Deep-Learning-Based Lattice Reduction Preprocessing for Time-Correlated MIMO Systems
- Resource Type
- Conference
- Authors
- Li, Yi-Mei; Chi, Jung-Chun; Huang, Yuan-Hao
- Source
- 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2023 Asia Pacific. :230-237 Oct, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Adaptation models
Simulation
Asia
Lattices
Reinforcement learning
Information processing
Time-varying channels
- Language
- ISSN
- 2640-0103
The lattice-reduction (LR) preprocessing can effectively improve the performance of multiple-input multiple-output (MIMO) systems especially in the highly correlated channel. This study designed a deep-learning model for the LR preprocessing in the MIMO system. Based on the AlphaGo Zero architecture, this paper proposes a deep learning-based lattice reduction (DLLR) algorithm by using reinforcement learning in the AlphaGo Zero. This work investigated the performances of the DLLR in the time-varying correlated MIMO channel environments. Compare with the traditional LR-aided MIMO algorithm, the simulation results show that the DLLR can greatly improve the orthogonality of the MIMO matrices for the detection and the DLLR-aided MIMO system has better bit-error-rate performance than the LR-aided MIMO system.