Noise Mitigation Methods for Digital Visible Light Communication (DVLC) - IJCNC Vol.18, No.1

Table of Contents

1. Introduction

Visible Light Communication (VLC) has emerged as a promising complementary technology to RF-based systems, leveraging the ubiquitous lighting infrastructure for data transmission. Digital VLC (DVLC) employs modulation schemes like OOK and PPM. However, its performance is severely hampered by optical noise from ambient light sources (e.g., fluorescent lamps), leading to waveform distortion and increased Bit Error Rates (BER). This paper from IJCNC Vol.18, No.1 (2026) by Uemura and Hamano addresses this critical challenge by proposing and evaluating two distinct noise mitigation methods.

2. Visible Light Communication (VLC)

VLC operates within the 380-780 nm visible spectrum. White LEDs are common transmitters. In digital pulse modulation (e.g., OOK), an ON light state represents a binary HIGH, and OFF represents LOW. Data is transmitted as a sequence of these time slots. The receiver typically applies a voltage threshold to distinguish between states.

3. Noise Problems in VLC Systems

Optical noise superimposed on the VLC signal can cause incorrect symbol detection during the thresholding process at the receiver, degrading communication reliability.

3.1 Periodic Noise (AC Power-line Interference)

This noise originates from AC-powered ambient light sources (e.g., fluorescent lamps). Its frequency is tied to the local power grid (50/60 Hz). In this study, experiments were conducted under 60 Hz conditions (Western Japan). The noise waveform exhibits a predictable, periodic nature.

3.2 Non-Periodic Noise

This category includes unpredictable noise from various sources, lacking a fixed periodic structure, making it more challenging to mitigate with simple synchronous methods.

4. Proposed Method 1: Periodic Noise Subtraction

This method targets periodic interference from AC-powered lights.

4.1 Principle and Implementation

The core idea is to sample one complete cycle of the noise waveform (during a known silent period or by estimation). This sampled noise profile, $n_{sample}(t)$, is then subtracted from the received signal $r(t)$, which contains both the desired signal $s(t)$ and noise $n(t)$: $r(t) = s(t) + n(t)$. The cleaned signal is approximated as: $s_{cleaned}(t) \approx r(t) - n_{sample}(t)$.

4.2 Technical Details & Mathematical Formulation

The effectiveness relies on accurate synchronization to the noise period $T_{noise}$ (e.g., 1/60 s). The subtraction is performed in the digital domain after Analog-to-Digital Conversion (ADC). A key challenge is phase alignment; a small phase error $\phi$ can lead to residual noise: $n_{residual}(t) = n(t) - n_{sample}(t - \phi)$.

5. Proposed Method 2: ANC-Inspired Real-Time Noise Cancellation

Inspired by acoustic Active Noise Control (ANC), this method handles both periodic and non-periodic noise.

5.1 System Architecture

The system introduces an auxiliary photodetector placed strategically to capture primarily the ambient noise component $n(t)$ while minimizing reception of the intended VLC signal $s(t)$. This provides a reference noise signal.

5.2 Subtraction Circuit Design

An analog subtraction circuit (e.g., based on a differential amplifier) receives two inputs: the primary signal $r(t) = s(t) + n(t)$ and the reference noise $n_{ref}(t) \approx n(t)$. The circuit outputs: $s_{cleaned}(t) \approx r(t) - G \cdot n_{ref}(t)$, where $G$ is a gain factor adjusted to match the noise amplitude in the primary channel. This enables real-time, adaptive noise cancellation.

6. Experimental Results & Performance Evaluation

The performance was quantified using the standard metric of Bit Error Rate (BER) versus Energy-per-bit to Noise power spectral density ratio ($E_b/N_0$).

6.1 BER vs. Eb/N0 Analysis

The results clearly show that both proposed methods shift the BER vs. $E_b/N_0$ curve downwards compared to the conventional receiver. For a target BER (e.g., $10^{-3}$), the ANC-inspired method achieves this at a lower $E_b/N_0$, indicating higher power efficiency and robustness.

6.2 Comparative Performance

Method 1 is simpler and effective for dominant periodic noise but fails against non-periodic components. Method 2 is more complex (requires an extra photodiode and circuit) but offers comprehensive, real-time protection, making it suitable for dynamic, mixed-noise environments.

7. Analysis Framework & Case Example

Scenario: A DVLC system for indoor positioning in a supermarket. Fluorescent lights (60 Hz) cause periodic noise, and sunlight from windows causes non-periodic, time-varying noise.

Framework Application:

1. Noise Profiling: Use the auxiliary photodiode (Method 2) to log the composite noise signature over time.
2. Method Selection: Implement the ANC-inspired method as the primary canceller for its adaptability.
3. Parameter Tuning: Dynamically adjust the subtraction gain $G$ based on the correlation between the primary and reference channels. A simple adaptive filter like the Least Mean Squares (LMS) algorithm could be implemented in a microcontroller: $G_{k+1} = G_k + \mu \cdot e_k \cdot n_{ref,k}$, where $e_k$ is the error signal (cleaned output) and $\mu$ is the step size.
4. Validation: Measure positioning accuracy (e.g., error in cm) with and without the noise mitigation system enabled.

This framework demonstrates a systematic approach to deploying the research in a real-world context.

8. Application Outlook & Future Directions

Immediate Applications: Robust VLC for Li-Fi in offices/industries with harsh lighting, VLC-based indoor positioning/navigation, and secure communication in noise-prone environments.

Future Research Directions:

• AI-Enhanced Cancellation: Integrating machine learning (e.g., recurrent neural networks) to predict and cancel complex, non-stationary noise patterns beyond traditional ANC.
• Integrated Photonic Circuits: Miniaturizing the ANC system (photodiode + subtraction circuit) into a single photonic integrated chip (PIC) for cost-effective mass deployment.
• Hybrid RF/VLC Systems: Using the noise-reference signal from the VLC receiver to also mitigate interference in co-located RF systems (e.g., WiFi), as explored in cross-technology interference studies.
• Standardization: Proposing these mitigation techniques as part of future IEEE 802.15.7r1 (VLC) or other Li-Fi standard amendments for improved interoperability.

9. References

1. Uemura, W., & Hamano, T. (2026). Noise Mitigation Methods for Digital Visible Light Communication. International Journal of Computer Networks & Communications (IJCNC), Vol.18, No.1, pp.51-52.
2. Kahn, J. M., & Barry, J. R. (1997). Wireless Infrared Communications. Proceedings of the IEEE, 85(2), 265-298.
3. Haas, H., Yin, L., Wang, Y., & Chen, C. (2016). What is LiFi? Journal of Lightwave Technology, 34(6), 1533-1544.
4. Kuo, S. M., & Morgan, D. R. (1996). Active Noise Control Systems: Algorithms and DSP Implementations. John Wiley & Sons. (Foundational text on ANC principles).
5. IEEE Standard for Local and Metropolitan Area Networks–Part 15.7: Short-Range Wireless Optical Communication Using Visible Light. (2018). IEEE Std 802.15.7-2018.

10. Original Analysis & Expert Commentary