Advanced Jamming-Resilient RF Front-End Architecture for Secure, Low-Latency Mission-Critical IoT Systems

Abstract

The wireless communications that are heavily used in mission-critical Internet of Things (IoT) situations, like those involved in an industrial control system, smart defense infrastructure, and emergency response networks are very vulnerable to purposeful radio frequency (RF) jamming which present high risks of operational resilience, data integrity as well as response time. The present paper suggests a sophisticated, simulation-proven, and hardware-in-the-loop (HIL)-tested jamming-tolerant RF front end system architecture that guarantees a secure, low-latency communication out of aggressive spectral environments. The system architecture includes wideband low-noise amplifier (LNA) that is reconfigurable, tunable bandpass filters realized using MEMS, and frequency-agile local oscillators that can support real-time frequency hopping spread spectrum (FHSS). To achieve adaptive threat mitigation, there has been lightweight convolutional neural network (CNN)-based jamming engine at the RF front-end to achieve dynamic spectral awareness and jamming pattern classification (e.g., tone, sweeping, and reactive jammers). Real-time signal-to-interference-plus-noise ratio (SINR) and bit error rate (BER) measurements determine whether to hop or not in a closed-loop control system. The design is confirmed with full-wave simulations in CST and behavioral modeling in MATLAB/Simulink after which the design is tested on the USRP SDRs and embedded controller in the hardware-in-the-loop testing. It has shown more than 94% a jamming resistance, latencies below 2 ms, and high throughput performance in a jammed 2.4 GHz ISM band system. The suggested RF front-end architecture ensures the provision of a scalable, hardware-efficient technique of ensuring robust and resilient wireless connection in timesensitive and security-relevant IoT implementations.