IEEE websites place cookies on your device to give you the best user experience. By using our websites, you agree to the placement of these cookies. To learn more, read our Privacy Policy.
The Ukraine invasion has become an old-fashioned slog, enabling Russia to unleash its electronic weapons
The Krasukha-4 is a centerpiece of Russia’s complement of electronic-warfare systems.
A month into Russia’s invasion, Ukrainian troops stumbled upon a nondescript shipping container at an abandoned Russian command post outside Kyiv. They did not know it then, but the branch-covered box left by retreating Russian soldiers was possibly the biggest intelligence coup of the young war.
Inside were the guts of one of Russia’s most sophisticated electronic warfare (EW) systems, the Krasukha-4. First fielded in 2014, the Krasukha-4 is a centerpiece of Russia’s strategic EW complement. Designed primarily to jam airborne or satellite-based fire control radars in the X- and Ku-bands, the Krasukha-4 Is often used alongside the Krasukha-2, which targets lower-frequency S-band search radars. Such radars are used on stalwart U.S. reconnaissance platforms, such as the E-8 Joint Surveillance Target Attack Radar System (JSTARS) and Airborne Warning and Control System, or AWACS, aircraft.
And now Ukraine, including by extension its intelligence partners in NATO, had a Krasukha-4 to dissect and analyze.
That Russian troops would ditch the heart of such a valuable EW system was surprising in March, when Moscow was still making gains across the country and threatening Kyiv. Five months into the war, it is now apparent that Russia’s initial advance was already faltering when the Krasukha-4 was left by the roadside. With highways around Kyiv clogged by armored columns, withdrawing units needed to lighten their load.
The abandoned Krasukha-4 was emblematic of the puzzling failure of Russian EW in the first few months of Russia’s invasion. After nearly a decade of owning the airwaves during a Moscow-backed insurgency in eastern Ukraine, EW was not decisive when Russia went to war in February. The key questions now are, why was this so, what is next for Russian EW in this oddly anachronistic war, and how might it affect the outcome?
At least three of Russia’s five electronic warfare brigades are engaged in Ukraine. And with more exposure to NATO-supplied radios, experienced Russian EW operators who cut their teeth in Syria are beginning to detect and degrade Ukrainian communications.
Electronic warfare is a pivotal if invisible part of modern warfare. Military forces rely on radios, radars, and infrared detectors to coordinate operations and find the enemy. They use EW to control the spectrum, protecting their own sensing and communications while denying access to the electromagnetic spectrum by enemy troops.
U.S. military doctrine defines EW as comprising electronic attack (EA), electronic protection, and electronic support. The most familiar of these is EA, which includes jamming, where a transmitter overpowers or disrupts the waveform of a hostile radar or radio. For instance, the Russian R-330Zh Zhitel jammer can reportedly shut down—within a radius of tens of kilometers—GPS, satellite communications, and cellphone networks in the VHF and UHF bands. Deception is also part of EA, in which a system substitutes its own signal for an expected radar or radio transmission. For example, Russian forces sent propaganda and fake orders to troops and civilians during the 2014–2022 insurgency in eastern Ukraine by hijacking the local cellular network with the RB-341V Leer-3 system. Using soldier-portable Orlan-10 drones managed by a truck-mounted control system, the Leer-3 can extend its range and impact VHF and UHF communications over wider areas.
The Zhitel jamming system can shut down, over tens of kilometers, GPS and satellite communications. This image shows the base of one of the four antennas in a typical setup.informnapalm.org
The converse of electronic attack is electronic support (ES), which is used to passively detect and analyze an opponent’s transmissions. ES is essential for understanding the potential vulnerabilities of an adversary’s radars or radios. Therefore, most Russian EA systems include ES capabilities that allow them to find and quickly characterize potential jamming targets. Using their ES capabilities, most EA systems can also geolocate enemy radio and cellphone transmissions and then pass that information on so that it can be used to direct artillery or rocket fire—with often devastating effects.
A few Russian systems conduct ES exclusively; one example is the Moskva-1, which is a precision HF/VHF receiver that can use the reflections of TV and radio signals to conduct passive coherent location or passive radar operations. Basically, the system picks up the radio waves of commercial TV and radio transmitters in an area, which will reflect off targets like ships or aircraft. By triangulating among multiple sets of received waves, the target can be pinpointed with sufficient accuracy to track it and, if needed, shoot at it.
Russia uses specialized electronic-warfare units to conduct its EA and ES operations. In its ground forces, dedicated EW brigades of several hundred soldiers are assigned to the five Russian military districts—West, South, North, Central, and East—to support regional EW operations that include disrupting enemy surveillance radars and satellite communication networks over hundreds of kilometers. EW brigades are equipped with the larger Krasukha-2 and -4, Leer-3, Moskva-1, and Murmansk-BN systems (the latter of which detects and jams HF radios). Each Russian army maneuver brigade also includes an EW company of about 100 personnel that is trained to support local actions within about 50 kilometers using smaller systems, like the R-330Zh Zhitel.
Militaries use electronic protection (EP), also known as electronic countermeasures, to defend against EA and ES. Long considered an afterthought by western forces after the Cold War, EP has risen again to be perhaps the most important aspect of EW as Russia and China field increasingly sophisticated jammers and sensors. EP includes tactics and technologies to shield radio transmissions from being detected or jammed. Typical techniques include using narrow beams or low - power transmissions, as well as advanced waveforms that are resistant to jamming.
Experts have long touted Russia as having some of the most experienced and best-equipped EW units in the world. So in the early days of the 24 February invasion, analysts expected Russian forces to quickly gain control of, and then dominate, the electromagnetic spectrum. Since the annexation of Crimea in 2014, EW has been a key part of Russian operations in the “gray zone,” the shadowy realm between peace and war, in the Donbas region. Using Leer-3 EW vehicles and Orlan-10 drones, Moscow-backed separatists and mercenaries would jam Ukrainian communications and send propaganda over local mobile-phone networks. When Russian forces were ready to strike, the ground and airborne systems would detect Ukrainian radios and target them with rocket attacks.
But after nearly a decade of rehearsals in eastern Ukraine, when the latest escalation and invasion began in February, Russian EW was a no-show. Ukrainian defenders did not experience the jamming they faced in the Donbas and were not being targeted by drones or ground-based electronic surveillance. Although Russian forces did blow up some broadcast radio and television towers, Ukraine’s leaders continued to reach the outside world unimpeded by Russian EW.
Using counter-drone systems provided by the United States before the invasion, Ukrainian troops have downed hundreds of Russian drones by jamming their GPS signals or possibly by damaging their electronics with high-powered microwave beams.
Russia is gaining the upper hand now, having consolidated control in Ukraine's east and south as the invaded country begins running out of soldiers, weapons, and time. With more defined front lines and better logistics support from their homeland, Russian troops are now using their EW systems to guide artillery and rocket strikes. But instead of being the leading edge of Russia’s offensive, EW is coming into play only after Moscow resorted to siege tactics that call to mind the origins of EW in World War I.
The RF spectrum was a lot less busy then. Commanders used their new radios to coordinate troop movements and direct fire and employed early passive direction-finding equipment to locate or listen to enemy radio transmissions. While communications jamming emerged at the same time, it was not widely employed. Radio operators realized that simply keying their systems could send out a blast of white noise to drown the transmissions of other radios operating at the same frequencies. But this tactic had limited operational value, because it also prevented forces doing the jamming from using the same radio frequencies to communicate. Moreover, warfare happened slowly enough that the victim could simply wait out the jammer.
Thus, World War I EW was exemplified by passive detection of radio transmissions and infrequent, rudimentary jamming. The shift to more sophisticated EW systems and tactics occurred with World War II, when technological advances made airborne radars and jammers practical, better tuners allowed jamming and communicating on separate frequencies, and the increased tempo of warfare gave combatants an incentive to not just jam enemy transmissions but to intercept and exploit them as well.
Consider the Battle of Britain, when the main challenge for German pilots was reaching the right spot to drop their bombs. Germany used a radio-beacon system it called Knickebein (“crooked leg” in English) to guide its bombers to British aircraft factories, which the British countered with fake beacons that they code-named Aspirin. To support British warplanes attacking Germany in 1942, the Royal Air Force (RAF) fielded the GEE hyperbolic radio navigation system that allowed its bomber crews to use transmissions from British ground stations to determine their in-flight positions. Germany countered with jammers that drowned out the GEE transmissions.
The World War II EW competition extended to sensing and communication networks. RAF and U.S. bombers dispensed clouds of metallic chaff called Window that confused German air-defense radars by creating thousands of false radar targets. And they used VHF communication jammers, which the British called Jostle, to interfere with German ground controllers attempting to vector fighters toward allied bombers.
The move-countermove cycle accelerated in response to Soviet military aggressions and advances in the 1950s. Active countermeasures such as jammers or decoys proliferated, thanks to technological advances that enabled EW systems with greater power, wider frequency ranges, and more complex waveforms, and which were small enough to fit aircraft as well as ships.
Later, as Soviet military sensors, surface-to-air missiles, and antiship cruise missiles grew in their sophistication and numbers, the U.S. Department of Defense sought to break out of the radar-versus-electronic-attack competition by leveraging emerging materials, computer simulation, and other technologies. In the years since, the U.S. military has developed multiple generations of stealth aircraft and ships with severely reduced radio-frequency, infrared, acoustic, and visual signatures. Russia followed with its own stealth platforms, albeit more slowly after the Soviet Union’s collapse.
But today, years of underfunded aviation training and maintenance and the rapid introduction by NATO of Stinger shoulder-launched surface-to-air missiles have largely grounded Russian jets and helicopters during the Ukraine invasion. So when Russian troops crossed the border, they faced a situation not unlike the armies of World War I.
Without airpower, the Russian assault crawled at the speed of their trucks and tanks. And although they proved effective in the Donbas during the last decade, Russian drones are controlled by line-of-sight radios operating in the Ka- and Ku-bands, which prevented them from straying too far from their operators on the ground. With Russian columns moving along multiple axes into Ukraine and unable to send EW drones well over the horizon, any jamming of Ukrainian forces, some of which were interspersed between Russian formations, would have also taken out Russian radios.
Russian EW units did use Leer-3 units to find Ukrainian fighters via their radio and cellphone transmissions, as they had in the Donbas. But unlike Ukraine’s rural east, the areas around Kyiv are relatively densely populated. With civilian cellphone transmissions mixed in with military communications, Russian ES systems were unable to pinpoint military transmitters and use that information to target Ukrainian troops. Making matters worse for the Russians, Ukrainian forces also began using the NATO Single-Channel Ground and Airborne Radio System, or SINCGARS.
Ukrainian troops had trained for a decade with SINCGARS, but the portable VHF combat radios were scarce until the lead-up to the Russian invasion, when the flood of NATO support sent SINCGARS radios to nearly every Ukrainian ground unit. Unlike Ukraine’s previous radios, which were Russian-built and included backdoors for the convenience of Russian intelligence, SINCGARS have built-in encryption. To protect against jamming and interception, SINCGARS automatically hops among frequencies up to 100 times a second across its overall coverage of 30 to 88 megahertz. Because SINCGARS can control signals within 25-kilohertz bands, the user can select among more than 2,000 channels.
As in World War I, the lack of airpower also affected the speed of conflict. The widely circulated videos of Russian armored convoys stuck along the roads around Kyiv were a stark reminder that ground operations can only move as fast as their fuel supply. In World War II and the Cold War, bombing missions and other air operations happened so quickly that even if jamming impacted friendly forces, the effect would be temporary, as the positions of jammers, jamming targets, and bystanders would quickly change. But when Russian forces were trundling toward the urban areas of northern Ukraine, they were going so slowly that they were unable to exploit changing geometries to get their jammers into positions from which they could have substantial effects. At the same time, Russian troops were not sitting still, which prevented them from setting up a large system like the Krasukha-4 to blind NATO radars in the air and in space.
Russian EW is gaining an advantage only now because Moscow’s strategy of quickly taking Kyiv failed, and it shifted to a grinding war of attrition in Ukraine’s south.
So what’s next? The Kremlin’s fortunes have improved now that its soldiers are fighting from Russian-held territory in Ukraine’s east. No longer spread out along multiple lines in suburban areas, invading troops are now able to use EW to support a strategy of incrementally gaining territory by finding Ukrainian positions and overwhelming them with Russia’s roughly 10-to-1 advantage in artillery.
As of this writing, at least three of Russia’s five EW brigades are engaged in Ukraine. And with more exposure to NATO-supplied radios, experienced Russian EW operators who cut their teeth in the last decade of war in Syria are beginning to detect and degrade Ukrainian communications. EW brigades are using the Leer-3’s Orlan-10 drones to detect Ukrainian artillery positions based on their radio emissions, although the encryption and frequency hopping of SINCGARS radios makes them hard to intercept and exploit. Because the front lines are now better defined compared to the early war around Kyiv, Russian forces can assume the detections are from Ukrainian military units and direct artillery and rocket fire against those locations.
Russian troops are using Orlan-10 drones [foreground] in conjunction with the Leer-3 electronic-warfare system (which includes the truck in the background) to identify and attack Ukrainian units. iStockphoto
The Krasukha-4, which was too powerful and unwieldy to be useful during the assault on Kyiv, is also making a reappearance. Exploiting Russia’s territorial control in the Donbas, EW brigades are using the Krasukha-4 to jam the radars on such Ukrainian drones as the Bayraktar TB2, and to interfere with their communication links, preventing Ukrainian forces from locating Russian artillery emplacements.
To gain flexibility and mobility leading up to the invasion, the Russian army broke its 2,000-soldier maneuver brigades into smaller battalion tactical groups (BTGs) of 300 to 800 personnel in such a way that each included a portion of the original maneuver brigade’s EW company. Today, BTGs operating in southern and eastern Ukraine are employing shorter-range VHF-UHF electronic attack systems like the R-330Zh Zhitel to disable Ukrainian drones ranging from Bayraktar TB2s to small DJI Mavics by jamming their GPS signals. BTGs are also attacking Ukrainian communications using R-934B VHF and SPR-2 VHF/UHF jammers, with some success. Although Ukrainian soldiers have SINCGARS radios, they still rely on vulnerable cellphones and radios without encryption or frequency hopping when SINCGARS is down or unavailable.
But Ukraine is fighting back against Russia’s spectrum assault. Using counter-drone systems provided by the United States before the invasion, Ukrainian troops have downed hundreds of Russian drones by jamming their GPS signals or possibly by damaging their electronics with high-powered microwave beams, a specific type of EA where electromagnetic energy is used to generate high voltages in sensitive microelectronics that damage transistors and integrated circuits.
Ukrainian forces are also leveraging U.S.-supplied EW systems and training to jam Russian communications. Unlike their Ukrainian counterparts, Russian troops do not have a system like SINCGARS and often rely on cellphones or unencrypted radios to coordinate operations, making them susceptible to Ukrainian geolocation and jamming. In this way, stabilization of the front lines also helps Ukraine’s EW efforts because it allows quick correlation of transmissions to locations. Ukraine’s defenders also exploited a weakness of the large and powerful Russian EW systems—they are easy to find. Using U.S.-supplied ES gear, Ukrainian troops have been able to detect transmissions from systems like the Leer-3 or Krasukha-4 and direct rocket, artillery, and drone counterattacks against the truck-borne Russian systems.
The Ukraine invasion shows EW can change the course of a war, but it’s also showing that the fundamentals still matter. Without airpower or satellite-guided drones, Russia’s army could not get jammers over the horizon to degrade Ukrainian communications and radars in advance of troops moving on Kyiv. Forced to use short-range unmanned aircraft and ground systems, Russian EW brigades operating with BTGs had to worry about interfering with friendly operations and could not distinguish Ukrainian troops from civilians. They also had to stay on the move, reducing the utility of their large multivehicle EW systems. Russian EW is gaining an advantage only now because Moscow’s strategy of quickly taking Kyiv failed, and it shifted to a grinding war of attrition in Ukraine’s south.
So for now, unable to reach over the horizon, Russian EW ground units can jam Ukrainian troops only when they are separated by clearly defined battle lines. They are relying on systems like the Leer-3 to find Ukrainian emissions so Russian artillery can then overwhelm the defenders with volleys of shells and rockets. Russian EW systems like the Krasukha-4 and R-330Zh Zhitel can disable GPS or radars on Ukrainian drones, but it’s not substantially different from shooting down aircraft with guns. And although ES systems like the Moskva-4 could hear signals over the horizon, Russia is running out of the long-range missiles that could exploit such detections.
Perhaps the biggest lesson from Ukraine for EW is that winning the airwaves does not equal winning the war. Russia is on top of the EW war now only because its lighting assault became a pulverizing slog. The situation could quickly flip if Kyiv’s troops, with western support, regain control of Ukraine’s skies, where they could electronically and physically disrupt the management and logistics that keep Russia’s rickety war machine trundling along.
Bryan Clark is a senior fellow at the Hudson Institute and director of the Institute’s Center for Defense Concepts and Technology. He is an expert in electronic warfare, naval operations, autonomous systems, military competitions, and war-gaming. Earlier in his career, Clark was special assistant to the U.S. chief of naval operations and director of his commander’s action group, where he led development of U.S. Navy strategy and implemented new initiatives in electromagnetic spectrum operations, undersea warfare, expeditionary operations, and in personnel and readiness management.
Question: Why is IEEE letting Russian EW specialists belong as members? They are participating in murder and the Institute is silent!!
This is a really good overview of EW fundamentals through a lens of the Russo-Ukrainian War. However, there is one error – Electronic Protection is not also known as electronic countermeasures (ECM). The older re-designated Cold War-era terms are:
Electronic Countermeasures (ECM) became Electronic Attack (EA)
https://en.wikipedia.org/wiki/Electronic_countermeasure
Electronic Support Measures (ESM) became Electronic Warfare Support (ES)
https://en.wikipedia.org/wiki/Electronic_warfare_support_measures
Electronic Counter-Countermeasures (ECCM) became Electronic Protection (EP).
https://en.wikipedia.org/wiki/Electronic_counter-countermeasure
The older terms are still used to described specific techniques like chaff (mechanical ECM) in training and planning.
ESM, ECM, ECCM have been huge enablers for decades.
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.