Mitigating Smart Jammers in MU-MIMO via Joint Channel Estimation and Data Detection

TL;DR

This paper introduces MAED, a joint estimation and detection method that effectively mitigates smart jamming attacks in MU-MIMO systems without prior knowledge of attack specifics.

Contribution

The paper proposes a novel joint approach called MAED that combines jammer estimation, channel estimation, and data detection for MU-MIMO systems, addressing smart jammers.

Findings

MAED effectively mitigates various smart jamming attacks.

The method does not require prior knowledge of attack types.

MAED outperforms existing mitigation techniques.

Abstract

Wireless systems must be resilient to jamming attacks. Existing mitigation methods require knowledge of the jammer's transmit characteristics. However, this knowledge may be difficult to acquire, especially for smart jammers that attack only specific instants during transmission in order to evade mitigation. We propose a novel method that mitigates attacks by smart jammers on massive multi-user multiple-input multiple-output (MU-MIMO) basestations (BSs). Our approach builds on recent progress in joint channel estimation and data detection (JED) and exploits the fact that a jammer cannot change its subspace within a coherence interval. Our method, called MAED (short for MitigAtion, Estimation, and Detection), uses a novel problem formulation that combines jammer estimation and mitigation, channel estimation, and data detection, instead of separating these tasks. We solve the problem approximately with an efficient iterative algorithm. Our results show that MAED effectively mitigates a wide range of smart jamming attacks without having any a priori knowledge about the attack type.