We present a common quantum model describing noise generation and accumulation from both optical amplification and nonlinearity in long-haul WDM transmission, yielding predictions for optimal bit-error-rate and information-capacity performance
We analyze the information-capacity limitations of the optical communication channel, as determined by noise accumulation from optical amplification and nonlinear wave-mixing. We review the concepts of signal-to-noise ratio and entropy for binary-coded and continuous communications, leading to a definition of ultimate capacity for the optically-amplified channel. A unified quantum model, describing both amplification and nonlinearity limitations, makes possible to determine the power transmission window within which the channel capacity can be maximized.
In this letter, we investigate the performance of low-density parity-check (LDPC) codes in long-haul optical communication systems for carrier-suppressed return-to-zero signal format at 40 Gb/s per channel in a wavelength-division-multiplexing environment. We are particularly concerned with high-rate codes based on projective geometries. These codes have large minimum distance and simple iterative decoding algorithms, which makes them good candidates for such high-speed application. We consider iterative decoding based on min-sum algorithm since it requires only simple addition and minimum operations and, as such, is suitable for high-speed optical transmission. Contrary to the common practice of considering the performance of error controlling schemes using the additive white Gaussian noise channel assumption, we consider the performance of the proposed LDPC schemes, taking into account in a natural way all major impairments in long-haul optical transmission such as amplifier spontaneous emission noise, pulse distortion due to fiber nonlinearities, chromatic dispersion or polarization dispersion, crosstalk effects, intersymbol interference, etc.
Achieving ultra-low Bit Error Rate (BER) in Dense Wavelength Division Multiplexing (DWDM) systems is crucial for next-generation optical networks. This paper proposes a novel AI-enhanced QPSK-WDM System that integrates hybrid deep learning equalization and Schrödinger-informed digital backpropagation to mitigate fiber nonlinearities and noise impairments. A CNN-LSTM equalizer is employed to compensate for dispersion and nonlinear distortions, outperforming traditional DSP-based techniques. Additionally, a Neural Network-Based Quantum-inspired LDPC Decoder enhances error correction beyond conventional methods. We introduce a GAN-based Optical Noise Predictor to further optimize transmission performance, enabling proactive compensation for system impairments. Simulation results demonstrate a BER improvement to 10⁻¹³, significantly outperforming conventional equalization and FEC techniques. These findings suggest that AI-driven optical systems can enhance spectral efficiency and system robustness, paving the way for practical deployment in high-capacity optical networks.