LAUSR

This article addresses the challenges of traditional base station planning and the limited coverage radius of 5G high-frequency bands by proposing an adaptive beam algorithm based on synchronization signal reference signal received power (SS-RSRP), with signal extension as the entry point. The algorithm achieves efficient beam pointing adjustment in complex indoor environments while maintaining low computational resource consumption and ensuring high-precision pointing accuracy (errors below 1°), thereby effectively enhancing indoor 5G signal coverage. Furthermore, to improve the accuracy of coverage assessment, we develop an indoor coverage model by integrating ray-tracing and 3-D scene data. By combining the adaptive beam optimization algorithm with this data model for specific scenarios, this article optimizes beam pointing based on environmental characteristics and refines the precision of coverage evaluation. Experimental results demonstrate that the optimized scheme significantly improves indoor signal coverage, achieving a 9.36% increase in coverage ratio with measured error fluctuations constrained within $\pm ~3$ dB. This study provides a novel technical solution to 5G indoor coverage challenges and introduces innovative methodologies for future development of 5G indoor coverage enhancement techniques.