Performance Analysis of a Mission-Critical Portable LTE System in Targeted RF Interference

TL;DR

This paper evaluates the robustness of a mission-critical LTE system against RF interference using SDR technology, demonstrating how targeted jamming affects throughput and proposing a classification method for attack detection.

Contribution

It introduces a configurable interference waveform testing methodology and applies machine learning for RF attack detection in LTE networks.

Findings

Synchronization signal interference significantly reduces throughput.

K-nearest neighbor classification effectively detects RF signaling attacks.

Dynamic performance evaluation aids in attack mitigation.

Abstract

Mission-critical wireless networks are being up-graded to 4G long-term evolution (LTE). As opposed to capacity, these networks require very high reliability and security as well as easy deployment and operation in the field. Wireless communication systems have been vulnerable to jamming, spoofing and other radio frequency attacks since the early days of analog systems. Although wireless systems have evolved, important security and reliability concerns still exist. This paper presents our methodology and results for testing 4G LTE operating in harsh signaling environments. We use software-defined radio technology and open-source software to develop a fully configurable protocol-aware interference waveform. We define several test cases that target the entire LTE signal or part of it to evaluate the performance of a mission-critical production LTE system. Our experimental results show that synchronization signal interference in LTE causes significant throughput degradation at low interference power. By dynamically evaluating the performance measurement counters, the k- nearest neighbor classification method can detect the specific RF signaling attack to aid in effective mitigation.