34 citations · 61 across the 12 of their papers we have counts for
12 papers
Towards xAI: Configuring RNN Weights using Domain Knowledge for MIMO Receive Processing
Shashank Jere, Lizhong Zheng, Karim Said +1
Deep learning is making a profound impact in the physical layer of wireless communications. Despite exhibiting outstanding empirical performance in tasks such as MIMO receive proce…
Learning at the Speed of Wireless: Online Real-Time Learning for AI-Enabled MIMO in NextG
Jiarui Xu, Shashank Jere, Yifei Song +3
Integration of artificial intelligence (AI) and machine learning (ML) into the air interface has been envisioned as a key technology for next-generation (NextG) cellular networks.…
Anonymous Jamming Detection in 5G with Bayesian Network Model Based Inference Analysis
Ying Wang, Shashank Jere, Soumya Banerjee +3
Jamming and intrusion detection are critical in 5G research, aiming to maintain reliability, prevent user experience degradation, and avoid infrastructure failure. This paper intro…
Towards Explainable Machine Learning: The Effectiveness of Reservoir Computing in Wireless Receive Processing
Shashank Jere, Karim Said, Lizhong Zheng +1
Deep learning has seen a rapid adoption in a variety of wireless communications applications, including at the physical layer. While it has delivered impressive performance in task…
Universal Approximation of Linear Time-Invariant (LTI) Systems through RNNs: Power of Randomness in Reservoir Computing
Shashank Jere, Lizhong Zheng, Karim Said +1
Recurrent neural networks (RNNs) are known to be universal approximators of dynamic systems under fairly mild and general assumptions. However, RNNs usually suffer from the issues…
Bayesian Inference-assisted Machine Learning for Near Real-Time Jamming Detection and Classification in 5G New Radio (NR)
Shashank Jere, Ying Wang, Ishan Aryendu +2
The increased flexibility and density of spectrum access in 5G New Radio (NR) has made jamming detection and classification a critical research area. To detect coexisting jamming a…
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…
Federated Dynamic Spectrum Access
Yifei Song, Hao-Hsuan Chang, Zhou Zhou +2
Due to the growing volume of data traffic produced by the surge of Internet of Things (IoT) devices, the demand for radio spectrum resources is approaching their limitation defined…
Learning with Knowledge of Structure: A Neural Network-Based Approach for MIMO-OFDM Detection
Zhou Zhou, Shashank Jere, Lizhong Zheng +1
In this paper, we explore neural network-based strategies for performing symbol detection in a MIMO-OFDM system. Building on a reservoir computing (RC)-based approach towards symbo…
Learning for Integer-Constrained Optimization through Neural Networks with Limited Training
Zhou Zhou, Shashank Jere, Lizhong Zheng +1
In this paper, we investigate a neural network-based learning approach towards solving an integer-constrained programming problem using very limited training. To be specific, we in…
Federated Learning in Mobile Edge Computing: An Edge-Learning Perspective for Beyond 5G
Shashank Jere, Qiang Fan, Bodong Shang +2
Owing to the large volume of sensed data from the enormous number of IoT devices in operation today, centralized machine learning algorithms operating on such data incur an unbeara…
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…