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On Optimizing VLC Networks for Downlink Multi-User Transmission: A Survey

Mohanad Obeed, Anas M. Salhab, Mohamed-Slim Alouini, Salam A. Zummo

arXiv:1808.05089v1cs.IT

TL;DR

Rising traffic and crowded RF networks motivate VLC as a complementary or replacement technology for future high-rate wireless systems. This survey synthesizes VLC channel, optimization, access, harvesting, coordination, and security research, then identifies open problems centered on performance objectives including sum rate, fairness, energy efficiency, secrecy rate, and harvested energy.

  • Problem

    High-rate services and growing connected-device populations are stressing traditional RF and WiFi networks, while VLC still faces coverage, blockage, handover, interference, and standardization challenges.

  • Method

    The paper reviews VLC channel-capacity derivations, performance metrics, network optimization techniques, multiple-access schemes, energy harvesting, and security research.

  • Results

    The survey identifies open research problems for optimizing VLC networks across sum rate, fairness, energy efficiency, secrecy rate, and harvested energy.

  • Takeaways & Limitations

    VLC is presented as a potential complement to or replacement for RF networks, with future work needed for integrated, optimized wireless systems.

Abstract

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The evolving explosion in high data rate services and applications will soon require the use of untapped, abundant unregulated spectrum of the visible light for communications to adequately meet the demands of the fifth-generation (5G) mobile technologies. Radio-frequency (RF) networks are proving to be scarce to cover the escalation in data rate services. Visible light communication (VLC) has emerged as a great potential solution, either in replacement of, or complement to, existing RF networks, to support the projected traffic demands. Despite of the prolific advantages of VLC networks, VLC faces many challenges that must be resolved in the near future to achieve a full standardization and to be integrated to future wireless systems. Here, we review the new, emerging research in the field of VLC networks and lay out the challenges, technological solutions, and future work predictions. Specifically, we first review the VLC channel capacity derivation, discuss the performance metrics and the associated variables; the optimization of VLC networks are also discussed, including resources and power allocation techniques, user-to-access point (AP) association and APs-to-clustered-users-association, APs coordination techniques, non-orthogonal multiple access (NOMA) VLC networks, simultaneous energy harvesting and information transmission using the visible light, and the security issue in VLC networks. Finally, we propose several open research problems to optimize the various VLC networks by maximizing either the sum rate, fairness, energy efficiency, secrecy rate, or harvested energy.

I. INTRODUCTION

VLC is presented as a promising complement or alternative to congested RF networks, using abundant visible-light spectrum and energy-efficient LEDs. The paper surveys optimization techniques, emerging RF-network technologies adapted to VLC, and open research challenges.

  • Motivation: Rapidly growing traffic and crowded RF/WiFi networks motivate higher-capacity 5G technologies, including VLC.Traffic is reported at approximately 7.24 exabyte-per-month in 2016 and predicted to reach 48.95 exabyte-per-month in 2021.
  • VLC technology: VLC uses untapped visible-light spectrum and LEDs to transmit illumination and information signals, while respecting illumination and LED physical constraints.White LEDs offer high modulation performance, conversion efficiency, long life, low cost, and high operational speed.
  • Challenges: VLC still faces limited LED bandwidth, channel-estimation and capacity challenges, blockage, distance-dependent intensity loss, and lighting-generated noise or interference.Optimization and hybrid VLC/RF networking are identified as common ways to partially address these limitations.
  • Emerging technologies: The survey maps RF-network technologies into VLC, including NOMA, SWIPT, cooperative transmission, SDMA, and physical layer security.It also reviews energy harvesting and secure VLC communications as distinct research areas.
  • Survey scope: The paper reviews VLC optimization methods for power and resource allocation, user-to-AP association, cell formation, and AP cooperation.These methods target performance improvement and mitigation of VLC disadvantages.
  • Open problems: The paper identifies open research areas in NOMA-VLC, energy harvesting, and secure VLC networks while outlining future optimization directions.Its stated objectives include maximizing sum rate, fairness, energy efficiency, secrecy rate, or harvested energy.

II. FUNDAMENTALS OF VLC INDOOR SYSTEMS

Indoor VLC uses intensity-modulated optical signals transmitted by LEDs and detected by photodiodes or other optical receivers. Its channel includes direct line-of-sight and reflected components, while illumination and LED operating constraints shape communication design.

  • Channel and transmission fundamentals: VLC encodes information through positive, real-valued optical intensity rather than phase or frequency.The transmitted peak power is constrained by LED dynamic range and illumination requirements.
  • Transmitter and receiver: LED lamps serve both illumination and communication by varying the drive current to modulate brightness.The driver circuit changes the flowing-in current, which modifies light intensity.
  • Transmitter and receiver: Blue-phosphor LEDs offer lower cost and simpler implementation, whereas RGB LEDs provide higher reported data rates through color shift keying.Reported rates are 1 Gbps for blue-phosphor LEDs and 3.4 Gbps for RGB-based communication.
  • Transmitter and receiver: VLC receivers include photodetectors, imaging or camera sensors, and solar panels.Photodetectors convert received light intensity into a modulated current, while solar panels can operate without an external power supply.
  • Channel model: The indoor VLC channel combines a line-of-sight link with first-order reflected links, while higher-order reflections are treated as negligible.The line-of-sight gain depends on receiver area, distance, irradiance and incidence angles, optical filtering, and concentrator characteristics.

C. VLC Modulation Schemes

VLC modulation must support intensity modulation and direct detection using real, non-negative signals. The survey reviews OOK and optical OFDM variants while emphasizing VLC-specific channel and signal constraints.

  • Modulation principles: VLC modulation varies LED light intensity and uses direct photodetection because phase and frequency do not carry the data.On-off keying represents data with two light-intensity levels.
  • Optical OFDM: Nonlinear VLC channel responses can cause inter-symbol interference, requiring OFDM adaptations compatible with real, non-negative optical signals.Conventional complex bipolar OFDM signals must be transformed before optical transmission.
  • Optical OFDM: DCO-OFDM adds a positive direct-current bias and modulates all subcarriers to maximize spectral efficiency.The bias ensures that the transmitted signal remains non-negative.
  • Optical OFDM: ACO-OFDM modulates only odd subcarriers, producing a symmetric time-domain signal.This structure supports compatibility with the non-negative intensity-modulation requirement.
  • VLC-specific constraints: Traditional RF techniques cannot be directly applied because VLC optimization must account for dimming, peak intensity, illumination, and non-negative real-valued signals.The VLC channel is also modeled with geometrical parameters through a Lambertian channel model.

1) System Capacity or Sum Rate:

The survey distinguishes system capacity from achieved throughput and energy efficiency, then reviews resource and power-allocation strategies for optimizing these objectives under practical constraints.

  • System capacity or sum rate: VLC capacity derivations account for dimming, peak optical intensity, illumination, LED dynamic range, and non-negative real-valued input constraints.The common capacity expression estimates system capacity under VLC-specific optical restrictions.
  • Throughput: Actual throughput differs from system capacity because it depends on bit error rate, coding, modulation, and received signal-to-noise ratio.For OFDM, subcarrier spectrum efficiency incorporates the modulation scheme, coding scheme, and received SNR.
  • Throughput: OFDM throughput depends on modulation bandwidth, subcarrier count, subcarrier spectrum efficiency, and the OFDM-specific factor β.The survey also gives a TDMA expression based on assigned time slots and their spectrum efficiencies.
  • Energy efficiency: Energy efficiency can be optimized by resource allocation, power allocation, energy transfer, harvesting, and hardware solutions.An alternative formulation minimizes transmitted power under QoS constraints; Dinkelbach’s method converts non-convex EE maximization into sequential convex problems.
  • Energy efficiency: Energy efficiency is defined as system benefit divided by total consumed transmitter power, with sum rate used as the benefit in the stated formulation.The corresponding variables are total sum rate RT and total consumed power PT.

4) Fairness:

Fairness is central to VLC network optimization because distance-dependent channel loss and small coverage areas create unequal service conditions. The survey relates fairness objectives to Jain’s metric, proportional-fair utility, and illumination-aware constraints.

  • Fairness: VLC fairness is challenged by sharp distance-related channel loss and frequent frequency reuse across small cells, which can intensify interference.These conditions can leave users unable to move from crowded cells to less crowded ones.
  • Fairness metrics: Jain’s formula measures fairness for an individual cell and for the whole cellular system using user association and data-rate variables.The notation includes Ni users in cell i, Nap cells, and Rj,i for user j’s rate in cell i.
  • Fairness optimization: Fairness-aware optimization can maximize utility with proportional-fairness guarantees or incorporate QoS constraints.The generalized objective combines a utility function with a proportion factor α.
  • Fairness optimization: α = 0 ignores fairness, α = 1 yields proportional fairness, and α →∞ approaches max-min fairness.The proportion factor controls the balance between utility and fairness.
  • Required illumination constraints: VLC optimization must jointly consider communication and illumination, including peak optical power, dimming, flicker reduction, and LED dynamic-range limits.Dimming can support power saving through schemes such as M-PPM and variable OOK, while flicker frequency must exceed 200 Hz.
  • Coverage and illumination: Coverage probability is important because VLC LEDs cover small areas and coverage decreases sharply as transmitter-receiver distance increases.The user’s field of view increases coverage probability but decreases channel quality, making FoV optimization significant.

6) Coverage Probability:

Coverage probability depends strongly on VLC geometry and user FoV, while optical power creates a trade-off between desired signal strength and interference. VLC systems can also use LED light for simultaneous communication and energy harvesting, requiring joint parameter design.

  • Coverage Probability: Increasing optical power strengthens the serving AP-to-user link but also increases interference from other APs.
  • Coverage Probability: Coverage probability increases with user FoV, but channel quality decreases as FoV increases, making FoV optimization important.These effects are shown for different transmitter-receiver distances and zero irradiance and incidence angles.
  • The Harvested Energy: LEDs can simultaneously provide illumination, communication, and power transfer through received modulated light converted by a solar panel.The receiver can harvest energy without an external power supply.
  • The Harvested Energy: Energy-harvesting VLC networks must design parameters to compromise among illumination, communication, and energy-transfer functions.

8) Secrecy Capacity:

In multi-user VLC networks, secrecy capacity measures the rate advantage of legitimate receivers over eavesdroppers, while optimization must address VLC-specific assignment and coordination challenges. These challenges include limited coverage, blockages, handover, and inter-cell interference that can degrade service fairness.

  • 8) Secrecy Capacity: Secrecy capacity is the maximum legitimate-receiver information rate minus the maximum eavesdropper information rate.
  • 8) Secrecy Capacity: Under amplitude constraints, capacity-achieving input distributions are difficult to determine, although lower and upper bounds can be obtained.
  • 8) Secrecy Capacity: Multi-user VLC optimization considers access-point assignment, resource management, power allocation, and AP coordination to maximize objectives under system constraints.
  • 8) Secrecy Capacity: Small coverage areas, non-line-of-sight failures, frequent handovers, and inter-cell interference can produce uneven service quality and fairness.Objects may block line-of-sight links while also blocking interference from adjacent APs.

A. Optimizing Hybrid VLC/RF Networks

Hybrid VLC/RF optimization distributes users and resources across complementary AP types to balance VLC’s capacity advantages against RF’s broader coverage and robustness. The reviewed approaches include association, resource allocation, mobility and blockage handling, heuristic and learning-based load balancing, and joint AP association with power allocation.

  • User association and load balancing: Hybrid VLC/RF networks assign users according to channel conditions, coverage, interference, blockages, and mobility to improve overall performance.RF APs can serve users affected by VLC blockages or interference, while other users remain on VLC APs.
  • User association and load balancing: Load balancing jointly addresses AP assignment and resource allocation, including TDMA time slots or OFDMA sub-carriers.Users may be distributed dynamically between RF and VLC APs, with resources allocated among the associated users.
  • Algorithmic load balancing: Dynamic-system studies compare optimization, evolutionary game theory, and fuzzy-logic approaches, with fuzzy logic outperforming the alternatives when handover is considered.For static systems, optimization-based algorithms provide the best performance, with only a slight improvement over evolutionary game theory.
  • Joint optimization: Joint AP association and power-allocation algorithms are designed to improve both system capacity and fairness through iterative user and power distribution.The assignment, power allocation, and exact interference information are treated as interlinked problems.
  • Performance objectives: Hybrid-network studies also optimize energy efficiency, QoS-constrained power and bandwidth, outage probability, coverage, and user-rate performance.Reported formulations include Dinkelbach and sub-gradient methods for nonconvex problems and convex optimization for total-power minimization.

B. Optimizing the Standalone VLC Networks

Standalone VLC optimization addresses coverage, handover, blockage, interference, and performance limitations through RF support, deployment design, resource allocation, AP cooperation, and user-centric cell formation.

  • RF networks commonly supplement standalone VLC to address handover, line-of-sight blockages, coverage, and inter-cell interference.
  • AP arrangement: Hexagonal AP deployment achieved the best performance, while random deployment achieved the worst, based on SINR distributions, outage probabilities, and data rates.Co-channel interference had a stronger effect than multipath, and VLC generally outperformed indoor RF and mmWave networks.
  • Resource allocation: OFDMA resource allocation jointly considers DC bias, power, and subcarriers, while accounting for frequency-dependent channel quality.Several algorithms trade performance against complexity, and lower-frequency channels generally provide better quality.
  • AP cooperation: AP cooperation can beamform signals, mitigate interference, increase spatial diversity and coverage, reduce handover overhead, and decrease received-SNR fluctuations.Coordinated transmitters can form one cell through joint transmission.
  • Interference mitigation: Precoding and SDMA techniques address VLC-specific interference and non-negative signal constraints in multi-user MISO and MIMO systems.Reported approaches include zero-forcing, dirty-paper coding, block diagonalization, adaptive precoding, and spatially separated beams.
  • Cell formation: User-centric cell formation is suited to dense VLC networks with fewer users than APs, using user clustering, AP association, and power allocation.Related methods target fairness, sum utility, energy efficiency, and reduced inter-cell interference.

IV. NOMA IN VLC

NOMA multiplexes multiple VLC users on shared resources using power-domain separation and successive interference cancellation. Its application must account for VLC bandwidth, illumination, blockage, distance, and channel-control constraints.

  • NOMA targets higher throughput, lower latency, improved fairness, and better connectivity by sharing one resource component among multiple users.Examples include power-domain, pattern-division, and sparse-code multiple access.
  • Successive interference cancellation: Successive interference cancellation lets users decode superposed signals, but decoding complexity increases with the number of users and residual interference can remain.The lowest-channel-gain users receive the highest power coefficients.
  • VLC constraints: NOMA-VLC design must consider limited LED bandwidth, illumination-constrained transmit power, blockages, and channel deterioration with distance.Receiver FoV and tunable transmitter semi-angles can be selected to improve performance.
  • Evaluation scenarios: Figures 8 and 9 simulate NOMA versus OMA for two users and one AP under changing weak-user distance, incidence angle, and irradiance angle.The cited studies also compare NOMA-VLC with OMA under target-rate and opportunistic-rate scenarios.
  • Multi-AP NOMA: Multi-AP and multi-cell NOMA studies use gain-ratio or static power allocation and interference-based resource-block assignment.Reported settings include two APs with three users and frequency reuse FR = 2.

V. ENERGY HARVESTING IN VLC SYSTEMS

VLC energy harvesting uses optical light to support power transfer alongside communication and illumination, while security research addresses information leakage through direct and reflected paths.

  • Energy harvesting converts RF signals or light intensity into electrical voltage or current, supporting growing IoT power-transfer demands.
  • Harvesting mechanisms: VLC can harvest energy from the transmitted DC component, which is separated from the modulated signal using capacitors or related receiver circuitry.Solar cells can support simultaneous information reception and energy harvesting.
  • Spatial energy harvesting: Indoor harvested energy depends on receiver FoV; simulations considered an 8 × 8 room with 16 VLC APs.
  • Optical power transfer: Laser-diode optical power transfer improved efficiency over inductive transfer by approximately 2.7 times and delivered 7.2 W over 30 m using 42 laser diodes.
  • Hybrid systems: Hybrid VLC/RF systems can transfer data and energy to a relay optically, after which the relay forwards data using harvested energy.
  • Security: VLC physical-layer security must account for reflected light, channel correlation, and eavesdropper placement because reflections can enable unauthorized information access.Secrecy outage probability depends on legitimate-user position, LED design, and eavesdropper location relative to reflecting points.

VII. SUMMARY AND OPEN RESEARCH PROBLEMS

The paper reviews optimization techniques for hybrid and standalone VLC networks across coordination, NOMA, energy harvesting, and eavesdropping scenarios, then identifies challenges for future investigation.

  • The review covers hybrid VLC/RF networks, standalone coordinated VLC networks, NOMA-VLC networks, energy-harvesting users, and eavesdroppers.
  • Future work should investigate the challenges and open research problems identified from existing VLC-network research.

A. NOMA-VLC Networks

This section surveys NOMA-VLC research and identifies unresolved challenges involving user grouping, QoS diversity, cooperation, modulation, coordinated transmission, and hybrid SDMA/NOMA designs.

  • NOMA-VLC Networks: Hybrid NOMA groups users into multiple clusters and assigns each cluster a resource block, potentially reducing decoding complexity for large user populations.Without clustering, the strongest user may need to decode all users’ signals before decoding its own.
  • NOMA-VLC Networks: Power allocation for users with different QoS requirements remains challenging, particularly when weak-channel users require higher data rates.Applications may range from video streaming to low-rate IoT sensing.
  • NOMA-VLC Networks: Open directions include cooperative NOMA, where strong users or relays forward weak users’ signals, and modulation and coding schemes tailored to VLC’s IM/DD constraints.Cooperation is intended to compensate weak users affected by co-channel interference.
  • NOMA-VLC Networks: CoMP and hybrid SDMA/NOMA designs require decisions about user ordering, grouping, precoding, and directional LED assignments to manage inter-cell interference.SDMA uses angle-diversity transmitters to generate parallel narrow beams directed toward users.

B. Harvesting the Energy in VLC Systems

This section reviews energy harvesting and information transfer in VLC, emphasizing joint optimization of illumination, resources, bias, user placement, and transmission strategy. It also identifies security and deployment challenges for future VLC systems.

  • Harvesting the Energy in VLC Systems: Simultaneous light-wave information, illumination, and power transfer may violate illumination requirements, motivating joint optimization of DC bias, transmit power, and available resources.The proposed research direction treats the three light functions simultaneously.
  • Harvesting the Energy in VLC Systems: Joint DC-bias and resource allocation is proposed to improve VLC performance while preserving energy that users can harvest under SLIPT.The passage frames effective user-resource allocation as a way to maintain high harvestable energy.
  • Harvesting the Energy in VLC Systems: Cooperative NOMA-VLC could let a strong user harvest energy first and then use it to forward a weak user’s signal, requiring joint DC-bias and information-power optimization.This approach addresses the strong user’s reluctance to spend its own power on forwarding.
  • Harvesting the Energy in VLC Systems: For cooperating multi-AP MISO-VLC systems with more users than APs, the choice between OMA and NOMA affects scheduling, required information power, DC bias, and harvested energy.The passage reports that NOMA can achieve required user data rates with less information power than OMA.
  • Harvesting the Energy in VLC Systems: Placing energy-harvesting users remains challenging because their positions must support maximum harvested energy while preserving required QoS for information users.The setting includes information-only users and uplink IoT devices focused on harvesting energy.
  • Harvesting the Energy in VLC Systems: Security research must address beamforming with active or passive eavesdroppers, NOMA-VLC physical-layer security, and user-centric cell formation when eavesdropper CSI is incomplete or unavailable.Open questions include clustering users, associating APs, and selecting participating or inactive APs.
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