Shi Jin

Deep Learning for Wireless Physical Layer: Opportunities and Challenges

2017 · arXiv (Cornell University)

Machine learning (ML) has been widely applied to the upper layers of wireless communication systems for various purposes, such as deployment of cognitive radio and communication network. However, its application to the physical layer is …

Federated edge learning for the wireless physical layer: Opportunities and challenges

2022 · China Communications

Deep learning (DL) has been applied to the physical layer of wireless communication systems, which directly extracts environment knowledge from data and outperforms conventional methods either in accuracy or computation complexity. However, most related research …

Quantum simulation of Maxwell's equations via Schrödingersation

2023 · arXiv (Cornell University)

We present quantum algorithms for electromagnetic fields governed by Maxwell's equations. The algorithms are based on the Schrödingersation approach, which transforms any linear PDEs and ODEs with non-unitary dynamics into a system evolving under unitary …

Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients

2024 · arXiv (Cornell University)

Federated learning (FL) is an emerging distributed training paradigm that aims to learn a common global model without exchanging or transferring the data that are stored locally at different clients. The Federated Averaging (FedAvg)-based algorithms …

Deep CSI Compression for Dual-Polarized Massive MIMO Channels with Disentangled Representation Learning

2024 · arXiv (Cornell University)

Channel state information (CSI) feedback is critical for achieving the promised advantages of enhancing spectral and energy efficiencies in massive multiple-input multiple-output (MIMO) wireless communication systems. Deep learning (DL)-based methods have been proven effective in …

Facilitating AI-Based CSI Feedback Deployment in Massive MIMO Systems With Learngene

2024 · IEEE Transactions on Wireless Communications

Recent advances in artificial intelligence offer groundbreaking alternatives to conventional codebook-based channel state information (CSI) feedback techniques. Confronted with the influx of CSI data from simulations and real-world environments, leveraging neural networks to mine valuable …

Quantum simulation of Maxwell’s equations <i>via</i> Schrödingerisation

2024 · ESAIM Mathematical Modelling and Numerical Analysis

We present quantum algorithms for electromagnetic fields governed by Maxwell’s equations. The algorithms are based on the Schrödingerisation approach, which transforms any linear PDEs and ODEs with non-unitary dynamics into a system evolving under unitary …

Quantum Algorithms for Stochastic Differential Equations: A Schrödingerisation Approach

2024 · arXiv (Cornell University)

Quantum computers are known for their potential to achieve up-to-exponential speedup compared to classical computers for certain problems. To exploit the advantages of quantum computers, we propose quantum algorithms for linear stochastic differential equations, utilizing …