Coverage and Cell Capacity optimization in Private LTE network based on Position and Expected Channel Knowledge

This paper presents an innovative approach to maximize cell coverage and capacity in a private LTE network by optimizing resource block (RB) and power allocation. The proposed method decomposes the cell into sub-cells and employs two possible solutions: genetic algorithm (GA) and a water-filling based algorithm to find optimal resource distribution for each sub-cell. A priori knowledge of the expected channel gain is assumed. Both algorithms explore an efficient RB and power allocation, based on expected channel conditions and capacity requirements. The reported numerical results validate the effectiveness of the proposed approach, offering valuable insights for enhancing private LTE network performance through optimized resource allocation.