DeepAlloc: Deep Learning Approach to Spectrum Allocation in Shared Spectrum Systems
Shared spectrum systems facilitate spectrum allocation to unlicensed users without harming
the licensed users; they offer great promise in optimizing spectrum utility, but their …
the licensed users; they offer great promise in optimizing spectrum utility, but their …
Shared spectrum allocation via pathloss estimation in crowdsensed shared spectrum systems
The RF spectrum is a natural resource in great demand. The research community has
addressed this unabated increase in demand via development of shared spectrum …
addressed this unabated increase in demand via development of shared spectrum …
Machine learning towards enabling spectrum-as-a-service dynamic sharing
A Moubayed, T Ahmed, A Haque… - 2020 IEEE Canadian …, 2020 - ieeexplore.ieee.org
The growth in wireless broadband users, devices, and novel applications has led to a
significant increase in the demand for new radio frequency spectrum. This is expected to …
significant increase in the demand for new radio frequency spectrum. This is expected to …
A deep reinforcement learning framework for contention-based spectrum sharing
The increasing number of wireless devices operating in unlicensed spectrum motivates the
development of intelligent adaptive approaches to spectrum access. We consider …
development of intelligent adaptive approaches to spectrum access. We consider …
A comprehensive survey of spectrum sharing schemes from a standardization and implementation perspective
As the services and requirements of next-generation wireless networks become increasingly
diversified, it is estimated that the current frequency bands of mobile network operators …
diversified, it is estimated that the current frequency bands of mobile network operators …
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 …
A Case Study of Spectrum Analysis Using Unsupervised Machine Learning
V Nagpure, S Vaccaro, C Hood - 2019 IEEE International …, 2019 - ieeexplore.ieee.org
Shared spectrum is a building block for 5G. Understanding how allocated spectrum is
utilized in time and space is necessary to identify sharing opportunities. Analysis of spectrum …
utilized in time and space is necessary to identify sharing opportunities. Analysis of spectrum …
Distributive dynamic spectrum access through deep reinforcement learning: A reservoir computing-based approach
Dynamic spectrum access (DSA) is regarded as an effective and efficient technology to
share radio spectrum among different networks. As a secondary user (SU), a DSA device …
share radio spectrum among different networks. As a secondary user (SU), a DSA device …
A deep Q-learning dynamic spectrum sharing experiment
We report results of an experiment in applying deep Q-learning for dynamic spectrum
sharing (DSS) in the Alleys of Austin scenario from the DARPA Spectrum Collaboration …
sharing (DSS) in the Alleys of Austin scenario from the DARPA Spectrum Collaboration …
Traffic learning: A deep learning approach for obtaining accurate statistical information of the channel traffic in spectrum sharing systems
OH Toma, M Lopez-Benitez - IEEE Access, 2021 - ieeexplore.ieee.org
In recent works, the statistical information of the channel traffic has been increasingly
exploited to make effective decisions in spectrum sharing systems. However, these statistics …
exploited to make effective decisions in spectrum sharing systems. However, these statistics …