In this work, we consider the use of model-driven deep learning (DL) techniques for signal detection in massive multiple-input multiple-output (MIMO) system. Massive MIMO promises improved spectral efficiency, coverage and reliability, compared to conventional MIMO systems. Unfortunately, these benefits usually come at the cost of significantly increased computational complexity. To address this difficulty, a learned conjugate gradient descent network, referred to as LcgNet, is presented by unfolding the iterative conjugate gradient descent (CG) detector. In the proposed network, instead of calculating the exact values of the scalar step-sizes for every problem instance, we explicitly learn their universal values. We show that the performance of the proposed network can be greatly improved by augmenting the dimensions of these step-sizes. Furthermore, due to the limited learnable parameters to be optimized, the proposed networks are easy and fast to train. Numerical results demonstrate that this approach can achieve superior performance over some state-of-the-art MIMO detectors such as the CG detector, the linear minimum mean squared error (LMMSE) detector etc., with much lower computational complexity.
|Original language||English (US)|
|Title of host publication||2020 IEEE International Conference on Communications, ICC 2020 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - Jun 2020|
|Event||2020 IEEE International Conference on Communications, ICC 2020 - Dublin, Ireland|
Duration: Jun 7 2020 → Jun 11 2020
|Name||IEEE International Conference on Communications|
|Conference||2020 IEEE International Conference on Communications, ICC 2020|
|Period||6/7/20 → 6/11/20|
Bibliographical noteFunding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 91938202, in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LQ20F010010, in part by the Fundamental Research Funds for the Central Universities under Grant 2019QNA5011, and in part by the National Key Research and Development Project under grant 2018YFB1802303.
- Conjugate gradient descent
- deep learning
- massive MIMO detection
- model-driven method