Name
Abstract | ||
|---|---|---|
In the downlink transmission scenario, power allocation and beamforming design at the transmitter are essential when using multiple antenna arrays. This paper considers a multiple input-multiple output broadcast channel to maximize the weighted sum-rate under the total power constraint. The classical weighted minimum mean-square error (WMMSE) algorithm can obtain suboptimal solutions but involves high computational complexity. To reduce this complexity, we propose a fast beamforming design method using unsupervised learning, which trains the deep neural network (DNN) offline and provides real-time service online only with simple neural network operations. The training process is based on an end-to-end method without labeled samples avoiding the complicated process of obtaining labels. Moreover, we use the "APoZ" -based pruning algorithm to compress the network volume, which further reduces the computational complexity and volume of the DNN, making it more suitable for low computation-capacity devices. Finally, the experimental results demonstrate that the proposed method improves computational speed significantly with performance close to the WMMSE algorithm. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2018.2887308 | IEEE ACCESS |
Keywords | Field | DocType |
MIMO,beamforming,deep learning,unsupervised learning,network pruning | Beamforming,Transmitter,Computer science,Algorithm,MIMO,Unsupervised learning,Artificial intelligence,Deep learning,Artificial neural network,Computational complexity theory,Telecommunications link,Distributed computing | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 6 |
PageRank | References | Authors |
0.42 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Huang | 1 | 589 | 104.49 |
| Wenchao Xia | 2 | 40 | 3.92 |
| Jian Xiong | 3 | 130 | 9.78 |
| Yang, J. | 4 | 89 | 9.03 |
| Gan Zheng | 5 | 2199 | 115.78 |
| Xiaomei Zhu | 6 | 44 | 5.48 |