We propose a probabilistic shaping (PS) scheme based on a single-layer lookup table (LUT) that employs only one LUT for symbol mapping while achieving favorable system performance. This scheme reduces the average power of the signal by adjusting the symbol distribution using a specialized LUT architecture and a flexible shaping proportion. The simulation results indicate that the proposed PS scheme delivers performance comparable to that of the conventional constant-composition distribution-matching-based probabilistic shaping (CCDM-PS) algorithm. Specifically, it reduces the bit error rate (BER) from 1.2376 $\times {10}^{-4}$ to 6.3256 $\times {10}^{-5}$, corresponding to a 48.89% improvement. The radial basis function neural network (RBFNN) effectively compensates for nonlinear distortions and further enhances transmission performance due to its simple architecture and strong capacity for nonlinear learning. In this work, we combine lookup-table-based probabilistic shaping (LUT-PS) with RBFNN-based nonlinear equalization for the first time, completing the transmission of 16-QAM OFDM signals over a photonic terahertz-over-fiber system operating at 400 GHz. Simulation results show that the proposed approach reduces the BER by 81.45% and achieves a maximum Q-factor improvement of up to 23 dB.

Using DSM and LDPC coding, we successfully achieved error-free transmission for the PDM-1024QAM signal and high-quality transmission for the 2048QAM signal in the 400G photonic-assisted terahertz communication system. Without using LDPC, the BER of 1024-QAM signals can be reduced to 3.8e-3, while the BERs for 2048-QAM and 4096-QAM signals can be decreased to below 4.2e-2. When LDPC is employed for error correction, 1024-QAM signals achieve error-free transmission, and the BER of 2048-QAM signals can be further reduced to 9.8e-5.