Efficient self-optimization of neighbour cell lists in macrocellular networks

The neighbour cell list (NCL) in cellular networks has an important impact on the number of dropped calls and is traditionally optimized manually with the help of planning tools. In this paper, a method for automatically optimizing a NCL is presented, which consists of an initialization using a self-configuration phase, followed by a self-optimization phase that further refines the NCL based on measurements provided by mobile stations during the network operation. The performance of the proposed methods is evaluated for different user speeds and different NCL sizes. Besides, the convergence speed of the proposed self-optimization method is evaluated. It is shown that when about 6000 measurements are reported by mobile stations, the proposed self-optimization method attains a stable maximum performance about 99% of success rate.