Optimising Data Retransmission Performance Through Intelligent Set-Associative Cache Memory Management: An NS-3 Simulation Informed by Emergency-Department WLAN Traffic

Modern network communication systems require reliable data transmission while maintaining high performance and low latency. This study proposes a retransmission-aware Set-Associative Memory Management (SAMM) architecture integrating adaptive replacement, priority-aware retention, admission control, and heuristic prefetching. Emergency-department WLAN characteristics observed using CommView at Charlotte Maxeke Johannesburg Academic Hospital provided representative network and workload context for the simulation; SAMM itself was not deployed or evaluated in the hospital network. Cache-management performance was evaluated using a reproducible ns-3.42 simulation framework. The implemented retransmission-likelihood estimator was deterministic and ACK/NACK-driven; no trained machine-learning model was experimentally evaluated. Across five independent ns-3.42 runs, SAMM achieved a higher aggregate cache hit rate than set-associative LRU (18.25% versus 14.76%), reduced mean access latency from 43.97 to 42.17 ms, and increased throughput from 219.66 to 229.96 Mbps. Values were evaluated with 95% confidence intervals across the five runs. A complementary five-seed trace-driven sensitivity analysis provided paired statistical support for improvements in cache hit rate, access latency, and throughput relative to SA-LRU (all p < 0.001). However, SAMM showed lower modelled memory efficiency (94.56% versus 98.94%) and slightly higher modelled CPU overhead (0.69% versus 0.66%), while the network-optimised cache achieved a higher retransmission-specific hit rate. These findings indicate a performance–complexity trade-off rather than uniform superiority across all evaluated metrics.