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.

Parameterizing Solid Models for ANSYS HFSS

ANSYS HFSS features an integrated “history-based modeler”. This means that an object’s final shape is dependent on each and every operation performed on that object. History-based modelers are a perfect choice for analysis since they naturally support parameterization for design exploration and optimization. However, editing imported solid 3D Mechanical CAD (or MCAD) models can sometimes be challenging with a history-based modeler since there are no imported parameters, the order of operation is important, and operational dependencies can sometimes lead to logic errors. Conversely, direct modelers are not bound by previous operations which can offer more freedom to edit geometry in any order without historic logic errors. This makes direct modelers a popular choice for CAD software but, since dependencies are not maintained, they are not typically the natural choice for parametric analysis. If only there was a way to leverage the best of both worlds… Well, with ANSYS, there is a way.

As discussed in a previous blog post, since the release of ANSYS 18.1, ANSYS SpaceClaim Direct Modeler (SCDM) and the MCAD translator used to import geometry from third-party CAD tools are now packaged together. The post also covered a few simple procedures to import and prepare a solid model for electromagnetic analysis. However, this blog post will demonstrate how to define parameters in SCDM, directly link the model in SCDM to HFSS, and drive a parametric sweep from HFSS. This link unites the geometric flexibility of a direct modeler to the parametric flexibility of a history-based modeler.

You can download a copy of this model here to follow along. If you need access to SCDM, you can contact us at info@padtinc.com. It’s also worth noting that the processes discussed throughout this article work the same for HFSS-IE, Q3D, and Maxwell designs as well.

[1] To begin, open ANSYS SpaceClaim and select File > Open to import the step file.

[2] Split the patch antenna and reference plane from the dielectric. Click here for steps to splitting geometry. Notice the objects can be renamed and colors can be changed under the Display tab.

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[1] Click and hold the center mouse button to rotate the model, zoom into the microstrip feed using the mouse scroll, then select the side of the trace.

[2] Rotate to the other side of the microstrip feed, hold the Ctrl key, and select the other side of the trace. Note the distance between the faces is shown as 3mm in the Status Bar at the bottom of the screen, which is the initial trace width.

[3] Select Design > Edit > Pull and select No merge under Options – Pull.

[4] Click the yellow arrow in the model, and drag the side of the trace. Notice how both faces move in or out to change the trace width. After releasing the mouse, a P will appear next to the measurement box. Click this P to create a parameter.

[5] Select the Groups panel under the Structure tree. Change “Group1” to “traceWidth” and reset the Ruler dimension to 0mm. Then, save the project as UWB_Patch_Antenna_PCB.scdoc and leave SCDM open.

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[1] Open ANSYS Electronics Desktop (AEDT), insert a new HFSS Design, and select the menu item Modeler > SpaceClaim Link > Connect to Active Session… Notice that there is an option to browse and open any SCDM project if the session is not currently active (or open).

[2] Select the active UWB_Patch_Antenna_PCB session and click Connect.

[3] The geometry from SCDM is automatically imported into HFSS.

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[1] Double-click the SpaceClaim1 model in the HFSS modeler tree and select the Parameters tab in the pop-up dialogue box. Notice the SCDM parameter can now be controlled within HFSS. Change the Value of traceWidth to SCDM_traceWidth to create a local variable and set SCDM_traceWidth equal to -1mm. Then click OK. Notice a lightning bolt over the SpaceClaim1 model to indicate changes have been made.

[2] Right-click SpaceClaim1 in the modeler tree and select Send Parameters and Generate.

[3] Notice how the HFSS geometry reflects the changes.

[4] Notice how the SCDM also reflects the changes. In practice, it is generally recommended to browse to unopen SCDM projects (rather than connecting to an active session) to avoid accidentally editing the same geometry in two places.

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At this point, not only can the geometry in SCDM be controlled by variables in HFSS, but a parametric analysis can now be performed on geometry within a direct modeler. The best of both worlds!

Use the typical steps within HFSS to setup a parametric sweep or optimization. When performing a parametric analysis, the geometry will automatically update the link between HFSS and SCDM, so step [2] above does not need to be performed manually. Be sure to follow the typical HFSS setup procedures such as assigning materials, defining ports and boundaries, and creating a solution setup before solving.

Here are some additional pro-tips:

  1. Create local variables in HFSS that can be used for both local and linked geometry. For example, create a variable in HFSS for traceWidth = 3mm (which was the previously noted width). Define SCDM_traceWidth = (traceWidth-3mm)/2. Now the port width can scale with the trace width.

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  1. Link to multiple SCDM projects. Either move and rotate parts as needed or create a separate coordinate system for each component. For example, link an SMA end connector to the same HFSS project to analyze both components. Notice that each component has variables and the substrate thickness changes both SCDM projects.

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  1. Design other objects in the native HFSS history-based modeler that are dependent on the SCDM design variables. For example, the void in an enclosure could be a function of SCDM_dielectricHeight. Notice that the enclosure void is dependent on the SCDM dielectric height.

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Importing and Splitting Solid Models for ANSYS HFSS 18.0

Importing solid 3D Mechanical CAD (or MCAD) models into ANSYS HFSS has always been and remains to be a fairly simple process. After opening ANSYS Electronics Desktop and creating an HFSS design, from the menu bar, select Modeler > Import. A dialog box will open to navigate to and directly open the model.

The CAD will automatically be translated and loaded into the HFSS 3D Modeler. If the geometry is correct and does not require any editing, the import process is complete and analysis can begin! However, if there are any errors with the geometry, there is excessive or invalid detail, or if it’s not organized into separate bodies conducive for electromagnetic analysis, you may soon realize that the editing capability is limited to scaling, reorienting, or Boolean operations. This approach can be particularly troublesome when portions of the model (or all of the model) which consist of different materials are not split into different objects. For example, notice the outer conductor, inner conductor, and dielectric of the imported SMA below are all one solid object.

Unless you’re lucky enough to work with the creator of the CAD, you will need to find a way to split this model into the inner and outer conductors, and the dielectric. However, since the release of ANSYS R18.1, the power of SpaceClaim Direct Modeler (SCDM) and the MCAD translator will be packaged together. The good news is, the process described above will continue to work. The better news is, SCDM offers new capabilities to directly edit or clean imported geometry. So, here are a few simple steps to quickly split this SMA connector using SCDM. You can download a copy of this model here to follow along. If you need access to SCDM, you can contact us at info@padtinc.com. It’s worth noting, at this point, that the processes discussed throughout this article work the same for HFSS-IE, Q3D, and Maxwell designs as well.

[1] First, after opening ANSYS SpaceClaim, the step file can be imported through the menu File > Open or by simply dragging and dropping the file into the SCDM window. [2] To separate the dielectric from the outer conductor, select Design > Intersect > Split Body. [3] Click and hold the center mouse button to rotate the model so the boundary between the dielectric and outer conductor is visible. Hold the Ctrl key and click the center mouse button to pan, and use the center mouse scroll to zoom in and out. Finally, press ‘z’ on the keyboard to fit the view window. [4] When positioned, click on the object to split (in this case it is the entire model). [5] Then, click on the face which defines the boundary between the dielectric and outer conductor. [6] Finally, press the Esc key. The first split is done!

Repeat the Split Body process to separate the center conductor from the dielectric. Notice under the structure tree that there are now three separate objects.

The split body function is also useful to simplify a structure for analysis. For example, the female side of the SMA could be simplified as a solid center conductor. [1] Reposition the connector to view the female side. [2]-[3] Control the visibility of each body with the object’s checkbox in the structure tree. [4] Measure the length of the female side by pressing the letter ‘e’ on the keyboard and selecting the top edge (note the line length of 2.95mm for later). [5] Then, repeat the Split Body process to split the center conductor at the boundary between the male and female sides. [6]-[7] However, rather than pressing the Esc key, click on the female receiver to automatically remove the body.

[1] To extend the center pin to its original length, select Design > Edit > Pull. [2] Click on the face where the female side was originally attached and select the Up To option. [3] Type in the previously measured length of 2.95mm. [4] Finally, press Enter (press Esc 3x to exit the Pull command).

Repeat the Split Body and Pull processes until the model has been divided into different bodies for each material type and is sufficiently simplified.

Once the model is ready, select File > Save As to save the geometry as the preferred format. Perhaps the most familiar approach to HFSS users would be to save the new model as a STEP file, then to import the model into HFSS as described in the first paragraph.

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.