# Planar Array Diagnostic Tool for Millimeter-Wave Wireless Communication Systems
## 7 Conclusions  
A faster and robust antenna array diagnosis procedure from far-field radiation pattern measurement points using Bayesian compressive sensing (BCS) approach was proposed in this paper. Previous compressive sensing procedures exhibit shortcomings based on reliability with noisy data, and require a large number of far-field measurement points. The proposed method solves these problems by formulating planar array diagnosis within the concept of the BCS framework, resolved using fast relevance machine (RVM). We are not the first to apply the BCS approach to antenna array diagnosis. It was applied only to linear configurations in References [15; 16] without practical measurements, which are fundamental for testing any procedure. To the best knowledge of the authors, this is the first attempt to apply the BCS approach to planar antenna array diagnosis from far-field measurement points, validated with experimental measurements. Diagnoses from simulated and measured far-field points from a designed microstrip patch antenna array show the method's robustness to additive data noise, as well as its reconstruction accuracy and faster diagnosis speed, which is desired in practical applications. Hence, the proposed method is a better practical choice whenever an efficient, faster, and more reliable antenna array diagnosis (testing) is needed. Also, it is important to comment on the choice of sampling strategy. We considered a random selection of measurement points from a uniform lattice. The choice of the sampling technique is not critical because it affects all the techniques in the same manner at the far-field. However, it was pointed out, from a non-uniform near-field lattice, that proper non-uniform random sampling (NURS) using a priori information on the problem provides meaningful reduction in the cardinality of the set of measured data compared to uniform random sampling and random sampling from a \(\lambda/2\) equispaced dataset [39]. Moreover, the BCS-based technique was compared to other methods using the data reported in the literature. In the future, we will compare different techniques using experimental data from controlled and uncontrolled environments, and the same parameters in order to quantify the error affecting the result of different techniques. For example, we will determine what happens if we use 30 measurements instead of 64 measurements in the experimental data using \(\ell_{1}\) minimization, i.e., the same number of data used by the BCS, as well as the error compared to BCS. A complete comparison among the techniques using real data is still absent in the present literature. Conceptualization, methodology, writing-review and editing, original draft preparation, O.J.F. ; resources, Z.Z. ; visualization, software, A.F. and R.Z. ; supervision, F.L. This research received no external funding. The APC was funded by University of Science and Technology of China. The majority of the work was conducted at MESIC (a joint lab of USTC and IMECAS), and partially carried out at the USTC Center for Micro and Nanoscale Research and Fabrication. The authors would like to thank the Information Science Laboratory Center of USTC for software and hardware services. The authors acknowledge the Applied Electromagnetics and Microwave Engineering Group of Helen Normal University for the provision of the anechoic chamber. The support of the Chinese Academy of Science and the World Academy of Science (CAS-TWAS) is appreciated. The authors appreciate [PERSON] [40] and the CVX research group [41] for making useful codes accessible online. The authors declare no conflict of interest.