# Optical ISAC: Fundamental Performance Limits and Transceiver Design

arXiv: https://arxiv.org/abs/2408.11792 · HTML: https://mlsanity.com/paper/2408.11792/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 21 Aug 2024.

## Builds on (most cited first, 7 of 7)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Fundamental CRB-Rate Tradeoff in Multi-Antenna ISAC Systems with Information Multicasting and Multi-Target Sensing](https://mlsanity.com/paper/2307.11337/) (2023)
- [Seventy Years of Radar and Communications: The Road from Separation to Integration](https://mlsanity.com/paper/2210.00446/) (2022)
- [Outage Tradeoff Analysis in a Downlink Integrated Sensing and Communication Network](https://mlsanity.com/paper/2305.15388/) (2023)
- [Joint User Association and UAV Location Optimization for Two-Tired Visible Light Communication Networks](https://mlsanity.com/paper/2306.01482/) (2023)
- [Optical Integrated Sensing and Communication: Architectures, Potentials and Challenges](https://mlsanity.com/paper/2312.13640/) (2023)
- [Probabilistic Constellation Shaping for Enhancing Spectral Efficiency in NOMA VLC Systems](https://mlsanity.com/paper/2402.13715/) (2024)

## Abstract (arXiv)

This paper characterizes the optimal capacity-distortion (C-D) tradeoff in an optical point-to-point system with single-input single-output (SISO) for communication and single-input multiple-output (SIMO) for sensing within an integrated sensing and communication (ISAC) framework. We consider the optimal rate-distortion (R-D) region and explore several inner (IB) and outer bounds (OB). We introduce practical, asymptotically optimal maximum a posteriori (MAP) and maximum likelihood estimators (MLE) for target distance, addressing nonlinear measurement-to-state relationships and non-conjugate priors. As the number of sensing antennas increases, these estimators converge to the Bayesian Cram'er-Rao bound (BCRB). We also establish that the achievable rate-Cram'er-Rao bound (R-CRB) serves as an OB for the optimal C-D region, valid for both unbiased estimators and asymptotically large numbers of receive antennas. To clarify that the input distribution determines the tradeoff across the Pareto boundary of the C-D region, we propose two algorithms: i) an iterative Blahut-Arimoto algorithm (BAA)-type method, and ii) a memory-efficient closed-form (CF) approach. The CF approach includes a CF optimal distribution for high optical signal-to-noise ratio (O-SNR) conditions. Additionally, we adapt and refine the deterministic-random tradeoff (DRT) to this optical ISAC context.