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Open Access research which pushes advances in bionanotechnology

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SIPBS is a major research centre in Scotland focusing on 'new medicines', 'better medicines' and 'better use of medicines'. This includes the exploration of nanoparticles and nanomedicines within the wider research agenda of bionanotechnology, in which the tools of nanotechnology are applied to solve biological problems. At SIPBS multidisciplinary approaches are also pursued to improve bioscience understanding of novel therapeutic targets with the aim of developing therapeutic interventions and the investigation, development and manufacture of drug substances and products.

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An advanced SOM algorithm applied to handover management within LTE

Sinclair, Neil and Harle, David and Glover, Ian and Irvine, James and Atkinson, Robert (2013) An advanced SOM algorithm applied to handover management within LTE. IEEE Transactions on Vehicular Technology, 62 (5). pp. 1883-1894. ISSN 0018-9545

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Abstract

Abstract—A novel approach to handover management for LTE femtocells is presented. Within LTE, the use of Self Organizing Networks is included as standard and handover management is one of its use cases. Base stations can autonomously decide whether handover should take place and assign the values of relevant parameters. Due to the limited range of femtocells, handover requires more delicate attention in an indoor scenario to allow for efficient and seamless handover from indoor femtocells to outdoor macrocells. As a result of the complexities of the indoor radio environment, frequent ping-pong handovers between the femtocell and macrocell layers can occur. A novel approach requiring a small amount of additional processing using neural networks is presented. A modified Self Organizing Map is used to allow the femtocell to learn the locations of the indoor environment from where handover requests have occurred and, based on previous experience, decide whether to permit or prohibit these handovers. Once the regions that coincide with unnecessary handovers have been detected, the algorithm can reduce the total number of handovers that occur by up to 70% while still permitting any necessary handover requests to proceed. By reducing the number of handovers, the system’s overall efficiency will improve as the consequence of a reduction in associated but unnecessary signaling. Using machine learning for this task complies with the plug-n-play functionality required from Self Organizing Networks in LTE systems.