## 4 Experimental Results and Discussion  
Figure 5 shows the spectrum of sub-terahertz 16 QAM signals with sampling rates of 80 GHz, intermediate frequencies of approximately 12.8 GHz, and baud rates of 10 Gbaud, 20 Gbaud, and 30 Gbaud, respectively. From the spectral diagrams, it is evident that the signal spectrum shifts upward at different baud rates, specifically showing that the actual frequency is slightly higher than the anticipated 12.80 GHz. This phenomenon may be attributed to the phase noise and frequency instability caused by the laser linewidth. A larger laser linewidth leads to higher phase noise, which increases the phase noise in the beat frequency signal, subsequently causing frequency drift and jitter. It could also be due to insufficient frequency stability in the laser itself, resulting in an output frequency higher than expected, possibly caused by internal noise or temperature fluctuations. Additionally, frequency-selective fading in fiber transmission or terahertz channels may also cause the spectrum to shift upward. These  
Figure 4: **(a)** Schematic diagram of a 4.6 km 2 \(\times\) 2 MIMO photonic-assisted terahertz communication system architecture in the D-band; **(b)** flowchart of the digital signal processing at the receiving end; and **(c)** the 4.6 km 2 \(\times\) 2 MIMO photonic-assisted terahertz experimental setup. **(I)** Transmitter, before single-mode fiber, **(II)** transmitter, after single-mode fiber, **(III)** receiver, signal processing, and **(IV)** receiver, lens. factors increase inter-symbol interference (ISI) and phase errors, impacting the accuracy of signal demodulation and increasing the BER. Therefore, advanced DSP algorithms need to be employed at the receiving end to compensate for these effects and improve the BER. At the receiving end, we employed traditional DSP demodulation methods. The BERs obtained for the 10 Gbaud, 20 Gbaud, and 30 Gbaud signals in both polarization directions after applying DDLMS were 0.0426, 0.0362, 0.0773, 0.0776, 0.1826, and 0.1856, respectively. After applying the MIMO-VNLE, the BERs were measured as 0.00392, 0.00378, 0.00843, 0.00827, 0.0221, and 0.0192, respectively. Since the results of the MIMO-VNLE did not meet the hard decision threshold requirements, we employed a three-layer neural network after the DDLMS to further reduce the BER. The network structure was \(n_{i}\)-360-260-1, meaning the first layer had 360 neurons, the second layer had 260 neurons, and the output layer had 1 neuron. The data length was 98304, and the length of the training data was 32768. By analyzing Figure 6, we discern the neural network's convergence trend, and note that the average loss for the 30 Gbaud data is initially significantly higher than that for the 10 Gbaud and 20 Gbaud data, but eventually, the average loss for all three rates converges to a similar level. This phenomenon arises because data with higher baud rates exhibit more complex signal characteristics and noise patterns, resulting in higher initial losses. Consequently, the network requires more time at the outset to adapt to these features associated with higher baud rates. As training progresses, the network gradually learns to handle the characteristics of high-baud-rate data by adjusting its weights and parameters, leading to a reduction in loss until the average losses for all rates converge to a similar level. This indicates that the network is capable of effectively processing data at different rates while maintaining stable performance. Such convergence underscores the network's robust generalization capabilities and adaptability. Figure 5: Signal spectra: (**a**) V-pol-10 Gbaud, (**b**) V-pol-20 Gbaud, (**c**) V-pol-30 Gbaud, (**d**) H-pol-10 Gbaud, (**e**) H-pol-20 Gbaud, and (**f**) H-pol-30 Gbaud. Figure 6: Neural network training epochs vs. average loss. (**a**) V-pol and (**b**) H-pol. After processing with a three-layer neural network, the calculated BERs were 0.0025, 0.0021, 0.0081, 0.0063, 0.0093, and 0.0121, respectively.