Full spectrum sharing in cognitive radio networks toward 5G: a survey
Open accessAbstract
With the development of wireless communication technology, the need for bandwidth is increasing continuously, and the growing need makes wireless spectrum resources more and more scarce. Cognitive radio (CR) has been identified as a promising solution for the spectrum scarcity, and its core idea is the dynamic spectrum access. It can dynamically utilize the idle spectrum without affecting the rights of primary users, so that multiple services or users can share a part of the spectrum, thus achieving the goal of avoiding the high cost of spectrum resetting and improving the utilization of spectrum resources. In order to meet the critical requirements of the fifth generation (5G) mobile network, especially theWider-Coverage,Massive-Capacity,Massive-Connectivity, andLow-Latencyfour application scenarios, the spectrum range used in 5G will be further expanded into the full spectrum era, possibly from 1 GHz to 100 GHz. In this paper, we conduct a comprehensive survey of CR technology and focus on the current significant research progress in the full spectrum sharing towards the four scenarios. In addition, the key enabling technologies that may be closely related to the study of 5G in the near future are presented in terms of full-duplex spectrum sensing, spectrum-database based spectrum sensing, auction based spectrum allocation, carrier aggregation based spectrum access. Subsequently, other issues that play a positive role for the development research and practical application of CR, such as common control channel, energy harvesting, non-orthogonal multiple access, and CR based aeronautical communication are discussed. The comprehensive overview provided by this survey is expected to help researchers develop CR technology in the field of 5G further.
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Blockchain for 5G and beyond networks: A state of the art survey
Nguyen, D. C., Pathirana, P. N., Ding, M., & Seneviratne, A. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102693 · Cited by 412 · see citing works
A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access
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- Authors
- Hu, F., Chen, B., & Zhu, K.
- Affiliations
- Nanjing University of Aeronautics and Astronautics (CN)
- Venue
- IEEE Access
- Published
- 2018
- Volume
- 6
- Pages
- 15754-15776
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2789470429
- Cited by
- 321
- References
- 138
- Citations by year
- 2026: 9 · 2025: 22 · 2024: 24 · 2023: 38 · 2022: 52 · 2021: 45 · 2020: 65 · 2019: 50 · 2018: 16
- FWCI
- 27.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Key Research and Development Program of China (2017YFB0802303)
- National Natural Science Foundation of China (61672283)
- Funding of Jiangsu Innovation Program for Graduate Education (KYLX15_0325)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Cognitive Radio Networks and Spectrum Sensing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Harvesting in Wireless Networks
- Keywords
- cognitive radio, 5G, dynamic spectrum access, spectrum scarcity, spectral library, carrier aggregation, common control channel, energy harvesting, non-orthogonal multiple access, aviation communication
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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