Search Results for author: Lexi Xu

Found 6 papers, 1 papers with code

RadioGAT: A Joint Model-based and Data-driven Framework for Multi-band Radiomap Reconstruction via Graph Attention Networks

no code implementations25 Mar 2024 Xiaojie Li, Songyang Zhang, Hang Li, Xiaoyang Li, Lexi Xu, Haigao Xu, Hui Mei, Guangxu Zhu, Nan Qi, Ming Xiao

Multi-band radiomap reconstruction (MB-RMR) is a key component in wireless communications for tasks such as spectrum management and network planning.

Graph Attention

Over-the-Air Computation in OFDM Systems with Imperfect Channel State Information

no code implementations7 Jul 2023 Yilong Chen, Huijun Xing, Jie Xu, Lexi Xu, Shuguang Cui

In particular, we consider two scenarios with best-effort and error-constrained computation tasks, with the objectives of minimizing the average computation mean squared error (MSE) and the computation outage probability over the multiple subcarriers, respectively.

Output-Dependent Gaussian Process State-Space Model

1 code implementation15 Dec 2022 Zhidi Lin, Lei Cheng, Feng Yin, Lexi Xu, Shuguang Cui

Gaussian process state-space model (GPSSM) is a fully probabilistic state-space model that has attracted much attention over the past decade.

MetaLoc: Learning to Learn Wireless Localization

no code implementations8 Nov 2022 Jun Gao, Dongze Wu, Feng Yin, Qinglei Kong, Lexi Xu, Shuguang Cui

The framework introduces two paradigms for the optimization of meta-parameters: a centralized paradigm that simplifies the process by sharing data from all historical environments, and a distributed paradigm that maintains data privacy by training meta-parameters for each specific environment separately.

Meta-Learning

Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey

no code implementations18 Mar 2021 Kai Chen, Qinglei Kong, Yijue Dai, Yue Xu, Feng Yin, Lexi Xu, Shuguang Cui

Empowered by big data and machine learning, next-generation data-driven communication systems will be intelligent with the characteristics of expressiveness, scalability, interpretability, and especially uncertainty modeling, which can confidently involve diversified latent demands and personalized services in the foreseeable future.

BIG-bench Machine Learning Gaussian Processes

A General Architecture for Behavior Modeling of Nonlinear Power Amplifier using Deep Convolutional Neural Network

no code implementations6 Jun 2020 Xin Hu, Zhijun Liu, You Li, Lexi Xu, Sun Zhang, Qinlong Li, Jia Hu, WenHua Chen, Weidong Wang, Mohamed Helaoui, Fadhel M. Ghannouchi

In this work, a low complexity, general architecture based on the deep real-valued convolutional neural network (DRVCNN) is proposed to build the nonlinear behavior of the power amplifier.

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