activity
20132024
most citedEnd-to-end Deep Learning of Optical Fiber Communications

429 citations · 939 across the 56 of their papers we have counts for

collaborators
Showing 2021Show all

5 papers · 1 filter

eess.SP2021★ 19 cited

Geometric Constellation Shaping for Phase-noise Channels Using a Differentiable Blind Phase Search

Andrej Rode, Benedikt Geiger, Laurent Schmalen

We perform geometric constellation shaping with optimized bit labeling using a binary autoencoder including a differential blind phase search (BPS). Our approach enables full end-t…

cs.IT2021★ 2 cited

Improved Soft-aided Error-and-erasure Decoding of Product Codes with Dynamic Reliability Scores

Sisi Miao, Lukas Rapp, Laurent Schmalen

We propose a novel soft-aided low-complexity decoder for product codes based on dynamic reliability scores and error-and-erasure decoding. We observe coding gains of up to 1.2 dB c…

cs.NI2021

Deep Reinforcement Learning for Wireless Resource Allocation Using Buffer State Information

Eike-Manuel Bansbach, Victor Eliachevitch, Laurent Schmalen

As the number of user equipments (UEs) with various data rate and latency requirements increases in wireless networks, the resource allocation problem for orthogonal frequency-divi…

cs.IT2021

Low Rate Protograph-Based LDPC Codes for Continuous Variable Quantum Key Distribution

Kadir Gümüs, Laurent Schmalen

Error correction plays a major role in the reconciliation of continuous variable quantum key distribution (CV-QKD) and greatly affects the performance of the system. CV-QKD require…

cs.IT2021★ 19 cited

Error-and-Erasure Decoding of Product and Staircase Codes

Lukas Rapp, Laurent Schmalen

High-rate product codes (PCs) and staircase codes (SCs) are ubiquitous codes in high-speed optical communication achieving near-capacity performance on the binary symmetric channel…