Name
Title | ||
|---|---|---|
Constrained Deep Neural Network based Hybrid Beamforming for Millimeter Wave Massive MIMO Systems |
Abstract | ||
|---|---|---|
Hybrid beamforming is a promising technology to reduce power consumption and provide high spectrum efficiency for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system. However, it is intractable to obtain global optima for similar constrained joint optimization problems by limitation of hardware architecture. In this work, we proposed a constrained deep neural network (constrained-DNN) based hybrid beamforming for mmWave massive MIMO system, which employs neural networks to replace the beamforming matrices in traditional hybrid beamforming to achieve end-to-end autonomous hybrid beamforming. Traditional hybrid beamforming optimization problem is transformed into a neural network optimization problem, which break the limitation of non-convex optimization. We also present numerical results on the performance of the proposed algorithms, which exhibits significant improvement on bit error rate (BER) performance compared with existing hybrid beamforming schemes. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICC.2019.8761742 | IEEE International Conference on Communications |
Keywords | Field | DocType |
Millimeter-wave massive MIMO,hybrid beamforming,constrained deep learning network | Beamforming,Extremely high frequency,Computer science,MIMO,Electronic engineering,Real-time computing,Spectral efficiency,Artificial neural network,Optimization problem,Hardware architecture,Bit error rate | Conference |
ISSN | Citations | PageRank |
1550-3607 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiyun Tao | 1 | 3 | 1.73 |
| Qi Wang | 2 | 73 | 40.49 |
| Siyu Luo | 3 | 0 | 0.68 |
| Jienan Chen | 4 | 84 | 13.64 |