49 citations · 88 across the 16 of their papers we have counts for
7 papers · 1 filter
Learning to Learn Quantum Turbo Detection
Bryan Liu, Toshiaki Koike-Akino, Ye Wang +1
This paper investigates a turbo receiver employing a variational quantum circuit (VQC). The VQC is configured with an ansatz of the quantum approximate optimization algorithm (QAOA…
Variational Quantum Compressed Sensing for Joint User and Channel State Acquisition in Grant-Free Device Access Systems
Bryan Liu, Toshiaki Koike-Akino, Ye Wang +1
This paper introduces a new quantum computing framework integrated with a two-step compressed sensing technique, applied to a joint channel estimation and user identification probl…
Huffman-Coded Sphere Shaping for Extended-Reach Single-Span Links
Pavel Skvortcov, Ian Phillips, Wladek Forysiak +4
Huffman-coded sphere shaping (HCSS) is an algorithm for finite-length probabilistic constellation shaping, which provides nearly optimal energy efficiency at low implementation com…
Huffman-coded Sphere Shaping and Distribution Matching Algorithms via Lookup Tables
Tobias Fehenberger, David S. Millar, Toshiaki Koike-Akino +3
In this paper, we study amplitude shaping schemes for the probabilistic amplitude shaping (PAS) framework as well as algorithms for constant-composition distribution matching (CCDM…
Analysis of Nonlinear Fiber Interactions for Finite-Length Constant-Composition Sequences
Tobias Fehenberger, David S. Millar, Toshiaki Koike-Akino +3
In order to realize probabilistically shaped signaling within the probabilistic amplitude shaping (PAS) framework, a shaping device outputs sequences that follow a certain nonunifo…
Neural Turbo Equalization: Deep Learning for Fiber-Optic Nonlinearity Compensation
Toshiaki Koike-Akino, Ye Wang, David S. Millar +2
Recently, data-driven approaches motivated by modern deep learning have been applied to optical communications in place of traditional model-based counterparts. The application of…