Adaptive Detection of Weak Light Signals in Underwater Laser Communication via Piecewise-Exponential Unsaturated Bistable Stochastic Resonance

We introduce a piecewise-exponential unsaturated bistable stochastic resonance (PEUBSR) model designed to enhance the detection of weak signals in underwater optical wireless communication (UOWC). To optimize the system performance, we develop an improved Newton-Raphson-based optimizer (INRBO) algorithm. This algorithm integrates an adaptive piecewise decision factor (DF) strategy with a Cauchy-Gaussian hybrid variance approach, thereby facilitating the adaptive optimization of parameters within a stochastic resonance system. Moreover, in real-world scenarios where prior information is challenging to obtain, we develop a novel metric: the correlation-phase weighted signal-to-noise ratio (CP-WSNR). This metric integrates a multidomain evaluation of the signal and demonstrates a robust anti-interference capability. The simulation results indicate a marked improvement in the weak-signal detection performance under high-noise conditions compared to the classical bistable stochastic resonance (CBSR) and piecewise unsaturated bistable stochastic resonance (PUBSR). Compared with PEUBSR systems based on other detection metrics, the PEUBSR system based on the CP-WSNR metric achieves a higher probability of successful detection at lower SNRs values, with a detection probability of 90% at an input SNR of −24 dB. Experimental results demonstrated that our method enhanced the SNR from −22.5370 to −9.8377dB, achieving a 12.6993 dB improvement in the SNR. The efficacy and robustness of the proposed method is verified by theoretical research, simulation, and the collected experimental data.