activity
20092026
most citedQuantum Communication

611 citations · 2.4k across the 80 of their papers we have counts for

collaborators

80 papers

eess.SP2026

Regular Perturbation on the Group-Velocity Dispersion Parameter for Dual-Polarization Short-Reach Systems

Dario Cellini, Vinícius Oliari, Erik Agrell +3

The Manakov equation governs the propagation of signals in dual-polarization systems. Its solution is usually approximated by regular perturbation on the nonlinear Kerr parameter.…

eess.SP2026

Channel Estimation and LDPC Decoding for Bursty Phase Noise

Han Cui, Frank R. Kschischang, Magnus Karlsson +1

Time-varying distortions in communication systems can significantly degrade the performance of soft-decision forward error correction. This paper presents a burst-aware (BA) low-de…

eess.SP2026

PIDT: Physics-Informed Digital Twin for Optical Fiber Parameter Estimation

Zicong Jiang, Magnus Karlsson, Erik Agrell +1

We propose physics-informed digital twin (PIDT): a fiber parameter estimation approach that combines a parameterized split-step method with a physics-informed loss. PIDT improves a…

cs.IT2024★ 1 cited

On the Capacity of Correlated Phase-Noise Channels: An Electro-Optic Frequency Comb Example

Mohammad Farsi, Hamdi Joudeh, Gabriele Liga +3

The capacity of a discrete-time channel with correlated phase noises is investigated. In particular, the electro-optic frequency comb system is considered, where the phase noise of…

eess.SP2024

Pilot Distributions for Phase Noise Estimation in Electro-Optic Frequency Comb Systems

Mohammad Farsi, Magnus Karlsson, Erik Agrell

We explore the optimal pilot positioning for phase tracking in electro-optic frequency comb setups. We show that, in contrast to previous results for regular multichannel systems,…

eess.SP2024

Learning to Extract Distributed Polarization Sensing Data from Noisy Jones Matrices

Mohammad Farsi, Christian Häger, Magnus Karlsson +1

We consider the problem of recovering spatially resolved polarization information from receiver Jones matrices. We introduce a physics-based learning approach, improving noise resi…