The fidelity is as high as ~98%. The "AI + Optics" research of Guangzhou University of Technology is published in the Nature sub-journal. Deep learning empowers non-orthogonal optical multiplexing.

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In optical multiplexing, the orthogonality between channels plays a crucial role. This orthogonality ensures that signals between different channels do not interfere with each other, enabling efficient data transmission. The optical multiplexing system can transmit multiple channels of data at the same time, effectively improving the utilization of optical fibers. However, such a system also inevitably imposes an upper limit on multiplexing capacity.

Here, the Key Laboratory of Synaesthetic Fusion Photonics Technology of the Ministry of Education of Guangdong University of Technology developed a non-orthogonal optical multiplexing on multi-mode fiber (MMF) based on a deep neural network, Called Speckle light field retrieval network (SLRnet), it can learn complex mapping relationships between multiple non-orthogonal input light fields containing information encoding and their corresponding single intensity outputs.

Through principle verification experiments, SLRnet successfully solved the ill-posed problem of non-orthogonal optical multiplexing on MMF. It is able to utilize a single shot speckle output to unambiguously retrieve multiple non-orthogonal input signals mediated by the same polarization, wavelength and spatial location with 98% fidelity. This research paves the way for realizing high-capacity optical multiplexing utilizing non-orthogonal channels and is an important step towards this goal.

This research will promote potential applications in the fields of optics and photonics and provide new insights into the exploration of broader disciplines such as information science and technology.

The related research was titled "Non-orthogonal optical multiplexing empowered by deep learning" and was published in "Nature Communications" on February 21, 2024.

Paper link: https://www.nature.com/articles/s41467-024-45845-4

Optical multiplexing problem

Multiplexing is the cornerstone of optical communications, in which physical orthogonality between multiplexed channels is a prerequisite for large-scale coded information transmission.

Considering the demultiplexing of multiple orthogonal signals, transmission matrix methods (such as MMF) can even solve this problem on strongly scattering media.

Recently, deep learning has been widely used in the fields of optics and photonics for the reverse design of optical devices and computational optics. Specifically, deep neural networks have been used to improve the performance of orthogonal multiplexing over multiple scattering media.

However, all reported multiplexing scenarios to date strictly rely on physical orthogonality between multiplexed channels. There have been no attempts to leverage the nonlinear modeling capabilities of deep learning to achieve non-orthogonal optical multiplexing over MMFs.

Unfortunately, the multiplexing of non-orthogonal channels mediated by the same polarization or wavelength even in single-mode fiber is still very challenging due to the lack of efficient demultiplexing methods or digital signals Processing is overburdened. Therefore, developing a new method to decode the information encoded in non-orthogonal input channels is critical for eventual optical multiplexing.

Non-orthogonal optical multiplexing on MMF based on deep neural network

Here, researchers demonstrate that preliminary non-orthogonal optical multiplexing can be achieved via MMF with the support of SLRnet.

As a proof-of-concept demonstration, non-orthogonal input channels can be used to achieve multiplexed transmission of information through MMF, including general natural scene images, unrelated random binary data and images that do not belong to the same type of training data set, It is beneficial to realize non-orthogonal multiplexing transmission of optical information.

Through data-driven techniques to establish complex relationships between non-orthogonal input channels and outputs, trained deep neural networks can retrieve non-orthogonal Encoded information for traffic lanes. Even non-orthogonal multiplexed channels sharing the same polarization, wavelength and input spatial region can be efficiently decoded.

Figure 1: Schematic diagram of non-orthogonal optical multiplexing on MMF. (Source: Paper)

Neural Network Architecture

DepthNeural NetworkAble to retrieve non-orthogonal optical multiplexing from a single speckle output of an MMF Signal. Multiple amplitude- and phase-encoded information mediated by arbitrary polarization combinations can be efficiently retrieved by SLRnet after propagating in the MMF.

As shown in Figure 2a, even the typical scenario of non-orthogonal input channels with the same polarization, wavelength and input space region can be explicitly decoded. This is achieved through a deep neural network, the architecture of which is shown in Figure 2b, which is a variant of Unet based on the unique multiple scattering process of MMF. It consists of a fully connected (FC) layer and ResUnet.

Figure 2: Non-orthogonal optical multiplexing on MMF via deep learning. (Source: paper)

Experimental results

First consider the case where the MMF length is 1m. Figure 3a shows the evolution of the retrieval fidelity for two multiplexed light field channels with arbitrary combinations of polarization states during SLRnet training. Overall, there will be four encoding channels in the amplitude and phase dimensions, which can be non-orthogonal depending on the polarization state. Retrieval fidelity was measured by Pearson's correlation coefficient (PCC).

Figure 3: Non-orthogonal multiplexing performance using SLRnet. (Source: paper)

As can be seen from the figure, the evolution of PCC retrieved using the same SLRnet training configuration after 100 epochs is greater than 0.97. At the same time, the evolution of retrieval fidelity is essentially the same for the twelve multiplexing scenarios, demonstrating the excellent robustness of non-orthogonal multiplexing to arbitrary polarization combinations.

Additionally, Figure 3b provides the fidelity retrieved in each amplitude and phase multiplexed channel separately using different polarization combinations. The average retrieval fidelity in the amplitude and phase dimensions is nearly identical (~0.98), highlighting the ability of SLRnet to demultiplex information encoded in multiple non-orthogonal input channels.

In order to perform sensory evaluation of the retrieval information of wavefront encoding, the typical demultiplexing results of four polarization combinations (0° and 0°, 0° and 10°, 0° and 90°, and 0° and ellipse) are as follows: Figure 4 shows.

Figure 4: Non-orthogonal multiplexing results on 1 m MMF. (Source: paper)

It can be seen that four grayscale images multiplexed on the amplitude and phase of the input wavefront using the same polarization can be effectively demultiplexed using a single speckle output. Other results retrieved with similar fidelity under different polarization combinations demonstrate that SLRnet is capable of unprecedented non-orthogonal input channel multiplexing even when the encoding wavefront is scrambled by MMF.

Figure 5: Non-orthogonal multiplexing results on 50 m MMF. (Source: paper)

To further consolidate the superiority of SLRnet in more realistic scenarios, non-orthogonal optical multiplexing results using the same polarization state on 50 m MMF are proposed, as shown in Figure 5. As can be seen from Figures 4 and 5, the demultiplexing results for the 1 m MMF are better than the 50 m case because the scattering properties of the longer MMF are more susceptible to environmental influences. Demultiplexing performance can be further improved by optimizing the network structure. Research shows that SLRnet is an effective means of multiplexing non-orthogonal channels in MMF.

Figure 6: Results of non-orthogonal multiplexing on 1 m MMF for general natural scene images and images not belonging to the ImageNet database. (Source: paper)

Finally, to demonstrate the versatility of SLRnet for different image sets, the study shows that SLRnet has good generalization.

Although the MMF-based non-orthogonal optical multiplexing concept proposed at this stage cannot be directly used for medical diagnosis that usually requires uniform fidelity, high-precision non-orthogonal multiplexing of non-correlated binary digital information It shows that the realization of non-orthogonal multiplexing transmission of optical information through MMF is a step forward.

This research may not only pave the way for utilizing high-throughput MMFs for communication and information processing, but may also provide a paradigm shift for optical multiplexing in optics and other fields, which can greatly increase the degree of freedom and freedom of optical systems. capacity.

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