Shifting Maximum Eigenvalue Detection in Low SNR Environment

TL;DR

This paper introduces the SMED algorithm that shifts the maximum eigenvalue out of the Marchenko-Pastur bulk, transforming its Tracy-Widom distribution into a Gaussian, thereby simplifying detection and improving performance in low SNR environments.

Contribution

The paper proposes the SMED algorithm, which shifts the maximum eigenvalue to enable Gaussian approximation, enhancing detection accuracy in low SNR conditions.

Findings

SMED outperforms traditional MED and FMD algorithms.

The shifted eigenvalue follows a Gaussian distribution, simplifying analysis.

Simulation results confirm improved detection performance.

Abstract

Maximum eigenvalue detection (MED) is an important application of random matrix theory in spectrum sensing and signal detection. However, in small signal-to-noise ratio environment, the maximum eigenvalue of the representative signal is at the edge of Marchenko-Pastur (M-P) law bulk and meets the Tracy-Widom distribution. Since the distribution of Tracy-Widom has no closed-form expression, it brings great difficulty in processing. In this paper, we propose a shifting maximum eigenvalue (SMED) algorithm, which shifts the maximum eigenvalue out of the M-P law bulk by combining an auxiliary signal associated with the signal to be detected. According to the random matrix theory, the shifted maximum eigenvalue is consistent with Gaussian distribution. The proposed SMED not only simplifies the detection algorithm, but also greatly improve the detection performance. In this paper, the performance of SMED, MED and trace (FMD) algorithm is analyzed and the theoretical performance comparisons are obtained. The algorithm and theoretical results are verified by the simulations in different signal environments.