Key result
A reinforcement learning-based joint optimization algorithm for dynamic spectrum access and coding was proposed and verified by simulations to enable cognitive spectrum collaboration.
Why the study?
In mass connection scenarios where everything is connected, emerging coding technologies can address packet erasure caused by dynamic spectrum access and enable cognitive spectrum collaboration.
Key points are not available for this paper at this time.
Design
Simulation study
Provides overview of ML-enabled cognitive spectrum tech; leaves open empirical validation in dynamic mass-connection scenarios.
For a future scenario where everything is connected, cognitive technology can be used for spectrum sensing and access, and emerging coding technologies can be used to address the erasure of packets caused by dynamic spectrum access and realize cognitive spectrum collaboration among users in mass connection scenarios. Machine learning technologies are being increasingly used in the implementation of smart networks. In this paper, after an overview of several key technologies in the cognitive spectrum collaboration, a joint optimization algorithm of dynamic spectrum access and coding is proposed and implemented using reinforcement learning, and the effectiveness of the algorithm is verified by simulations, thus providing a feasible research direction for the realization of cognitive spectrum collaboration.
No takes yet. Share an insight, caveat, or question.
Cai et al. (2020) studied this question. A reinforcement learning-based joint optimization algorithm for dynamic spectrum access and coding was proposed and verified by simulations to enable cognitive spectrum collaboration.
Synapse has enriched 4 closely related papers on similar clinical questions. Consider them for comparative context: