Interference-Aware Adaptive Beam Alignment for Hyper-Dense IEEE 802.11ax Internet-of-Things Networks

Area of Science:

• Wireless communication networks
• Internet of Things (IoT) systems
• Network interference mitigation

Background:

• Increasing density of Internet of Things (IoT) devices leads to significant wireless interference.
• Degraded communication quality is a major challenge in hyper-dense wireless networking systems.
• The IEEE 802.11ax standard aims to address interference issues in such environments.

Purpose of the Study:

• To identify a potential delay issue within the multiple network allocation vector technology used in IEEE 802.11ax.
• To propose an adaptive beam alignment algorithm for resolving interference among IoT devices.
• To analyze communication delays in IoT devices under interference conditions using the proposed algorithm.

Main Methods:

• Identification of a problem in the multiple network allocation vector (MANV) approach.
• Development of an adaptive beam alignment algorithm for interference resolution.
• Simulation of the proposed algorithm in densely deployed IoT environments.

Main Results:

• The proposed adaptive beam alignment algorithm effectively mitigates interference in hyper-dense wireless networks.
• Simulations demonstrate reduced communication delays for IoT devices.
• Enhanced fair utilization of the air interface for IEEE 802.11ax devices compared to conventional methods.

Conclusions:

• The multiple network allocation vector may introduce communication delays in dense IoT networks.
• The adaptive beam alignment algorithm offers a viable solution for interference management.
• The proposed method improves the performance and fairness of IEEE 802.11ax-based IoT communications.