Genetic Algorithm-Based Grouping Strategy for IEEE 802.11ah Networks

Area of Science:

• Computer Science
• Electrical Engineering
• Wireless Communication

Background:

• The IEEE 802.11ah standard targets Internet of Things (IoT) applications.
• The Restricted Access Window (RAW) mechanism in IEEE 802.11ah aims to reduce contention and improve energy efficiency by scheduling station transmissions.
• Optimal configuration of RAW parameters is not specified, posing a challenge for network performance.

Purpose of the Study:

• To propose a genetic algorithm (GA)-based grouping strategy for optimizing RAW parameters in IEEE 802.11ah networks.
• To address the challenge of heterogeneous stations with varying Modulation and Coding Schemes (MCS).
• To improve system throughput and fairness in IEEE 802.11ah networks.

Main Methods:

• A genetic algorithm (GA) is developed for grouping stations under the RAW mechanism.
• A fitness function is defined by combining predicted system throughput and fairness.
• GA parameters are tuned for optimal results and efficient convergence.
• The proposed strategy is evaluated through simulations and tested on a Raspberry Pi 3B+.

Main Results:

• The GA-based grouping strategy effectively optimizes RAW parameters for heterogeneous IEEE 802.11ah networks.
• The method demonstrates convergence in approximately 5 seconds on a Raspberry Pi 3B+.
• Comparisons show the proposed mechanism achieves a favorable balance between throughput and fairness.

Conclusions:

• The developed GA-based grouping strategy offers an effective solution for optimizing IEEE 802.11ah RAW mechanisms.
• This approach enhances performance in IoT scenarios with diverse station capabilities.
• The study validates the practical applicability and efficiency of the proposed method.