Abstract

Dual connectivity (DC) was first proposed in 3GPP Release 12 which allows one piece of user equipment (UE) to connect to two base stations in heterogeneous networks (HetNet) at the same time, to increase the flexibility of resource utilization. DC has been further extended to multiple connectivity in 5G New Radio (NR). On the other hand, different UE tends to have different bandwidth requirements. Thus, in DC, one of the challenging issues is how to integrate resources from two base stations to enhance the quality of service (QoS) as well as the data transfer rate of each UE. In this paper, we proposed novel resource management mechanisms to improve the QoS of UE in the co-channel dual connectivity network. In terms of resource allocation, we designed the (MTS) which, in principle, allocates a resource block to the UE with the best channel quality while considering the issues of intercell resource allocation and the QoS requirement of each UE. In order to balance the load of different cells, we designed a novel cell selection scheme based on the HetNet Congestion Indicator (HCI) which considers not only the signal quality of UE but also the remaining resources of each base station. To improve the QoS of cell edge UE, cell range expansion (CRE) and the Almost Blank Subframe (ABS) were proposed in 3GPP. In this paper, based on Q-learning, we designed an adaptive mechanism which dynamically adjusts the ABS ratio according to the network condition to improve resource utilization. Our simulation results showed that our MTS scheduler was able to achieve a 31.44% higher data rate than the Proportional Fairness Scheduler; our HCI cell selection scheme yielded a 2.98% higher data rate than the signal-to-interference plus noise ratio (SINR) cell selection scheme; the QoS satisfaction rate of our Q-learning dynamic ABS scheme was 4.06% higher than that of the Static ABS scheme. Finally, for the cell edge users who often suffer poor data transfer rate, by integrating the mechanisms of DC, CRE, and ABS, our experimental results showed that the QoS satisfaction ratio of cell edge UEs could be improved by 10.76% as compared to the single connectivity and no ABS situation.

Highlights

  • Due to the rapidly growing mobile data traffic, 4G is not able to meet the current traffic demand.With the limited wireless radio resource, dual connectivity (DC) technology and heterogeneous network (HetNet) architecture can improve the data transfer rate beyond 4G

  • In order to balance the load of different cells, we designed a novel cell selection scheme based on the HetNet Congestion Indicator (HCI) which considers the signal quality of user equipment (UE) and the remaining resources of each base station

  • Our simulation results showed that our min Threshold Scheduler (MTS) scheduler was able to achieve a 31.44% higher data rate than the Proportional Fairness Scheduler; our HCI cell selection scheme yielded a 2.98% higher data rate than the signal-to-interference plus noise ratio (SINR) cell selection scheme; the quality of service (QoS) satisfaction rate of our Q-learning dynamic Almost Blank Subframe (ABS) scheme was 4.06% higher than that of the Static ABS scheme

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Summary

Introduction

Due to the rapidly growing mobile data traffic, 4G is not able to meet the current traffic demand. In [6], authors indicated that MeNB and SeNB have different scheduling requirements, proposing the coordination mechanism to prevent the user equipment from using beyond its maximum transmission power. Many other factors are considered in cell selection in the literature, such as load balance, utility function of proportional fair, dynamic strategy, and data transfer rate [18,19,20,21]. The numerical results show that our proposed a guaranteed fix transmission rate, while that of MBR is a guaranteed of minimum rate and aschemes limit of had significant the QoS satisfaction ratio thanschemes previoushad works in the literature.

Co-Channel Networks with Dual Connectivity
Optimal Resource Allocation
Proposed Schemes
Max–Min Threshold Scheduler
Max–Min Scheduling
A TB consists of several
HetNet Congestion Indicator
A UE continuously the BSs
Classify a UE as a MUE or PUE Type
Q-Learning Dynamic Almost Blank Subframe
Related Works
Simulation Assumption
Q-Learning-Based Dynamic Almost Blank Subframe
Output Indicators
Evaluation for the Cell Selection of HCI Scheme
Evaluation for for thethe
Evaluation for the Resource Allocation of PF Scheme
Evaluation for the E-ICIC of QD-ABS Scheme
Evaluation for the Dual Connectivity Combined with CRE and ABS
Conclusions and and Future
Full Text
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