Resilient Heterogeneous Network Architecture: A Deep Reinforcement Learning And Cryptographic Provenance Approach For Performance Optimization Under Cascading Failures

Abstract

Cascading failures in telecommunications infrastructure during large-scale disasters (bushfires, hurricanes) isolate emergency operations centers from field personnel, preventing real-time situational awareness required for coordinated response. This paper presents a heterogeneous network (HetNet) architecture integrating terrestrial cellular (LTE, Public Safety Band), satellite backhaul (Ku-band, L-band emergency systems), land mobile radio (VHF/UHF), and mobile ad-hoc networks (IEEE 802.11p mesh) with AI-driven dynamic resource allocation to maintain communications resilience during cascading infrastructure failures. The proposed architecture employs: (1) multi-path routing optimization to maximize redundancy across heterogeneous links, (2) spectrum sensing algorithms (MIMO, adaptive modulation and coding) to maintain signal quality in degraded RF environments, (3) cryptographic audit trails enabling post-event forensic analysis, and (4) machine learning-based traffic classification for priority-based resource allocation. Mathematical modeling establishes network reliability as a function of redundancy parameter k, with significant packet delivery ratio (PDR) improvement from legacy cellular-only to proposed HetNet configurations under cascading failure scenarios. Simulation results using NS-3 demonstrate rapid system recovery time across all frequency bands and latency maintenance for critical emergency dispatch despite substantial infrastructure outage conditions. The design principles apply to telecommunications resilience requirements in public safety networks, essential communications infrastructure, and disaster response operations.