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Found 11 papers, 0 papers with code
Learning at the Speed of Wireless: Online Real-Time Learning for AI-Enabled MIMO in NextG
At the air interface, multiple-input multiple-output (MIMO) and its variants such as multi-user MIMO (MU-MIMO) and massive/full-dimension MIMO have been key enablers across successive generations of cellular networks with evolving complexity and design challenges.
Anonymous Jamming Detection in 5G with Bayesian Network Model Based Inference Analysis
To address this, an unsupervised auto-encoder-based anomaly detection is presented with an AUC of 0. 987.
Towards Explainable Machine Learning: The Effectiveness of Reservoir Computing in Wireless Receive Processing
With this groundwork, we incorporate the available domain knowledge in the form of the statistics of the wireless channel directly into the weights of the ESN model.
Universal Approximation of Linear Time-Invariant (LTI) Systems through RNNs: Power of Randomness in Reservoir Computing
Our work results in clear signal processing-based model interpretability of RC and provides theoretical explanation/justification for the power of randomness in randomly generating instead of training RC's recurrent weights.
Bayesian Inference-assisted Machine Learning for Near Real-Time Jamming Detection and Classification in 5G New Radio (NR)
The increased flexibility and density of spectrum access in 5G New Radio (NR) has made jamming detection and classification a critical research area.
Distributed Learning Meets 6G: A Communication and Computing Perspective
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 research interest towards exploring Distributed Learning (DL) frameworks to realize stringent key performance indicators (KPIs) that are expected in next-generation/6G cellular networks.
Federated Dynamic Spectrum Access
In this article, we introduce a Federated Learning (FL) based framework for the task of DSA, where FL is a distributive machine learning framework that can reserve the privacy of network terminals under heterogeneous data distributions.
Learning with Knowledge of Structure: A Neural Network-Based Approach for MIMO-OFDM Detection
In this paper, we explore neural network-based strategies for performing symbol detection in a MIMO-OFDM system.
Learning for Integer-Constrained Optimization through Neural Networks with Limited Training
In this paper, we investigate a neural network-based learning approach towards solving an integer-constrained programming problem using very limited training.
Federated Learning in Mobile Edge Computing: An Edge-Learning Perspective for Beyond 5G
Thus, in this paper, we design a novel edge computing-assisted federated learning framework, in which the communication constraints between IoT devices and edge servers and the effect of various IoT devices on the training accuracy are taken into account.
RCNet: Incorporating Structural Information into Deep RNN for MIMO-OFDM Symbol Detection with Limited Training
In this paper, we investigate learning-based MIMO-OFDM symbol detection strategies focusing on a special recurrent neural network (RNN) -- reservoir computing (RC).
