2021 Fiscal Year Annual Research Report
Research on Coding for Large-Scale Sensor Networks
| Project/Area Number |
18K04132
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| Research Institution | Gifu University |
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Principal Investigator |
LU SHAN 岐阜大学, 工学部, 助教 (30755385)
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| Co-Investigator(Kenkyū-buntansha) |
程 俊 同志社大学, 理工学部, 教授 (00388042)
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| Project Period (FY) |
2018-04-01 – 2022-03-31
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| Keywords | unsourced random access |
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| Outline of Annual Research Achievements |
In the unsourced random access (U-RA), many users with only a certain number are active in the same time slot. Each user employs the same codebook, and the task of the decoder is to recover a list of transmitted messages regardless of the user's identity. The compressed sensing(CS) approach is straightforward but with high computational complexity in addressing the U-RA problem.
A concatenated coding approach called a coded compressed sensing scheme is a low complexity method that splits each message into L sub-slots. First, an outer tree code connects the messages of all sub-slots. Then, the active users send a column from an inner CS matrix in each sub-slot. However, there is a limitation that the inner CS decoding only decodes the support of a sparse vector, which leads to each user at the same sub-slot must send a different message, and the maximum tolerable active user number is low.
We consider the inner CS decoding scheme that first decodes the amplitudes of a sparse vector and quantity them to show the number of active users choosing the same columns. Therefore, the constraint of each user sending different messages at the same sub-slot vanishes, and the maximum tolerable number of active users increases. We show the maximum tolerable active user number with various codelengths and improve the survival probabilities' upper and lower bounds of the outer tree encoder.
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