[HTML][HTML] Analysis of spectrum sensing using deep learning algorithms: CNNs and RNNs
Spectrum sensing is a critical component of cognitive radio systems, enabling the detection
and utilization of underutilized frequency bands. Convolutional neural networks (CNNs) and …
and utilization of underutilized frequency bands. Convolutional neural networks (CNNs) and …
Intelligent spectrum sensing and access with partial observation based on hierarchical multi-agent deep reinforcement learning
Dynamic spectrum access (DSA) has been regarded as a viable solution to the spectrum
shortage problem. To find idle spectrum, partial spectrum sensing could be employed by …
shortage problem. To find idle spectrum, partial spectrum sensing could be employed by …
Collaborative wideband spectrum sensing and scheduling for networked UAVs in UTM systems
SR Chintareddy, K Roach, K Cheung… - … 2023-2023 IEEE …, 2023 - ieeexplore.ieee.org
In this paper, we propose a data-driven framework for collaborative wideband spectrum
sensing and scheduling for networked unmanned aerial vehicles (UAVs), which act as the …
sensing and scheduling for networked unmanned aerial vehicles (UAVs), which act as the …
Multi-Flow Transmission in Wireless Interference Networks: A Convergent Graph Learning Approach
We consider the problem of multi-flow transmission in wireless networks, where data signals
from different flows can interfere with each other due to mutual interference between links …
from different flows can interfere with each other due to mutual interference between links …
SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel Allocation in Cognitive Interference Networks
We consider the problem of dynamic channel allocation (DCA) in cognitive communication
networks with the goal of maximizing a global signal-to-interference-plus-noise ratio (SINR) …
networks with the goal of maximizing a global signal-to-interference-plus-noise ratio (SINR) …
A joint scheme on spectrum sensing and access with partial observation: A multi-agent deep reinforcement learning approach
Dynamic spectrum access (DSA) has been regarded as a promising solution to mitigate the
serious spectrum shortage problem in the 6G networks, in which secondary users (SUs) are …
serious spectrum shortage problem in the 6G networks, in which secondary users (SUs) are …
Federated Learning-based Collaborative Wideband Spectrum Sensing and Scheduling for UAVs in UTM Systems
SR Chintareddy, K Roach, K Cheung… - arXiv preprint arXiv …, 2024 - arxiv.org
In this paper, we propose a data-driven framework for collaborative wideband spectrum
sensing and scheduling for networked unmanned aerial vehicles (UAVs), which act as the …
sensing and scheduling for networked unmanned aerial vehicles (UAVs), which act as the …
Spectrum utilization improvement for multi‐channel EH‐CRN with spectrum sensing
Due to the ever‐growing applications and services of the Internet of Things (IoT), designing
energy‐efficient and spectral‐efficient transmission schemes to support IoT devices for the …
energy‐efficient and spectral‐efficient transmission schemes to support IoT devices for the …
Simultaneous Sensing and Channel Access based on Partial Observations via Deep Reinforcement Learning
This paper is eligible for the Jack Keil Wolf ISIT Student Paper Award. In this paper we study
dynamic spectrum access (DSA) in cognitive wireless networks, consisting of primary users …
dynamic spectrum access (DSA) in cognitive wireless networks, consisting of primary users …
Joint Sensing Times Detection Thresholds and Users Association Optimization in Multi-Channel Multi-Antenna Cognitive Radio Networks
M Sadeghian Kerdabadi, R Ghazizadeh… - International Journal of …, 2023 - ije.ir
Energy consumption and throughput optimization in cognitive radio networks (CRNs) are
two critical issues that have attracted more attention in recent years. In this paper, we …
two critical issues that have attracted more attention in recent years. In this paper, we …