Optical Wireless ISAC Integration

Updated 27 October 2025

Optical Wireless ISAC is the integration of high-speed optical communication and optical sensing using shared hardware like lasers and photodetectors.
It leverages advanced waveform designs and resource allocation techniques, including optical OFDM variants and dual-domain methods, to optimize data rates and sensing accuracy.
OW-ISAC supports multifunctional applications in beyond-5G/6G networks, enabling vehicular, IoT, and robotics systems through efficient spectrum use and dynamic environmental mapping.

Optical Wireless Integrated Sensing and Communication (OW-ISAC) refers to the integration of high-speed optical wireless communication (OWC) with optical sensing functionalities, leveraging the broad optical spectrum and unique propagation characteristics of light to enable simultaneous data transmission and environmental awareness. This paradigm is motivated by the quest for spectrum efficiency, multifunctionality, and ultra-reliable low-latency services envisioned for beyond-5G/6G networks. OW-ISAC architectures typically combine free-space optical (FSO) or visible light communication (VLC) links with sensing techniques such as laser radar, optical ranging, localization, and environmental mapping, aiming for shared hardware, spectrum, and advanced signal processing to jointly optimize sensing and communication performance.

1. System Architectures and Integration Principles

OW-ISAC system architectures are structured around unified transceivers and front-end components—such as shared laser diodes, photodetectors, and A/D converters—enabling dual communication and sensing functionality (Wen et al., 2023). At the transmitter, the modulated signal (encoding data and/or sensing waveform) undergoes DC biasing to satisfy intensity modulation/direct detection (IM/DD) requirements, then is launched by the optical source into free space. After propagation and reflection (possibly involving environmental targets or objects to be sensed), the signal is received at an optical front end, split between communication decoding and sensing parameter estimation (e.g., time-of-flight, Doppler shift).

OW-ISAC architectures may operate in various configurations:

Hybrid RF/Optical Integration: Combining OWC with RF technologies (e.g., LiFi-femtocell indoor hybrids, RF/optical vehicular networks) to exploit bandwidth and interference mitigation (e.g., integrated handover and zoning for improved SINR, group handover in vehicular applications) (Chowdhury et al., 2018).
Fog/Cloud Networking: Employing cloud/fog architectures to distribute signal processing tasks across optical access points and processing nodes to maximize SINR and minimize energy consumption, with resource allocation optimally modeled via mixed-integer linear programming (Alsulami et al., 2019).
Programmable Surfaces/IRSs: Incorporating optical intelligent reflecting surfaces (IRSs) or RISs for dynamic manipulation of propagation, power/spectral efficiency, and channel coupling control (Chepuri et al., 2022, Yesilkaya et al., 2023).
Cooperative and Multi-domain Designs: Supporting multi-user, multi-frequency cooperation, crowdsourced environmental mapping, and high-precision localization, potentially fusing sensing information across different spectral bands (Yang et al., 2021).

2. Waveform Design and Resource Allocation

OW-ISAC mandates waveforms that enable joint sensing and communication while respecting the non-negativity and real-valued requirements of IM/DD systems (Wen et al., 2023). Key classes include:

Pulsed Waveforms: E.g., pulse-position modulation (PPM) and m-sequence-based pulse patterns, providing ToF-based ranging while carrying data.
Constant-Modulus Waveforms: Subcarrier intensity modulation using schemes such as LFM-CPM (linear frequency modulation–continuous phase modulation), with DC bias adjusted for IM/DD.
Multi-carrier Waveforms: Optical OFDM variants (notably DC-biased optical OFDM, DCO-OFDM), supporting high spectral efficiency, with joint optimization for power allocation, DC bias setting, and per-subcarrier tradeoff between BER (comm.) and Cramér–Rao Bound (sensing) (Wen et al., 2023).
Dual-domain ISAC Waveforms: The superposition of a conventional OFDM signal (confined to in-band resources by regulation) and a low-power, high-resolution sensing waveform designed in the delay-Doppler domain and mapped to frequency-time using DFT-IDFT transformations (Tagliaferri et al., 2022). This allows virtual bandwidth extension for sensing, achieving sharper CRB on delay estimation with minimal penalization of achievable rate; the sensing signal is typically downscaled by ≥30 dB relative to the communication signal.
OTFS: Orthogonal Time Frequency Space modulation to mitigate Doppler spread and inter-carrier interference (ICI), mapping symbols natively onto the delay–Doppler domain, synergistically supporting communications and radar-style operations (Shtaiwi et al., 15 Feb 2024).

Resource allocation must consider the hardware-constrained signaling space (e.g., real, non-negative intensity), the dynamic range of optical modulators, and regulatory constraints such as adjacent channel leakage ratio (ACLR) limiting out-of-band emissions. Trade-offs are articulated mathematically: e.g., the distance resolution scales inversely with bandwidth (ΔR ∝ 1/B), and water-filling based power allocation must be balanced against clipping distortion and sensing performance metrics (Wen et al., 2023).

3. Sensing-Communication Trade-offs and Capacity Limits

OW-ISAC system performance is inherently a multi-objective trade-off, most precisely characterized via information-theoretic models (Wen et al., 20 Oct 2025, Lu et al., 2023):

Mutual Information and Channel Capacity: For systems such as FMCW-based OW-ISAC, channel capacity under a harmonic-mean constraint on the transmitted envelope X (i.e., E[1/X] ≤ ϑ) provides lower and upper bounds on achievable rates given non-negativity and sensing performance constraints (necessary to prevent excessive noise amplification when processing beat signals for ranging). The optimal envelope distribution transitions from Bernoulli (2-PAM) at low SNR to truncated exponential (max-entropy subclass) at high SNR (Wen et al., 20 Oct 2025).
Pareto-Optimal Waveform Design: Joint design is formulated as maximizing a weighted sum of communication and sensing mutual informations:

$\operatorname{maximize} \quad \rho I(X;Y_c|H_c) + (1-\rho)I(H_s;Y_s|X) \quad \text{subject to constraints}$

where ρ ∈ [0,1] balances priorities (Lu et al., 2023).

Resolution Metrics: Sensing performance (e.g., delay/angle/Doppler estimation accuracy) is limited by waveform bandwidth and signal structure, often quantified by the Cramér–Rao Bound (CRB). As the smoothing parameter (harmonic-mean constraint, envelope amplitude, bandwidth) is tightened, capacity may decrease but range/radial error improves (Wen et al., 20 Oct 2025, Tagliaferri et al., 2022).

Numerical studies confirm that dual-domain and multi-carrier designs (e.g., DCO-OFDM with optimized resource allocation) can approach the theoretical capacity limits while enabling sub-centimeter ranging and high data rates concurrently (Tagliaferri et al., 2022, Wen et al., 2023, Wen et al., 20 Oct 2025).

4. Advanced Signal Processing and Practical Implementations

Practical OW-ISAC systems require efficient signal processing to extract both data and sensing parameters with low latency and hardware overhead. Notable advancements include:

Phase Coding and Anti-Noise Processing: Applying codes such as Golay sequences to enhance SNR and reduce sidelobes/ambiguity, directly reducing the CRB (for distance estimation) and supporting coherent integration (Wei et al., 2023).
Iterative Algorithms: Short- and long-range sensing can exploit low-complexity iterative 2D FFT and cyclic cross-correlation algorithms, significantly reducing root mean square error (RMSE) in distance and velocity estimation, even with high bandwidth (Wei et al., 2023).
Cloud/Fog Optimization: Resource assignment for both optical and processing resources is formulated and solved via MILP, distributing communication and computational tasks according to real-time load, latency, and energy consumption (Alsulami et al., 2019).
Hybrid Visual/Optical Sensing: Integrated schemes (e.g., OISAC for cooperative mobile robotics) combine visual sensing (camera-based pose estimation) and SCC to achieve ultra-low-latency real-time formation control with quantifiable improvements over EKF-based approaches (Wang et al., 2023).
Emerging Hardware: Advanced detectors and arrays (VCSELs, photodiodes, metasurface-based IRS panels) support dynamic beam steering, programmable propagation, and fine-grained sensing/communication control (Yesilkaya et al., 2023, Krishnamoorthy et al., 21 Dec 2024).

5. Environmental Control and Reconfigurable Surfaces

The deployment of intelligent reflecting surfaces (IRSs) or RISs in OW-ISAC systems enables dynamic environmental reconfiguration for coverage, channel conditioning, and coupling optimization between communication and sensing paths (Chepuri et al., 2022, Yesilkaya et al., 2023):

Fundamentals: IRSs comprise sub-wavelength controllable “meta-atoms” that can impose spatially-programmable amplitude/phase shifts, steering reflected beams, producing anomalous reflection, or multiplexing by wavelength decoupling.
System Models: The electromagnetic response is characterized by a complex reflection coefficient,

$\Gamma(\theta, \lambda) = A(\theta, \lambda) e^{j\phi(\theta, \lambda)}$

The overall channel is modeled as a cascaded product across Tx–RIS and RIS–Rx, with performance optimized via beamformer / phase shift design (typically under unit-modulus constraints).

Enhanced Sensing and Communication: IRSs can increase DC channel gain, induce tailored multipath, and dramatically boost achievable rates and SNR for targets in shadowed or NLoS zones. Controlled RMS delay spread and individualized control per user/device are essential for robust ISAC operation.
Challenges: Include scalable and accurate channel state information (CSI) acquisition for ultra-large IRSs, mitigation of inter-symbol interference due to increased multipath, and real-time optimization—often requiring machine learning or dynamic programming approaches.

6. Challenges, Trade-offs, and Future Research Directions

While OW-ISAC offers substantial performance enhancements—particularly in spectral efficiency, spatial resolution, and interference reduction—a number of challenges remain:

Device/Physics Constraints: Etendue constrains the simultaneous attainment of high gain and wide FOV—limiting robust, non-directional networking (Krishnamoorthy et al., 21 Dec 2024); optical-to-electrical conversion bandwidth is a critical bottleneck for exploiting the available spectrum.
Algorithmic Complexity: Delay–Doppler domain and IRS/RIS-enabled systems demand adaptive and computationally efficient signal processing and channel estimation algorithms tailored to high-bandwidth, low-latency operation (Shtaiwi et al., 15 Feb 2024, Yesilkaya et al., 2023).
Resource Optimization: Unified performance metrics (e.g., Pareto-optimal fronts) for communication and sensing remain an open problem, as does cross-layer resource allocation and the design of security/privacy-protecting ISAC protocols (Lu et al., 2023, Wen et al., 2023).
Diversity and Interoperability: Operational scenarios
- Hybrid RF–optical solutions: Mitigating blockages and blind spots inherent in highly directional optical links (e.g., via collaborative RF fallbacks or dual-band architectures) (Wen et al., 2023).
- SLIPT/energy harvesting: Balancing energy transfer and high-bandwidth detection requirements, especially via photovoltaic receivers (Krishnamoorthy et al., 21 Dec 2024).
New Application Domains: OW-ISAC is poised to transform vehicular (V2X), drone/satellite, underwater, IoT, and mobile robotics networks by providing centimeter-level positioning, collision avoidance, and non-contact gesture recognition with integrated communication (Wang et al., 2023, Krishnamoorthy et al., 21 Dec 2024).
Emerging Technologies: OAM-based modulation, IRS/RIS/ORIS, optical phased arrays, and deep learning for adaptive resource optimization and semantic-aware ISAC are key areas of ongoing research (Wen et al., 2023, Krishnamoorthy et al., 21 Dec 2024).

Future research will center on bridging hardware advances (miniaturized OPAs, high-speed photodetectors) with theoretical and algorithmic foundations (joint waveform/resource optimization under real-world constraints), developing scalable architectures for multi-user and multi-node cooperation, and extending these principles to multi-application, resilient, and energy-efficient optical network-of-networks.

The convergence of optical communication and sensing within the OW-ISAC framework is poised to become a cornerstone of advanced wireless networks. Its realization requires continued exploration of physical-layer integration, joint optimization strategies, adaptive resource management, robust signal processing, and cross-disciplinary innovations spanning hardware, algorithms, and network architectures.