Abstract
Deep unfolding is a method of growing popularity that fuses iterative optimization algorithms with tools from neural networks to efficiently solve a range of tasks in machine learning, signal and image processing, and communication systems. This survey summarizes the principle of deep unfolding and discusses its recent use for communication systems with focus on detection and precoding in multi-Antenna (MIMO) wireless systems and belief propagation decoding of error-correcting codes. To showcase the efficacy and generality of deep unfolding, we describe a range of other tasks relevant to communication systems that can be solved using this emerging paradigm. We conclude the survey by outlining a list of open research problems and future research directions.
| Original language | English |
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| Title of host publication | 2019 IEEE International Workshop on Signal Processing Systems, SiPS 2019 |
| Publisher | Institute of Electrical and Electronics Engineers |
| Pages | 266-271 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781728119274 |
| DOIs | |
| Publication status | Published - Oct 2019 |
| Event | 33rd IEEE Workshop on Signal Processing Systems, SiPS 2019 - Nanjing, China Duration: 20 Oct 2019 → 23 Oct 2019 Conference number: 33 |
Conference
| Conference | 33rd IEEE Workshop on Signal Processing Systems, SiPS 2019 |
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| Abbreviated title | SiPS 2019 |
| Country/Territory | China |
| City | Nanjing |
| Period | 20/10/19 → 23/10/19 |
Funding
The work of ABS was supported by the Swiss NSF project PZ00P2 179686. The work of CS was supported in part by Xilinx Inc. and the US NSF under grants ECCS-1408006, CCF-1535897, CCF-1652065, CNS-1717559, and ECCS-1824379. The authors would like to thank O. Castañeda and T. Goldstein for discussions on deep unfolding.
Keywords
- channel coding
- deep unfolding
- Machine learning
- massive MIMO