IEEE Day 2017: Smart Antennas for IoT and 5G

IEEE Day celebrates the first time in history when engineers worldwide and IEEE members gathered to share their technical ideas in 1884. Events were held around the world by 846 IEEE Chapters this year. So, to celebrate, I attended a joint chapter meeting in at The Museum of Flight in Seattle with technical presentations focused on “Smart Antennas for IoT and 5G”. There were approximately 60 in attendance, so assuming this was the average attendance globally results in over 50,000 engineers celebrating IEEE Day worldwide!

The Seattle seminar featured three speakers that spanned theory, design, test, integration, and application of smart antennas. There was much discussion about the complexity and challenges of meeting the ambitious goals of 5G, which extend beyond mobile broadband data access. Some key objectives of 5G are to increase capacity, increase data rates, reduce latency, increase availability, and improve spectral and energy efficiency by 2020. A critical technology behind achieving these goals is beamforming antenna arrays, which were at the forefront of each presentation.

Anil Kumar from Boeing focused on the application of mmWave technology on aircraft. Test data was used to analyze EM radiation leakage through coated and uncoated aircraft windows. However, since existing regulations don’t consider the increased path loss associated with such high frequencies, the integration of 5G wireless applications may be restricted or delayed. Beyond this regulatory challenge, Anil discussed how multipath reflectors and absorbers will present significant challenges to successful integration inside the cabin. Although testing is always required for validation, designing the layout of the onboard transceivers may be impractical to optimize without an asymptotic EM simulation tool that can account for creeping waves, diffraction, and multi-bounce.

Considering the test and measurement perspective, Jari Vikstedt from ETS-Lindgren focused on the challenges of testing smart antenna systems. Smart or adaptive antenna systems will not likely perform the same in an anechoic chamber test as they would in real systems. Of particular difficulty, radiation null placement is just as critical as beam placement. This poses a difficult challenge to the number and location of probes in a test environment. Not only would a large number of probes become impractical, there is significant shadowing at mmWave frequencies which can negatively impact the measurement. Furthermore, compact ranges can significantly impact testing and line of sight measurements become particularly challenging. While not a purely test-oriented observation, this lead to considering the challenge of tower hand off. If a handset and tower use beamforming to maintain a link, if is difficult for an approaching tower to even sense the handset to negotiate the hand-off.

In contrast, if the handset was continuously scanning, the approaching tower could be sensed to negotiate the hand-off before the link is jeopardized.

The keynote speaker, who also traveled from Phoenix to Seattle, was ASU Professor Dr. Constantine Balanis. Dr. Balanis opened his presentation by making a distinction between conventional “dumb antennas” and “smart antennas”. In reality, there are no smart antennas, but instead smart antenna systems. This is a critical point from an engineering perspective since it highlights the complexity and challenge of designing modern communication systems. The focus of his presentation was using an adaptive system to steer null points in addition to the beam in an antenna array using a least mean square (LMS) algorithm. He began with a simple linear patch array with fixed uniform amplitude weights, since an analytic solution was practical and could be used to validate a simulation setup. However, once the simulation results were verified for confidence, designing a more complex array with weighted amplitudes accompanying the element phase shift was only practical through simulation. While beam steering will create a device centric system by targeting individual users on massive multiple input multiple output (MIMO) networks, null steering can improve efficiency by minimizing interference to other devices.

Whether spatial processing is truly the “last frontier in the battle for cellular system capacity”, 5G technology will most certainly usher in a new era of high capacity, high speed, efficient, and ubiquitous means of communication. If you would like to learn more about how PADT approaches antenna simulation, you can read about it here and contact us directly at info@padtinc.com.

Exploring High-Frequency Electromagnetic Theory with ANSYS HFSS

I recently had the opportunity to present an interesting experimental research paper at DesignCon 2017, titled Replacing High-Speed Bottlenecks with PCB Superhighways. The motivation behind the research was to develop a new high-speed signaling system using rectangular waveguides, but the most exciting aspect for me personally was salvaging a (perhaps contentious) 70 year old first-principles electromagnetic model. While it took some time to really understand how to apply the mathematics to design, their application led to an exciting convergence of theory, simulation, and measurement.

One of the most critical aspects of the design was exciting the waveguide with a monopole probe antenna. Many different techniques have been developed to match the antenna impedance to the waveguide impedance at the desired frequency, as well as increase the bandwidth. Yet, all of them rely on assumptions and empirical measurement studies. Optimizing a design to nanometer precision empirically would be difficult at best and even if the answer was found it wouldn’t inherently reveal the physics. To solve this problem, we needed a first-principles model, a simulation tool that could quickly iterate designs accurately, and some measurements to validate the simulation methodology.

A rigorous first-principles model was developed by Robert Collin in 1960, but this solution has since been forgotten and replaced by simplified rules. Unfortunately, these simplified rules are unable to deliver an optimal design or offer any useful insight to the critical parameters. In fairness, Collin’s equations are difficult to implement in design and validating them with measurement would be tedious and expensive. Because of this, empirical measurements have been considered a faster and cheaper alternative. However, we wanted the best of both worlds… we wanted the best design, for the lowest cost, and we wanted the results quickly.

For this study, we used ANSYS HFSS to simulate our designs. Before exploring new designs, we first wanted to validate our simulation methodology by correlating results with available measurements. We were able to demonstrate a strong agreement between Collin’s theory, ANSYS HFSS simulation, and VNA measurement.

Red simulated S-parameters strongly correlated with blue measurements.

To perform a series of parametric studies, we swept thousands of antenna design iterations across a wide frequency range of 50 GHz for structures ranging from 50-100 guide wavelengths long. High-performance computing gave us the ability to solve return loss and insertion loss S-parameters within just a few minutes for each design iteration by distributing across 48 cores.

Sample Parametric Design Sweep

Finally, we used the lessons we learned from Collin’s equations and the parametric study to develop a new signaling system with probe antenna performance never before demonstrated. You can read the full DesignCon paper here. The outcome also pertains to RF applications in addition to potentially addressing Signal Integrity concerns for future high-speed communication channels.

Rules-of-thumb are important to fast and practical design, but their application can many times be limited. Competitive innovation demands we explore beyond these limitations but the only way to match the speed and accuracy of design rules is to use simulations capable of offering fast design exploration with the same reliability as measurement. ANSYS HFSS gave us the ability to, not only optimize our design, but also teach us about the physics that explain our design and allow us to accurately predict the behavior of new innovative designs.