no code implementations • 4 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.
1 code implementation • 4 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.
no code implementations • 28 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.
no code implementations • 6 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.
no code implementations • 11 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.