Simply put, 5G is the 5th generation mobile network. It is a new global wireless standard (coming after 1G, 2G, 3G, and 4G). This new standard is designed to connect virtually everyone (and everything) together, from phones to even machines.

5G technology was designed to deliver extremely fast speeds (multi-Gbps peak speeds!), very low latency, more reliability, and a larger network capacity. These features empower user experiences and connect new industries.

Underlying technologies

So what are the underlying technologies that make up 5G? This answer is going to be a bit complicated.

5G is based on Orthogonal frequency-division multiplexing (OFDM), which is a method of reducing interference by modulating a digital signal across various different channels. 5G also uses 5G NR (5G New Radio) air interface, as well as wider bandwidth technologies such as sub-6 GHz and mmWave.

5G OFDM operates using the same mobile networking principles as 4G, but the new 5G NR air interface can further enhance OFDM and deliver a much higher level of scalability and flexibility. This allows greater access to 5G for more people and devices, for a variety of different use cases.

5G allows for wider bandwidths by expanding the use of spectrum resources, from sub-3 GHz, used in 4G, to 100GHz and beyond. It can operate in both the lower bands (sub-6 GHz, for example), in addition to mmWave (24 GHz and up, for example). This is what allows for the extreme capacity, fast throughput, and low latency.

What’s the difference between 4G and 5G?

In order to answer this question and provide additional context, let’s actually look at all of the previous generations: 1G, 2G, 3G, and then 4G.

First Generation — 1G

• Launched in 1979 in Japan
• Brought to the US in 1983 (and Canada in the mid-1980s)
• Delivered analog voice
• No encryption (anyone with a radio scanner could listen in to a call)
• According to Wikipedia, the only remaining 1G network in operation is in Russia.

Second Generation — 2G

• Launched on the Global System for Mobile Communications (GSM) in Finland in 1991
• Introduced digital voice e.g. CDMA (Code Division Multiple Access)
• Introduced encrypted calls
• Introduced text messages (SMS) and multimedia messages (MMS)

Third Generation — 3G

• Launched in 2001 in Japan
• Introduced mobile data (e.g. CDMA2000)
• Introduced international roaming services (due to the standardized protocol)
• Video streaming became possible
• Gave the ability to use the internet, visiting websites, and streaming music
• 2G offered some of these features, however the increased speed of 3G made it much more accessible

Fourth Generation — 4G LTE

• Introduced in Norway near the end of 2009
• Brought to Canada in mid-2011
• Brought the era of mobile broadband
• Providing high-quality video streaming, fast internet access, HD videos, and even online mobile gaming

Logically, following 4G is 5G, which is an improvement on the previous generations of the standard. 5G will impact every industry, making things like safer transportation and even remote healthcare, among other things, a reality.

What improvements are there from 4G to 5G?

There are quite a few differences between the two standards, and many reasons why 5G is superior, for example:

• 5G is quite a bit faster than 4G
• up to 20 Gbps peak data rates
• 100+ Mbps average data rates
• 5G has a larger capacity than 4G
• designed to support a 100x increase in traffic capacity and network efficiency
• 5G has much lower latency than 4G
• allows for more instantaneous, real-time access
• 10x decrease in end-to-end latency, down to 1ms
• 5G is a unified platform that is more capable than 4G
• 4G focused on providing faster services than 3G
• 5G was designed to elevate mobile broadband experiences, and also support new services like mission-critical communications and IoT
• 5G also natively supports all spectrum types and bands, a wide range of deployment models, and new ways to interconnect
• 5G uses spectrum better than 4G
• designed to get the most out of every bit of spectrum, from low bands below 1 GHz, to mid bands from 1 GHz to 6 GHz, to high bands, known as millimetre wave (mmWave)

Economic impact of 5G

5G is driving global economic growth. Through a 5G Economy study, it was found that 5G’s full economic impact will likely be realized globally by 2035, supporting a wide range of industries and even potentially enabling up to $13.2 trillion worth of goods & services.

This impact is much higher than with previous generations. The development requirements of the new 5G network are also expanding beyond the traditional mobile networking players to industries, like the automotive industry.

This same study also revealed that the 5G value chain (including OEMs, operators, content creators, app developers, and consumers) could support up to 22.8 million jobs.

There are other potential impacts 5G could have on the economy, but only time will tell what the full effect will be.

The Controversy & Conspiracy Theories

I’m sure you’ve seen people doubting the safety of 5G online. There are plenty of conspiracy theories involving 5G, and of course, completely disregarding the science.

One example of a conspiracy theory involving 5G is the claim that 5G is a hazard to your health. This claim dates back to the 1920s, when mobile phone usage was incredibly limited, with critics claiming the 2G airwaves could cause cancer.

Although the vast majority of the world rejects these rumours, the emergence of 5G seems to have caused a rebirth of these claims. Numerous public health authorities, like the World Health Organization (WHO), and the International Commission on Non-Ionizing Radiation Protection (ICNIRP), which is the Germany-based scientific body in charge of setting limits on exposure to radiation, have both stated on numerous occasions the airwaves used by mobile communications is not harmful to health.

In order to determine whether these higher-frequency waves are harmful to our health or not, let’s take a look at the electromagnetic spectrum.

What makes radio frequencies harmful?

As you can see at the bottom of the chart, there are two types of radiation listed: non-ionizing and ionizing. Ionizing radiation is what makes some radio frequencies more dangerous. When ionized atoms come into contact with living tissue, it causes the cells to be ripped apart, causing things like radiation burns, damage to cells, sickness, cancer, and in some cases, even death.

However, there is absolutely no reason to worry, 5G is very safely inside the non-ionizing range. Although 5G waves can extend up to 300 GHz, only waves up to 100 GHz are used for 5G deployments.

Let’s take another look at the electromagnetic spectrum, this time with the 5G range highlighted:

As you can see, the waves created by 5G are very safely within the non-ionizing range. In fact, these frequencies are between about 2,500 to 14,000 times less potent than the power we receive from the sun. If anything, we should be a lot more afraid of the sun and its UV rays causing health problems than this.

5G and COVID-19

One of the more ridiculous claims is that 5G is the cause of COVID-19. In March, Dr. Thomas Cowan, a US doctor who is now on disciplinary probation, claimed that 5G poisoned cells in the body, forcing them to excrete waste, which eventually became known as COVID-19. The video went viral, being reposted by multiple celebrities, but has been disproven by several scientists who questioned the validity of the evidence Cowan presented. The video has also since been removed from YouTube.

“Viruses are not just debris,” Jason Kindrachuk, a virologist and Canada research chair in emerging viruses at the University of Manitoba, said in an interview with CBC. “Viruses don’t just get created as a way to deal with poison.”

Scientists have been able to recreate the virus in a lab, proving it is not simply a secretion from human cells. Cowan made several other outlandish claims in the video, suggesting the emergence of the Spanish Flu (in 1918) coincided with the launch of commercial radio services (in 1920), and also claiming the fact Wuhan is ground-zero for COVID-19 and the first city to have 5G (which it was not, Shanghai was the first city with coverage).

People are burning down cell towers

These conspiracy theories have lead to people burning down many cell towers. In this CNET article from May 2020, 77 cell towers were burned down in the UK alone, and states that as of April 21, 2020, 40 employees of one UK carrier were attacked physically or verbally, with one even being stabbed and hospitalized as a result of the attack.

The issue is not restricted to the UK, with towers being burned down in other places as well, including in Quebec, Canada, the Netherlands, and the US.

In the US, one woman even shot at cellphone tower workers (while they were up in the tower, working), and barricaded herself in her home with two axes, before being arrested by police.

Funnily enough, 5G towers apparently look quite similar to towers carrying crucial 4G and 3G services, and very few of the attacked towers were actually correctly identified. This, however, resulted in outages in service for thousands of people, and also crippled the coverage for emergency services, while the damage to the towers was fixed.

My thoughts on 5G

This is something that interests me quite a bit. I’m looking forward to seeing 5G in my local area in the future (hopefully soon!), and to see the innovations enabled by this new technology around the world. This new standard looks very exciting, and I’m thrilled to see where it will go.

The reality of today’s existing 5G networks is that nearly all of them are still in their infancy. It’s hard to predict where the world will go, and what we will be able to do with this, but from what we know now, it looks very promising.

As I was sitting in front of my computer one day, I found myself thinking about the future of computers, which led to me thinking about quantum computing. I realized I really didn’t know much about the concept of quantum computing, and that led me to explore my curiosity and do some research.

Quantum computing sounds really cool. We’ve all read about the massive investments that have been made in making it a reality, and it has a lot of potential to make breakthroughs in many industries. One thing I found is that the news about quantum computing often glosses over what it is and how it works. I came to find that it was for a reason: quantum computing is very different from “normal” digital computing and it requires thinking about things in a non-intuitive way. Plus, there’s a lot of math involved.

Quantum Computing Concepts

Quantum computers use qubits instead of the traditional bits (binary digits) that our computers currently use. Qubits are different from traditional bits, because until they are read out (measured), they can exist in an indeterminate state, where we can’t tell whether or not they’ll be measured as a 0 or a 1. That’s due to a unique property, called superposition.

Now, superposition certainly makes qubits interesting, but the really interesting phenomenon is entanglement. Entangled qubits can interact instantly. In order to make functional qubits, quantum computers must be cooled to near absolute zero. Even when supercooled, qubits don’t maintain their entangled state for very long.

This makes programming them rather difficult. Quantum computers are programmed using different sequences of various kinds of logic gates, but the programs must run quickly enough that the qubits don’t lose coherence before they are measured. People who may have taken a logic class, or looked at building circuits using flip-flops, could find quantum logic gates somewhat familiar, although quantum computers themselves are essentially analog. However, the combination of entanglement and superposition make the process a lot more confusing.

Qubits & Superposition

The ordinary bits we use in everyday digital computers are either 0 or 1. They can be read whenever you want, and unless there is a hardware issue, they won’t change. However, qubits don’t behave like that. They have a probability of being 0 and a probability of being 1, but until they are measured, they may be in an indefinite state. That state, along with some other state information that can allow for more computational complexity, can be described as being an arbitrary point on a sphere (of radius 1) that reflects both the probability of being measured as a 0 or 1 (which are the north & south poles).

The qubit’s state is a combination of the values along all three axes. This is superposition. Some articles describe this property as “being in all possible states at the same time”, while others feel that’s somewhat misleading, and that we’re better off with the probability explanation. Either way, a quantum computer can actually perform math operations on the qubit while it’s in superposition (modifying the probabilities in various ways through logic gates) before eventually outputting a result by measuring it. In all cases, though, once a qubit is read, it is either 1 or 0, and it loses its other state information.

Qubits generally start life at 0, although they are frequently then moved into an indeterminate state using a Hadamard Gate, which results in a qubit that will read out as 0 half the time, and 1 the other half. Other gates are also available to flip the state of a qubit by varying amounts and directions, both relative to the 0 and 1 axes, and also a third axis that represents phase, and provides additional possibilities for representing information. However, the specific gates & operations available depend on the quantum computer and toolkit being used.

Entanglement

Groups of independent qubits, by themselves, aren’t powerful enough to create the massive breakthroughs that are promised by quantum computing. The magic really begins to happen when the concept of quantum entanglement is introduced. One industry expert likened qubits without entanglement as being like a “very expensive classical computer.” Entangled qubits affect each other instantly when measured, no matter how far apart they are. In terms of classic computing, this is almost like having a logic gate connecting every bit in memory to every other bit.

You can start to see how powerful that might be, compared to a traditional computer needing to read and write from each element of memory separately before being able to operate on it. As a result of this, there are some very large gains that become possible from entanglement. The first is a large increase in the complexity of programming that could be executed, at least for certain types of problems. Another one is the modeling of complex molecules and materials that are extremely difficult to simulate with classical computers. Another could be innovations in long-distance secured communication, if and when it becomes possible to preserve quantum state over large distances. Programming using entanglement usually starts with the C-NOT gate, which flips the state of an entangled particle if its partner is read out as a 1. This is somewhat like a traditional XOR gate, except for the fact that it only operates when a measurement is made.

Quantum computers and cryptography

Superposition and entanglement are impressive phenomena, but leveraging them to do computation requires a very different mindset and programming model. You can’t just throw your Python code on a quantum computer and expect it to run, and certainly not to run faster. Fortunately, mathematicians and physicists were thinking ahead, and developed algorithms that take advantage of quantum computers decades before the machines even started to appear.

I remember reading about the fact that quantum computers one day could break most cryptography systems. They will be able to do this because there are some algorithms design to run on quantum computers that can solve a hard math problem, which can then, in turn, be used to factor very large numbers. One of the most famous examples of this is Shor’s Factoring Algorithm. The difficulty of factoring large numbers is essential to the security of all public-private key systems, which are the most commonly used system today. Current quantum computers don’t have nearly enough qubits to attempt the task, but several experts predict they will, within the next 3–8 years. This leads to some potentially dangerous situations, such as if only governments and the extremely-rich had access to the ultra-secure encryption provided by quantum computers.

Why is building quantum computers so hard?

There are a bunch of reasons quantum computers are taking a long time to develop. To start, you need to find a way to isolate and control a physical object that implements a qubit. Part of that process also requires cooling it to nearly absolute zero (in the case of IBM’s Quantum One, it is cooled to .015° K). Even with this extreme cooling, qubits are only stable for a very short amount of time, which greatly limits the flexibility of programmers in how many operations they can perform before needing to read out a result.

Not only do programs need to be constrained, but they also need to be run several times, as current qubit implementations have a pretty high error rate. Additionally, entanglement isn’t easy to implement in hardware either. In many designs, only some of the qubits are entangled, so the computer needs to have the ability to swap bits around as needed to help simulate a system where all the bits can potentially be entangled.

Moore’s Law

You may have heard about something called Moore’s Law, which is “the observation that the number of transistors in a dense integrated circuit doubles about every two years”. The “law” is in fact just an empirical relationship, and not a physical or natural law. It was named after Gordon Moore, the CEO and co-founder of Intel, whose 1965 paper described a 2x increase in the number of components per integrated circuit per year, and he projected this rate of growth would continue for at least another decade. In 1975, he revised the forecast to doubling every 2 years. This prediction ended up being used in the semiconductor industry to guide long-term planning, and to set targets for research and development. As of 2018, leading semiconductor manufacturers have integrated circuit fabrication processed in mass production with 10 nm and 7 nm features, which keep pace with Moore’s law.

However, with quantum computers this law doesn’t make sense. According to an article by Quanta Magazine, this led to the creation of a new idea, known as Neven’s Law. The law, named after Hartmut Neven, the director of the Quantum Artificial Intelligence Lab at Google, who first discovered the phenomenon, dictates how quickly quantum processors are improving, relative to traditional digital computers.

As it turns out, they’re gaining on ordinary computers at an extremely fast, “doubly exponential rate”. This means that processing power grows by a factor of 2^2^1(4), then 2^2^2 (16), then 2^2^3 (256), then 2^2^4 (65,536), and so on. With this model, as you can see, numbers get mind-bogglingly big very, very fast. Doubly-exponential growth is so fast, it’s hard to find anything that grows so quickly in the natural world, according to Quanta.

Based on this observation, Neven suggested that quantum supremacy could occur in 2019. Quantum supremacy is defined as “the goal of demonstrating that a programmable quantum device can solve a problem that no classical computer can feasibly solve.” Sure enough, in October 2019, Google declared quantum supremacy, on target with Neven’s prediction.

Overall, quantum computing is a very interesting field, and I’m looking forward to seeing what the future holds for us. What are your thoughts about quantum computing?

On March 25, 2020, the Folding@Home network announced that it now has over an exaflop of computing power, after hitting 470 petaflops just days prior. Some estimates by others have suggested that it could be more, as much as up to 1.5 exaflops at that time, but still, either number is a remarkable achievement.

To compare, the world’s most powerful supercomputer (according to the 54th TOP500, in November 2019), Summit, scored 148.6 petaFLOPS on the LINPACK benchmark thanks to its 202,752 CPU cores. As of April 8, 2020, the Folding@Home network is now up to 1182.7 petaFLOPS (with over 9 million CPU cores!), meaning it is nearly 10x more powerful than Summit.

In fact, if the Folding@Home network is “only” one exaflop, then it is still as fast as the 25 fastest supercomputers on Earth combined. If it has reached the 1.5-exaflop mark, as some members of the PCMR Folding@Home team have speculated, it’s currently as fast as just over the top 100 systems on the TOP500 combined.

What on earth is a FLOP?

FLOP is an acronym for Floating-Point Operation, often referred to by FLOPS, meaning Floating Point Operations Per Second. The FLOPS is a measure of a computer’s performance, specifically in fields of scientific calculations that heavily use floating-point calculations.

To scale this up, we can then talk about a TFLOP (or Teraflop), which is a trillion flops (or 10¹² FLOPS). Scaling up again, we have the Petaflop (10¹⁵ FLOPS). Finally, the big one, the Exaflop, which is a thousand petaflops, or a quintillion (10¹⁸) FLOPS. Insanity.

So What?

To understand the impacts of the Folding@Home project, we must further understand proteins, and more specifically, protein folding.

The Folding@home project (FAH) is dedicated to understanding protein folding, the diseases that result from protein misfolding and aggregation, and novel computational ways to develop new drugs in general. Here, we briefly describe our goals, what we are doing, and some highlights so far.

What is protein folding?

Proteins are necklaces of amino acids, long-chain molecules. They are the basis of how biology effectively gets things done. As enzymes, they are the driving force behind all of the biochemical reactions that make biology work. As structural elements, they are the main constituent of our bones, muscles, hair, skin, and blood vessels. As antibodies, they recognize invading elements and enable the immune system to get rid of the unwanted invaders. Due to all of these reasons, scientists have sequenced the human genome (the blueprint for all of the proteins in biology), but how can we understand what the proteins do and how they work?

The issue is, only knowing this sequence doesn’t tell us a lot about what the protein does and how it works. In order to carry out their function, they have to take on a particular shape, which is also known as a “fold”. Therefore, before proteins can do their work, they assemble themselves. This self-assembly process is called “folding”.

How does this relate to disease?

Diseases, such as Alzheimer’s disease, Huntington’s disease, cystic fibrosis, BSE (mad cow disease), an inherited form of emphysema, and even many cancers are believed to be caused by protein misfolding. When this happens, proteins can clump together and gather in the brain, where they are believed to cause the symptoms of Mad Cow or Alzheimer’s disease.

Coronavirus & Folding@Home

Now, viruses also have proteins that they use to suppress our immune systems and reproduce themselves. FAH’s mission is to understand how viral proteins work and how to design therapeutics to stop them. There are a bunch of methods for finding protein structures, and while extremely powerful, they only reveal a single snapshot of a protein’s shape. However, since proteins have lots of moving parts, we want to see the protein in action. These structures that we can’t see experimentally, could be the key to discovering a new therapeutic.

Using football as an analogy for this situation, it’s like you could only see the players lined up for the snap (the single arrangement the players spend the most time in), but couldn’t see anything else about the rest of the game.

Seeing a single protein structure is important information, but has a lot of missing information. FAH’s specialty is using computer simulations to understand these proteins’ moving parts. Watching how the atoms in a protein move relative to one another is important, because it captures valuable information that is inaccessible by other means. Taking the experimental “snapshots” as starting points, it is possible to simulate how all the atoms in the protein move, effectively filling in that gap that experiments miss.

This is truly fascinating, as this cannot only be applied to COVID-19, but other diseases as well. For example, in a recent paper by the FAH team, they simulated a protein from the Ebola virus that is typically considered “undruggable” because the snapshots from experiments don’t have obvious druggable sites. But FAH simulations discovered an alternative structure that does have a druggable site. This led to experiments that confirmed the prediction, and now the search for drugs that bind the newly discovered binding site.