David J. Love

Channel Estimation,Unmanned Aerial Vehicles,Federated Learning,Base Station,Computational Complexity,Internet Of Things,Machine Learning Models,Time Slot,User Equipment,Wireless Networks,Amazon Web Services,Channel Model,Linear Solver,Low Earth Orbit,Massive Multiple-input Multiple-output,Multiple-input Multiple-output,Nonzero Entries,Signal-to-interference-plus-noise Ratio,Sparse Matrix,Sparsity,Additive Noise,Communication Delay,Computational Speed,Convolutional Neural Network,Deep Neural Network,Edge Devices,Global Model,Model Aggregation,Number Of Nonzero Entries,Numerical Stability,Random Combinations,Worker Nodes,Central Node,Channel Capacity,Decoding Error,Deep Learning,Distributed Computing,Edge Computing,Edge Server,Error Probability,Fraction Of Matrix,Ground Users,Heterogeneous Systems,Local Computing,Local Dataset,Local Updates,Loss Of Generality,Matrix Multiplication,Multiple-input Multiple-output Systems,Numerical Experiments,