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Detail
Journal Article
Enhanced deep soft interference cancellation for multiuser symbol detection
- Authors
- Jihyung Kim, Junghyun Kim, Moon-Sik Lee
- Issue Date
- 2023-12
- Citation
- ETRI Journal, v.45, no.6, pp.929-938
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원
- Language
- English
- Type
- Journal Article
- Abstract
- The detection of all the symbols transmitted simultaneously in multiuser systems using limited wireless resources is challenging. Traditional model‐based methods show high performance with perfect channel state information (CSI); however, severe performance degradation will occur if perfect CSI cannot be acquired. In contrast, data‐driven methods perform slightly worse than model‐based methods in terms of symbol error ratio performance in perfect CSI states; however, they are also able to overcome extreme performance degradation in imperfect CSI states. This study proposes a novel deep learning‐based method by improving a state‐of‐the‐art data‐driven technique called deep soft interference cancellation (DSIC). The enhanced DSIC (EDSIC) method detects multiuser symbols in a fully sequential manner and uses an efficient neural network structure to ensure high performance. Additionally, error‐propagation mitigation techniques are used to ensure robustness against channel uncertainty. The EDSIC guarantees a performance that is very close to the optimal performance of the existing model‐based methods in perfect CSI environments and the best performance in imperfect CSI environments.
- KSP Keywords
- Best performance, Channel State Information(CSI), High performance, Interference cancellation, Multiuser systems, Neural network structure, Optimal Performance, Wireless resources, channel uncertainty, deep learning(DL), imperfect CSI
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
