.png){kind=icon}
Return
Deep Learning Based Interference Exploitation in 1-Bit Massive MIMO Precoding
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2023.3244928.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, we focus on one-bit precoding approach for downlink massive multiple-input multiple-output (MIMO) systems, where we exploit the concept of constructive interference (CI) employing deep learning (DL) techniques. One of the main performance limiting factors in wireless communication systems is interference, which needs to be minimized or mitigated. By controlling the interference signals in order to add up constructively at the receiver side, there is a possibility to improve the system performance. This paper presents a DL-based one-bit precoding scheme that improves the massive MIMO performance via CI exploitation in the presence of one-bit digital to analog converters (DAC) as a hardware impairment. More precisely, for phase shift keying signaling, we first formulate the optimization problem in order to maximize the CI effects in the case of a base station equipped with one-bit DACs. Then, after solving the optimization problem and creating a large enough dataset, a DL network is trained to do the precoding. Numerical results show that the DL-based solution approaches the performance of the conventional interference exploitation one-bit precoding schemes in the massive MIMO systems while having an order of magnitude less complexity.
Keywords:
Precoding
Interference
Massive MIMO
Symbols
Radio frequency
Phase shift keying
Deep learning
Codes
interference exploitation
one-bit DAC
precoding
deep learning
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
Organization
Cited Papers
Phototriggered Linkage Isomerization in Ruthenium−Dimethylsulfoxyde Complexes: Insights from Theory
PREAI
ShareSaveShareSaveShareSaveAn Overview of Machine Learning-Based Techniques for Solving Optimization Problems in Communications and Signal Processing.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAIShareSaveShareSaveShareSave
View more