Kai Chen

Kai Chen

Shanghai Fudan Microelectronics Group Co., Ltd

Publications

Sub-Array Hybrid Precoding for Massive MIMO Systems: A CNN-Based Approach

Published in Journal, 2021

In order to reduce the computation time of hybrid precoding processing while improving the spectrum efficiency (SE) of massive multiple-input multiple-output (MIMO) systems, in this letter, we investigate the sub-array hybrid precoding based on the convolutional neural network (CNN). A constraint-relaxation alternating minimization (CR Alt-Min) algorithm is proposed to create the training set of the CNN. To reduce the computation time caused by iterations in the Alt-Min algorithm, a CNN-based algorithm is proposed. Simulation results show that the CNN-based algorithm reduces the computation time in hybrid precoding processing by an order of magnitude. Moreover, the maximum SE is improved by 26.64% by the CNN-based algorithm, compared with the Alt-Min algorithm.

Recommended citation: K. Chen, J. Yang, Q. Li and X. Ge, "Sub-Array Hybrid Precoding for Massive MIMO Systems: A CNN-Based Approach," in IEEE Communications Letters, vol. 25, no. 1, pp. 191-195, Jan. 2021, doi: 10.1109/LCOMM.2020.3022898. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9189869

Complex-BP-Neural-Network-based Hybrid Precoding for Millimeter Wave Multiuser Massive MIMO Systems

Published in Conference, 2019

The high energy consumption of massive multi-input multi-out (MIMO) system has become a prominent problem in the millimeter wave(mm-Wave) communication scenario. The hybrid precoding technology greatly reduces the number of radio frequency (RE) chains by handing over part of the coding work to the phase shifting network, which can effectively improve energy efficiency. However, conventional hybrid precoding algorithms based on mathematical means often suffer from performance loss and high computational complexity. In this paper, a novel BPneural-network-enabled hybrid precoding algorithm is proposed, in which the full-digital zero-forcing(ZF) precoding is set as the training target. Considering that signals at the base station are complex, we choose the complex neural network that has a richer representational capacity. Besides, we present the activation function of the complex neural network and the gradient derivation of the back propagation process. Simulation results demonstrate that the performance of the proposed hybrid precoding algorithm can optimally approximate the ZF precoding.

Recommended citation: K. Chen, J. Yang, X. Ge and Y. Li, "Complex-BP-Neural-Network-based Hybrid Precoding for Millimeter Wave Multiuser Massive MIMO Systems," 2019 Computing, Communications and IoT Applications (ComComAp), Shenzhen, China, 2019, pp. 100-105, doi: 10.1109/ComComAp46287.2019.9018725. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9018725

Energy Efficiency Optimization of Generalized Spatial Modulation with Sub-connected Hybrid Precoding

Published in Conference, 2018

Energy efficiency (EE) optimization of millimeter wave (mm-Wave) massive multiple-input multiple-output (MI-MO) systems is emerging as an important challenge for the fifth generation (5G) mobile communication systems. However, the power of radio frequency (RF) chains increases sharply due to the high carrier frequency in mm-Wave massive MIMO systems. To overcome this issue, a new energy efficiency optimization solution is proposed based on the structure of the generalized spatial modulation (GSM) and sub-connected hybrid precoding (HP). Moreover, the computation power of mm-Wave massive MIMO systems is considered for optimizing the EE. Simulation results indicate that the EE of the GSM-HP scheme outperforms the full digital precoding (FDP) scheme in the mm-Wave massive MIMO scene, and 88% computation power can be saved by the proposed GSM-HP scheme.

Recommended citation: K. Chen, J. Yang, X. Ge, Y. Li, L. Tian and J. Shi, "Energy Efficiency Optimization of Generalized Spatial Modulation with Sub-connected Hybrid Precoding," 2018 IEEE 4th International Conference on Computer and Communications (ICCC), Chengdu, China, 2018, pp. 17-22, doi: 10.1109/CompComm.2018.8780889. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8780889