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Existing smartphones will connect with new satellite constellations in 2023
This rendering of AST SpaceMobile’s BlueWalker 3 test satellite, launched on 10 September 2022, shows the satellite’s appearance when the antennas are fully deployed. The antennas’ surface area—64 square meters—enables the satellite to function as a long-distance cell tower for phones on Earth’s surface.
In 2023, you or someone you know will be able to send a text message through space. Late in 2022, hardware behemoths Huawei and Apple released cellular telephones capable of texting on traditional satellite-communications networks. A pair of ambitious startups, AST SpaceMobile and Lynk Global, also started building new low Earth orbit (LEO) satellite networks designed to reach conventional 5G cellphones outside terrestrial coverage.
“Offering direct satellite access to smartphones without modifications would allow access to billions of devices worldwide,” says Symeon Chatzinotas, the head of the University of Luxembourg’s SigCom research group.
Users looking to connect via satellite won’t need the bulky, expensive commercial satphones that have been available since the late 1990s—but they also won’t have conventional calling or high-bandwidth data streaming just yet. Satellite connections are still plenty useful, though. To begin with, people could use texting to signal for help if need be, no matter where they are, as long as they have a clear view of the sky. That is, their mobile phones will have capabilities similar to existing pocket devices like Garmin’s inReach communicator.
Huawei has not said when its service will begin working, but Apple’s partnership with Globalstar, dubbed Emergency SOS via satellite, has been operational since November 2022. As of this writing, Lynk Global has agreements with 23 telecom providers to begin commercial operations in 2023. AST SpaceMobile says it plans to launch its first five commercial satellites late in 2023, has agreements or understandings with more than 25 telecom providers around the world, and should begin commercial operations in 2024.
Splashy announcements of satellite-cellular connectivity from Apple, Starlink, and T-Mobile in the third quarter of 2022 promoted the idea of anywhere, any-kind connectivity. The first services won’t be that slick, though. Apple and Huawei will both connect initially to older satellites in higher orbits, for which it could take more than 10 minutes to establish a connection. Even the newer LEO networks, such as Lynk Global’s, currently advertise satellite texting but are not yet promising the higher-capacity link that a voice or video call would require.
AST SpaceMobile says that as the company adds satellites, it will be up to its mobile-network-operator (MNO) partners to decide whether to market the bandwidth in small increments to many users for texting or voice-only calls or to offer data-heavy services to select users. Lynk doesn’t mind its competitors’ aspirational advertising campaigns, says Lynk Global CEO Charles Miller: “They educated the market. It’s only going to make people want more.” 
These new offerings are possible thanks to a handful of advances that are now maturing. Advances include the declining cost of satellite manufacturing and the shrinking size of satellites themselves, making it affordable to build many more satellites than in the past. And with many more of them, it’s possible to put the satellites into lower orbits, between 300 to 600 kilometers above Earth, where each covers less ground. But closer satellites allow handsets with less power to reach them.
Another improvement is in software-defined radios—chips that can transmit and receive on different wavelengths modulated by software running aboard the satellite. In the past, sending and receiving such a wide range of different wavelengths required distinct hardware. Digital signal processing enables these chips to do the work of a complicated array of hardware. “Software-defined radio means the phased-array antennas can do frequency hopping as we switch from country to country,” Miller says. That technology makes it viable to pack more antenna capability into less space—Lynk will start with relatively small 1-square-meter antennas, but it plans to install bigger, more effective ones on its satellites in the future.
AST SpaceMobile chief strategy officer Scott Wisniewski says larger antennas are a big part of AST’s strategy: “We think that’s very important to communicate with low-power, low-signal-strength phones.” AST plans to deploy antennas up to around 400 m2, which would be the largest commercial telecom arrays in LEO.
Block 1 Bluebirds:
64 m2 antennas
Block 2 Bluebirds: 128 m2 antennas
First generation:
1 m2 satellites
Second generation:
4 m2 satellites
Even so, having phones communicate with satellites rather than cell towers is tricky because of the much larger signal delays. “Everything about a phone is built around time-synching on the order of 5 to 10 milliseconds,” Wisniewski says. “That works just fine with a tower that’s a quarter mile away, 3 miles away even, but not for orbit.” AST is developing hardware solutions with Nokia and Rakuten that tell the core network how to wait longer for satellite signals.
In 2023, Apple and Huawei will be testing how much use they can get from older communications satellites through their flagship handsets, equipped with new chips. Meanwhile, if things go according to Lynk Global’s plan, by spring of 2023 the company will be offering commercial service to its MNO partners. AST may have its first commercial satellites in space but would still be testing and configuring them.
Network operators “historically asked ‘How is this possible?’” Wisniewski says. “Lately it’s more about ‘How can we use this best, when can we use this, what’s the best market strategy for each market?’” For people living in certain countries, 2023 could be the year when they are no longer troubled by the words “No Service.”
Lucas Laursen is a journalist covering global development by way of science and technology with special interest in energy and agriculture. He has lived in and reported from the United States, United Kingdom, Switzerland, and Mexico.
Your yearly selection of awesome robot holiday videos
Evan Ackerman is a senior editor at IEEE Spectrum. Since 2007, he has written over 6,000 articles on robotics and technology. He has a degree in Martian geology and is excellent at playing bagpipes.
Video Friday is your weekly selection of awesome robotics videos (special holiday edition!) collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.
Enjoy today’s videos!
[ Boston Dynamics ]
[ FZI ]
Thanks, Arne!
[ Leverage Robotics ]
Thanks, Roman!
[ BruBrotics ]
Thanks, Bram!
[ IHMC Robotics ]
[ BHT ]
Thanks, Hannes!
[ ABB ]
Helping with the office tree, from Sanctuary AI.
Flavor text from the video description: “Decorated Christmas trees originated during the 16th-century in Germany. Protestant reformer Martin Luther is known for being among the first major historical figures to add candles to an evergreen tree. It is unclear whether this was, even then, considered to be a good idea.”
[ Sanctuary ]
Merry Christmas from qbrobotics!
[ qbrobotics ]
Christmas, delivered by robots!
[ Naver Labs ]
[ Max Planck ]
[ Kawasaki Robotics ]
Robotnik wishes you a Merry Christmas 2022.
[ Robotnik ]
[ Cybathlon ]
Here’s what LiDAR-based SLAM in a snow gust looks like. Enjoy the weather out there!
[ NORLAB ]
[ Paper ]
Thanks, Poramate!
“Every day in a research job :)”
[ Chengxu Zhou ]
[ Agility Robotics ]
[ Kuka ]
[ JPL ]
The key to overcoming complexity in modern wireless systems design
This is a sponsored article brought to you by MathWorks.
The evolution of mobile wireless technology, from 3G/4G to 5G, and introduction of Industry 4.0, have resulted in the ever-increasing complexity of wireless systems design. Wireless networks have also become more difficult to manage due to requirements necessitating optimal sharing of valuable resources to expanding sets of users. These challenges force engineers to think beyond traditional rules-based approaches with many are turning to artificial intelligence (AI) as the go-to solution to face the challenges introduced by modern systems.
From managing communications between autonomous vehicles, to optimization of resource allocations in mobile calls, AI has brought the sophistication necessary for modern wireless applications. As the number and scope of devices connected to networks expands, so too will the role of AI in wireless. Engineers must be prepared to introduce it into increasingly complex systems. Knowing the benefits and current applications of AI in wireless systems, as well as the best practices necessary for optimal implementation, will be key for the future success of the technology.
The transition to 5G has brought about the optimization of speed and quality of mobile broadband networks, as well as the need for ultra-reliable low rates and massive machine-type communication for time-sensitive connections between Industry 4.0 devices – three distinct use-cases in a modern network and the contending forces driving engineers to the adoption of AI.
As devices compete for the resources of the network, with the number of users and applications of a wireless system increasing all the time, formerly linear patterns of designs once understood by human-based rules, cease to be sufficient. AI techniques, however, can better solve non-linear problems by extracting any pattern automatically and efficiently, beyond the ability of human-based approaches.
By AI in this context, we mean those machine learning and deep learning systems used to recognize patterns within communications channels that link devices and people. These systems then optimize the resources given to that link to improve performance. Simply put, running a network for those disparate use cases without exploiting AI methodologies becomes a near impossible task.
Beyond bringing sophistication and optimization, AI also brings project management benefits. Incorporating simulated environments into an algorithmic model through estimating the behaviour of source environments can enable engineers to quickly study a system’s dominant effect using minimal computational resources. This leaves more time to explore design and carry out more iterations faster, reducing cost and development time. 
AI for Wireless workflow: Data generation, AI training, validation and testing and deployment on hardware. Click on the image to watch on-demand webinar: Apply AI Techniques to Solve Wireless Communications System Design Challenges. MathWorks
Data size and quality are vital for effective deployment of AI models, because they are only as effective as the data they are trained with. To deal with a range of real-world scenarios, these models need to be trained with a broad range of data. By synthesizing new data based on primitives or by extracting them from over-the-air signals, applications like MathWorks’ 5G Toolbox provide the data variability necessary for 5G network designers to train AI robustly. Failure to explore a large training data set and iterate on different algorithms based on that data could result in a narrow local optimization instead of an overall global one.
A robust approach to testing AI models in the field is similarly critical to success. Variability in signal needed for testing AI techniques is a problem, where signals captured in a narrow, localized geography may adversely impact how an engineer might optimize quality of their design. Without field iterations, the parameters of individual cases cannot be used to optimize AI for specific locations, negatively impacting call performance.
Digital transformation in areas like telecommunications and automotive necessitate the use of AI and is the primary driver for its application. Placing electronic communications in areas once mechanical orientated generates large amounts of data as applications like smart cities, telecommunication networks and autonomous vehicles (AV) connect. As they do so, the resources of the network joining them becomes stretched.
In telecommunications, AI is deployed at two levels – at the physical layer (PHY) and above PHY. The application of AI for improving performance in a line connecting two users is referred to as operating at PHY. Applications of AI techniques to physical layers includes digital pre-distortion, channel estimation and channel resource optimization, as well as automatic adjustments to transceiver parameters during a call otherwise known as autoencoder design.
Channel optimization is the enhancement of the connection between two devices, notably network infrastructure and user equipment. Often, this means using AI to overcome signal variability in localized environments through techniques such as fingerprinting and channel state information compression.
With fingerprinting, AI is used to optimize positioning and localization for wireless networks by mapping disruptions to propagation patterns in indoor environments, caused by individuals entering them. AI then estimates based on these individualized 5G signal variations the position of the user. Meanwhile, channel state information compression is the use of AI to compress feedback data from user equipment to a base station, ensuring that the feedback loop informing the station’s attempt to improve call performance does not exceed the available bandwidth thus consequently causing a dropped call.
Above-PHY uses are mainly in network management and resource allocation. Applications, such as scheduling, beam management and spectrum allocation, are functions that manage and optimize the resources of core systems for the competing users and use-cases of the network. As the number of users and use-cases on the network increase, network designers have turned to AI techniques in order to respond to allocation demands in real time.
In the automotive industry, using AI for wireless connectivity is making safe autonomous driving possible. Autonomous vehicles (AV) rely on data from multiple sources, including LiDAR, radar, and wireless sensors, to interpret the environment in which they are situated. The hardware present in AVs must handle data from these many competing sources. AI enables sensor fusion, fusing competing signals to allow the vehicle’s software to make sense of its location and establish how it will interact with its environment.
As the use cases for wireless technology expand, so too does the need to implement AI within those systems. Without it, systems such as 5G, autonomous vehicles and IoT applications would not have the sophistication necessary to function effectively. While AI’s place in engineering, particularly wireless system design, has been increasing in recent years, it can be expected for this to keep rising – and faster – as the use cases and the number of network users grow.
To learn more about this topic, see the additional resource below or email at hzarrink@mathworks.com:
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