Friday, June 18, 2021

Fast computers, 5G networks and radar that passes through walls are bringing ‘X-ray vision’ closer to reality

Seeing through walls has long been a staple of comics and science fiction. Something like it could soon be a reality. Paul Gilligan/Photodisc via Getty Images
Aly Fathy, University of Tennessee

Within seconds after reaching a city, earthquakes can cause immense destruction: Houses crumble, high-rises turn to rubble, people and animals are buried in the debris.

In the immediate aftermath of such carnage, emergency personnel desperately search for any sign of life in what used to be a home or office. Often, however, they find that they were digging in the wrong pile of rubble, and precious time has passed.

Imagine if rescuers could see through the debris to spot survivors under the rubble, measure their vital signs and even generate images of the victims. This is rapidly becoming possible using see-through-wall radar technology. Early versions of the technology that indicate whether a person is present in a room have been in use for several years, and some can measure vital signs albeit under better conditions than through rubble.

I’m an electrical engineer who researches electromagnetic communication and imaging systems. I and others are using fast computers, new algorithms and radar transceivers that collect large amounts of data to enable something much closer to the X-ray vision of science fiction and comic books. This emerging technology will make it possible to determine how many occupants are present behind a wall or barrier, where they are, what items they might be carrying and, in policing or military uses, even what type of body armor they might be wearing.

These see-through-wall radars will also be able to track individuals’ movements, and heart and respiration rates. The technology could also be used to determine from a distance the entire layout of a building, down to the location of pipes and wires within the walls, and detect hidden weapons and booby traps.

See-through-wall technology has been under development since the Cold War as a way to replace drilling holes through walls for spying. There are a few commercial products on the market today, like Range-R radar, that are used by law enforcement officers to track motion behind walls.

How radar works

Radar stands for radio detection and ranging. Using radio waves, a radar sends a signal that travels at the speed of light. If the signal hits an object like a plane, for example, it is reflected back toward a receiver and an echo is seen in the radar’s screen after a certain time delay. This echo can then be used to estimate the location of the object.

In 1842, Christian Doppler, an Austrian physicist, described a phenomenon now known as the Doppler effect or Doppler shift, where the change in frequency of a signal is related to the speed and direction of the source of the signal. In Doppler’s original case, this was the light from a binary star system. This is similar to the changing pitch of a siren as an emergency vehicle speeds toward you, passes you and then moves away. Doppler radar uses this effect to compare the frequencies of the transmitted and reflected signals to determine the direction and speed of moving objects, like thunderstorms and speeding cars.

The Doppler effect can be used to detect tiny motions, including heartbeats and chest movement associated with breathing. In these examples, the Doppler radar sends a signal to a human body, and the reflected signal differs based on whether the person is inhaling or exhaling, or even based on the person’s heart rate. This allows the technology to accurately measure these vital signs.

How radar can go through walls

Like cellphones, radars use electromagnetic waves. When a wave hits solid walls like drywall or wood walls, a fraction of it is reflected off the surface. But the rest travels through the wall, especially at relatively low radio frequencies. The transmitted wave can be totally reflected back if it hits a metal object or even a human, because the human body’s high water content makes it highly reflective.

If the radar’s receiver is sensitive enough – a lot more sensitive than ordinary radar receivers – it can pick up the signals that are reflected back through the wall. Using well-established signal processing techniques, the reflections from static objects like walls and furniture can be filtered out, allowing the signal of interest – like a person’s location – to be isolated.

A diagram showing a square on the left, a vertical rectangle in the middle and a sphere on the right. A series of four diminishing sine waves pass from the square to the wall, the wall to the sphere, the sphere back to the wall and from the wall to the sq
The key to using radar to track objects on the other side of a wall is having a very sensitive antenna that can pick up the greatly diminished reflected radio waves. Abdel-Kareem Moadi, CC BY-ND

Turning data into images

Historically, radar technology has been limited in its ability to aid in disaster management and law enforcement because it hasn’t had sufficient computational power or speed to filter out background noise from complicated environments like foliage or rubble and produce live images.

Today, however, radar sensors can often collect and process large amounts of data – even in harsh environments – and generate high-resolution images of targets. By using sophisticated algorithms, they can display the data in near real-time. This requires fast computer processors to rapidly handle these large amounts of data, and wideband circuits that can rapidly transmit data to improve the images’ resolution.

Recent developments in millimeter wave wireless technology, from 5G to 5G+ and beyond, are likely to help further improve this technology, providing higher-resolution images through order-of-magnitude wider bandwidth. The wireless technology will also speed data processing times because it greatly reduces latency, the time between transmitting and receiving data.

My laboratory is developing fast methods to remotely characterize the electrical characteristics of walls, which help in calibrating the radar waves and optimize the antennas to make the waves more easily pass through the wall and essentially make the wall transparent to the waves. We are also developing the software and hardware system to carry out the radar systems’ big data analyses in near real-time.

On the left, a laboratory set up showing a cinderblock wall and a foil-covered cardboard silhouette of a person, and, on the right, a radar image showing a corresponding silhouette in a three-dimensional space
This laboratory wall-penetrating radar provides more detail than today’s commercial systems. Aly Fathy

Better electronics promise portable radars

Radar systems at the low frequencies usually required to see through walls are bulky due to the large size of the antenna. The wavelength of electromagnetic signals corresponds to the size of the antenna. Scientists have been pushing see-through-wall radar technology to higher frequencies in order to build smaller and more portable systems.

In addition to providing a tool for emergency services, law enforcement and the military, the technology could also be used to monitor the elderly and read vital signs of patients with infectious diseases like COVID-19 from outside a hospital room.

One indication of see-through-wall radar’s potential is the U.S. Army’s interest. They’re looking for technology that can create three-dimensional maps of buildings and their occupants in almost real-time. They are even looking for see-through-wall radar that can create images of people’s faces that are accurate enough for facial recognition systems to identify the people behind the wall.

Whether or not researchers can develop see-through-wall radar that’s sensitive enough to distinguish people by their faces, the technology is likely to move well beyond blobs on a screen to give first responders something like superhuman powers.

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Aly Fathy, Professor of Electrical Engineering, University of Tennessee

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Sunday, April 11, 2021

Top Stories

Embrace the unexpected: To teach AI how to handle new situations, change the rules of the game

Most of today’s AI’s come to a grinding halt when they encounter unexpected conditions, like a change in the rules of a game. LightFieldStudios/iStock via Getty Images
Mayank Kejriwal, University of Southern California

My colleagues and I changed a digital version of Monopoly so that instead of getting US$200 each time a player passes Go, the player is charged a wealth tax. We didn’t do this to gain an advantage or trick anyone. The purpose is to throw a curveball at artificial intelligence agents that play the game.

Our aim is to help the agents learn to handle unexpected events, something AIs to date have been decidedly bad at. Giving AIs this kind of adaptability is important for futuristic systems like surgical robots, but also algorithms in the here and now that decide who should get bail, who should get approved for a credit card and whose resume gets through to a hiring manager. Not dealing well with the unexpected in any of those situations can have disastrous consequences.

AI agents need the ability to detect, characterize and adapt to novelty in human-like ways. A situation is novel if it challenges, directly or indirectly, an agent’s model of the external world, which includes other agents, the environment and their interactions.

While most people do not deal with novelty in the most perfect way possible, they are able to to learn from their mistakes and adapt. Faced with a wealth tax in Monopoly, a human player might realize that she should have cash handy for the IRS as she is approaching Go. An AI player, bent on aggressively acquiring properties and monopolies, may fail to realize the appropriate balance between cash and nonliquid assets until it’s too late.

Adapting to novelty in open worlds

Reinforcement learning is the field that is largely responsible for “superhuman” game-playing AI agents and applications like self-driving cars. Reinforcement learning uses rewards and punishment to allow AI agents to learn by trial and error. It is part of the larger AI field of machine learning.

The learning in machine learning implies that such systems are already capable of dealing with limited types of novelty. Machine learning systems tend to do well on input data that are statistically similar, although not identical, to those on which they were originally trained. In practice, it is OK to violate this condition as long as nothing too unexpected is likely to happen.

Such systems can run into trouble in an open world. As the name suggests, open worlds cannot be completely and explicitly defined. The unexpected can, and does, happen. Most importantly, the real world is an open world.

However, the “superhuman” AIs are not designed to handle highly unexpected situations in an open world. One reason may be the use of modern reinforcement learning itself, which eventually leads the AI to be optimized for the specific environment in which it was trained. In real life, there are no such guarantees. An AI that is built for real life must be able to adapt to novelty in an open world.

Novelty as a first-class citizen

Returning to Monopoly, imagine that certain properties are subject to rent protection. A good player, human or AI, would recognize the properties as bad investments compared to properties that can earn higher rents and not purchase them. However, an AI that has never before seen this situation, or anything like it, will likely need to play many games before it can adapt.

Before computer scientists can even start theorizing about how to build such “novelty-adaptive” agents, they need a rigorous method for evaluating them. Traditionally, most AI systems are tested by the same people who build them. Competitions are more impartial, but to date, no competition has evaluated AI systems in situations so unexpected that not even the system designers could have foreseen them. Such an evaluation is the gold standard for testing AI on novelty, similar to randomized controlled trials for evaluating drugs.

In 2019, the U.S. Defense Advanced Research Projects Agency launched a program called Science of Artificial Intelligence and Learning for Open-world Novelty, called SAIL-ON for short. It is currently funding many groups, including my own at the University of Southern California, for researching novelty adaptation in open worlds.

One of the many ways in which the program is innovative is that a team can either develop an AI agent that handles novelty, or design an open-world environment for evaluating such agents, but not both. Teams that build an open-world environment must also theorize about novelty in that environment. They test their theories and evaluate the agents built by another group by developing a novelty generator. These generators can be used to inject unexpected elements into the environment.

Under SAIL-ON, my colleagues and I recently developed a simulator called Generating Novelty in Open-world Multi-agent Environments, or GNOME. GNOME is designed to test AI novelty adaptation in strategic board games that capture elements of the real world.

Diagram of a Monopoly game with symbols indicating players, houses and hotels
The Monopoly version of the author’s AI novelty environment can trip up AI’s that play the game by introducing a wealth tax, rent control and other unexpected factors. Mayank Kejriwal, CC BY-ND

Our first version of GNOME uses the classic board game Monopoly. We recently demonstrated the Monopoly-based GNOME at a top machine learning conference. We allowed participants to inject novelties and see for themselves how preprogrammed AI agents performed. For example, GNOME can introduce the wealth tax or rent protection “novelties” mentioned earlier, and evaluate the AI following the change.

By comparing how the AI performed before and after the rule change, GNOME can quantify just how far off its game the novelty knocked the AI. If GNOME finds that the AI was winning 80% of the games before the novelty was introduced, and is now winning only 25% of the games, it will flag the AI as one that has lots of room to improve.

The future: A science of novelty?

GNOME has already been used to evaluate novelty-adaptive AI agents built by three independent organizations also funded under this DARPA program. We have also built GNOMEs based on poker, and “war games” that are similar to Battleship. In the next year, we will also be exploring GNOMEs for other strategic board games like Risk and Catan. This research is expected to lead to AI agents that are capable of handling novelty in different settings.

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Making novelty a central focus of modern AI research and evaluation has had the byproduct of producing an initial body of work in support of a science of novelty. Not only are researchers like ourselves exploring definitions and theories of novelty, but we are exploring questions that could have fundamental implications. For example, our team is exploring the question of when a novelty is expected to be impossibly difficult for an AI. In the real world, if such a situation arises, the AI would recognize it and call a human operator.

In seeking answers to these and other questions, computer scientists are now trying to enable AIs that can react properly to the unexpected, including black-swan events like COVID-19. Perhaps the day is not far off when an AI will be able to not only beat humans at their existing games, but adapt quickly to any version of those games that humans can imagine. It may even be capable of adapting to situations that we cannot conceive of today.The Conversation

Mayank Kejriwal, Research Assistant Professor of Computer Science, University of Southern California

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Sunday, December 27, 2020

Top Stories

An AI tool can distinguish between a conspiracy theory and a true conspiracy – it comes down to how easily the story falls apart

In the age of social media, conspiracy theories are collective creations. AP Photo/Ted S. Warren
Timothy R. Tangherlini, University of California, Berkeley

The audio on the otherwise shaky body camera footage is unusually clear. As police officers search a handcuffed man who moments before had fired a shot inside a pizza parlor, an officer asks him why he was there. The man says to investigate a pedophile ring. Incredulous, the officer asks again. Another officer chimes in, “Pizzagate. He’s talking about Pizzagate.”

In that brief, chilling interaction in 2016, it becomes clear that conspiracy theories, long relegated to the fringes of society, had moved into the real world in a very dangerous way.

Conspiracy theories, which have the potential to cause significant harm, have found a welcome home on social media, where forums free from moderation allow like-minded individuals to converse. There they can develop their theories and propose actions to counteract the threats they “uncover.”

But how can you tell if an emerging narrative on social media is an unfounded conspiracy theory? It turns out that it’s possible to distinguish between conspiracy theories and true conspiracies by using machine learning tools to graph the elements and connections of a narrative. These tools could form the basis of an early warning system to alert authorities to online narratives that pose a threat in the real world.

The culture analytics group at the University of California, which I and Vwani Roychowdhury lead, has developed an automated approach to determining when conversations on social media reflect the telltale signs of conspiracy theorizing. We have applied these methods successfully to the study of Pizzagate, the COVID-19 pandemic and anti-vaccination movements. We’re currently using these methods to study QAnon.

Collaboratively constructed, fast to form

Actual conspiracies are deliberately hidden, real-life actions of people working together for their own malign purposes. In contrast, conspiracy theories are collaboratively constructed and develop in the open.

Conspiracy theories are deliberately complex and reflect an all-encompassing worldview. Instead of trying to explain one thing, a conspiracy theory tries to explain everything, discovering connections across domains of human interaction that are otherwise hidden – mostly because they do not exist.

People are susceptible to conspiracy theories by nature, and periods of uncertainty and heightened anxiety increase that susceptibility.

While the popular image of the conspiracy theorist is of a lone wolf piecing together puzzling connections with photographs and red string, that image no longer applies in the age of social media. Conspiracy theorizing has moved online and is now the end-product of a collective storytelling. The participants work out the parameters of a narrative framework: the people, places and things of a story and their relationships.

The online nature of conspiracy theorizing provides an opportunity for researchers to trace the development of these theories from their origins as a series of often disjointed rumors and story pieces to a comprehensive narrative. For our work, Pizzagate presented the perfect subject.

Pizzagate began to develop in late October 2016 during the runup to the presidential election. Within a month, it was fully formed, with a complete cast of characters drawn from a series of otherwise unlinked domains: Democratic politics, the private lives of the Podesta brothers, casual family dining and satanic pedophilic trafficking. The connecting narrative thread among these otherwise disparate domains was the fanciful interpretation of the leaked emails of the Democratic National Committee dumped by WikiLeaks in the final week of October 2016.

AI narrative analysis

We developed a model – a set of machine learning tools – that can identify narratives based on sets of people, places and things and their relationships. Machine learning algorithms process large amounts of data to determine the categories of things in the data and then identify which categories particular things belong to.

We analyzed 17,498 posts from April 2016 through February 2018 on the Reddit and 4chan forums where Pizzagate was discussed. The model treats each post as a fragment of a hidden story and sets about to uncover the narrative. The software identifies the people, places and things in the posts and determines which are major elements, which are minor elements and how they’re all connected.

The model determines the main layers of the narrative – in the case of Pizzagate, Democratic politics, the Podesta brothers, casual dining, satanism and WikiLeaks – and how the layers come together to form the narrative as a whole.

To ensure that our methods produced accurate output, we compared the narrative framework graph produced by our model with illustrations published in The New York Times. Our graph aligned with those illustrations, and also offered finer levels of detail about the people, places and things and their relationships.

Sturdy truth, fragile fiction

To see if we could distinguish between a conspiracy theory and an actual conspiracy, we examined Bridgegate, a political payback operation launched by staff members of Republican Gov. Chris Christie’s administration against the Democratic mayor of Fort Lee, New Jersey.

As we compared the results of our machine learning system using the two separate collections, two distinguishing features of a conspiracy theory’s narrative framework stood out.

First, while the narrative graph for Bridgegate took from 2013 to 2020 to develop, Pizzagate’s graph was fully formed and stable within a month. Second, Bridgegate’s graph survived having elements removed, implying that New Jersey politics would continue as a single, connected network even if key figures and relationships from the scandal were deleted.

The Pizzagate graph, in contrast, was easily fractured into smaller subgraphs. When we removed the people, places, things and relationships that came directly from the interpretations of the WikiLeaks emails, the graph fell apart into what in reality were the unconnected domains of politics, casual dining, the private lives of the Podestas and the odd world of satanism.

In the illustration below, the green planes are the major layers of the narrative, the dots are the major elements of the narrative, the blue lines are connections among elements within a layer and the red lines are connections among elements across the layers. The purple plane shows all the layers combined, showing how the dots are all connected. Removing the WikiLeaks plane yields a purple plane with dots connected only in small groups.

Two graphs, one above and one below, showing dots with interconnecting lines
The layers of the Pizzagate conspiracy theory combine to form a narrative, top right. Remove one layer, the fanciful interpretations of emails released by WikiLeaks, and the whole story falls apart, bottom right. Tangherlini, et al., CC BY

Early warning system?

There are clear ethical challenges that our work raises. Our methods, for instance, could be used to generate additional posts to a conspiracy theory discussion that fit the narrative framework at the root of the discussion. Similarly, given any set of domains, someone could use the tool to develop an entirely new conspiracy theory.

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However, this weaponization of storytelling is already occurring without automatic methods, as our study of social media forums makes clear. There is a role for the research community to help others understand how that weaponization occurs and to develop tools for people and organizations who protect public safety and democratic institutions.

Developing an early warning system that tracks the emergence and alignment of conspiracy theory narratives could alert researchers – and authorities – to real-world actions people might take based on these narratives. Perhaps with such a system in place, the arresting officer in the Pizzagate case would not have been baffled by the gunman’s response when asked why he’d shown up at a pizza parlor armed with an AR-15 rifle.The Conversation

Timothy R. Tangherlini, Professor of Danish Literature and Culture, University of California, Berkeley

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Wednesday, December 23, 2020

Top Stories

How tech firms have tried to stop disinformation and voter intimidation – and come up short

Facebook and the other social media platform companies are facing a reckoning for their handling of disinformation. AP Photo/Noah Berger
Scott Shackelford, Indiana University

Neither disinformation nor voter intimidation is anything new. But tools developed by leading tech companies including Twitter, Facebook and Google now allow these tactics to scale up dramatically.

As a scholar of cybersecurity and election security, I have argued that these firms must do more to rein in disinformation, digital repression and voter suppression on their platforms, including by treating these issues as a matter of corporate social responsibility.

Earlier this fall, Twitter announced new measures to tackle disinformation, including false claims about the risks of voting by mail. Facebook has likewise vowed to crack down on disinformation and voter intimidation on its platform, including by removing posts that encourage people to monitor polling places.

Google has dropped the Proud Boys domain that Iran allegedly used to send messages to some 25,000 registered Democrats that threatened them if they did not change parties and vote for Trump.

But such self-regulation, while helpful, can go only so far. The time has come for the U.S. to learn from the experiences of other nations and hold tech firms accountable for ensuring that their platforms are not misused to undermine the country’s democratic foundations.

Voter intimidation

On Oct. 20, registered Democrats in Florida, a crucial swing state, and Alaska began receiving emails purportedly from the far-right group Proud Boys. The messages were filled with threats up to and including violent reprisals if the receiver did not vote for President Trump and change their party affiliation to Republican.

Less than 24 hours later, on Oct. 21, U.S. Director of National Intelligence John Ratcliffe and FBI Director Christopher Wray gave a briefing in which they publicly attributed this attempt at voter intimidation to Iran. This verdict was later corroborated by Google, which has also claimed that more than 90% of these messages were blocked by spam filters.

The rapid timing of the attribution was reportedly the result of the foreign nature of the threat and the fact that it was coming so close to Election Day. But it is important to note that this is just the latest example of such voter intimidation. Other recent incidents include a robo-call scheme targeting largely African American cities such as Detroit and Cleveland.

It remains unclear how many of these messages actually reached voters and how in turn these threats changed voter behavior. There is some evidence that such tactics can backfire and lead to higher turnout rates in the targeted population.

Disinformation on social media

Effective disinformation campaigns typically have three components:

  • A state-sponsored news outlet to originate the fabrication
  • Alternative media sources willing to spread the disinformation without adequately checking the underlying facts
  • Witting or unwitting “agents of influence”: that is, people to advance the story in other outlets

Pages from the U.S. State Department's Global Engagement Center report released on Aug. 5, 2020
Russia is using a well-developed online operation to spread disinformation, according to the U.S. State Department. AP Photo/Jon Elswick

The advent of cyberspace has put the disinformation process into overdrive, both speeding the viral spread of stories across national boundaries and platforms with ease and causing a proliferation in the types of traditional and social media willing to run with fake stories.

To date, the major social media firms have taken a largely piecemeal and fractured approach to managing this complex issue. Twitter announced a ban on political ads during the 2020 U.S. election season, in part over concerns about enabling the spread of misinformation. Facebook opted for a more limited ban on new political ads one week before the election.

The U.S. has no equivalent of the French law barring any influencing speech on the day before an election.

Effects and constraints

The impacts of these efforts have been muted, in part due to the prevalence of social bots that spread low-credibility information virally across these platforms. No comprehensive data exists on the total amount of disinformation or how it is affecting users.

Some recent studies do shed light, though. For example, one 2019 study found that a very small number of Twitter users accounted for the vast majority of exposure to disinformation.

Tech platforms are constrained from doing more by several forces. These include fear of perceived political bias and a strong belief among many, including Mark Zuckerberg, in a robust interpretation of free speech. A related concern of the platform companies is that the more they’re perceived as media gatekeepers, the more likely they will be to face new regulation.

The platform companies are also limited by the technologies and procedures they use to combat disinformation and voter intimidation. For example, Facebook staff reportedly had to manually intervene to limit the spread of a New York Post article about Hunter Biden’s laptop computer that could be part of a disinformation campaign. This highlights how the platform companies are playing catch-up in countering disinformation and need to devote more resources to the effort.

Regulatory options

There is a growing bipartisan consensus that more must be done to rein in social media excesses and to better manage the dual issues of voter intimidation and disinformation. In recent weeks, we have already seen the U.S. Department of Justice open a new antitrust case against Google, which, although it is unrelated to disinformation, can be understood as part of a larger campaign to regulate these behemoths.

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Another tool at the U.S. government’s disposal is revising, or even revoking, Section 230 of the 1990s-era Communications Decency Act. This law was designed to protect tech firms as they developed from liability for the content that users post to their sites. Many, including former Vice President Joe Biden, argue that it has outlived its usefulness.

Another option to consider is learning from the EU’s approach. In 2018, the European Commission was successful in getting tech firms to adopt the “Code of Practice on Disinformation,” which committed these companies to boost “transparency around political and issue-based advertising.” However, these measures to fight disinformation, and the related EU’s Rapid Alert System, have so far not been able to stem the tide of these threats.

Instead, there are growing calls to pass a host of reforms to ensure that the platforms publicize accurate information, protect sources of accurate information through enhanced cybersecurity requirements and monitor disinformation more effectively. Tech firms in particular could be doing more to make it easier to report disinformation, contact users who have interacted with such content with a warning and take down false information about voting, as Facebook and Twitter have begun to do.

Such steps are just a beginning. Everyone has a role in making democracy harder to hack, but the tech platforms that have done so much to contribute to this problem have an outsized duty to address it.The Conversation

Scott Shackelford, Associate Professor of Business Law and Ethics; Executive Director, Ostrom Workshop; Cybersecurity Program Chair, IU-Bloomington, Indiana University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Monday, December 21, 2020

What's cellular about a cellphone?

Daniel Bliss, Arizona State University

Editor’s note: Daniel Bliss is a professor of electrical engineering at Arizona State University and the director of the Center for Wireless Information Systems and Computational Architecture. In this interview, he explains the ideas behind the original cellular networks and how they evolved over the years into today’s 5G (fifth generation) and even 6G (sixth generation) networks.

Daniel Bliss provides a brief history of cellular networks.

How did wireless phones work before cellular technology?

The idea of wireless communications is quite old. Famously, the Marconi system could talk all the way across the Atlantic Ocean. It would have one system, which was the size of a building, talking to another system, which was the size of a building. But in essence, it just made a radio link between the two. Eventually people realized that’s a really useful capability. So they put up a radio system, say at a high point in the city, and then everybody – well, those few who had the right kind of radio system – talked to that high point. So if you like, there was only one cell – it wasn’t cellular in any sense. But because the amount of data you can send over time is a function of how far away you are, you want to get these things closer together. And so that’s the the invention of the cellular system.

The CenturyLink Building in Minneapolis with a microwave antenna on the top. It looks like a black spiky crown on the top of the building.
The CenturyLink building in Minneapolis has a microwave antenna on the top which was used in early wireless phone networks. Mulad via Wikimedia Commons, CC BY-SA

How are cellular systems different?

The farther your phone and the base station are from each other, the harder it is to send a signal across. If you just have one base station and you’re too far away from it, it just doesn’t work. So you want to have many base stations and talk to the one that’s closest to you.

If you draw a boundary between those base stations and look down on it on a map, you see these different little cell towers which your phone is supposed to talk to. That’s where the technology gets its name. The amazing thing that happened during the development of cellular systems is that it automatically switched which base station the phone talks to as its location changed, such as while driving. It’s really remarkable that this system works as well as it does, because it’s pretty complicated and you don’t even notice.

A diagram of a cellular network
Cellular technology gets its name from the diagrams of the networks which are divided into cells. This diagram shows cellular phone towers at corners of each haxagon cell. Greensburger via Wikimedia Commons

What are the major improvements to cellular networks that have enabled faster data rates?

If you go back to the first-generation cellular systems, those were primarily analog systems. It was just a way of converting your voice to an analog signal.

The second-generation systems focused on taking your voice, digitizing it and then sending it as a data link to improve stability and security. As an accident, it could also send data across. People found that it’s really useful to send a photo or send some other information as well. So they started using the same link to send data, but then complained that it’s not fast enough.

Subsequent generations of cellular networks allocated increasingly wider bandwidths using different techniques and were powered by a denser network of base stations. We tend to notice the big tall towers. But if you start looking around, particularly in a city, you’ll notice these boxes sitting on the sides of buildings all over the place. They are actually cellular base stations that are much lower down. They’re intended to reach people within just a kilometer or a half-kilometer.

The easiest way to achieve much higher data rates is for your phone to be close to a signal source. The other way is to have antenna systems that are pointing radio waves at your phone, which is one of the things that’s happening in 5G.

5G networks are still being rolled out around the country, but work on 6G technologies is already underway. What can we expect from that?

We don’t really know which technologies that are being developed right now will be used in 6G networks, but I can talk about what I think what’s going to happen.

6G networks will allow a much broader set of user types. What do I mean by that? Cellular systems, from the very start, were designed for humans to communicate. So it had certain constraints on what you needed. But now, humans are now a minority of users, because we have so many machines talking to each other too, such as smart appliances, for example. These machines have varying needs. Some want to send lots of data, and some need to send almost no data and maybe send nothing for months at a time. So 6G technologies need to work well for humans as well as a broad range of devices.

Another piece of this is that we often think about communication systems as being the only users of the radio frequency spectrum, but it’s very much not true. Radars use spectrum too, and pretty soon you won’t be able to buy a car that doesn’t have a suite of radars on it for safety or autonomous driving. There’s also position navigation and timing, which are necessary for, say, cars to know the distance between each other. So with 6G, you’ll have these multi-function systems.

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And then there is a push to go to yet higher frequencies. These frequencies work for only very, very short links. But a lot of our problems are over very short links. You can potentially send really huge amounts of data over short distances. If we can get the prices down, then it can potentially replace your Wi-Fi.

We can also expect a refinement of the technologies currently used in 5G – such as improving the pointing of the antenna to your phone, as I mentioned earlier.The Conversation

Daniel Bliss, Professor of Electrical Engineering, Arizona State University

This article is republished from The Conversation under a Creative Commons license. Read the original article.