A Survey on Resource Allocation for 5G Heterogeneous Networks: Current Research, Future Trends, and Challenges
書誌事項
- 公開日
- 2021
- 権利情報
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- https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
- https://doi.org/10.15223/policy-029
- https://doi.org/10.15223/policy-037
- DOI
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- 10.1109/comst.2021.3059896
- 公開者
- Institute of Electrical and Electronics Engineers (IEEE)
説明
In the fifth-generation (5G) mobile communication system, various service requirements of different communication environments are expected to be satisfied. As a new evolution network structure, heterogeneous network (HetNet) has been studied in recent years. Compared with homogeneous networks, HetNets can increase the opportunity in the spatial resource reuse and improve users’ quality of service by developing small cells into the coverage of macrocells. Since there is mutual interference among different users and the limited spectrum resource in HetNets, however, efficient resource allocation (RA) algorithms are vitally important to reduce the mutual interference and achieve spectrum sharing. In this article, we provide a comprehensive survey on RA in HetNets for 5G communications. Specifically, we first introduce the definition and different network scenarios of HetNets. Second, RA models are discussed. Then, we present a classification to analyze current RA algorithms for the existing works. Finally, some challenging issues and future research trends are discussed. Accordingly, we provide two potential structures for 6G communications to solve the RA problems of the next-generation HetNets, such as a learning-based RA structure and a control-based RA structure. The goal of this article is to provide important information on HetNets, which could be used to guide the development of more efficient techniques in this research area.
収録刊行物
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- IEEE Communications Surveys & Tutorials
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IEEE Communications Surveys & Tutorials 23 (2), 668-695, 2021
Institute of Electrical and Electronics Engineers (IEEE)
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詳細情報 詳細情報について
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- CRID
- 1360861293074419072
-
- ISSN
- 1553877X
- 2373745X
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- データソース種別
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- Crossref
- OpenAIRE