A skipping spectrum sensing scheme based on deep reinforcement learning for transform domain communication systems Article Swipe
Ce Li
,
Yanhua Wu
,
Rangang Zhu
,
Ruochen Wu
,
Zhengkun Zhang
,
Zunhui Wang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-024-83140-w
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-024-83140-w
Spectrum sensing is a key technology and prerequisite for Transform Domain Communication Systems (TDCS). The traditional approach typically involves selecting a working sub-band and maintaining it without further changes, with spectrum sensing being conducted periodically. However, this approach presents two main issues: on the one hand, if the selected working band has few idle channels, TDCS devices are unable to flexibly switch sub-bands, leading to reduced performance; on the other hand, periodic sensing consumes time and energy, limiting TDCS's transmission efficiency. In contrast to previous studies that unrealistically modeled the problem as a Markov Decision Process (MDP), this study accounts for the fact that TDCS devices cannot fully observe the entire spectrum state and must rely on historical observations, along with the current state of sub-bands, to make informed decisions. We innovatively model this as a Partially Observable Markov Decision Process (POMDP). Moreover, we consider both the number of skipped time slots and the selection of idle sub-bands, establishing distinct termination conditions for each action. By assigning different weights to balance sensing overhead and spectrum utilization while reducing conflicts, the algorithm's adaptability and performance are improved. To address the Q-value overestimation problem inherent in traditional Deep Recurrent Q-Network (DRQN) due to the use of a single network, we propose a DDRQN-BandShift strategy that combines Double Deep Q-Network (DDQN) and DRQN. Simulation results show that the proposed scheme significantly improves TDCS transmission efficiency while effectively reducing sensing costs.
Related Topics
Concepts
Partially observable Markov decision process
Computer science
Markov decision process
Overhead (engineering)
Reinforcement learning
Transmission (telecommunications)
Process (computing)
Markov chain
Adaptability
Real-time computing
Distributed computing
Markov model
Markov process
Artificial intelligence
Telecommunications
Machine learning
Mathematics
Operating system
Statistics
Biology
Ecology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-024-83140-w
- https://www.nature.com/articles/s41598-024-83140-w.pdf
- OA Status
- gold
- Cited By
- 1
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405865788
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405865788Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1038/s41598-024-83140-wDigital Object Identifier
- Title
-
A skipping spectrum sensing scheme based on deep reinforcement learning for transform domain communication systemsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-28Full publication date if available
- Authors
-
Ce Li, Yanhua Wu, Rangang Zhu, Ruochen Wu, Zhengkun Zhang, Zunhui WangList of authors in order
- Landing page
-
https://doi.org/10.1038/s41598-024-83140-wPublisher landing page
- PDF URL
-
https://www.nature.com/articles/s41598-024-83140-w.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.nature.com/articles/s41598-024-83140-w.pdfDirect OA link when available
- Concepts
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Partially observable Markov decision process, Computer science, Markov decision process, Overhead (engineering), Reinforcement learning, Transmission (telecommunications), Process (computing), Markov chain, Adaptability, Real-time computing, Distributed computing, Markov model, Markov process, Artificial intelligence, Telecommunications, Machine learning, Mathematics, Operating system, Statistics, Biology, EcologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- References (count)
-
20Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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