221 citations · 232 across the 8 of their papers we have counts for
8 papers
Distributed Learning Meets 6G: A Communication and Computing Perspective
Shashank Jere, Yifei Song, Yang Yi +1
With the ever-improving computing capabilities and storage capacities of mobile devices in line with evolving telecommunication network paradigms, there has been an explosion of re…
Adversarial Classification of the Attacks on Smart Grids Using Game Theory and Deep Learning
Kian Hamedani, Lingjia Liu, Jithin Jagannath +2
Smart grids are vulnerable to cyber-attacks. This paper proposes a game-theoretic approach to evaluate the variations caused by an attacker on the power measurements. Adversaries c…
Making Intelligent Reflecting Surfaces More Intelligent: A Roadmap Through Reservoir Computing
Zhou Zhou, Kangjun Bai, Nima Mohammadi +2
This article introduces a neural network-based signal processing framework for intelligent reflecting surface (IRS) aided wireless communications systems. By modeling radio-frequen…
Differential Privacy Meets Federated Learning under Communication Constraints
Nima Mohammadi, Jianan Bai, Qiang Fan +3
The performance of federated learning systems is bottlenecked by communication costs and training variance. The communication overhead problem is usually addressed by three communi…
Deep Echo State Q-Network (DEQN) and Its Application in Dynamic Spectrum Sharing for 5G and Beyond
Hao-Hsuan Chang, Lingjia Liu, Yang Yi
Deep reinforcement learning (DRL) has been shown to be successful in many application domains. Combining recurrent neural networks (RNNs) and DRL further enables DRL to be applicab…
Delay-aware Resource Allocation in Fog-assisted IoT Networks Through Reinforcement Learning
Qiang Fan, Jianan Bai, Hongxia Zhang +2
Fog nodes in the vicinity of IoT devices are promising to provision low latency services by offloading tasks from IoT devices to them. Mobile IoT is composed by mobile IoT devices…
RCNet: Incorporating Structural Information into Deep RNN for MIMO-OFDM Symbol Detection with Limited Training
Zhou Zhou, Lingjia Liu, Shashank Jere +3
In this paper, we investigate learning-based MIMO-OFDM symbol detection strategies focusing on a special recurrent neural network (RNN) -- reservoir computing (RC). We first introd…
Distributive Dynamic Spectrum Access through Deep Reinforcement Learning: A Reservoir Computing Based Approach
Hao-Hsuan Chang, Hao Song, Yang Yi +3
Dynamic spectrum access (DSA) is regarded as an effective and efficient technology to share radio spectrum among different networks. As a secondary user (SU), a DSA device will fac…