## 4 Experimental Evaluation
### Results and Performance Analysis  
In this experimental section, we compare the BER performance of two receiver setups within a two-transmitter, one-receiver W-band photonic mmWave communication system: a conventional receiver using cascaded matched filters and an LMS equalizer as well as an NNWSC-based receiver. In total, 81,920 data sets are used for training the NNWSC receiver; 80% of the data are used as the training set, and 20% of the data are used as the test set. The data used for evaluation are different from the data used for training the NNWSC receiver. After the completion of training the NNWSC receiver, we changed the random seed and regenerated the QAM symbols for transmission. We selected a Vpp (peak-to-peak voltage) value of 200 mV as the operating voltage for experiments. Vpp represents the amplitude of the electrical signal, defined as the difference between the maximum and minimum voltage levels of the waveform. This value was chosen to provide an optimal balance between signal strength and system performance, ensuring reliable data transmission while minimizing nonlinear distortions. To more accurately emulate the practical system, we introduced optical fiber links of varying lengths for transmission in the two channels. Channel 1, responsible for transmitting S1 and S3, was equipped with a 10 km fiber link, while Channel 2, which carries S2, used a 5 km fiber link. The relationship between BER and the sub-band spacing factor in the experiment is shown in Figure 6. The overall BER of S3 is slightly lower than that of S1, which  
Figure 5: The experimental setup and offline DSP (AWG: arbitrary waveform generator; ECL: external cavity laser; EA: low-noise amplifier; MZM: Mach–Zehnder modulator; EDFA: erbium-doped fiber amplifier; PD: photodetector; LO: local oscillator; PA: power amplifier; LNA: low-noise amplifier; OSC: oscilloscope). (**a**) shows the spectrum of the output signals from AWG Channel 1. (**b**) shows the spectrum of the output signal from AWG Channel 2.  
can be attributed to the combined effects of hardware limitations and the comparatively lower interference experienced by the sub-band with the highest center frequency. The experimental results are in good agreement with the simulation results. To validate the rationality of the NNWSC, we conducted an additional experiment to investigate the impact of increasing the number of hidden layers and neurons on model performance. Specifically, we added a hidden layer with 20 inputs and 10 outputs to the existing model and measured the BER performance of the model with an additional hidden layer (2 HL) compared to the current model with only one hidden layer (1 HL) at a roll-off factor of 0.4 and multiple sub-band spacing factors. The experimental results are shown in Figure 7. From the experimental results, it can be observed that there is no significant difference in BER performance between the 2 HL and 1 HL models after adding the hidden layer. This indicates that under the current signal complexity, a single hidden layer is already sufficient to capture the characteristics of the signal, and further increasing the number of hidden layers does not lead to a noticeable performance improvement. Additionally, adding hidden layers increases the algorithm complexity, thereby affecting computational efficiency. Therefore, in the current scenario, a model with one hidden layer is already adequate to meet the requirements, and adding more hidden layers not only fails to significantly enhance performance but may also introduce unnecessary computational overhead. This result further validates the rationality and efficiency of the current model design. Figure 6: **(a)** The relationship between the BER and sub-band spacing factor per sub-band at a roll-off factor of 0.4; **(b)** constellation diagrams at sub-band spacing factors of 0.2. Figure 7: The relationship between the BER and sub-band spacing factor of two models.