| Paper ID | SPCOM-8.4 |
| Paper Title |
A LOW-COMPLEXITY ADMM-BASED MASSIVE MIMO DETECTORS VIA DEEP NEURAL NETWORKS |
| Authors |
Isayiyas Nigatu Tiba, Quan Zhang, Jing Jiang, Yongchao Wang, Xidian University, China |
| Session | SPCOM-8: Deep learning for communications |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Signal Processing for Communications and Networking: [SPC-MOD] Modulation, demodulation, encoding and decoding |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
An alternate direction method of multipliers (ADMM)-based detectors can achieve good performance in both small and large-scale multiple-input multiple-output (MIMO) systems. However, due to the difficulty of choosing the optimal penalty parameters, their performance is limited. This paper presents a deep neural network (DNN)-based massive MIMO detection method which can overcome the above limitation. It exploits the unfolding technique and learns to estimate the penalty parameters. Additionally, a computationally cheaper detector is also proposed. The proposed methods can handle the higher-order modulation signals. Numerical results are presented to demonstrate the performances of the proposed methods compared with the existing works. |
