Abstract
Data volume demand has increased dramatically due to huge user device increasement along with the development of cellular networks. And macrocell in 5G networks may encounter sudden traffic due to dense users caused by sports or celebration activities. To resolve such temporal hotspot, additional network access point has become a new solution for it, and unmanned aerial vehicle equipped with base stations is taken as an effective solution for coverage and capacity improvement. How to plan the best three-dimensional location of the aerial base station according to the users’ business needs and service scenarios is a key issue to be solved. In this article, first, aiming at maximizing the spectral efficiency and considering the effects of line-of-sight and non-line-of-sight path loss for 5G mmWave networks, a mathematical optimization model for the location planning of the aerial base station is proposed. For this model, the model definition and training process of deep Q-learning are constructed, and through the large-scale pre-learning experience of different user layouts in the training process to gain experience, finally improve the timeliness of the training process. Through the simulation results, it points out that the optimization model can achieve more than 90% of the theoretical maximum spectral efficiency with acceptable service quality.
Highlights
Along with varieties of services and the Internet-ofThings (IoT) devices data communication requirements for different scenarios in 5G networks, traffic generations take on drastic spatial and temporal variations, which have caused tremendous pressure on the basic macro base stations (BSs). 5G networks integrate different network architectures, such as cloud radio access networks, ultra-dense networks with heterogeneous cells to improve network density and coverage, achieving higher transmission rate and network capacity
The remaining content is organized as follows: System model is analyzed in section ‘‘System model.’’ And deep Q-network (DQN) procedures for aerial-BSs’ 3D deployment are shown in section ‘‘DQN-based aerial-BS location optimization framework.’’ And simulations are taken in section ‘‘Simulation results.’’ conclusions and recommendations are given in section ‘‘Conclusion and future work.’’
We have studied multiple aerialBSs’ optimal locations to cope with sudden traffic with optimal spectrum efficiency
Summary
Along with varieties of services and the Internet-ofThings (IoT) devices data communication requirements for different scenarios in 5G networks, traffic generations take on drastic spatial and temporal variations, which have caused tremendous pressure on the basic macro base stations (BSs). 5G networks integrate different network architectures, such as cloud radio access networks, ultra-dense networks with heterogeneous cells to improve network density and coverage, achieving higher transmission rate and network capacity. The mmWave includes wireless frequencies of 30–300 GHz and the wavelength range of the radio wave in this frequency band is from 1 to 10 mm, so it is called millimeter wave This frequency has obvious advantages, because it supports higher bandwidth, so it is very suitable for wireless infrastructure applications. In addition to the advantages of large bandwidth and high-speed rate, millimeter wave has narrow beam, good directivity, and high spatial resolution, which improves the transmission efficiency, so mmWave transmission suitable for direct communications can be a feasible method here. 2. Applying DQN algorithm to the optimization of 3D deployment of aerial-BSs and sudden traffic accommodation. The remaining content is organized as follows: System model is analyzed in section ‘‘System model.’’ And DQN procedures for aerial-BSs’ 3D deployment are shown in section ‘‘DQN-based aerial-BS location optimization framework.’’ And simulations are taken in section ‘‘Simulation results.’’ conclusions and recommendations are given in section ‘‘Conclusion and future work.’’
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