Joint Jammer Mitigation and Data Detection for Smart, Distributed, and Multi-Antenna Jammers

TL;DR

This paper introduces a joint jammer mitigation and data detection approach for MIMO systems that eliminates the need for dedicated training phases, effectively countering smart and dynamic jammers through a novel estimation method.

Contribution

It proposes a new paradigm for MIMO jammer mitigation that jointly estimates jammer interference and detects data, improving robustness against smart and dynamic jammers.

Findings

JMD effectively mitigates various jammer types in simulations.

SANDMAN algorithm demonstrates practical viability and robustness.

Eliminates the need for dedicated training phases, maintaining communication rate.

Abstract

Multi-antenna (MIMO) processing is a promising solution to the problem of jammer mitigation. Existing methods mitigate the jammer based on an estimate of its subspace (or receive statistics) acquired through a dedicated training phase. This strategy has two main drawbacks: (i) it reduces the communication rate since no data can be transmitted during the training phase and (ii) it can be evaded by smart or multi-antenna jammers that are quiet during the training phase or that dynamically change their subspace through time-varying beamforming. To address these drawbacks, we propose joint jammer mitigation and data detection (JMD), a novel paradigm for MIMO jammer mitigation. The core idea is to estimate and remove the jammer interference subspace jointly with detecting the transmit data over multiple time slots. Doing so removes the need for a dedicated rate-reducing training period while enabling the mitigation of smart and dynamic multi-antenna jammers. We instantiate our paradigm with SANDMAN, a simple and practical algorithm for multi-user MIMO uplink JMD. Extensive simulations demonstrate the efficacy of JMD, and of SANDMAN in particular, for jammer mitigation.