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Sub-column pixel neural network scheme for modulation format shifting based optical camera communications.

Jin Shi, Jing He, Xinda Yan

|December 23, 2022

Summary

This summary is machine-generated.

A novel sub-column pixel neural network (SCPNN) enhances optical camera communication by enabling modulation format shifting (MFS) to boost data rates. This method improves bit-error-rate performance, even in low light conditions.

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Area of Science:

Optical Camera Communication
Machine Learning in Communications

Background:

Optical Camera Communication (OCC) systems offer a wireless communication channel using visible light.
Modulation Format Shifting (MFS) in OCC can increase data rates by encoding additional information.
Existing methods like multi-threshold decision (MTD) struggle with noise and inter-symbol interference (ISI).

Purpose of the Study:

To propose a Sub-Column Pixel Neural Network (SCPNN) scheme for improved pixel pattern recognition in MFS-OCC systems.
To enhance data transmission rates in OCC without requiring multi-level modulation.
To mitigate the effects of ISI and noise on signal decision.

Main Methods:

Development and implementation of a Sub-Column Pixel Neural Network (SCPNN).
Utilizing all pixels within a one-bit period for signal decision making.
Experimental validation under low illuminance conditions (300 lux).

Main Results:

The SCPNN scheme achieved a data rate of 8.16 Kbps with a bit-error-rate (BER) of 2.2 × 10^-3, below the HD-FEC limit.
Significant BER performance improvement compared to MFS-OCC with MTD and conventional on-off keying (OOK)-OCC systems.
Effective mitigation of ISI and noise impacts on signal decision.

Conclusions:

The proposed SCPNN scheme offers a viable solution for high-speed and reliable MFS-OCC.
SCPNN demonstrates superior performance over conventional methods, particularly in challenging low-light environments.
This approach enhances the efficiency of optical wireless communication systems.