Joint user association and resource allocation in HetNets based on user mobility prediction
Author | Cheng, Zhipeng |
Author | Chen, Ning |
Author | Liu, Bang |
Author | Gao, Zhibin |
Author | Huang, Lianfen |
Author | Du, Xiaojiang |
Author | Guizani, Mohsen |
Available date | 2022-11-27T13:01:00Z |
Publication Date | 2020-08-04 |
Publication Name | Computer Networks |
Identifier | http://dx.doi.org/10.1016/j.comnet.2020.107312 |
Citation | Cheng, Z., Chen, N., Liu, B., Gao, Z., Huang, L., Du, X., & Guizani, M. (2020). Joint user association and resource allocation in HetNets based on user mobility prediction. Computer Networks, 177, 107312. |
ISSN | 13891286 |
Abstract | Virtual small cell (VSC) formed by directional beams is seen as an alternative for the small base station (SBS) within the coverage of macro base station (MBS), to increase system capacity and reduce site cost. However, the flexibility of development for VSC poses challenges to user association and resource allocation in heterogeneous networks. In this paper, we consider the joint problem of user association and resource allocation in VSC aided multi-tier heterogeneous networks. To better analyze the formation of VSC and the impact of user mobility on the system, a user mobility prediction model is firstly constructed based on the Markov model. Then the joint user association and resource allocation problem is formulated to maximize the system capacity. Since the aforementioned problem is a coupling problem, two different solutions, namely, a decoupling solution and a coupling solution, are proposed based on the multi-agent Q-learning (MAQL) method, to find the optimal user association and resource allocation strategy. Moreover, to overcome the state and action space explosion in MAQL and accelerate convergence, the deep Q-network (DQN) is applied. Simulation results reveal that the deployment of VSC can increase the system capacity and spectrum efficiency. The coupling solution achieves better performance than the decoupling solution under a large number of users. |
Sponsor | This work was supported by National Natural Science Foundation of China (Grant Number 61971365,61871339). |
Language | en |
Publisher | Elsevier B.V. |
Subject | Deep Q-network Heterogeneous networks Multi-agent Q-learning Resource allocation User association User mobility prediction Virtual small cell |
Type | Article |
Volume Number | 177 |
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