This paper characterizes the optimal capacity-distortion (C-D) tradeoff in an optical point-to-point (P2P) system with single-input single-output for communication and single-input multiple-output for sensing (SISO-SIMO-C/S) within an integrated sensing and communication (ISAC) framework. 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. Our results show these estimators converge to the Bayesian Cramer-Rao bound (BCRB) as sensing antennas increase. We also demonstrate that the achievable rate-CRB (AR-CRB) serves as an outer bound (OB) for the optimal C-D region. To optimize input distribution across the Pareto boundary of the C-D region, we propose two algorithms: an iterative Blahut-Arimoto algorithm (BAA)-type method and a memory-efficient closed-form (CF) approach, including a CF optimal distribution for high optical signal-to-noise ratio (O-SNR) conditions. Additionally, we extend and modify the Deterministic-Random Tradeoff (DRT) to this optical ISAC context.
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