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Found 9 papers, 1 papers with code
DRL-based Dolph-Tschebyscheff Beamforming in Downlink Transmission for Mobile Users
With the emergence of AI technologies in next-generation communication systems, machine learning plays a pivotal role due to its ability to address high-dimensional, non-stationary optimization problems within dynamic environments while maintaining computational efficiency.
First line of defense: A robust first layer mitigates adversarial attacks
We demonstrate that a carefully designed first layer of the neural network can serve as an implicit adversarial noise filter (ANF).
Energy Efficient Fair STAR-RIS for Mobile Users
We then formulate a novel optimization problem by concurrently optimizing the phase shifts of the STAR-RIS and subsurface assignment variable.
Rotate the ReLU to implicitly sparsify deep networks
We show that this activation wherein the rotation is learned via training results in the elimination of those parameters/filters in the network which are not important for the task.
Binarized ResNet: Enabling Robust Automatic Modulation Classification at the resource-constrained Edge
The proposed MC method with RBLResNets has an adversarial accuracy of $87. 25\%$ over a wide range of SNRs, surpassing the robustness of all existing SOTA methods to the best of our knowledge.
BayesAoA: A Bayesian method for Computation Efficient Angle of Arrival Estimation
The proposed method achieves $92\%$ accuracy in a channel of noise variance $10^{-6}$ with $19. 3\%$ of the brute-force method's computation.
Understanding Learning Dynamics of Binary Neural Networks via Information Bottleneck
Compact neural networks are essential for affordable and power efficient deep learning solutions.
Green DetNet: Computation and Memory efficient DetNet using Smart Compression and Training
This paper introduces an incremental training framework for compressing popular Deep Neural Network (DNN) based unfolded multiple-input-multiple-output (MIMO) detection algorithms like DetNet.
Deep Reinforcement Learning based Blind mmWave MIMO Beam Alignment
Directional beamforming is a crucial component for realizing robust wireless communication systems using millimeter wave (mmWave) technology.
