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Found 9 papers, 0 papers with code
Multi-Agent Hybrid SAC for Joint SS-DSA in CRNs
In CRNs, both spectrum sensing and resource allocation (SSRA) are critical to maximizing system throughput while minimizing collisions of secondary users with the primary network.
Minimum Description Feature Selection for Complexity Reduction in Machine Learning-based Wireless Positioning
In this work, we design a novel positioning neural network (P-NN) that utilizes the minimum description features to substantially reduce the complexity of deep learning-based WP.
Smart Information Exchange for Unsupervised Federated Learning via Reinforcement Learning
Numerical analysis shows the advantages in terms of convergence speed and straggler resilience of the proposed method to different available FL schemes and benchmark datasets.
Complexity Reduction in Machine Learning-Based Wireless Positioning: Minimum Description Features
A recent line of research has been investigating deep learning approaches to wireless positioning (WP).
Coding for Gaussian Two-Way Channels: Linear and Learning-Based Approaches
In this work, we aim to enhance and balance the communication reliability in GTWCs by minimizing the sum of error probabilities via joint design of encoders and decoders at the users.
Constant Modulus Waveform Design with Block-Level Interference Exploitation for DFRC Systems
Dual-functional radar-communication (DFRC) is a promising technology where radar and communication functions operate on the same spectrum and hardware.
A Reinforcement Learning-Based Approach to Graph Discovery in D2D-Enabled Federated Learning
Augmenting federated learning (FL) with direct device-to-device (D2D) communications can help improve convergence speed and reduce model bias through rapid local information exchange.
Coded Matrix Computations for D2D-enabled Linearized Federated Learning
Federated learning (FL) is a popular technique for training a global model on data distributed across client devices.
A Decentralized Pilot Assignment Algorithm for Scalable O-RAN Cell-Free Massive MIMO
Radio access networks (RANs) in monolithic architectures have limited adaptability to supporting different network scenarios.
