# Planar Array Diagnostic Tool for Millimeter-Wave Wireless Communication Systems
## 6 Antenna Array Diagnosis from Measured Data  
### Measurement Set-Up  
The proposed diagnostic technique was subjected to an experimental test, as presented in Figure 16. Although, this was a controlled environment (anechoic chamber), a more practical condition (uncontrolled environment) was also possible using the same set-up in Figure 16, without the chamber. The AUT was a 10 \(\times\) 10 microstrip patch antenna array (see Figure 17a), reradiating a signal tilted in the two planes. Figure 17b,c show the measured radiation patterns for an ideal antenna and defected antenna, respectively. It can be see that the failure causes higher sidelobe level, reduced gain, lower front-to-back ratio (FBR), wider beamwidth, and a boresight pointing error.  
The antenna was particularly designed and fabricated for this purpose, and each element had its feeding port which was excited using a power divider. Five radiating elements in the array were not excited (zero excitation) to successfully emulate the failure of elements. The AUT set-up is depicted in Figure 18. About 1000 co-polar and cross-polar measurements were taken on the far-field half-sphere at 28.9 GHz in an anechoic chamber (see Figure 18).  
Radiation pattern measurements obtained from the array with five faulty elements were fed into the proposed algorithm for post-processing. The reconstructed excitation error which identified the specific faulty elements is depicted in Figure 19, and the corresponding dB equivalence is shown in Figure 20. Moreover, the performance metrics of the BCS-based approach were experimentally tested, and are shown in Figure 21. Figure 21a shows the obtained reconstruction error versus the measurement number at different degrees of failure. The error decreased as the number of measurement points increased irrespective of failure percentage. The reconstruction error profile increased with increased failure rate. In Figure 21b  
Figure 17: Photograph of (**a**) the fabricated antenna array, (**b**) measured radiation pattern without fault, and (**c**) measured radiation pattern with emulated failures.  
The imperfection of the curves (compared to the simulation) could be attributed to measurement errors, and errors due to experimental set-ups which provide different conditions from those in the simulations. The experiment was conducted in an anechoic chamber, which is a controlled environment. Hence, the results presented here may show a little variation if the experiment is conducted in a more practical environment (i.e., uncontrolled environment). Moreover, the BCS-based procedure presented here can be trusted to effectively and reliably address sparse recovery problems, particularly the detection of faulty radiators in planar arrays for next-generation 5G wireless communications. Once the suitable data are collected, and used to diagnose the array, then the array feeding network can be recalibrated to restore the needed radiation features via excitation reconfiguration of the healthy antenna elements. However, prior knowledge of the _golden_ array must be provided, and the failure rate is relatively smaller than the array size. Therefore, from the simulation results verified by the experiment, the BCS-based approach is adequate and reliable for noisy data. This technique overcomes the shortcomings of BPA, MMA, etc., demanding off-line phase training to form accurate mapping between the response of the AUT and the failure location. Hence, the proposed procedure will be highly useful for millimeter-wave planar array optimal performance.  
Figure 19: Reconstructed excitation error field by 30 random noisy measurement points with 20 dB signal-to-noise-ratio.  
Figure 18: Measurement set-up of AUT for diagnostic purpose.