantages: no electromagnetic radiation, no radio interference, and low implementation cost. It can be employed in areas with strict requirements of electromagnetic radiation, such as mining. Additionally, it can provide high-speed communication and high-precision positioning underground. The majority of current research on visible light communication and localization is focused on the indoor environment; however, research on the mining environment is limited, especially in the aspect of simultaneous consideration of multiple influencing factors in the channel model, there is still a large research space. This study proposes a method for constructing a visible channel model based on point cloud data in mines by considering two factors, irregular stone walls and random tilt at the receiver end, in the channel model, and dividing the reflective surface elements based on wall point cloud data using the point-by-point insertion method to compensate for the lack of integration of the theoretical channel model with real data. The effectiveness of the proposed model is verified by combining the genetic algorithm with the optimized back-propagation (BP) neural network localization algorithm. Methods First, the normal vectors of the tilted receiver end and reflective surface elements on the irregular wall are represented, and the incident angle of the reflective surface elements, radiation angle, and incident angle of the receiver end are calculated using the normal vectors. The calculated angles are replaced by the corresponding angles in the conventional model to complete the theoretical modeling. Subsequently, the point cloud data collected by a binocular camera is converted to a coordinate system, and the normal vector is calculated by plane fitting using the least squares method to correct the plane of the point cloud image. Based on the processed point cloud data, the reflective surface elements are divided using the point-by-point insertion method. The coordinates of each triangular reflective surface element vertex are determined according to the reflective surface element topology, and the corresponding normal vector, area, and center of gravity of each surface element are calculated and replaced with the corresponding values in the theoretical model to combine the theoretical model and real data. Finally, based on the fingerprint localization method, the localization algorithm of BP neural network optimized by a genetic algorithm (GA) is used in the simulation space to complete the application of the proposed model. Results and Discussions The two real stone walls used for data acquisition are used as the two sides of the simulation space, which have a size of 5.0 m x 4.0 m x 3.5 m. In the case of the ideal wall, the average power of primary reflection is 0.1487 W, and the average contribution ratio is 15.22%. After considering the uneven wall, the average power of primary reflection increases to 0.1811 W, and the average contribution ratio becomes 30.37%. When considering both the uneven wall and the inclined receiver, the average power of primary reflection and the average contribution ratio are 0.1674 W and 29.48%, respectively. After considering the roughness of the real wall surface, the power distribution at the edge of the primary reflection power is affected by the irregular wall surface, showing unevenness, and the maximum power of the non-line of sight (NLOS) link is significantly increased, while the random tilt of the receiver end causes the uneven spatial distribution of the received power (Fig. 8). Using the model built in this study and GA-BP algorithm for positioning, the root mean square positioning errors when only considering direct light and considering primary reflection are 9.83 cm and 13.4 cm, respectively (Fig. 12). Because the model built in this study combines the real data of the wall, the relationship between the coordinates of each reference point and primary reflection power becomes more random. Therefore, the root mean square positioning error of the proposed model when considering primary reflection increases by 36.62% compared with that when only considering direct light, while the root mean square positioning error of the traditional channel model only increases by 0.8% (Table 4). In addition, this study also compares the localization effects of BP and GA- BP neural networks when using the model proposed in this study, and the root mean square localization error is 84.7 cm and 13.4 cm, respectively (Table 5). Compared with the BP neural network, the error of the GA-BP localization algorithm is reduced by 84.18%, which effectively improves the localization accuracy. Conclusions This study focuses on the effects of uneven walls and random tilting of the receiver end on the visible light communication (VLC) channel under a mine, proposes a method to construct a VLC channel model combining realistic data with 3D point cloud technology, and applies the established channel model to visible light localization using a GA- BP localization algorithm to explore the effects of primary reflection and tilted receiver end on the localization accuracy. In the simulated tunnel of 5.0 m x 4.0 mx 3.5 m, the average total received powers obtained by using the conventional and proposed channel models are 0.1487 W and 0.1674 W, respectively. The average contribution ratio of primary reflection is about twice that of the conventional model, suggesting that primary reflection and wall concavity must be considered when studying visible light communication and localization underground. However, the localization accuracy of the conventional channel model is 2.51 cm while that of the proposed channel model is 13.4 cm when using the GA-BP algorithm for the case with primary reflection. The channel model developed in this study provides an effective way to research visible light communication and localization in mines and has good application value.