Compound TCP Performance for Industry 4.0 WiFi: A Cognitive Federated Learning Approach

摘要

Understanding the performance of compound transmission control protocol (C-TCP) in wireless settings is complicated because of C-TCP's hybrid congestion control, and the complex interdependencies between losses due to wireless channel errors, medium access control (MAC)-layer collisions, and access point (AP) buffer overflows. In this article, we develop a comprehensive model to study the performance of long-lived C-TCP flows over Industry 4.0 WiFi infrastructure, taking all losses into account. Our mathematical model includes WiFi system parameters, such as the retransmissions limit and the AP buffer size, in order to see how they affect transport-layer throughput and fairness. More importantly, we extend the analytical model to multiple APs, and compare the performance of a dual AP scenario with a conventional single AP scenario. Our results show that using cognitive radio and federated learning techniques in the industrial multiple APs scenario can substantially improve the performance.