Work

Accelerating Broadband DOA Estimation: A Real-Valued and Coherent Sparse Bayesian Approach for 5G Sensing

Year: 2025

Type: article

Abstract: For applications like smart cities and autonomous driving, high-precision direction-of-arrival (DOA) estimation for 5G broadband signals is essential. A primary obstacle for existing methods is the spatial incoherence caused by multi-frequency propagation. We present a sparse Bayesian learning (SBL) algorithm specifically designed to resolve this issue while also minimizing computational load. The algorithm synergistically combines three key components: first, a multiple-signal classification (MUSIC)-like focusing technique ensures a coherent sparse model; second, a real-valued transformation significantly cuts down on computational complexity; and third, an optimized variational Bayesian inference accelerates convergence via root-finding. Validation against MUSIC and rootSBL confirms our method’s marked superiority in low-SNR, limited-snapshot, and multipath conditions delivering both higher accuracy and faster convergence. This work, thus, contributes an effective and practical solution for real-time 5G DOA sensing.

Source: Electronics

DOI: 10.3390/electronics14163174

Authors: Xin Tong, Yinzhe Hu, Zhongliang Deng, Enwen Hu

Institution: Beijing University of Posts and Telecommunications

Language: English

Topic: Direction-of-Arrival Estimation Techniques

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896