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September 27, 2025Open Access

A Novel Framework for Enhancing WiFi Performance Through Adaptive Channel Allocation and AI-Driven Interference Mitigation

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Authors

SBSneha Vinayak Bhambure

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Overview

This framework improves wifi throughput and quality of service in dense environments, suggesting AI-driven solutions for interference reduction.

Key Points

  • The framework significantly improves throughput and reduces congestion in wifi networks, enhancing overall performance.
  • Experimental simulations show a notable increase in quality of service (QoS) compared to static allocation methods.
  • The framework utilizes adaptive channel allocation alongside AI-driven interference mitigation for optimal performance.
  • This research addresses challenges in spectrum utilization by implementing a scalable solution in modern wifi deployments.

Cite This Study

Sneha Vinayak Bhambure (2025) studied this question.

synapsesocial.com/papers/68d7cc6aeebfec0fc5238e1bhttps://doi.org/10.31224/5456
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Real-Time Interference Management in Next-Generation Wireless Systems2025
  2. 2AI-Enhanced Distributed Channel Access for Collision Avoidance in Future Wi-Fi 82025
  3. 3Research on Mobile Communication Interference Suppression Technology and Network Optimization for Heterogeneous Networks2026
  4. 4Optimization of Wireless Networks using Artificial Intelligence Algorithms2025
  5. 5A Reinforcement Learning Approach to Improve WiFi Network Performance Coexisting with LTE2024 · 3 citations