Output details

An Advanced SOM Algorithm Applied to Handover Management within LTE

As mobile communication systems approach the Shannon bound, increased capacity can be met (most directly) by using smaller cells in higher numbers. Since the manual configuration and tuning of large numbers of base stations would be prohibitively expensive, system self-management becomes necessary. This paper, which acknowledges the contribution of Pelleg and Moore from the School of Computer Science, Carnegie Mellon University, uses machine learning techniques to autonomically optimise handover between radio cells, thereby reducing network operational expenditure. Its importance, relevance and timeliness are evinced by the inclusion of SONs (self-organizing networks) within the mobile communication LTE (Long Term Evolution) standard.