Search Results for author: Yinan Zou

Found 5 papers, 1 papers with code

Proximal Gradient-Based Unfolding for Massive Random Access in IoT Networks

no code implementations4 Dec 2022 Yinan Zou, Yong Zhou, Xu Chen, Yonina C. Eldar

Simulations show that the proposed unfolding neural network achieves better recovery performance, convergence rate, and adaptivity than current baselines.

Action Detection Activity Detection +1

Gan-Based Joint Activity Detection and Channel Estimation For Grant-free Random Access

1 code implementation4 Apr 2022 Shuang Liang, Yinan Zou, Yong Zhou

Joint activity detection and channel estimation (JADCE) for grant-free random access is a critical issue that needs to be addressed to support massive connectivity in IoT networks.

Action Detection Activity Detection +1

Knowledge-Guided Learning for Transceiver Design in Over-the-Air Federated Learning

no code implementations28 Mar 2022 Yinan Zou, Zixin Wang, Xu Chen, Haibo Zhou, Yong Zhou

Based on the convergence analysis, we formulate an optimization problem to minimize the upper bound to enhance the learning performance, followed by proposing an alternating optimization algorithm to facilitate the optimal transceiver design for AirComp-assisted FL.

Federated Learning

Learning Proximal Operator Methods for Massive Connectivity in IoT Networks

no code implementations6 Dec 2021 Yinan Zou, Yong Zhou, Yuanming Shi, Xu Chen

To mitigate all the aforementioned limitations, we in this paper develop an effective unfolding neural network framework built upon the proximal operator method to tackle the JADCE problem in IoT networks, where the base station is equipped with multiple antennas.

Action Detection Activity Detection

Optimal Receive Beamforming for Over-the-Air Computation

no code implementations11 May 2021 Wenzhi Fang, Yinan Zou, Hongbin Zhu, Yuanming Shi, Yong Zhou

In this paper, we consider fast wireless data aggregation via over-the-air computation (AirComp) in Internet of Things (IoT) networks, where an access point (AP) with multiple antennas aim to recover the arithmetic mean of sensory data from multiple IoT devices.

Denoising

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