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In this paper, we apply deep learning for communication over dispersivechannels with power detection, as encountered in low-cost optical intensitymodulation/direct detection (IM/DD) links. We consider an autoencoder based onthe recently proposed sliding window bidirectional recurrent neural network(SBRNN) design to realize the transceiver for optical IM/DD communication. Weshow that its performance can be improved by introducing a weighted sequenceestimation scheme at the receiver. Moreover, we perform bit-to-symbol mappingoptimization to reduce the bit-error rate (BER) of the system. Furthermore, wecarry out a detailed comparison with classical schemes based on pulse-amplitudemodulation and maximum likelihood sequence detection (MLSD). Our investigationshows that for a reference 42\,Gb/s transmission, the SBRNN autoencoderachieves a BER performance comparable to MLSD, when both systems account forthe same amount of memory. In contrast to MLSD, the SBRNN performance isachieved without incurring a computational complexity exponentially growingwith the processed memory.

Deep Learning for Communication over Dispersive Nonlinear Channels: Performance and Comparison with Classical Digital Signal Processing