Antenna Health-Aware Selective Beamforming for Hardware-Constrained DFRC Systems II

TL;DR

This paper presents a novel antenna health-aware beamforming method for DFRC systems that improves resilience by focusing on reliable antennas, using advanced optimization to balance spectral efficiency and system reliability.

Contribution

It introduces a new beamforming approach incorporating antenna health information and applies GPGDA optimization to enhance system robustness and efficiency in sparse array selection.

Findings

Increasing sparsity weight improves spectral efficiency and data rate.

Perfect reliability achieved at high sparsity weights, with a performance threshold.

Analysis highlights computational complexity and power consumption considerations.

Abstract

This study introduces an innovative beamforming design approach that incorporates the reliability of antenna array elements into the optimization process, termed "antenna health-aware selective beamforming". This method strategically focuses transmission power on more reliable antenna elements, thus enhancing system resilience and operational integrity. By integrating antenna health information and individual power constraints, our research leverages advanced optimization techniques such as the Group Proximal-Gradient Dual Ascent (GPGDA) to efficiently address nonconvex challenges in sparse array selection. Applying the proposed technique to a Dual-Functional Radar-Communication (DFRC) system, our findings highlight that increasing the sparsity promotion weight ($\rho_s$) generally boosts spectral efficiency and communication data rate, achieving perfect system reliability at higher $\rho_s$ values but also revealing a performance threshold beyond which further sparsity is detrimental. This underscores the importance of balanced sparsity in beamforming for optimizing performance, particularly in critical communication and defense applications where uninterrupted operation is crucial. Additionally, our analysis of the time complexity and power consumption associated with GPGDA underscores the need for optimizing computational resources in practical implementations.