| Budget Amount *help |
¥11,570,000 (Direct Cost: ¥8,900,000、Indirect Cost: ¥2,670,000)
Fiscal Year 2018: ¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
Fiscal Year 2017: ¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2016: ¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
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| Outline of Final Research Achievements |
In the next-generation mobile network settings, for instance 5G networks with ultra-dense small cells, the network operators are burdened with the anticipation to provide both highly reliable and secure services to the users. Due to the contrasting goals of quality of service performance and security, it is critical to first model and analyze the traffic models carefully and then consider a balanced integration. The purpose of this research is to formulate this problem of finding a balanced set of tunable Quality of Service/Experience and security levels, and propose appropriate methods to optimally solve the problem.
Mature results were obtained and published in both international flagship conferences and the more analytically intensive papers submitted to the high impact factor IEEE journals/transactions. Deep learning based methods were demonstrated to be effective for intelligently routing packets in the wireless/mobile backbone network and fronthaul.
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