Interference Reduction Assisted by Indoor Localization and Machine Learning Techniques

• Main Authors: Sheng-Kai Chiu, 邱聖凱
• Other Authors: Jia-Chin Lin
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/fjzs7h

碩士 === 國立中央大學 === 通訊工程學系 === 105 === Recently, the demand for high-quality mobile communications has significantly increased. From the 4th generation of mobile phone mobile communication technology standards to the future 5th, in order to meet the large and wide variety of needs, there will be many different back-end networks and different power of base stations coexist. Therefore, heterogeneous network not only help Telecommunications corporations building Ultra-dense network, but could also fulfill the upcoming high-speed Internet access and user experience needs. Femtocell network are considered suitable for next-generation mobile communication because of its small size, which can solve inadequate coverage of mobile communication problem. Femtocell network has been discussed at the end of 3G, but the reason that it could not have been developed is the problem of interference coordination. Interference is divided into two categories, the first is the interference from large base stations and the second is the interference between femtocell base stations, which will both affect network quality of users and cause signal disconnection problems. We are going to discuss the second problem mentioned above. In this thesis, we will present a system including an indoor localization method, clustering methods from machine learning, and two widely used inter-cell interference coordination algorithms. Through this system, we could reduce the interference between femtocell base stations.