Journal
IEEE COMMUNICATIONS LETTERS
Volume 24, Issue 8, Pages 1724-1728Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LCOMM.2020.2989672
Keywords
MIMO communication; Detectors; Mathematical model; Training; Complexity theory; Machine learning; Neural networks; Massive MIMO; MIMO detection; deep learning; model-driven
Categories
Funding
- National Key Research and Development Program of China [2018AAA0102401]
- National Natural Science Foundation of China [61971191, 61661021, 61831013, 61771274]
- Beijing Natural Science Foundation [L182018]
- National Science and Technology Major Project of the Ministry of Science and Technology of China [2016ZX03001014-006]
- Open Research Fund of National Mobile Communications Research Laboratory, Southeast University [2017D14]
Ask authors/readers for more resources
In this letter, an efficient massive multiple-input multiple-output (MIMO) detector is proposed by employing a deep neural network (DNN). Specifically, we first unfold an existing iterative detection algorithm into the DNN structure, such that the detection task can be implemented by deep learning (DL) approach. We then introduce two auxiliary parameters at each layer to better cancel multiuser interference (MUI). The first parameter is to generate the residual error vector while the second one is to adjust the relationship among previous layers. We further design the training procedure to optimize the auxiliary parameters with pre-processed inputs. The so derived MIMO detector falls into the category of model-driven DL. The simulation results show that the proposed MIMO detector can achieve preferable detection performance compared to the existing detectors for massive MIMO systems.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available