Using Resource Allocation in Cognitive Wireless Networks to Maximize the Signal to Interference plus Noise Ratio

碩士 === 元智大學 === 工業工程與管理學系 === 100 === Wireless Network communication technology continues to develop, no matter on the existing mobile communications network or on developing smart phone network system. In the future wireless communication network, Users will need more and more transmission quality...

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Bibliographic Details
Main Authors: Shang-Hsuan Lee, 李尚軒
Other Authors: Yee-MingChen
Format: Others
Language:zh-TW
Online Access:http://ndltd.ncl.edu.tw/handle/52958257598576466013
Description
Summary:碩士 === 元智大學 === 工業工程與管理學系 === 100 === Wireless Network communication technology continues to develop, no matter on the existing mobile communications network or on developing smart phone network system. In the future wireless communication network, Users will need more and more transmission quality and communications stability. Therefore users demand will be a substantial increase, it maybe cause a serious imbalance of the transmission quality of a network communication system. The users’ transmission signals deterioration or inequitable distribution problem result in bandwidth resources. The wireless communications base station, in fact, can't increase unrestricted and the output power is also restricted. This study develop a channel resource allocation system in Matlab which implements the channel resource allocation with femtocell access point (FAP) self-adjustment of the pilot signal power under the environmental assumptions. The channel resource allocation is that each user has a quality of service, such as Signal-to-Interference-Plus-Noise-Ratio (SINR). Our objective is to assign radio resources, namely, self-adjustment power, to maximize either the total SINR summed over all users, subject to constraints on the desired transmission level and channel available at the base station. Finally, a case study on a particular problem shows that the heuristic search of Simulated Annealing (SA) and mathematical programming (MP) can achieve good performance. But SA performs less computational time in comparison with MP counterpart method in this radio resource allocation case.