A Kronecker-Based Sparse Compressive Sensing Matrix for Millimeter Wave Beam Alignment

TL;DR

This paper introduces a deterministic, Kronecker-structured sparse sensing matrix for millimeter wave beam alignment, improving efficiency and performance over existing methods, especially in low SNR conditions.

Contribution

It proposes a novel Kronecker-based deterministic sensing matrix that satisfies RIP, enhancing sparse channel reconstruction in millimeter wave beam alignment.

Findings

Outperforms existing random beamforming techniques in low SNR scenarios

Satisfies the RIP condition ensuring accurate sparse vector recovery

Computationally efficient due to its sparse, structured design

Abstract

Millimeter wave beam alignment (BA) is a challenging problem especially for large number of antennas. Compressed sensing (CS) tools have been exploited due to the sparse nature of such channels. This paper presents a novel deterministic CS approach for BA. Our proposed sensing matrix which has a Kronecker-based structure is sparse, which means it is computationally efficient. We show that our proposed sensing matrix satisfies the restricted isometry property (RIP) condition, which guarantees the reconstruction of the sparse vector. Our approach outperforms existing random beamforming techniques in practical low signal to noise ratio (SNR) scenarios.