PROJECT DESCRIPTION

TRENDS AND OPPORTUNITIES

Traditionally, Wi-Fi optimization has been focused on maximizing throughput, but today’s priority is minimizing latency. While managed QoS solutions can improve Wi-Fi latency, they aren’t always suitable for unmanaged environments or when the goal is to enhance latency across all applications on the network. As such, optimizing EDCA parameters is essential for meeting the performance demands of modern users and ensuring Wi-Fi networks are future-proof.

Wi-Fi deployments vary widely, from residential and enterprise setups to public venues, each with distinct characteristics. EDCA (Enhanced Distributed Channel Access) is a critical mechanism in Wi-Fi networks that significantly impacts performance, particularly latency and throughput. However, with the growing number of devices and the expanding variety of applications, EDCA can operate sub-optimally, leading to high and variable latency. This often results in poor user experiences, which highlights the need for more effective optimization strategies.

BUSINESS & MARKET BENEFITS

This project optimizes network performance by refining EDCA parameters to improve latency, quality of service, and throughput across residential, enterprise, and automotive environments, while ensuring fairness for legacy systems. By enhancing user experience through reduced latency and improved service quality, the project drives customer satisfaction and loyalty.

The optimization reduces infrastructure upgrade costs, improves operational performance, supports scalability to handle growing device numbers without major hardware changes, and ensures smooth transitions with legacy systems.

Businesses offering optimized Wi-Fi networks gain a competitive edge, attracting more customers. This solution provides a cost-effective, scalable approach to enhancing network performance and user satisfaction across diverse environments.

EXPECTED OUTCOMES

  • Enhance Wi-Fi network latency by optimizing EDCA parameters across different deployment types, while limiting impact on overall capacity.
  • Explore innovative approaches like Adaptive EDCA parameterization and predictive models, using feature vectors for training. Base guidelines on data gathered from simulations, tests, and new techniques such as machine learning.
  • Develop strategies to improve latency without compromising throughput, ensuring scalability and performance across diverse environments.

KEY PARTICIPANTS