End-to-End Deep Learning of Optical Fiber Communications

Abstract

In this paper, we implement an optical fiber communication system as an end-to-end deep neural network, including the complete chain of transmitter, channel model, and receiver. This approach enables the optimization of the transceiver in a single end-to-end process. We illustrate the benefits of this method by applying it to intensity modulation/direct detection (IM/DD) systems and show that we can achieve bit error rates below the 6.7% hard-decision forward error correction (HD-FEC) threshold. We model all componentry of the transmitter and receiver, as well as the fiber channel, and apply deep learning to find transmitter and receiver configurations minimizing the symbol error rate. We propose and verify in simulations a training method that yields robust and flexible transceivers that allow-without reconfiguration-reliable transmission over a large range of link dispersions. The results from end-to-end deep learning are successfully verified for the first time in an experiment. In particular, we achieve information rates of 42 Gb/s below the HD-FEC threshold at distances beyond 40 km. We find that our results outperform conventional IM/DD solutions based on 2 and 4 level pulse amplitude modulation (PAM2/PAM4) with feedforward equalization (FFE) at the receiver. Our study is the first step towards end-to-end deep learning-based optimization of optical fiber communication systems.

Does deep learning always outperform simple linear regression in optical imaging?

“…In recent years, deep learning receives much attention in many research fields including optical design [1,2] and optical imaging [3]. In previous works, deep learning has been extensively applied for many optical imaging problems including phase retrieval [4][5][6][7], microscopic image enhancement [8][9], scattering imaging [10][11], holography [12][13][14][15][16][17][18], single-pixel imaging [19,20], super-resolution [21][22][23][24], Fourier ptychography [25][26][27], optical interferometry [28,29], wavefront sensing [30,31], and optical fiber communications [32].…”

End-to-End Learning of Geometrical Shaping Maximizing Generalized Mutual Information

“…A key advantage of this method is that it can be applied to arbitrary channels, including nonlinear optical ones. This was done for example in [7]- [9], where the objective function was a lower bound on the mutual information (MI). In practice, binary forward-error correction (FEC) is typically employed, in which case the generalized mutual information (GMI) is a more suitable performance metric.…”

Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation

“…More generally, one may regard the entire communication system design as an end-to-end reconstruction task and jointly optimize transmitter and receiver NNs [1]. Both traditional [2][3][4] and end-to-end learning [5][6][7] have received considerable attention for optical fiber systems. However, the reliance on NNs as universal (but sometimes poorly understood) function approximators makes it difficult to incorporate existing domain knowledge or interpret the obtained solutions.…”