Abstract
In this paper, we consider the saturation problem in the 3GPP LTE cellular system caused by the expected huge number of machine‐type communication (MTC) devices, leading to a significant impact on both machine‐to‐machine (M2M) and human‐to‐machine H2H traffic. M2M communications are expected to dominate traffic in LTE and beyond cellular networks. In order to address this problem, we proposed an advanced architecture designed for 5G LTE networks to enable the coexistence of H2H/M2M traffic, supported by different priority strategies to meet QoS for each traffic. The queuing strategy is implemented with an M2M gateway that manages four queues allocated to different types of MTC traffic. The optimal radio resource allocation method in LTE and beyond cellular networks was developed. This method is based on adaptive selection of channel bandwidth depending on the QoS requirements and priority traffic aggregation in the M2M gateway. Additionally, a new simulation model is proposed which can help in studying and analyzing the mutual impact between M2M and H2H traffic coexistence in 5G networks while considering high and low priority traffics for both M2M and H2H devices. This simulator automates the proposed method of optimal radio resource allocation between the M2M and H2H traffic to ensure the required QoS. Our simulation results proved that the proposed method improved the efficiency of radio resource utilization to 13% by optimizing the LTE frame formation process.
Highlights
Due to the rapid growth of mobile data traffic, the popularity of Internet of Things (IoT), and M2M, mobile operators are constantly focused on improving the quality of service (QoS) provision, developing 4G networks into the future software-defined heterogeneous 5G/6G networks based on Long-Term Evolution (LTE) technology [3,4,5,6]
The remainder of this paper is organized as follows: Section 2 presents the related works, Section 3 presents the proposed LTE network architecture for 5G/6G mobile communication systems, Section 4 describes the radio resource allocation algorithm between Human-Type Communications (HTC) and machine-type communications (MTC) to ensure QoS, Section 5 presents the development of an optimal radio resource allocation method in 5G LTE networks based on adaptive channel bandwidth selection of the conclusions of the study, Section 6 presents simulation results, and Section 7 presents the conclusions of the study
Based on our review of works, we found that the known methods of radio resource allocation do not have the necessary flexibility to serve mobile users and M2M sensors
Summary
Due to the rapid development of IoT technologies and the constant growth of the number of mobile users, the actual scientific and practical task is to increase the efficiency of radio resource utilization and quality of service in the new-generation mobile communication systems by improving flexible information flow management models and methods for optimal allocation of network resources. This method improved the efficiency of licensed radio resource utilization by optimizing the LTE frame formation process and reducing the share of signal traffic in these frames (2) A new simulation model for research of the state-ofthe-art mobile communication network functioning process was developed This simulator takes into account a significant basic number of technical parameters of 5G LTE 3GPP standard functioning to create real research conditions and automates the proposed method of optimal radio resource allocation between the traffic of mobile users and MTC devices to ensure the required QoS. The remainder of this paper is organized as follows: Section 2 presents the related works, Section 3 presents the proposed LTE network architecture for 5G/6G mobile communication systems, Section 4 describes the radio resource allocation algorithm between HTC and MTC to ensure QoS, Section 5 presents the development of an optimal radio resource allocation method in 5G LTE networks based on adaptive channel bandwidth selection of the conclusions of the study, Section 6 presents simulation results, and Section 7 presents the conclusions of the study
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