102 citations · 134 across the 14 of their papers we have counts for
6 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…
Universal Physiological Representation Learning with Soft-Disentangled Rateless Autoencoders
Mo Han, Ozan Ozdenizci, Toshiaki Koike-Akino +2
Human computer interaction (HCI) involves a multidisciplinary fusion of technologies, through which the control of external devices could be achieved by monitoring physiological st…
Disentangled Adversarial Transfer Learning for Physiological Biosignals
Mo Han, Ozan Ozdenizci, Ye Wang +2
Recent developments in wearable sensors demonstrate promising results for monitoring physiological status in effective and comfortable ways. One major challenge of physiological st…
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…
Learning to Modulate for Non-coherent MIMO
Ye Wang, Toshiaki Koike-Akino
The deep learning trend has recently impacted a variety of fields, including communication systems, where various approaches have explored the application of neural networks in pla…