{"title":"Analysis of diffraction loss in NLOS small cell backhaul links deployed in urban canyons","authors":"B. Malila, O. Falowo, N. Ventura","doi":"10.1109/EUROCON.2015.7313755","DOIUrl":null,"url":null,"abstract":"The dense deployment of low cost small cell access networks in urban areas has generally been accepted as a solution to the capacity and coverage problem in 5G access networks. This deployment strategy has however, resulted in the need for high capacity, low cost backhaul systems. Millimeter-wave (mmW) technologies with inherent Gbps capacity are currently being considered as possible solutions. However, like all wireless systems deployed in urban, mmW backhaul solutions will require clear line-of-sight for proper operation. The systems must also have autonomous capabilities to achieve the scalability and flexibility expected of 5G transport systems. Recent measurement campaigns have shown that usable links based on diffracted signals are achievable under non-line-of-sight (NLOS) conditions in urban environments. In our earlier work, an intelligent NLOS SCB system which uses diffracted signals has been proposed. In this paper we study existing propagation models for urban environments. We use the Single Knife Edge Diffraction (SKED) model to predict diffraction loss on the propagation path due to knife edge obstructions for street level to roof top NLOS SCB links. Using ideal SKED parameter values to be used in the design and optimization of typical NLOS SCB systems, a numerical analysis of the effect of varying these parameters on diffraction loss is carried out. The results obtained show that the model can be used in the development of algorithms for SCB radio devices with cognitive and self-organizing capabilities. Future work will involve the development of such algorithms using Genetic Algorithm.","PeriodicalId":133824,"journal":{"name":"IEEE EUROCON 2015 - International Conference on Computer as a Tool (EUROCON)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE EUROCON 2015 - International Conference on Computer as a Tool (EUROCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EUROCON.2015.7313755","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
The dense deployment of low cost small cell access networks in urban areas has generally been accepted as a solution to the capacity and coverage problem in 5G access networks. This deployment strategy has however, resulted in the need for high capacity, low cost backhaul systems. Millimeter-wave (mmW) technologies with inherent Gbps capacity are currently being considered as possible solutions. However, like all wireless systems deployed in urban, mmW backhaul solutions will require clear line-of-sight for proper operation. The systems must also have autonomous capabilities to achieve the scalability and flexibility expected of 5G transport systems. Recent measurement campaigns have shown that usable links based on diffracted signals are achievable under non-line-of-sight (NLOS) conditions in urban environments. In our earlier work, an intelligent NLOS SCB system which uses diffracted signals has been proposed. In this paper we study existing propagation models for urban environments. We use the Single Knife Edge Diffraction (SKED) model to predict diffraction loss on the propagation path due to knife edge obstructions for street level to roof top NLOS SCB links. Using ideal SKED parameter values to be used in the design and optimization of typical NLOS SCB systems, a numerical analysis of the effect of varying these parameters on diffraction loss is carried out. The results obtained show that the model can be used in the development of algorithms for SCB radio devices with cognitive and self-organizing capabilities. Future work will involve the development of such algorithms using Genetic Algorithm.