Masked Amazon engineer seated at a work table tests a Project Kuiper antenna
Project Kuiper antenna development and testing is occurring at the team's facility in Redmond, Washington.

Nima Mahanfar discusses the science behind Project Kuiper customer terminal antenna

The senior manager of hardware and antenna development for Project Kuiper answers questions related to the development of new custom-built antenna that advances this ambitious project to provide affordable broadband to unserved and underserved communities around the globe.

Amazon today provided an update on Project Kuiper, its initiative to increase broadband access through a constellation of 3,236 low-Earth orbit (LEO) satellites. The team released information on the Ka-band phased array antenna for its low-cost customer terminal, signaling another milestone in the company’s efforts to provide fast and affordable broadband access to communities around the world.

The prototype antenna is based on a new architecture designed and developed by the Project Kuiper team. The initial prototype is delivering speeds of up to 400 Mbps, despite a form factor that is approximately 12 inches in diameter and significantly lighter than legacy antenna designs. The reduction in size and complexity will allow Amazon to reduce production costs, contributing to the team’s goal of providing customers a terminal that is affordable and easy to install.

Nima Mahanfar
Nima Mahanfar

The antenna design and manufacturing effort is led in-house by the Project Kuiper team at Amazon. In advance of today’s announcement, Amazon Science asked Nima Mahanfar, senior manager of antenna development for Project Kuiper, about designing and developing the antenna, the science and engineering challenges his team encountered, tradeoffs the team had to wrestle with, and more.

The Kuiper prototype antenna is smaller and lighter than legacy Ka-band antennas. How did your team achieve that goal? 

The key advancement was combining transmit and receive phased-array antennas into one aperture. This can be done in other frequency bands, but Project Kuiper plans to operate in Ka-Band, which has transmit and receive frequencies that are much further apart from one another. This makes it difficult, nearly impossible in fact, to combine transmit and receive into one aperture. Phased arrays are a class of radiating system, where multiple antennas — it could be two, it could be thousands— are on the same aperture, creating a focused beam of radio waves. The distance between the antennas — or the relation between these antennas — is decided by the frequency. If the frequencies are close to each other, as with Ku-Band, you can combine the transmit and receive function into one and it works. When the frequencies are far apart, as with Ka-Band, it’s much more difficult to utilize the same lattice for both. This has never been done before — until now. 

Our design involves hundreds of antennas in each aperture, with receive antennas operating at 18 to 20 gigahertz (GHz) and transmit antennas operating at 28 to 30 GHz. Our breakthrough came from the realization that we could get to a single lattice by looking at each antenna element uniquely — helping reduce the size and cost of our entire terminal.

What were the science and engineering tradeoffs your team had to wrestle with in developing this new architecture, and what tenets guided your team’s work?

There were several, but I’ll focus on two key ones.

One is balancing the tradeoff between the transmit and receive functionality. From a customer obsession perspective, we realized that what affects the customer most is the receive function. In other words, we typically receive more information than we transmit. So we always err on the side of improving receive performance. On the transmit side, if you compromise performance, you can always increase the transmitted power by a little.

Project Kuiper customer terminal animation
"The key advancement was combining transmit and receive phased-array antennas into one aperture," Nima Mahanfar, senior manager of antenna development for Project Kuiper said. "Our breakthrough came from the realization that we could get to a single lattice by looking at each antenna element uniquely – helping reduce the size and cost of our entire terminal."

The second tradeoff relates to how easy our antenna is to manufacture. If our design was overly complex, it couldn’t be built affordably, or it couldn’t be scaled in production. We had to ask ourselves, at what point does this combined aperture become twice as complex as the single aperture, and would it still make sense? Our objective was to ensure our antenna was mass producible by mainstream circuit board manufacturers, allowing us to take advantage of economies of scale and produce millions at low cost. We had to keep our design as simple as possible to satisfy this objective, and this is an area where collaboration between scientists and manufacturing and hardware engineers was so important.

You received your PhD in high-frequency electronics and microwaves from the Université de Limoges and have been working in this field for a couple of decades. What are some of the key advancements that make Project Kuiper viable today? What are some of the interesting science and engineering challenges still left to address?

There are several important trends that make a project like this possible, and interestingly, few of them have to do with aerospace and satellite technology.

One is silicon. CMOS [complementary metal oxide semiconductor] technology has improved significantly, is available at a low cost, and can operate at higher and higher frequencies. Components that were once a luxury and confined primarily to the space and military industries, now are readily available and cost just a few dollars, or even cents. That has opened up this whole area for exploration.

The second trend comes from cellular technology and the cloud. As adoption and demand for these technologies has increased, we’ve seen higher frequency materials, and components available at larger scale and lower cost.  At one time, building a printed circuit board at 30GHz was a niche thing.  There were only a couple of manufacturers in the United States who could do this. They were expensive and they didn’t scale. Right now with 5G and even 4G cellular technology, basically we have more and more mass production of radio frequency (RF) at scale and at very high frequencies and customers are benefitting from this. 

So those are the two technology trends that are helping us innovate easier, better and cheaper. The whole area of RF used to be exclusive to the space industry, and the military.  That’s not the case anymore, and that’s great news for engineers like myself.

As for the challenges left to solve, I feel we can make phased array technology even more affordable. Not just by buying cheaper materials, but by developing new technologies and architectures that could be fundamentally different from today’s approach. We should be open-minded about the possibilities, and we are pursuing many of them already.

View from space of a connected network around planet Earth representing the Internet of Things.
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Another relates to the phased array technology we are building for our satellites in space, where the challenges are a little different. Cost is still important, but more importantly, we want to reduce watts per gigabits per second. Solving power challenges in space is hard, and dissipating the heat from that power is even harder. There’s no air to cool it. So having a low-power system that can provide many gigabytes of service to customers is key. How can we reduce the power consumption of these space-borne phased arrays? That’s one of the other big challenges facing anyone deploying phased array antennas in low earth orbit.

What type of scientist and engineer are you seeking to help address those challenges?

We’re seeking individuals with strong, fundamental science skills who understand the physical limits of what’s possible, and can deliver performance to that physical limit, or who can explain the imperfections for why we can’t. We want to deliver the best possible performance at the lowest possible price for our customers. To do this, we need to understand what possible based on physics rather than an arbitrary limit based on what’s been achieved in legacy systems. 

Project Kuiper is made up of engineers and scientists with decades of experience in satellite communications, many of whom hold multiple patents for their work. Most of the folks I work with are PhDs, many in the field of electromagnetics, who aren’t fearful of doing original work. This is particularly germane for phased array technology where we’ve shifted to using printed circuit board technology versus expensive ceramics and other materials. So we’re seeking individuals who can recognize science projects from the real thing, and can then build advanced designs that come close to the physical limits of what’s possible.  If this resonates with any of your readers, I hope they will visit our jobs page.


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Amazon’s High Value Messaging (HVM) Analytics team (part of Customer Behavior Analytics) is looking for a Senior Applied Scientist to spearhead the rapid growth of our Marketing Measurement solutions. The team focuses on building scalable scientific models to estimate the effectiveness of Amazon marketing efforts and provide actionable insights to the various marketing teams within Amazon. We are looking for a thought leader that has an aptitude for delivering customer-focused solutions and who enjoys working on the intersection of Big-Data analytics, Machine/Deep Learning, and Causal Inference.A successful candidate will be a self-starter, comfortable with ambiguity, able to think big and be creative, while still paying careful attention to detail. You should be able to translate how data represents the customer journey, be comfortable dealing with large and complex data sets, and have experience using machine learning and econometric modeling to solve business problems. You should have strong analytical and communication skills, be able to work with product managers and software teams to define key business questions and work with the analytics team to solve them. You will join a highly collaborative and diverse working environment that will empower you to shape the future of Amazon marketing, as well as allow you to be part of the large science community within the Customer Behavior Analytics (CBA) organization.The Customer Behavior Analytics (CBA) organization owns Amazon’s insights pipeline, from data collection to deep analytics. We aspire to be the place where Amazon teams come for answers, a trusted source for data and insights that empower our systems and business leaders to make better decisions. Our outputs shape Amazon product and marketing teams’ decisions and thus how Amazon customers see, use, and value their experience.The main responsibilities for this position include:· Apply expertise in ML and causal modeling to develop systems that describe how Amazon’s marketing campaigns impact customers’ actions· Own the end-to-end development of novel scientific models that address the most pressing needs of our business stakeholders and help guide their future actions· Improve upon and simplify our existing solutions and frameworks· Review and audit modeling processes and results for other scientists, both junior and senior· Work with marketing leadership to align our measurement plan with business strategy· Formalize assumptions about how models are expected to behave, creating definitions of outliers, developing methods to systematically identify these outliers, and explaining why they are reasonable or identifying fixes for them· Identify new opportunities that are suggested by the data insights· Bring a department-wide perspective into decision making· Develop and document scientific research to be shared with the greater science community at Amazon
US, WA, Seattle
Are you excited about powering Amazon’s physical stores’ expansion through the application of Machine Learning and Big Data technologies? Do you thrive in a fast-moving, innovative environment that values data-driven decision making, scalable solutions, and sound scientific practices? We are looking for experienced scientists to build the next level of intelligence that will help Amazon physical stores grow and succeed.Our team is responsible for building the core intelligence, insights, and algorithms that support the real estate acquisition strategies for Amazon physical stores. We are tackling cutting-edge, complex problems — such as predicting the optimal location for new Amazon stores — by bringing together numerous data assets from disparate sources inside and outside of Amazon, and using best-in-class modeling solutions to extract the most information out of them.You will have a proven track-record of delivering solutions using advanced science approaches. You will be comfortable using a variety of tools and data sources to answer high-impact business questions. You will transform one-off models into automated systems. You will be able to break down complex information and insights into clear and concise language and be comfortable presenting your findings to audiences with a broad range of backgrounds.Responsibilities:· Develop production software systems utilizing advanced algorithms to solve business problems.· Analyze and validate data to ensure high data quality and reliable insights.· Partner with data engineering teams across multiple business lines to improve data assets, quality, metrics and insights.· Proactively identify interesting areas for deep dive investigations and future product development.· Design and execute experiments, and analyze experimental results in collaboration with Product Managers, Business Analysts, Economists, and other specialists.· Leverage industry best practices to establish repeatable applied science practices, principles & processes.