1. Introduction

This paper investigates a Non-Orthogonal Multiple Access (NOMA) Visible Light Communication (VLC) system enhanced by Angle Diversity Receivers (ADRs). The primary challenge addressed is the limitation of conventional VLC systems in providing high data rates due to factors like Inter-Symbol Interference (ISI) and Co-Channel Interference (CCI). The proposed system combines the spectral efficiency of NOMA with the interference mitigation and signal capture capabilities of a 4-branch ADR, aiming to maximize user data rates in an indoor environment.

2. System Model

The system is modeled within an 8m × 4m × 3m empty room. The optical channel incorporates reflections from walls and ceilings, modeled as Lambertian reflectors with a reflectivity coefficient (ρ) of 0.8. Ray tracing is employed to simulate the multipath propagation of light signals.

2.1 Room and Channel Modeling

The indoor channel impulse response is calculated considering both line-of-sight (LOS) and diffuse (reflected) components. Reflective surfaces are divided into small elements of area dA. The channel DC gain for a receiver with detector area $A_{pd}$ and gain $T_s(\psi)$ is given by:

$H(0) = \frac{(m+1)A_{pd}}{2\pi d^2} \cos^m(\phi) T_s(\psi) g(\psi) \cos(\psi)$ for $0 \le \psi \le \Psi_c$

where $m$ is the Lambertian order, $d$ is the distance, $\phi$ is the irradiance angle, $\psi$ is the incidence angle, and $\Psi_c$ is the receiver's field-of-view (FOV).

2.2 Angle Diversity Receiver (ADR) Design

The ADR consists of four narrow-FOV photodetectors, each oriented in a different direction (e.g., towards room corners or specific access points). This design allows the receiver to select the branch with the strongest signal-to-noise ratio (SNR) or combine signals, effectively reducing the impact of ambient light, multipath dispersion, and co-channel interference.

2.3 NOMA Principle and Power Allocation

NOMA operates by superimposing signals for multiple users in the power domain at the transmitter. At the receiver, Successive Interference Cancellation (SIC) is used to decode signals. Power is allocated inversely to channel gain: users with better channel conditions (stronger signals) are allocated less power, while users with poorer conditions receive more power to ensure fairness. The achievable rate for user $i$ is:

$R_i = B \log_2 \left(1 + \frac{P_i |h_i|^2}{\sum_{j>i} P_j |h_i|^2 + \sigma^2}\right)$

where $B$ is bandwidth, $P_i$ is power allocated to user $i$, $h_i$ is the channel gain, and $\sigma^2$ is noise variance.

3. Simulation Results & Discussion

The performance of the NOMA-VLC system with ADR is compared against a baseline system using a single wide-FOV receiver.

3.1 Performance Comparison: ADR vs. Wide FOV

The key finding is that the ADR-based system achieves an average data rate improvement of 35% over the wide-FOV receiver system. This gain is attributed to the ADR's ability to selectively capture stronger, less distorted signals and reject interfering components from other transmitters or reflections.

3.2 Data Rate Analysis and Optimization

Simulations involve optimizing resource (power) allocation among users based on their instantaneous channel conditions, derived from the ADR branch selections. The optimization aims to maximize the sum data rate while maintaining user fairness, following the authors' prior approach [36]. Results demonstrate that the combination of adaptive branch selection and NOMA power allocation significantly enhances spectral efficiency.

Key Performance Metric

35% Average Data Rate Gain achieved by the ADR-based NOMA-VLC system compared to a wide-FOV receiver baseline.

4. Conclusion

The paper concludes that integrating Angle Diversity Receivers with NOMA in VLC systems is a highly effective strategy for overcoming key limitations like interference and limited bandwidth. The 4-branch ADR provides substantial gains in data rate by improving signal quality and enabling more efficient multi-user power allocation via NOMA. This work validates the potential of advanced receiver design combined with non-orthogonal multiplexing for next-generation optical wireless networks.

5. Core Analyst Insight

Core Insight: This paper isn't just about a marginal improvement; it's a strategic pivot. It correctly identifies that the bottleneck for dense, high-capacity VLC isn't just the transmitter (where most research focuses, e.g., on µLEDs or laser diodes) but critically, the receiver's ability to discriminate signals in a noisy, multipath environment. The 35% gain from a relatively simple 4-branch ADR is a powerful testament to this often-overlooked dimension.

Logical Flow: The argument is sound: 1) VLC suffers from interference (CCI/ISI), 2) ADRs mitigate interference by spatial filtering, 3) Cleaner signals enable more aggressive multiplexing (NOMA), 4) NOMA's power-domain multiplexing boosts spectral efficiency. The simulation in a standardized room model (similar to those used by the IEEE 802.15.7r1 task group) provides credible validation.

Strengths & Flaws: The strength lies in the pragmatic combination of two mature concepts (diversity reception and NOMA) for a clear, quantifiable gain. The methodology is robust. However, the flaw is in the simplicity of the ADR model. Real-world ADRs face challenges like branch correlation, increased hardware complexity, and the need for fast, low-power branch selection algorithms—issues only hinted at. Compared to cutting-edge research on adaptive optics or MIMO-based VLC using imaging receivers (as seen in works from MIT's Media Lab or UC Berkeley's BWRC), this approach is more immediately deployable but may have a lower ultimate capacity ceiling.

Actionable Insights: For industry practitioners, this paper is a green light to invest in receiver-side innovation. Product managers for Li-Fi or industrial VLC systems should prioritize integrating multi-element receivers. For researchers, the next steps are clear: 1) Investigate machine learning for dynamic, optimal ADR branch selection and NOMA user pairing. 2) Explore the integration with wavelength-division multiplexing (WDM) for multiplicative gains. 3) Conduct real-world tests with mobile users to validate the dynamic performance. Ignoring receiver diversity in future VLC standards would be a significant oversight.

6. Technical Details & Mathematical Formulation

The core technical contribution is the joint optimization of ADR branch selection and NOMA power allocation. The signal received at the $k$-th branch of the ADR for user $i$ is:

$y_{i,k} = h_{i,k} \sum_{u=1}^{U} \sqrt{P_u} x_u + n_{i,k}$

where $h_{i,k}$ is the channel gain from the transmitter to the $k$-th branch for user $i$, $P_u$ is the power allocated to user $u$'s signal $x_u$, and $n_{i,k}$ is additive white Gaussian noise. The receiver selects the branch $k^*$ for each user or decoding step that maximizes the effective SNR. The SIC process at a user with channel gain $|h_i|^2$ decodes signals in the order of increasing channel gain. The power allocation coefficients $\alpha_i$ (where $\sum \alpha_i = 1$, and $\alpha_i < \alpha_j$ if $|h_i|^2 > |h_j|^2$) are optimized to maximize the sum rate $\sum R_i$ under a total power constraint $P_T$.

7. Experimental Results & Chart Description

While the paper is simulation-based, the described results can be visualized through key charts:

  • Chart 1: Sum Rate vs. Transmit Power: This chart would show two curves, one for the ADR-NOMA system and one for the Wide-FOV-NOMA baseline. Both curves would increase with power, but the ADR curve would show a steeper slope and a higher plateau, clearly illustrating the 35% average gain across the power range.
  • Chart 2: User Rate Distribution: A bar chart or CDF showing the data rates achieved by individual users in the room. The ADR system would show a tighter, higher distribution, indicating more consistent and improved service for users in various locations (especially near walls or in corners where wide-FOV receivers suffer from multipath).
  • Chart 3: Branch Selection Frequency: A heatmap on the room floor indicating how often each of the ADR's four branches is selected as the "best" branch. This would visually demonstrate the ADR's adaptive nature, with different branches being optimal in different room regions.

8. Analysis Framework: A Case Study

Scenario: Designing a VLC network for a open-plan office with 20 workstations.

Framework Application:

  1. Problem Decomposition: Separate the link budget analysis into: (a) Transmitter Power & Modulation, (b) Channel Path Loss & Impulse Response (using ray-tracing), (c) Receiver Sensitivity & Field-of-View.
  2. ADR Benefit Quantification: For each workstation location, simulate the received signal strength and delay spread using a wide-FOV receiver and the 4-branch ADR. Calculate the potential SNR improvement and ISI reduction offered by the ADR's ability to reject late-arriving reflections.
  3. NOMA User Grouping: Cluster users into NOMA pairs/groups based on their channel gain disparity, which is now more pronounced and reliable due to the ADR's cleaner channel estimates.
  4. System-Level Simulation: Run a Monte Carlo simulation varying user activity and data demands. Compare the total network throughput and 5th-percentile user rate (a fairness metric) for the ADR-NOMA system vs. a traditional OFDMA-VLC system with wide-FOV receivers.
This framework allows a network designer to systematically evaluate the cost-benefit of deploying more complex ADR hardware against the promised capacity gains.

9. Future Applications & Research Directions

  • 6G Li-Fi Backhaul/Downlink: ADR-NOMA VLC is a prime candidate for high-density downlink in future 6G networks, complementing RF in stadiums, airports, and factories. Its resistance to RF interference is a key advantage.
  • Ultra-Reliable Industrial IoT: In automated warehouses or manufacturing lines, where low latency and reliability are critical, ADRs can provide robust links for machine-to-machine communication, with NOMA supporting massive sensor connectivity.
  • Underwater Optical Communications: The scattering environment underwater is analogous to a severe multipath scenario. ADRs could significantly improve the range and reliability of blue/green laser comms for autonomous underwater vehicles.
  • Research Directions:
    • Intelligent ADRs: Using micro-electromechanical systems (MEMS) or liquid crystal-based beam steering for continuous, fine-grained angle adjustment rather than fixed branches.
    • Cross-Layer Optimization: Jointly optimizing physical-layer ADR selection with medium-access control (MAC) layer scheduling and NOMA user clustering.
    • Hybrid RF/VLC Systems: Investigating how ADR-NOMA VLC can be seamlessly integrated with mmWave or sub-6 GHz RF in a heterogeneous network, with intelligent traffic offloading.

10. References

  1. Z. Ghassemlooy, W. Popoola, S. Rajbhandari, Optical Wireless Communications: System and Channel Modelling with MATLAB®, CRC Press, 2019. (Authority on VLC channel modeling)
  2. L. Yin, et al., "Non-orthogonal multiple access for visible light communications," IEEE Photonics Technology Letters, vol. 28, no. 1, 2016. (Seminal paper on NOMA-VLC)
  3. J. M. Kahn, J. R. Barry, "Wireless infrared communications," Proceedings of the IEEE, vol. 85, no. 2, 1997. (Foundational review)
  4. T. Fath, H. Haas, "Performance comparison of MIMO techniques for optical wireless communications in indoor environments," IEEE Transactions on Communications, vol. 61, no. 2, 2013. (Covers diversity techniques)
  5. IEEE Standard for Local and Metropolitan Area Networks–Part 15.7: Short-Range Optical Wireless Communications, IEEE Std 802.15.7-2018. (Relevant standard)
  6. M. O. I. Musa, et al., "Resource Allocation in Visible Light Communication Systems," Journal of Lightwave Technology, 2022. (Authors' prior work, ref [36])
  7. PureLiFi. "Li-Fi Technology." https://purelifi.com/ (Industry leader in VLC commercialization)
  8. Z. Wang, et al., "Angle diversity receiver for MIMO visible light communications," Optics Express, vol. 26, no. 10, 2018. (Specific ADR implementation study)