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Standard handsets on Earth, in some locations, will soon connect directly to satellites for remote roaming
Lynk Tower 1 launched in April 2022, deploying the world’s first commercial cell tower in space.
The next generation of cellphone networks won’t just be 5G or 6G—they will be zero g. In April, Lynk Global launched the first direct-to-mobile commercial satellite, and on 15 August a competitor, AST SpaceMobile, confirmed plans to launch an experimental direct-to-mobile satellite of its own in mid-September. Inmarsat and other companies are working on their own low Earth orbit (LEO) cellular solutions as launch prices drop, satellite fabrication methods improve, and telecoms engineers push new network capabilities.
LEO satellite systems such as SpaceX’s Starlink and Amazon’s Kuiper envision huge constellations of satellites. However, the U.S. Federal Communications Commission just rejected SpaceX’s application for some of the US $9 billion federal rural broadband fund—in part because the Starlink system requires a $600 ground station. Space-based cell service would not require special equipment, making it a potential candidate for rural broadband funds if companies can develop solutions to the many challenges that face satellite-based smartphone service.
“The main challenge is the link budget,” says electrical engineer Symeon Chatzinotas of the University of Luxembourg, referring to the amount of power required to transmit and receive data between satellites and connected devices. “Sending signals to smartphones outdoors could be feasible by using low Earth orbit satellites with sizable antennas in the sky. However, receiving info would be even more challenging since the smartphone antennas usually disperse their energy in all directions.”
“Network architectures are diverging. On the one hand, small cells are replacing Wi-Fi. On the other hand [telecom operators] are going to satellite-based systems with very wide coverage.” —Derek Long, Cambridge Consultants
The typical distance from a phone to an LEO satellite might be 500 kilometers, at least two orders of magnitude more than typical signal-transmission distances in urban settings, so the dispersion of the phone’s power would be at least eight times greater, and would be further complicated by the phone’s orientation. It is unlikely that a satellite-smartphone connection would work well when the handset is inside a building, for example.
Lynk Global’s initial offering, which it predicts will be available in late 2022, is narrowband—meaning limited voice calls, texting, and Internet of Things (IoT) traffic. That might not allow plutocrats to make 4K video calls from their ocean-faring yachts, but it would be enough for ship insurance companies or rescue services to remain in contact with vessels in places where they couldn’t be reached before, using off-the-shelf cellular devices. AST SpaceMobile’s is aiming for 4G and 5G broadband service for mobiles.
AST satellites will use a phased-array antenna, which consists of many antennas fanned out around the satellite. Each portion of the antenna will transmit within a well-defined cone terminating at the Earth’s surface; that will be the space-to-Earth equivalent of a cell originating from a single ground base station. The company plans for an initial fleet of 20 satellites to cover the equator and help fund the launch of subsequent satellites providing more global coverage.
The size of the coverage zone on the ground should exceed the limited size of those created by Alphabet’s failed balloon-based Project Loon. Broader coverage areas should allow AST to serve more potential customers with the same number of antennas. The low Earth orbit AST is experimenting with yields round-trip signal travel times of around 25 milliseconds or less, an order of magnitude faster than is the case for higher-orbit geostationary satellites that have provided satellite telephony until now.
Plenty of behind-the-scenes technical work remains. The relatively high speed of LEO satellites will also cause a Doppler shift in the signals for which the network will have to compensate, according to a recent review in IEEE Access. New protocols for handoffs between satellites and terrestrial towers will also have to be created so that an active call can be carried from one cell to the next.
The international telecoms standards group 3GPP began providing guidelines for so-called nonterrestrial networks in March in the 17th iteration of its cellular standards. “Nonterrestrial networks” refers not just to LEO satellites but also high-altitude platforms such as drones or balloons. Nonterrestrial networks will need further updates to 3GPP’s standards to accommodate their new network architecture, such as the longer distances between cell base stations and devices.
For example, Stratospheric Platforms earlier this year tested a drone-based network prototype that would fly at altitudes greater than 18,000 meters. Its behavior as part of a 5G network will differ from that of a Lynk Global or AST satellite.
“Network architectures are diverging. On the one hand, small cells are replacing Wi-Fi. On the other hand [telecom operators] are going to satellite-based systems with very wide coverage. In the middle, traditional macrocells, which are kind of difficult economically, are being squeezed,” says Derek Long, head of telecommunications at Cambridge Consultants. The company has advised Stratospheric Platforms and other companies working on nonterrestrial networks.
If telecom operators succeed, users won’t even notice their space-age smartphone networks.
“When you buy a phone, you expect it to work. Not just where someone says it will work, but everywhere. This is a step toward making that a possibility,” Long says.
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.
«small cells are replacing Wi-Fi» - I’m sorry but this is unfounded claim, maybe Derek can clarify where it is getting replaced?
There’s plenty of bandwidth available if we use reconfigurable intelligent surfaces
Ground level in a typical urban canyon, shielded by tall buildings, will be inaccessible to some 6G frequencies. Deft placement of reconfigurable intelligent surfaces [yellow] will enable the signals to pervade these areas.
For all the tumultuous revolution in wireless technology over the past several decades, there have been a couple of constants. One is the overcrowding of radio bands, and the other is the move to escape that congestion by exploiting higher and higher frequencies. And today, as engineers roll out 5G and plan for 6G wireless, they find themselves at a crossroads: After years of designing superefficient transmitters and receivers, and of compensating for the signal losses at the end points of a radio channel, they’re beginning to realize that they are approaching the practical limits of transmitter and receiver efficiency. From now on, to get high performance as we go to higher frequencies, we will need to engineer the wireless channel itself. But how can we possibly engineer and control a wireless environment, which is determined by a host of factors, many of them random and therefore unpredictable?
Perhaps the most promising solution, right now, is to use reconfigurable intelligent surfaces. These are planar structures typically ranging in size from about 100 square centimeters to about 5 square meters or more, depending on the frequency and other factors. These surfaces use advanced substances called metamaterials to reflect and refract electromagnetic waves. Thin two-dimensional metamaterials, known as metasurfaces, can be designed to sense the local electromagnetic environment and tune the wave’s key properties, such as its amplitude, phase, and polarization, as the wave is reflected or refracted by the surface. So as the waves fall on such a surface, it can alter the incident waves’ direction so as to strengthen the channel. In fact, these metasurfaces can be programmed to make these changes dynamically, reconfiguring the signal in real time in response to changes in the wireless channel. Think of reconfigurable intelligent surfaces as the next evolution of the repeater concept.
Reconfigurable intelligent surfaces could play a big role in the coming integration of wireless and satellite networks.
That’s important, because as we move to higher frequencies, the propagation characteristics become more “hostile” to the signal. The wireless channel varies constantly depending on surrounding objects. At 5G and 6G frequencies, the wavelength is vanishingly small compared to the size of buildings, vehicles, hills, trees, and rain. Lower-frequency waves diffract around or through such obstacles, but higher-frequency signals are absorbed, reflected, or scattered. Basically, at these frequencies, the line-of-sight signal is about all you can count on.
Such problems help explain why the topic of reconfigurable intelligent surfaces (RIS) is one of the hottest in wireless research. The hype is justified. A landslide of R&D activity and results has gathered momentum over the last several years, set in motion by the development of the first digitally controlled metamaterials almost 10 years ago.
This article was jointly produced by IEEE Spectrum and Proceedings of the IEEE with similar versions published in both publications.
RIS prototypes are showing great promise at scores of laboratories around the world. And yet one of the first major projects, the European-funded Visorsurf, began just five years ago and ran until 2020. The first public demonstrations of the technology occurred in late 2018, by NTT Docomo in Japan and Metawave, of Carlsbad, Calif.
Today, hundreds of researchers in Europe, Asia, and the United States are working on applying RIS to produce programmable and smart wireless environments. Vendors such as Huawei, Ericsson, NEC, Nokia, Samsung, and ZTE are working alone or in collaboration with universities. And major network operators, such as NTT Docomo, Orange, China Mobile, China Telecom, and BT are all carrying out substantial RIS trials or have plans to do so. This work has repeatedly demonstrated the ability of RIS to greatly strengthen signals in the most problematic bands of 5G and 6G.
To understand how RIS improves a signal, consider the electromagnetic environment. Traditional cellular networks consist of scattered base stations that are deployed on masts or towers, and on top of buildings and utility poles in urban areas. Objects in the path of a signal can block it, a problem that becomes especially bad at 5G’s higher frequencies, such as the millimeter-wave bands between 24.25 and 52.6 gigahertz. And it will only get worse if communication companies go ahead with plans to exploit subterahertz bands, between 90 and 300 GHz, in 6G networks. Here’s why. With 4G and similar lower-frequency bands, reflections from surfaces can actually strengthen the received signal, as reflected signals combine. However, as we move higher in frequencies, such multipath effects become much weaker or disappear entirely. The reason is that surfaces that appear smooth to a longer-wavelength signal are relatively rough to a shorter-wavelength signal. So rather than reflecting off such a surface, the signal simply scatters.
One solution is to use more powerful base stations or to install more of them throughout an area. But that strategy can double costs, or worse. Repeaters or relays can also improve coverage but here, too, the costs can be prohibitive. RIS, on the other hand, promises greatly improved coverage at just marginally higher cost
The key feature of RIS that makes it attractive in comparison with these alternatives is its nearly passive nature. The absence of amplifiers to boost the signal means that an RIS node can be powered with just a battery and a small solar panel.
RIS functions like a very sophisticated mirror, whose orientation and curvature can be adjusted in order to focus and redirect a signal in a specific direction. But rather than physically moving or reshaping the mirror, you electronically alter its surface so that it changes key properties of the incoming electromagnetic wave, such as the phase.
That’s what the metamaterials do. This emerging class of materials exhibits properties beyond (from the Greek meta) those of natural materials, such as anomalous reflection or refraction. The materials are fabricated using ordinary metals and electrical insulators, or dielectrics. As an electromagnetic wave impinges on a metamaterial, a predetermined gradient in the material alters the phase and other characteristics of the wave, making it possible to bend the wave front and redirect the beam as desired.
An RIS node is made up of hundreds or thousands of metamaterial elements called unit cells. Each cell consists of metallic and dielectric layers along with one or more switches or other tunable components. A typical structure includes an upper metallic patch with switches, a biasing layer, and a metallic ground layer separated by dielectric substrates. By controlling the biasing—the voltage between the metallic patch and the ground layer—you can switch each unit cell on or off and thus control how each cell alters the phase and other characteristics of an incident wave.
To control the direction of the larger wave reflecting off the entire RIS, you synchronize all the unit cells to create patterns of constructive and destructive interference in the larger reflected waves [ see illustration below]. This interference pattern reforms the incident beam and sends it in a particular direction determined by the pattern. This basic operating principle, by the way, is the same as that of a phased-array radar.
A reconfigurable intelligent surface comprises an array of unit cells. In each unit cell, a metamaterial alters the phase of an incoming radio wave, so that the resulting waves interfere with one another [above, top]. Precisely controlling the patterns of this constructive and destructive interference allows the reflected wave to be redirected [bottom], improving signal coverage.
An RIS has other useful features. Even without an amplifier, an RIS manages to provide substantial gain—about 30 to 40 decibels relative to isotropic (dBi)—depending on the size of the surface and the frequency. That’s because the gain of an antenna is proportional to the antenna’s aperture area. An RIS has the equivalent of many antenna elements covering a large aperture area, so it has higher gain than a conventional antenna does.
All the many unit cells in an RIS are controlled by a logic chip, such as a field-programmable gate array with a microcontroller, which also stores the many coding sequences needed to dynamically tune the RIS. The controller gives the appropriate instructions to the individual unit cells, setting their state. The most common coding scheme is simple binary coding, in which the controller toggles the switches of each unit cell on and off. The unit-cell switches are usually semiconductor devices, such as PIN diodes or field-effect transistors.
The important factors here are power consumption, speed, and flexibility, with the control circuit usually being one of the most power-hungry parts of an RIS. Reasonably efficient RIS implementations today have a total power consumption of around a few watts to a dozen watts during the switching state of reconfiguration, and much less in the idle state.
To deploy RIS nodes in a real-world network, researchers must first answer three questions: How many RIS nodes are needed? Where should they be placed? And how big should the surfaces be? As you might expect, there are complicated calculations and trade-offs.
Engineers can identify the best RIS positions by planning for them when the base station is designed. Or it can be done afterward by identifying, in the coverage map, the areas of poor signal strength. As for the size of the surfaces, that will depend on the frequencies (lower frequencies require larger surfaces) as well as the number of surfaces being deployed.
To optimize the network’s performance, researchers rely on simulations and measurements. At Huawei Sweden, where I work, we’ve had a lot of discussions about the best placement of RIS units in urban environments. We’re using a proprietary platform, called the Coffee Grinder Simulator, to simulate an RIS installation prior to its construction and deployment. We’re partnering with CNRS Research and CentraleSupélec, both in France, among others.
In a recent project, we used simulations to quantify the performance improvement gained when multiple RIS were deployed in a typical urban 5G network. As far as we know, this was the first large-scale, system-level attempt to gauge RIS performance in that setting. We optimized the RIS-augmented wireless coverage through the use of efficient deployment algorithms that we developed. Given the locations of the base stations and the users, the algorithms were designed to help us select the optimal three-dimensional locations and sizes of the RIS nodes from among thousands of possible positions on walls, roofs, corners, and so on. The output of the software is an RIS deployment map that maximizes the number of users able to receive a target signal.
An experimental reconfigurable intelligent surface with 2,304 unit cells was tested at Tsinghua University, in Beijing, last year.
Of course, the users of special interest are those at the edges of the cell-coverage area, who have the worst signal reception. Our results showed big improvements in coverage and data rates at the cell edges—and also for users with decent signal reception, especially in the millimeter band.
We also investigated how potential RIS hardware trade-offs affect performance. Simply put, every RIS design requires compromises—such as digitizing the responses of each unit cell into binary phases and amplitudes—in order to construct a less complex and cheaper RIS. But it’s important to know whether a design compromise will create additional beams to undesired directions or cause interference to other users. That’s why we studied the impact of network interference due to multiple base stations, reradiated waves by the RIS, and other factors.
Not surprisingly, our simulations confirmed that both larger RIS surfaces and larger numbers of them improved overall performance. But which is preferable? When we factored in the costs of the RIS nodes and the base stations, we found that in general a smaller number of larger RIS nodes, deployed further from a base station and its users to provide coverage to a larger area, was a particularly cost-effective solution.
The size and dimensions of the RIS depend on the operating frequency [see illustration below] . We found that a small number of rectangular RIS nodes, each around 4 meters wide for C-band frequencies (3.5 GHz) and around half a meter wide for millimeter-wave band (28 GHz), was a good compromise, and could boost performance significantly in both bands. This was a pleasant surprise: RIS improved signals not only in the millimeter-wave (5G high) band, where coverage problems can be especially acute, but also in the C band (5G mid).
To extend wireless coverage indoors, researchers in Asia are investigating a really intriguing possibility: covering room windows with transparent RIS nodes. Experiments at NTT Docomo and at Southeast and Nanjing universities, both in China, used smart films or smart glass. The films are fabricated from transparent conductive oxides (such as indium tin oxide), graphene, or silver nanowires and do not noticeably reduce light transmission. When the films are placed on windows, signals coming from outside can be refracted and boosted as they pass into a building, enhancing the coverage inside.
Planning and installing the RIS nodes is only part of the challenge. For an RIS node to work optimally, it needs to have a configuration, moment by moment, that is appropriate for the state of the communication channel in the instant the node is being used. The best configuration requires an accurate and instantaneous estimate of the channel. Technicians can come up with such an estimate by measuring the “channel impulse response” between the base station, the RIS, and the users. This response is measured using pilots, which are reference signals known beforehand by both the transmitter and the receiver. It’s a standard technique in wireless communications. Based on this estimation of the channel, it’s possible to calculate the phase shifts for each unit cell in the RIS.
The current approaches perform these calculations at the base station. However, that requires a huge number of pilots, because every unit cell needs its own phase configuration. There are various ideas for reducing this overhead, but so far none of them are really promising.
The total calculated configuration for all of the unit cells is fed to each RIS node through a wireless control link. So each RIS node needs a wireless receiver to periodically collect the instructions. This of course consumes power, and it also means that the RIS nodes are fully dependent on the base station, with unavoidable—and unaffordable—overhead and the need for continuous control. As a result, the whole system requires a flawless and complex orchestration of base stations and multiple RIS nodes via the wireless-control channels.
We need a better way. Recall that the “I” in RIS stands for intelligent. The word suggests real-time, dynamic control of the surface from within the node itself—the ability to learn, understand, and react to changes. We don’t have that now. Today’s RIS nodes cannot perceive, reason, or respond; they only execute remote orders from the base station. That’s why my colleagues and I at Huawei have started working on a project we call Autonomous RIS (AutoRIS). The goal is to enable the RIS nodes to autonomously control and configure the phase shifts of their unit cells. That will largely eliminate the base-station-based control and the massive signaling that either limit the data-rate gains from using RIS, or require synchronization and additional power consumption at the nodes. The success of AutoRIS might very well help determine whether RIS will ever be deployed commercially on a large scale.
Of course, it’s a rather daunting challenge to integrate into an RIS node the necessary receiving and processing capabilities while keeping the node lightweight and low power. In fact, it will require a huge research effort. For RIS to be commercially competitive, it will have to preserve its low-power nature.
With that in mind, we are now exploring the integration of an ultralow-power AI chip in an RIS, as well as the use of extremely efficient machine-learning models to provide the intelligence. These smart models will be able to produce the output RIS configuration based on the received data about the channel, while at the same time classifying users according to their contracted services and their network operator. Integrating AI into the RIS will also enable other functions, such as dynamically predicting upcoming RIS configurations and grouping users by location or other behavioral characteristics that affect the RIS operation.
Intelligent, autonomous RIS won’t be necessary for all situations. For some areas, a static RIS, with occasional reconfiguration—perhaps a couple of times per day or less—will be entirely adequate. In fact, there will undoubtedly be a range of deployments from static to fully intelligent and autonomous. Success will depend on not just efficiency and high performance but also ease of integration into an existing network.
6G promises to unleash staggering amounts of bandwidth—but only if we can surmount a potentially ruinous range problem.
The real test case for RIS will be 6G. The coming generation of wireless is expected to embrace autonomous networks and smart environments with real-time, flexible, software-defined, and adaptive control. Compared with 5G, 6G is expected to provide much higher data rates, greater coverage, lower latency, more intelligence, and sensing services of much higher accuracy. At the same time, a key driver for 6G is sustainability—we’ll need more energy-efficient solutions to achieve the “net zero” emission targets that many network operators are striving for. RIS fits all of those imperatives.
Start with massive MIMO, which stands for multiple-input multiple-output. This foundational 5G technique uses multiple antennas packed into an array at both the transmitting and receiving ends of wireless channels, to send and receive many signals at once and thus dramatically boost network capacity. However, the desire for higher data rates in 6G will demand even more massive MIMO, which will require many more radio-frequency chains to work and will be power-hungry and costly to operate. An energy-efficient and less costly alternative will be to place multiple low-power RIS nodes between massive MIMO base stations and users as we have described in this article.
The millimeter-wave and subterahertz 6G bands promise to unleash staggering amounts of bandwidth, but only if we can surmount a potentially ruinous range problem without resorting to costly solutions, such as ultradense deployments of base stations or active repeaters. My opinion is that only RIS will be able to make these frequency bands commercially viable at a reasonable cost.
The communications industry is already touting sensing—high-accuracy localization services as well as object detection and posture recognition—as an important possible feature for 6G. Sensing would also enhance performance. For example, highly accurate localization of users will help steer wireless beams efficiently. Sensing could also be offered as a new network service to vertical industries such as smart factories and autonomous driving, where detection of people or cars could be used for mapping an environment; the same capability could be used for surveillance in a home-security system. The large aperture of RIS nodes and their resulting high resolution mean that such applications will be not only possible but probably even cost effective.
And the sky is not the limit. RIS could enable the integration of satellites into 6G networks. Typically, a satellite uses a lot of power and has large antennas to compensate for the long-distance propagation losses and for the modest capabilities of mobile devices on Earth. RIS could play a big role in minimizing those limitations and perhaps even allowing direct communication from satellite to 6G users. Such a scheme could lead to more efficient satellite-integrated 6G networks.
As it transitions into new services and vast new frequency regimes, wireless communications will soon enter a period of great promise and sobering challenges. Many technologies will be needed to usher in this next exciting phase. None will be more essential than reconfigurable intelligent surfaces.
The author wishes to acknowledge the help of Ulrik Imberg in the writing of this article.