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IEEE also mourns the loss of a past IEEE Canada president and others
IEEE Life Fellow Merrill Skolnik served as superintendent of the radar division of the U.S. Naval Research Laboratory in Washington, D.C., for more than 30 years.
First recipient of the IEEE Dennis J. Picard Medal
Skolnik served as superintendent of the radar division of the U.S. Naval Research Laboratory in Washington, D.C., for more than 30 years. While there, he made significant contributions including helping to develop high-frequency, over-the-horizon radar; a system that can identify friend or foe during combat; and high-resolution radar techniques.
For his work in the field, he was named the first recipient of the IEEE Dennis J. Picard Medal for Radar Technologies and Applications, in 2000. Picard was chief executive of Raytheon and helped the company become a leader in tactical missile systems.
Skolnik began his career in 1955 at MIT’s Lincoln Laboratory. While there, he taught a course on radar at Northeastern University, in Boston. The course was the basis for his 1962 book Introduction to Radar Systems.
He left MIT in 1959 to join Electronic Communications, now part of Raytheon. There he gained experience working on antennas, electronic warfare, and phased arrays.
He then joined the Institute for Defense Analyses, in Alexandria, Va. It provides technical advice to the U.S. Defense Department, the Defense Advanced Research Projects Agency, and other government entities. While there, he did pioneering work on thinned arrays and self-phasing array antennas. He also contributed to the fields of bistatic radars and electronic countermeasures.
In 1965 he became superintendent of the radar division at the U.S. Naval Research Laboratory. He and his staff developed concepts for wideband shipboard air-surveillance radar with reduced susceptibility to electronic countermeasures; self-defense radar; and space-borne radar for detecting ships.
He continued to work as a consultant for the lab after he retired in 1996.
In 1944 Skolnik joined the American Institute of Electrical Engineers, one of IEEE’s predecessor societies. He served on the Proceedings of the IEEE editorial board in the late 1980s.
He earned bachelor’s and master’s degrees as well as a Ph.D. in engineering from Johns Hopkins University, in Baltimore.
Director emeritus of the IEEE Foundation
Behnke spent his entire career at Commonwealth Edison in Chicago. He retired in 1989 as vice chairman of the utility.
He was an active IEEE volunteer and held several leadership positions. He served as the 1988 president of the IEEE Power & Energy Society. He was the 1990–1991 Division VII director, and he directed the IEEE Foundation from 1999 to 2004.
Behnke served in the U.S. Navy during World War II and the Korean War before joining Commonwealth Edison. During his tenure at the company, he oversaw the design and construction of the Clinch River Breeder Reactor project, a sodium-cooled nuclear facility in Tennessee.
He earned a bachelor’s degree in electrical engineering in 1945 from Northwestern University, in Evanston, Ill.
Life senior member, 78; died 5 January
Kemp worked for 35 years at the Manitoba Telephone System in Canada.
An active IEEE volunteer, he served in several leadership positions including as 1998 president of IEEE Canada and director of Region 7. He was IEEE secretary in 2000.
He was an IEEE member for nearly 60 years. He helped establish an IEEE student branch at Red River College, in Winnipeg, Manitoba. He went on to hold several officer positions in the IEEE Winnipeg Section. He helped launch the IEEE Graduates of the Last Decade program, now IEEE Young Professionals.
Kemp served on numerous IEEE boards and committees including the IEEE Service Awards Committee, the IEEE History Committee, the IEEE Life Members Committee, and the IEEE Canadian Foundation.
After he retired as director of information technology at MTS, he was the business manager of industrial applications at the University of Manitoba’s Microelectronics Center, in Winnipeg. He also worked as a consultant in Europe.
He served on the boards of the Electronics Industry Association of Manitoba and the Canadian Institute of Management.
Kemp earned a bachelor’s degree in electronics engineering technology from the Manitoba Institute of Trades and Technology, in Winnipeg.
Recipient of the 1991 IEEE Nikola Tesla Award
Poloujadoff’s academic career spanned almost 40 years. His research focused on electrical machines, power electronics, and electrical lines.
He was an active volunteer in the IEEE France Section, and in 2004 he helped launch its IEEE Life Members Affinity Group.
He joined Grenoble University (now Université Grenoble Alpes) in France in 1961 as a professor of electrical engineering. He was a visiting professor in 1983 at McMaster University, in Hamilton, Ont., Canada. After returning to France, he became a professor of electrical engineering at the University Pierre et Marie Curie (now Sorbonne University), in Paris. He taught at the university until he retired in 2000.
Poloujadoff was a doctoral research advisor and mentored about 50 students during his career. He helped establish electrical engineering graduate programs in Egypt, France, and Tunisia.
He developed approaches for modeling squirrel-cage rotors—the rotating cylinder of steel laminations in induction motors—including harmonic magnetic fields and inter-bar currents. In 1965 he began conducting research on the numerical solution of electromagnetic field equations and later defined the basis of the first entirely 3D analysis of large transformers.
He served as chair of the France Section’s IEEE Life Members Affinity Group from 2004 to 2016. He was a distinguished lecturer for the IEEE Power & Energy Society and the IEEE Industry Applications Society.
After helping to found the International Conference on Electrical Machines in 1974, he served on ICEM’s steering committee from 1974 to 2000.
He received several honors including the 2012 ICEM Arthur Ellison Achievement Award, the 1994 IEEE Lamme Medal, and the 1991 IEEE Nikola Tesla Award.
Poloujadoff earned a bachelor’s degree in electrical engineering in 1955 from Supélec (now part of CentraleSupélec) in Paris. He received a master’s degree in computer science from Harvard. After returning to France, he earned his Ph.D. in electrical engineering in 1960 from the Université de Paris.
Vice president of engineering at AT&T
Life senior member, 86; died 26 November
During his career, Weingart worked at New York Telephone and Bell Atlantic (both now are part of Verizon) as well as AT&T. He retired in 1994 as AT&T’s vice president of engineering.
He was a pioneer in the wireless communications industry and helped launch cellular networks that are still used today. He holds several U.S. patents.
Weingart was a member of the U.S. Marine Corps Reserve.
Weingart’s family describes him as giving and community-minded. He was a member of several organizations including the Photographic Society of America, the Classic Car Club of America, and the Radio Club of America.
He earned a bachelor’s degree in electrical engineering from Polytechnic Institute of Brooklyn (now the New York University Tandon School of Engineering).
Spilman worked for several international technology companies in senior management positions. He was director of project management of open-source software at Arm, in England, when he died.
His family says he “always believed in building a strong team around him through supporting younger team members’ development.”
In his free time, Spilman enjoyed sailing, gardening, and skiing.
He earned a bachelor’s degree at the University of Southampton, in England, and a master’s degree at Stanford.
Joanna Goodrich is the assistant editor of The Institute, covering the work and accomplishments of IEEE members and IEEE and technology-related events. She has a master's degree in health communications from Rutgers University, in New Brunswick, N.J.
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.