Specific Multi-emitter Identification: Theoretical Limits and Low-complexity Design

TL;DR

This paper introduces a multi-emitter identification framework using multi-label learning, establishing theoretical performance bounds and proposing a low-complexity, attention-based method that effectively identifies overlapping signals in wireless networks.

Contribution

It presents the first multi-emitter identification approach with theoretical bounds, a reduced complexity formulation, and an attention-enhanced method for improved accuracy in overlapping scenarios.

Findings

Achieves high accuracy with linear complexity

The I-SMEI method outperforms existing techniques

Establishes performance bounds using Fano's inequality

Abstract

Specific emitter identification (SEI) distinguishes emitters by utilizing hardware-induced signal imperfections. However, conventional SEI techniques are primarily designed for single-emitter scenarios. This poses a fundamental limitation in distributed wireless networks, where simultaneous transmissions from multiple emitters result in overlapping signals that conventional single-emitter identification methods cannot effectively handle. To overcome this limitation, we present a specific multi-emitter identification (SMEI) framework via multi-label learning, treating identification as a problem of directly decoding emitter states from overlapping signals. Theoretically, we establish performance bounds using Fano's inequality. Methodologically, the multi-label formulation reduces output dimensionality from exponential to linear scale, thereby substantially decreasing computational complexity. Additionally, we propose an improved SMEI (I-SMEI), which incorporates multi-head attention to effectively capture features in correlated signal combinations. Experimental results demonstrate that SMEI achieves high identification accuracy with a linear computational complexity. Furthermore, the proposed I-SMEI scheme significantly improves identification accuracy across various overlapping scenarios compared to the proposed SMEI and other advanced methods.