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

Abstract

In the fifth-generation (5G) wireless communications system, various service requirements under different communication environments are expected to be satisfied. As a new evolution network structure, heterogeneous networks (HetNet) have been fully studied in recent years. In contrast to conventional homogeneous networks, the key feature of HetNet is to increase the opportunity of spatial resource reuse and improve the quality of service of users by allowing small cells to cooperate in macrocell networks. However, since the mutual interference among different users and the limited resource are existing in HetNets, efficient resource allocation (RA) schemes are very important to reduce the interference and achieve spectrum sharing. In this paper, we provide a comprehensive survey on RA in HetNets for 5G communications. Specifically, we first introduce the definition and different network scenarios of HetNet. Second, RA models are discussed. Then, we present classification to analyze current RA schemes in the existing references. Finally, some challenging open issues and future research trends are addressed in this field. We also provide two effective approaches for the sixth-generation (6G) communications to solve the RA problems of future HetNets, namely, a learning-based approach and a control theory-based approach. This paper provides important information on HetNets, which can be used to guide the development of more efficient RA schemes in this area.

Resource Allocation Schemes for 5G Network: A Systematic Review

“…Researchers in [ 62 ] presented a complete assessment on resource allocation (RA) in heterogeneous networks for 5G communications. First, they went through a description of HetNet and the various network situations.…”

Machine Learning-Aided Energy Efficiency Strategy for Multiuser Cooperative Networks

“…Owing to the rapid growth of machine learning (ML), many difficult research issues have been solved by MLbased methods, especially in future communication systems [6]. Thus, various ML-based approaches for wireless communication have been proposed in recent years [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. For example, in [8], the authors used an ML-aided method to predict trajectory and detect conflict for aerial vehicles.…”

RIS-Enhanced WPCNs: Joint Radio Resource Allocation and Passive Beamforming Optimization

“…However, most of the existing works [34]- [37] employed the HAP to transfer wireless energy and receive wireless information so that it leads to a 'doubly near-far' problem 1 . Moreover, most of the existing works aim to improve the system rate while neglecting system EE, which is also a critical issue for EHbased communication systems 2 .…”

“…This phenomenon may cause a certain unfairness. 2 For an EH-based communication network, the amount of the harvested energy and the energy-conversion efficiency are extremely low, so that considering a trade-off between data rates and energy consumption becomes an important design aspect for overall performance improvement. 3 The separated PS and the IR can well overcome the 'doubly near-far' phenomenon.…”