2021 Fiscal Year Final Research Report
Multiuser Detection based on Markov chain Monte Carlo Methods
| Project/Area Number |
19K04396
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 21020:Communication and network engineering-related
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| Research Institution | Keio University |
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Principal Investigator |
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| Project Period (FY) |
2019-04-01 – 2022-03-31
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| Keywords | 5G / マルコフ連鎖モンテカルロ法 / 多信号復調 |
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| Outline of Final Research Achievements |
Massive connection is one of the key features of a fifth generation (5G) mobile communication system. A base station with massive antenna elements has been implemented for 5G and it is possible to demodulate more than 100 signals at a time. However, conventional demodulation scheme demands huge computational complexity. Thsu, in this research, the applicaton of a Markov chain Monte Carlo method to mutliple signal detetion has been invetigated. More specifically, a candidate symbol selection with a novel selection probability curve has been proposed. Futhermore, a forcible symbol change scheme has been proposed. These schemes improve the bit error rate performance especially under high signal-to-noise ratio conditions.
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| Free Research Field |
無線通信
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| Academic Significance and Societal Importance of the Research Achievements |
これらの研究成果は従来とは違った多数接続アプリケーションのプラットフォームに適用することが期待できる．その例としては工場内の機器を無線でつなぎ，遠隔から工場を制御するスマート工場などが考えられる．このようなプラットフォームに第５世代移動通信で実用化された超多素子アンテナ基地局を用いれば，同時に100以上の信号を受信，復調することができる．
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