| Budget Amount *help |
¥18,590,000 (Direct Cost: ¥14,300,000、Indirect Cost: ¥4,290,000)
Fiscal Year 2020: ¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2019: ¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2018: ¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2017: ¥4,940,000 (Direct Cost: ¥3,800,000、Indirect Cost: ¥1,140,000)
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| Outline of Final Research Achievements |
Regarding the evaluation of mobile data 3D offloading aiming at maximizing space utilization efficiency, under what conditions and how the UE should transmit data to appropriately smooth the eNB load, we evaluated applying deep reinforcement learning to network simulation with various condition settings. In addition to the evaluation of transmission rate control models using DQN, we proceeded with the research focusing on the acquisition of effective communication control methods for 5G network slicing management. Based on the design that allocate network resources to one slice by distributed learning using Ape-X, we confirmed it was possible to optimally allocate resources independently of the number of slices by changing the number of agents.
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