.png){kind=icon}
.png){kind=icon}
Return
Machine Learning-Based Massive Augmented Spatial Modulation (ASM) for IoT VLC Systems
.png){kind=icon}
PRE
AI
DOI：10.1109/LCOMM.2020.3033123.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Massive Multiple-Input Multiple-Output (MIMO) technology aims to further the diversity/multiplexing gains of wireless communication systems. Spatial modulation (SM) is a renowned low-complexity MIMO scheme that jointly uses transmitting source indices along with the data stream to convey information. However, the reliability of the spatial stream in SM is significantly influenced by the correlation between channel coefficients. Hence, applying massive-MIMO in visible light communications (VLC) remains an under-investigated area of research, due to the ill-conditioned massive-MIMO VLC channel. Augmented SM (ASM) is an approach that overcomes the channel uniqueness requirement for SM-based VLC systems. This letter adopts massive-ASM for Internet-of-Things applications with a focus on investigating ASM's complexity and introducing different machine learning based receiver designs, including; support vector machine (SVM), logistic regression (LR), and a neural network (NN). The computational time and transmitter identification accuracy are compared and the system's bit-error-rate performance is evaluated.
Keywords:
Complexity theory
Optical transmitters
Receivers
Visible light communication
OFDM
Modulation
MISO communication
Augmented spatial modulation
IoT
machine learning
massive-MIMO
visible light communications
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.4
Papers:
1.3W
Citations:
2.2W