Search Results for author:
Found 8 papers, 3 papers with code
Reducing Satellite Interference to Radio Telescopes Using Beacons
We compare the beacon approach with two versions of Radio Quiet Zones (RQZs): fixed quiet zones on the ground and in the sky, and dynamic quiet zones that vary across satellites.
RadYOLOLet: Radar Detection and Parameter Estimation Using YOLO and WaveLet
Wavelet-CNN operates on continuous Wavelet transform of the captured signals, and we use it only when RadYOLO fails to detect any radar signal.
ProSpire: Proactive Spatial Prediction of Radio Environment Using Deep Learning
We carefully address several challenges in ProSpire, such as designing a framework that conveniently collects training data for learning, performing the predictions in a fast manner, enabling operations without an area map, and ensuring that the predictions do not lead to undesired interference.
A New Distributed Method for Training Generative Adversarial Networks
This paper proposes a new framework for training GANs in a distributed fashion: Each device computes a local discriminator using local data; a single server aggregates their results and computes a global GAN.
Deep Reinforcement Learning for Joint Spectrum and Power Allocation in Cellular Networks
In this paper, a learning framework is proposed to optimize both discrete and continuous decision variables.
Deep Actor-Critic Learning for Distributed Power Control in Wireless Mobile Networks
Deep reinforcement learning offers a model-free alternative to supervised deep learning and classical optimization for solving the transmit power control problem in wireless networks.
Scheduling for Cellular Federated Edge Learning with Importance and Channel Awareness
In this paper, a novel scheduling policy is proposed to exploit both diversity in multiuser channels and diversity in the "importance" of the edge devices' learning updates.
Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks
For a typical network architecture, the proposed algorithm is shown to achieve near-optimal power allocation in real time based on delayed CSI measurements available to the agents.
