Shashank Jere
Orcid: 0000-0001-6451-253X
According to our database1,
Shashank Jere
authored at least 20 papers
between 2014 and 2025.
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Bibliography
2025
Learning at the Speed of Wireless: Online Real-Time Learning for AI-Enabled MIMO in NextG.
IEEE Commun. Mag., January, 2025
2024
Bayesian Inference-Assisted Machine Learning for Near Real-Time Jamming Detection and Classification in 5G New Radio (NR).
IEEE Trans. Wirel. Commun., July, 2024
Towards xAI: Configuring RNN Weights using Domain Knowledge for MIMO Receive Processing.
CoRR, 2024
2023
Theoretical Foundation and Design Guideline for Reservoir Computing-Based MIMO-OFDM Symbol Detection.
IEEE Trans. Commun., September, 2023
Performance Analysis and Optimization for Layer-Based Scalable Video Caching in 6G Networks.
IEEE/ACM Trans. Netw., August, 2023
IEEE Wirel. Commun. Lett., May, 2023
IEEE Wirel. Commun., February, 2023
Universal Approximation of Linear Time-Invariant (LTI) Systems through RNNs: Power of Randomness in Reservoir Computing.
CoRR, 2023
Towards Explainable Machine Learning: The Effectiveness of Reservoir Computing in Wireless Receive Processing.
Proceedings of the IEEE Military Communications Conference, 2023
2022
Proceedings of the IEEE International Conference on Communications, 2022
Anonymous Jamming Detection in 5G with Bayesian Network Model Based Inference Analysis.
Proceedings of the 23rd IEEE International Conference on High Performance Switching and Routing, 2022
2021
RCNet: Incorporating Structural Information Into Deep RNN for Online MIMO-OFDM Symbol Detection With Limited Training.
IEEE Trans. Wirel. Commun., 2021
Proceedings of the 6th IEEE/ACM Symposium on Edge Computing, 2021
2020
Moving Toward Intelligence: Detecting Symbols on 5G Systems Through Deep Echo State Network.
IEEE J. Emerg. Sel. Topics Circuits Syst., 2020
Learning for Integer-Constrained Optimization through Neural Networks with Limited Training.
CoRR, 2020
Federated Learning in Mobile Edge Computing: An Edge-Learning Perspective for Beyond 5G.
CoRR, 2020
RCNet: Incorporating Structural Information into Deep RNN for MIMO-OFDM Symbol Detection with Limited Training.
CoRR, 2020
Learning with Knowledge of Structure: A Neural Network-Based Approach for MIMO-OFDM Detection.
Proceedings of the 54th Asilomar Conference on Signals, Systems, and Computers, 2020
2014
Proceedings of the 13th International Conference on Control Automation Robotics & Vision, 2014