# DeepGOMIMO: Deep Learning-Aided Generalized Optical MIMO with CSI-Free Detection
## 4 Simulation Results
### Impact of Input Pre-Processing  
It can be seen from Figure 2 that the preprocessing module, which preprocesses the input of the feedforward DNN module, plays a vital role to guarantee that the proposed CSI-free DNN detector can successfully perform MIMO detection without the need of CSI. In the next, we evaluate the impact of input preprocessing on the performance of the CSI-free DNN detector. Here, two different inputs of the feedforward DNN module are considered: one is \(\alpha\mathbf{y}\), i.e., the preprocessing module only performs amplitude scaling, and the other is \(\alpha\mathbf{F}\mathbf{y}\), i.e., the preprocessing module performs both amplitude scaling and feature extraction. Figure 6 compares the BER performance of the proposed CSI-free DNN  
Figure 5: BER comparison of the proposed CSI-free DNN detector and three benchmark detectors for GOSMP at (**a**) the center and (**b**) the corner.  
detector where the feedforward DNN module having different inputs for both GOSM and GOSMP with the receiver located at the center of the receiving plane. As we can see, for GOSM, the BER performance is only slightly improved when the input is changed from \(\alpha\mathbf{y}\) to \(\alpha\mathbf{Fy}\), and the SNR gain at BER = \(10^{-3}\) is only 0.8 dB. In contrast, for GOSMP, a noticeable BER improvement can be obtained by replacing the input \(\alpha\mathbf{y}\) with \(\alpha\mathbf{Fy}\), and the corresponding SNR gain at BER = \(10^{-3}\) is increased to 2.4 dB. The difference between BER improvements for GOSM and GOSMP can be explained as follows. As discussed in Section 3.4, because the feature matrix \(\mathbf{F}\) contains the spatial mapping information of GOSM, the feedforward DNN module can use the spatial mapping information to remove the channel crosstalk. As a result, the feedforward DNN module with input \(\alpha\mathbf{Fy}\) can efficiently mitigate the adverse effect of error propagation. However, in GOSM systems, the activated LEDs are used to transmit the same signal and hence error propagation only leads to reduced diversity gain, which might not significantly degrade the BER performance. In contrast, because the activated LEDs transmit different signals in GOSMP systems, error propagation leads to the missing of constellation information and hence results in significant BER degradation.  
Due to the substantial path loss during MIMO transmission, the received signal needs to be properly amplified before it can be fed into the feedforward DNN module. Figure 7a,b show the BER versus Log\({}_{10}\alpha\) with different transmitted SNRs for GOSM and GOSMP, respectively. For GOSM, as shown in Figure 7a, we can observe that a feasible range of \(\alpha\) is around [\(10^{5}\), \(10^{7}\)] when the receiver is located at the center of the receiving plane. Moreover, the feasible range of \(\alpha\) keeps the same for different transmitted SNR values. When the receiver is moved to the corner, the feasible range of \(\alpha\) is [\(10^{5}\), \(10^{8}\)]. For GOSMP, as shown in Figure 7b, the same feasible range of \(\alpha\) is obtained as that of GOSM when the receiver is located at the center of the receiving plane. However, the feasible range of \(\alpha\) for GOSMP is only around \(10^{6}\) when the receiver is moved to the corner. To successfully implement the proposed CSI-free DNN detector, the proper \(\alpha\) value with respect to each receiver location is determined in advance.  
Figure 6: BER comparison of the proposed CSI-free DNN detector where the feedforward DNN module having different inputs for both GOSM and GOSMP at the center of the receiving plane.