Interference-Aware Subcarrier Allocation for Massive Machine-Type Communication in 5G-Enabled Internet of Things.

Area of Science:

• Wireless Communication Networks
• Internet of Things (IoT)
• 5G Network Technologies

Background:

• Massive machine-type communication (mMTC) is a key scenario in 5th-generation (5G) networks.
• 5G-enabled Internet of Things (IoT) integrates enhanced mobile broadband (eMBB) and mMTC devices.
• Limited spectrum resources pose challenges for mMTC data transmission in 5G IoT.

Purpose of the Study:

• To address the subcarrier allocation problem in 5G-enabled IoT networks.
• To maximize the connectivity of mMTC devices while meeting Quality of Service (QoS) requirements for both eMBB and mMTC devices.
• To develop an efficient algorithm for managing spectrum resources in dense IoT environments.

Main Methods:

• Formulation of the subcarrier allocation problem as a mixed-integer non-linear programming (MINLP) problem.
• Development of an interference-aware subcarrier allocation algorithm for mMTC communication (IASA).
• Simulation-based performance evaluation of the proposed IASA algorithm against benchmark methods.

Main Results:

• The proposed IASA algorithm effectively maximizes the number of active mMTC devices.
• The algorithm significantly improves the utilization of the uplink spectrum in 5G IoT networks.
• Simulations demonstrate superior performance of IASA compared to three traditional benchmark methods.

Conclusions:

• The IASA algorithm offers a robust solution for subcarrier allocation in 5G-enabled IoT environments.
• Improved spectrum utilization and enhanced mMTC device connectivity are key benefits.
• The proposed approach is well-suited for supporting diverse IoT applications with varying communication needs.