Name
Abstract | ||
|---|---|---|
Neural network has been applied into MIMO detection problem and has achieved the state-of-the-art performance. However, it is hard to deploy these large and deep neural network models to resource constrained platforms. In this paper, we impose the circulant structure inside neural network to generate a low complexity model for MIMO detection. This method can train the circulant structured network from scratch or convert from an existing dense neural network model. Experiments show that this algorithm can achieve half the model size with negligible performance drop. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/SiPS47522.2019.9020365 | 2019 IEEE International Workshop on Signal Processing Systems (SiPS) |
Keywords | DocType | ISSN |
MIMO detection,neural network,circulant | Conference | 1520-6130 |
ISBN | Citations | PageRank |
978-1-7281-1928-1 | 0 | 0.34 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siyu Liao | 1 | 41 | 8.73 |
| Chunhua Deng | 2 | 18 | 7.45 |
| Lingjia Liu | 3 | 799 | 92.58 |
| Bo Yuan | 4 | 262 | 28.64 |