Joint Jammer Mitigation and Data Detection

2025-10-02

AI Summary

Traditional MIMO interference suppression suffers from spectral efficiency loss due to dedicated training phases and struggles against intelligent or time-varying multi-antenna interference. To address this, we propose a training-free paradigm that jointly performs interference suppression and data detection. By jointly estimating interference subspaces and detecting signals across multiple time slots, our approach simultaneously nulls interference subspaces and recovers legitimate signals—achieving, for the first time, tightly coupled joint processing of interference suppression and symbol detection, thereby effectively countering dynamic beamforming and covert interference. We design two complementary algorithms: SANDMAN (high-accuracy) and MAED (low-complexity), which trade off channel estimation strategy against performance–complexity requirements. Simulation results demonstrate significant detection performance gains across diverse interference scenarios, while maintaining data rates close to the interference-free upper bound—thus jointly enhancing communication efficiency and security.

Technology Category

• Machine Learning: Unsupervised & Self-Supervised Learning
• Cognitive Modeling & Cognitive Systems: Applications
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Multi-antenna (or MIMO) processing is a promising solution to the problem of jammer mitigation. Existing methods mitigate the jammer based on an estimate of its spatial signature that is 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 do not transmit 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 of JMD is to estimate and remove the jammer interference subspace jointly with detecting the legitimate transmit data over multiple time slots. Doing so removes the need for a dedicated and rate-reducing training period while being able to mitigate smart and dynamic multi-antenna jammers. We provide two JMD-type algorithms, SANDMAN and MAED, that differ in the way they estimate the channels of the legitimate transmitters and achieve different complexity-performance tradeoffs. Extensive simulations demonstrate the efficacy of JMD for jammer mitigation.

Problem

• Mitigating smart jammers without dedicated training phases
• Eliminating rate reduction caused by jammer mitigation training
• Addressing dynamic multi-antenna jammers with time-varying beamforming

Innovation

• Joint jammer mitigation and data detection
• No dedicated training phase required
• Mitigates smart and dynamic multi-antenna jammers