4.7 Article

A Survey on Resource Allocation for 5G Heterogeneous Networks: Current Research, Future Trends, and Challenges

Journal

IEEE COMMUNICATIONS SURVEYS AND TUTORIALS
Volume 23, Issue 2, Pages 668-695

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/COMST.2021.3059896

Keywords

Wireless communication; Macrocell networks; 5G mobile communication; Interference; Tutorials; Market research; Heterogeneous networks; Resource allocation; heterogeneous networks; spectrum efficiency; machine learning; 5G communications

Funding

  1. National Natural Science Foundation of China [61601071, 62071078]
  2. Natural Science Foundation of Chongqing [cstc2019jcyj-xfkxX0002]
  3. Major Project of the Ministry of Industry and Information Technology of China [TC190A3WZ-2]
  4. Six Top Talents Program of Jiangsu [XYDXX-010]
  5. Jiangsu Province Innovation and Entrepreneurship Team [CZ002SC19001]

Ask authors/readers for more resources

This article provides a comprehensive survey on RA in HetNets for 5G communications, covering definitions, scenarios, models, current algorithms, challenges, and future trends. It also proposes two potential structures for solving the RA problems in next-generation HetNets.
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.

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