Data Enhancement Assisted Path Loss Model Correction for 5G Deployment in Vertical Industries

5G is an effective communication technology for facilitating the intelligence and digitalization in vertical industries. Large-scale deployment of 5G generally requires an accurate channel model with path loss correction in specific scenario for link quality assessment and cell sites planning. However, it is difficult to obtain sufficient on-site channel measurement data to correct the path loss model in many industrial scenarios due to the factors such as personnel safety, site confidentiality, and building layout variability. In this paper, we propose a data enhancement assisted path loss model correction strategy (MGMDE) for 5G deployment in vertical industries when the available data is limited. In order to reduce computational complexity and improve the accuracy of correction, the metabolic grey model(MGM) is utilized to enhance and supplement the limited path loss data. The measurement results and numerical simulation illustrate that a 5G path loss model with high-precision for 500kV substation scenario is obtained by using the proposed method, and the ME and RMSE of the corrected model at 3.65GHz have been improved by 95.7% and 11%, respectively.