Category Archives: Technology

Cable Companies Continue to Upgrade

Jeff Baumgarner of Light Reading wrote an article detailing increased spending by cable companies as they continue to upgrade networks. The article notes a 40% increase in spending for the deployment of distributed access architecture (DAA).

Distributed Access Architecture is a network architecture that decentralizes cable networks by moving some of the brains and related functions to neighborhood nodes. Historically, cable company networks packed all of the network electronics at a centralized headend. There are significant benefits of moving broadband equipment into neighborhoods. The DAA upgrade is often accompanied by reducing the dnumber of customers on each neighborhood node, which alone increases the bandwidth distributed to the remaining customers. The upgrade to DAA generally means more overall bandwidth when cable companies upgrade to 10-gigabit bandwidth to feed each DAA node. In many cases, the transport reaching nodes still uses analog technology, and upgrading to a digital DAA improves bandwidth efficiency. Customers benefit from improved latency due to being closer to the core.

Part of the reason for the 2026 spending is that cable companies put network expansion plans on hold in 2025, waiting for the release of new Broadcom chips that enable the network to be expanded to 1.8 GHz of bandwidth. This higher bandwidth is enabling cable companies to significantly increase customer upload speeds by upgrading to symmetrical bandwidth with DOCSIS 4.0 or by using upgrades referred to as mid-splits to increase upload speeds on DOCSIS 3.1.

A lot of upgrades to DAA are restructuring cable networks for the future by using remote physical layer architecture (R-PHY) to move the modulation and demodulation functions to the neighborhood node. Remote MAC-PHY relocates both the PHY layer and the processing MAC layer to the node. This new configuration means the only thing left at headends are servers, switches, and routers, and opens the possibility of doing away with much of the headend and migrating the switching function to a regional data center.

Baumgartner notes other upgrades being made by cable companies. He notes that cable companies are integrating PON fiber technology into the network to serve new growth and rural markets. He quotes Jeff Heynen or Dell’Oro saying that sales of PON nodes to cable companies are up 71% year-over-year.

Baumgartner also provides some updates on DOCSIS 4.0 deployments. He says that Comcast is still leading the charge on DOCSIS 4.0 upgrades and has already upgraded millions of premises. He says Charter has plans to upgrade 35% of its footprint to DOCSIS 4.0 over the next few years and that it plans to accelerate the upgrades when it completes the merger with Cox. He also notes that Mediacom Communications is deploying D4.0 in some markets.

The one downside for vendors is that cable company spending on customer CPE is down 5% this year, probably reflecting the continuing loss of customers by every big cable company.

The reason for these upgrades is clear – customers still have more trust in fiber than in cable company HFC technology. PC Magazine recently conducted a nationwide survey that ranked customer satisfaction with ISPs. NextLight, a municipal network in Longmont, Colorado, got the highest rating of 9.7, followed close behind by GFiber at 9.4. The other big fiber ISPs ranged between 7.8 for Frontier to 8.3 for AT&T. The big cable companies ranged from 6.4 for Comcast to 6.8 for Optimum.

Is AI Changing Traffic Patterns?

Mitch Wagner of Fierce Network recently published an article that claims that AI is blowing up 30 years of traffic network assumptions. He claims that AI traffic is smoothing the daily peaks and dips in network traffic that all ISPs are familiar with. While every ISP is a little different, any ISP that serves a lot of end-user customers expects traffic peaks in the evening, smaller peaks during the daytime, and very low levels of network traffic at night.

Network engineers have always paid close attention to the peaks, which were the main factor in determining the size of network connections. Nobody wants to have a network that restricts bandwidth when customers want to use it the most. The cable companies learned this lesson the hard way during the pandemic when customers suddenly needed to work and school from home and found the broadband connections unable to meet their needs, particularly in homes where more than one person wanted to use the network at the same time. Every network engineer I know can cite the busy hour, busy day, and busy week on the networks they manage.

Wager says the peaks in traffic are evening out and that networks are seeing a more consistent demand throughout the day. Wagner cites Ed Fox, the CTO of MetTel, from an interview given for a Fierce Network Research report.

I have no reason not to believe Mr. Fox. However, MetTel is an ISP that operates in major urban centers and likely serves the kinds of businesses that have become heavy AI users. It’s not hard to imagine that an urban ISP serving businesses might be seeing a drastic change in traditional network traffic patterns. But I have to think that MetTel and other urban business-centric ISPs didn’t have the same traffic patterns as other ISPs before the advent of AI.

I work with a number of ISPs and I have not heard anybody talking about a big change in traffic patterns. My clients work in a variety of markets, from rural to urban, but none concentrate on the urban market business that MetTel is experiencing in places like New York City.

However, I appreciate the article, because if it’s true, then most ISPs, except for fully rural ones, might eventually see some shift in traffic patterns due to AI. I am curious to get feedback from this blog from IPS to hear if anybody is seeing anything like the big changes being experienced by MetTel.

The Wagner article also claims that upstream bandwidth is growing faster than downstream, reversing a 30-year design assumption built around heavy downloads. This is something that has been well documented by OpenVault. They’ve been shown that upstream usage has been growing faster than download usage starting in 2024. In the first quarter of 2024, national average upload usage increased by 13.2% compared to 7.7% for download. In the first quarter of this year, average upload growth was at 19.8% while download growth was at 7.8%. OpenVault credits most of the growth in upload usage to computer software synching with data centers.

Wagner claims the upsurge in upstream usage comes from AI inference traffic moving towards the edge, particularly for video processing. What he means by that is big growth coming from uses of video cameras for functions like AI-driven video surveillance at retail locations, camera-equipped wearables, and cloud-based operational technology in applications like oil and gas asset management. I hope that the big national companies that monitor traffic begin tracking this issue. I’d love to hear more about the trends in specific traffic, like video surveillance and wearables.

Network engineers all understand that upload traffic is usually a tiny fraction of download usage, with average download usage a dozen times more than upload. With the possible exception of cable companies, upload usage is largely an afterthought for network engineers, who barely consider it when sizing and designing networks.

I have no doubts that there are localized situations where AI traffic is making a big difference in network traffic. But for ISPs that mostly serve residential and small business customers, I still have to think it’s a tiny, possibly unnoticeable blip.

Network Challenges from AI Traffic

Cisco recently published a report that looks at the impact of AI traffic on networks. It’s an interesting paper because Cisco found that AI traffic does not operate the same as most other web traffic. While the volume of AI traffic is small today, Cisco predicts that we’ll have to make changes to the web over time to accommodate growing AI traffic volumes. Cisco predicts that by 2035, one fourth of all web traffic will be AI agents and AI models in data centers.

Cisco notes that we’ve spent decades optimizing a web that delivers burst traffic, like video. When a video is viewed from the web, the data stream doesn’t have to be delivered evenly in real time. Instead, all that is needed is for the transmission of the video to reach the viewer before they are ready to watch it. Anybody who has watched a video can see that the streamed video is always working to to stay ahead of what you are watching.

AI traffic is very different. Cisco uses the term AI inference traffic to mean the real-time transfer of AI traffic between the AI models operating in data centers and users. AI inference traffic is delivered at what Cisco calls software speed, meaning the receiving end is ready to digest and use the data as it is delivered, quite different than streamed video that is only trying to stay ahead of a viewer.

The difference between AI traffic and normal web traffic is significant. The typical burst of AI traffic lasts twice as a typical burst of video data. While individual bursts of video data are smaller, the flow rate for video, which means the actual delivery time, lasts ten times longer than AI traffic, since video bursts are spread over time in multiple small bursts.

AI traffic is also two-way and requires a good upstream connection. In fact, Cisco found that 9% of AI traffic requires more upstream traffic than downstream traffic. Cisco believes the need for network upload speeds will increase as AI agents mature.

Current network latency is not a bottleneck for AI traffic, but Cisco says latency will become a problem as the volume of AI traffic increases. This will require a major rework of web architecture when latency becomes an issue.

Cisco found that tasks performed by AI generate 450% more traffic than the same task performed in a more traditional way. In Cisco’s vocabulary, AI agents act as power users and use a lot of network resources.

The bottom line is that AI traffic is different from current web traffic and will not only increase traffic volumes on networks, but it will also change the shape, symmetry, and needed priority of traffic.

There have already been discussions of creating a private web to connect between AI data centers. But that would only solve part of the problem, because AI traffic is eventually delivered to users throughout the web. AI traffic is going to create an interesting new set of challenges for network engineers, something that nobody envisioned just a few years ago.

Life Left in HFC Networks

There was a time when it seemed certain that cable companies would have to bite the bullet and spend the money to upgrade to fiber. While there have been some upgrades by cable companies like Cox and Altice, most cable companies seem to be deciding that there is still good life left in DOCSIS cable networks. As you might expect, CableLabs has been quietly working behind the scenes to improve existing HFC technology.

DOCSIS 3.1 networks have become standard across the industry, and it’s now rare to see older technologies except for some small cable companies. Cable companies have been using DOCSIS 3.1 networks to deliver gigabit or faster download speeds, with the top speed depending on the overall size of the bandwidth being utilized in the internal radio network that controls the signal.

The whole cable industry got a shock during the pandemic when it became obvious to many of the millions of students and employees who began working out of homes that the slow upload speeds on DOCSIS 3.1 were a bottleneck. I think the inadequacies of this technology and slow upload speeds are what gave a big jumpstart to the public perception that fiber is far superior to cable technology.

CableLabs and vendors responded to the upload speed bottleneck by introducing two solutions that can add to the upstream portion of the cable network. Labeled as midsplit or highsplit, both solutions require some upgrades in the outside plant electronics, along with upgraded cable modems in homes. The midsplit upgrade is accomplished by increasing the frequencies used to support upload from 5-42 MHz to 5-85 MHz. The highsplit upgrade allocated even more frequency to uploading, as much as 204 MHz. Both of these upgrades increased upload speeds to 100 Mbps or faster, which eliminated the bottleneck for the average customer. In 2025, CableLabs offered an even better version of the midsplit upgrade by offering a new cable modem that can handle up to four more channels of bandwidth.

CableLabs is also improving DOCSIS 4.0 technology. This is an upgrade that became available for cable companies in early 2024 that can provide symmetrical broadband speeds. While the upgrade can deliver speeds up to 5 Gbps, most cable companies are using it to offer symmetrical 2 Gbps broadband. This upgrade makes it practical for a cable company to say it can match fiber speeds – or at least it did in 2024. There are now fiber ISPs offering residential broadband at speeds up to 10 Gbps.

CableLabs has demonstrated an upgrade to DOCSIS 4.0 that can mimic the faster advertised speeds of fiber providers. CableLabs recently released a new standard it is calling DOCSIS 4.0 Optional Annex. This standard works by increasing the network bandwidth inside the coaxial cable to 3 GHz. Cable networks operate by using radio frequencies inside the coaxial wires. Most DOCSIS 3.1 networks use 1.0 to 1.2 of total frequency. Some companies have upgraded to 1.6 GHz for DOCSIS 4.0. This new optional Annex, double that bandwidth and will supposedly support speeds up to 25 Gbps. CableLabs is also looking at a version of the new technology that would increase total network bandwidth to 6 GHz, which might support broadband speeds up to 50 Gbps.

These new options will give pause to any cable company thinking about upgrading to fiber. These new technologies provide a realistic alternative to fiber with DOCSIS 4.0.

Who Needs Moore’s Law?

I ran across a reference to Moore’s Law the other day. This was named after Gordon Moore, an engineer who later became one of the founders of Intel. In 1965, Moore observed that the number of transistors that could be squeezed into a given area of a circuit board was doubling every two years. He predicted this trend would last for perhaps another decade, but the microchip industry kept fulfilling his prediction for over fifty years, and the general consensus is that the Moore’s Law prediction died somewhere between 2016 and 2018. Chip density has continued to improve, but at a slower rate, and there is general consensus that we are getting close to reaching the maximum possible density of transistors, limited by the law of physics.

For a number of years, there were a lot of predictions that the end of Moore’s Law would mean the end of faster computing. For decades, our devices became obsolete every few years as the next generation of faster chips hit the electronics market. But chip makers have discovered a wide variety of ideas and technologies that continue to improve the speed of computing.

Consider some of the techniques that continue to improve computing power:

  • ASICs (Application-Specific Integrated Circuits): These are specialized chips designed for one specific task. For example, there are ASIC chips in data centers that are specifically designed to handle specific tasks related to processing masses of data.
  • GPUs (Graphics Processing Units): These chips take the opposite approach of ASICs and are designed to handle parallel tasks and tackle multiple functions at the same time. First designed for gaming, a GPU breaks a task into many independent threads and executes each thread using a different small core. GPUs keep improving as chip makers and software designers find better ways to coordinate and control the many threads.
  • 3D Stacking. Another new technique is stacking transistors and memory vertically as well as horizontally. This provides a huge boost to computing power. One of the more widely used 3D stacking technique is the use of chiplets, which are small chip components that can be stacked and fused to create a multi-layer chip. Chiplets benefit from Through-Silicon Vias (TSVs) that enables fast low-power communications between layers.
  • Memory Integration. One of the biggest bottlenecks for any chip is the process of moving data into and out of the chip core during the computing process. Memory integration creates temporary memory directly on the chip to store data that is still needed for the specific calculations being handled. Pulling needed information out of the chip’s own memory bypasses the normal data transfer issue.
  • Optical Computing. Optical computing uses light instead of electrons to transfer data around a chip. The primary benefit of light computing is that different colors of light can be used to allow for multiple streams of data transfer at the same time, instead of the single stream that comes from electrons. I recently wrote a blog that talked about a technology that can generate multiple wavelengths of light directly on a chip, which eliminates the need for bulky external lasers.
  • Optimized Algorithms: Some of the biggest improvements in chip speed come from rewriting software to be “hardware-aware”, meaning it perfectly aligns with a chip’s architecture.
  • Reconfigurable Computing. This is an architecture where portions of the chip can be programmed to change function or spatial configuration during the computing process.
  • Quantum Computing. Quantum computing increases computing power using qubits and the principles of superposition and entanglement. Qubits exist in multiple states instead of the two states of 1 and 0 for digital computing, which provides an exponential increase in calculation power.

We’ve just barely begun exploring many of these ideas, and there are likely many breakthroughs still to come. Picture what something like a reconfigurable architecture using multiple colors of chip-generated light waves in a quantum computer might mean.

We’re Drowning in Data

The analytic company IDC says the U.S. economy will be generating 394 trillion zettabytes of data annually by 2028 (a zettabyte is a trillion gigabytes). The majority of the energy used in data centers today is for storing some of this data in an accessible format. We don’t try to make all data available, and about 20% of the data we generate today is considered to be “hot data” that AI systems might want to draw on quickly. The remaining 80% of data is “cold data”, which we don’t put in data center storage, but which we also don’t discard, since it might still be of use in the future.

Today, hot data is largely stored on hard drives in data centers. This storage for quick retrievable uses a lot of electricity to operate the hard drives, and additional energy is used to cool the data center to offset the heat generated by the electronics. There is a growing trend of storing cold data on magnetic tapes, which also require energy for heating and cooling, since tapes are best stored at temperatures between 61 and 77 degrees. Tapes must also be replaced every 15-20 years by transferring the data – an intensive work effort.

The need to keep so much data at our fingertips to support AI means that we are literally drowning in data, and the problem is growing quickly every year. The solution to this is to find other ways to store massive amounts of data that don’t require a lot of electricity. There are several potential data storage methods on the horizon, and we’re going to need more.

One interesting possibility comes from Peter Kazansky, working at the University of Southampton in the UK. Back in 1999, while working with scientists at Kyoto University, Kazansky encountered a physical phenomenon that might provide the future for long-term data storage. The team at Kyoto found that when writing on glass with ultrafast femtocell lasers (a light pulse every quadrillionth of a second), the light traveling through the glass scattered in a way they could not explain.

It turns out that the researchers had discovered hidden nanostructures within silica glass created by micro-explosions from the lasers. The lasers had created tiny holes 1,000 times smaller than a human hair throughout the glass. The eureka moment came when researchers realized they could take advantage of this phenomenon by using lasers to print complex patterns inside the glass. After many years of research, Kazansky found that he could etch patterns in the glass that could store data in 5-dimensions – the normal x,y, and z coordinates, plus two additional coordinates related to voxels, or the scattering pattern of light.

This allows for storing massive amounts of data on a piece of etched silica glass. A 5-inch glass platter (slightly larger than a music CD) can store up to 360 terabytes of data. Unlike tape or hard disk storage, it looked like this technology creates forever memory that can be stored for the future. While energy is needed to etch the glass and encode the stored data, the process of reading the data uses light and is not energy-intensive. Kazanky founded SPhotonics in 2024 to commercialize the new storage method. Currently, the data can be retrieved at a speed of 30 MB per second, but he sees a path to reach 500 MB per second, which is faster than retrieving data from tapes.

Of course, storing data on etched glass is not without peril. A disc can break, and a fire or other disaster at a storage facility could destroy massive amounts of data, so most data will have to be stored at multiple sites. But at least the raw materials for silica glass are cheap and readily available. Probably the bigger issue facing the world is deciding how and when to ditch data that is no longer useful. Data scientists are already tackling this question today, but they are generally cautious and side with storing rather than destroying data if there is even a slight chance that it might be useful later.

Technology Shorts April 2026

The following topics discuss some interesting technologies that might someday influence the broadband industry.

Chip-level Photonics. Researchers at the CUNY Graduate Center have developed a thin, flat chip that can convert infrared light into precise frequencies of usable light that can be focused into a narrow, precise beam. The surface of the chip is patterned with tiny structures smaller than the wavelength of light. When hit with an infrared laser, tiny patterns convert the incoming light into a higher color as a narrow beam that can be steered by changing how the incoming light is polarized.

Scientists now envision a stack of different metasurfaces that could each be used to develop a different wavelength of light to use inside a chip to carry data. Effectively, this could create multiple laser light beams that were generated inside the chip without the expensive apparatus needed to inject external laser signals into each chip. Having a range of locally generated light signals could solve the problem of trying to move massive amounts of data into and out of the chip core – which is currently the biggest bottleneck to fast computing.

Dirt-Powered Fuel Cells. A team at Northwestern University has developed a device that can generate electricity by harvesting the power created by microbes that naturally reside in the soil and naturally break down organic matter. The fuel cell is about the size of a paperback book. The device has a disc-shaped anode that is buried in the soil with a second anode poking out near the surface. The device is large enough to tap the natural moisture in the soil at the bottom of the device. In testing, the device works across various soil conditions. The devices tested so far are creating 68 times more power than needed to operate the fuel cell, meaning there is a lot of power available to power other devices like agricultural sensors. The beauty of the technology is that it should work for many years without any need to replace batteries or other components like is needed for other power sources that could be used for similar applications. A fuel cell should work as long as there is enough carbon and moisture to fuel the natural microbes there.

New Laser Technology. Researchers at Tianjin University have created a new kind of optical device that can generate a light phenomena called skyrmions. The research shows that two skyrmions can be created, which are donut-shaped light patters that hold their shape – one that can be controlled by electric energy and the other by magnetic energy. The skyrmions are highly stable and resist interference, making them a good candidate for storing and transmitting data.

These new light sources could open up the use of terahertz wavelength lasers that could actively switch between light and magnetic mode, enabling a huge increase in the amount of data included in a laser transmission. This could provide the control of data flow needed to take full advantage of using a terahertz light source for data transmission. The key to making this work will be perfecting the constant flux between the light and magnetic pulses.

Forever Batteries? Scientists at the CSIRO Royal Melbourne Institute of Technology, and University of Melbourne, Australia, have developed a technology they are calling a quantum battery that can be recharged in a quadrillionths of a second, and that can work with six orders of magnitude of stored energy, making it practical for real-life applications. Unlike traditional batteries that use a chemical reaction to store and release energy, a quantum battery transfers energy using quantum coherence and collective interactions, rather than chemistry.

Normal batteries take a long time to recharge, and eventually the chemicals must be replaced, usually meaning replacing the battery. With traditional batteries, the larger the battery, the longer the time needed to recharge. Quantum batteries are the opposite, and the larger the battery, the faster it can be recharged. This is due to a phenomenon of collective effects between particles, which causes all of the storage units in a quantum battery to behave collectively.

The downside of quantum batteries is that the battery can also discharge all of its power quickly, and the challenge has been to find a way to control the output. The Australian team has demonstrated a full battery cycle from light absorption to storage to of electrical power output at room temperature and steady-state operation, showing that the quantum batteries have potential for real-life applications. The batteries can also be charged wirelessly, which opens up the possibility for remote charging.

Indoor Cellular Coverage

In an experience that is probably familiar to everybody, in the last few months I’ve found myself unable to get a cell signal in places I routinely visit. At the pharmacy, the only cell coverage I could find was directly next to the front windows. I went to my doctor and found I couldn’t get any reception while biding my time in a waiting room. There was no signal in the back half of the grocery store.

I know my experience is not unique, and I regularly see other people grumbling in these locations about the lack of cell signal. It seems extraordinary in today’s world, where people want nearly ubiquitous cellular coverage, to find so many places with poor or no cell coverage indoors.

My first reaction to this was surprise, since my cellphone data speeds are easily ten times faster than they were a decade ago. The fact that I can’t receive indoor cellular coverage is a reminder that cell reception is due a lot more to the power of the signal rather than the speeds being delivered.

There are several reasons why indoor cell coverage is getting worse. One big reason is that cell carriers have been migrating to higher frequencies. Years ago, cellular networks widely used frequencies like 700 MHz and 900 MHz, which were able to easily penetrate buildings. The higher frequencies used today do a much worse job of penetrating buildings. A second reason is that the building materials used in newer or upgraded buildings deflect a lot of the cell signal. Modern insulation materials are generally less friendly to cell signals. Ookla recently documented that low-E glass, which is used to reflect heat in many new buildings, reflects cell signals along with reflecting heat. The bottom line is that you aren’t imagining it if you notice that indoor cell coverage isn’t as good as it was in the past.

There are several possible fixes for this, but they aren’t cheap and aren’t widely deployed. One is for businesses to invest in a cellular repeater to put on the roof to aim downward to provide better cell coverage inside the building. Cell carriers have been pushing this technology for years, and many hotels, convention centers, and office buildings are willing to pay for the capital costs and recurring fees to provide better cell indoor coverage. But groceries, hardware stores, doctors’ offices, and pharmacies aren’t willing to make this kind of investment.

Another alternative is to provide free public WiFi inside large buildings. Many businesses where customers spend significant time do this today. A large percentage of the restaurants I visit have WiFi for customers, but most require a customer to find the password and log in, something I’m rarely willing to do during a quick trip to the grocery or pharmacy. Very few stores offer WiFi that doesn’t require a password.

There has been talk for years of implementing Hotspot 2.0, a technology that allows a subscriber to automatically connect to any WiFi router that is part of a larger Hotspot 2.0 network. Every year we hear of a few smaller cities or ISPs that put together this kind of network. However, the idea has never gotten enough traction to bring it to larger markets. I’m sure the issue is figuring out a way to monetize the effort to cover the costs of implementing it.

Another new concept for improving indoor coverage is to allow access to a neutral 5G host. This would involve a third-party infrastructure provider to build, own, and operate shared cellular infrastructure for buildings that can be used by any cellular carrier. The neutral 5G host would likely want some up-front money from building owners, but would expect to also charge the cell carriers for the extra reach provided to their networks.

One interesting technology solution is the use of small cellular repeaters that would work in conjunction with a neutral host. Ericsson markets a repeater called the Radio Dot, shown at the top of this blog. Repeaters can be distributed throughout a building to make sure the cell signal reaches all needed spaces, much like is done with WiFi extenders.

The FCC Opens the 900 MHz Band

The FCC voted in its recent open meeting to expand the use of 900 MHz spectrum. The order opens up the full 10 MHz available in the 900 MHz spectrum bands 896–901 and 935–940 MHz, for licensed broadband services. 900 MHz is an attractive band for users since the signals carry a long way and are good at penetrating buildings.

The licensed portion of the spectrum is not of interest to WISPs due to the small size of the channels, which won’t deliver the kinds of speeds expected by home broadband users. But the spectrum can easily support smartphone applications and is of interest to those wishing to deploy private 5G network.

This FCC change does impact the other bands of 900 MHz spectrum. For example, there are numerous uses allowed for the spectrum between 902 and 928 MHz, including ham radio, FM radio repeaters, alarm and security camera systems, video surveillance for law enforcement missions, and transmission of infrared scanner imagery during overflights of disaster areas. Some of these uses are restricted in Texas and New Mexico since this spectrum is also used to monitor the border.

The primary users of the expanded-use bands will be electric, gas, and water utilities that have been using the spectrum for automated meter reading and other network monitoring devices. The purpose of the FCC’s change is to provide more bandwidth and expanded capacity to utilities. The FCC order predicts that the changes to the spectrum usage will promote better smart metering, grid modernization, and network security and resilience. Under the former rules, transmissions in the band were restricted to 5 MHz licenses, which limited the ability for utilities to launch private 5G and LTE networks.

The new order provides different options for a current license holder to:

  • Continue to use the legacy configuration of 20 wideband channels and 200 narrowband channels.
  • Operate two paired 3 MHz channels and two segments of the remaining 4 MHz of spectrum to operate 159 narrowband channels.
  • Operate two paired 5 MHz channels to deploy more broadband use cases.

This change largely benefits Anterix. The company purchased a nationwide license for 6 MHz of the spectrum from Sprint in 2014. Anterix has been selling and leasing that spectrum to utilities to create private wireless networks. This new order gives the company the use of all 10 MHz of the spectrum.

One of the most interesting aspects of the new order is that it anticipates that the spectrum will be made available to others through voluntary negotiations and market-based transactions. The Anterix spectrum today is largely deployed on a county-by-county basis, and this order opens the door for entities other than utilities to license the spectrum to create local private 5G networks. This could be used by corporations or local governments looking for a private and secure wireless network outside of the public cellular networks.

I recently noted how the public cellular networks crashed in Western North Carolina after Hurricane Helene. While a number of cell sites sustained physical damage, many were still operational, but still failed since the backhaul fiber lines feeding the region were damaged or destroyed. While the lack of cell signal was a major inconvenience for the public, it was a crushing blow to first responders who found themselves unable to communicate. A private in-county 5G network for first responders using 900 MHz could have continued to work locally on the functional cell towers. This would have greatly benefited the search and rescue effort and the overall coordination of first responder resources.

It will take a while to see if this is a giveaway to Anterix or if this will really open up new opportunities for first responders and other local private wireless network providers.

Hydrogen Generators

There is an interesting technology that is slowly edging into the telecom industry. There are a handful of places that are using hydrogen fuel cell generators instead of the more standard diesel generators for backup power. Everybody who works with a telecom network is aware of the wide use of diesel backup generators that kick in when commercial power fails. Diesel generators are permanently installed for critical hub sites, and telecom companies use portable generators that can be quickly driven to remote powered sites like huts and cabinets.

Diesel generators have a few drawbacks. Diesel fuel in notoriously challenging to use in very cold weather. Diesel generators also expel clouds of oily smoke. The biggest downside to diesel generators is that they are loud – the larger the generator, the louder. The largest diesel generators used for large sites like data centers can operate at 110 decibels, the same sound level as a rock concert. One of the biggest complaints about neighbors of data centers is the loud noise when generators are being tested.

Hydrogen fuel cells offer an alternative to the shortcomings of diesel generators. They are nearly silent in operation. The technology doesn’t generate any heat. Most impressively, hydrogen generators don’t generate any pollution since the waste product of a hydrogen fuel cell is water.

Hydrogen fuel cells operate by a simple chemical reaction. In a hydrogen fuel cell, pure hydrogen is passed by an anode that separates the hydrogen molecule into protons and electrons. The electrons are used to power the applicable application, such as the electricity from the backup generator. The protons are passed through an electrolytic membrane where they combine with oxygen to form water.

Hydrogen fuel cell technology has been used in practical applications for decades. An early version of a hydrogen fuel cell was used to provide the electricity for the Apollo spacecraft in the 1960s. The technology began to be practically used in the 1990s when cities created zero-emission bus fleets operated by hydrogen. There are now delivery trucks that use hydrogen technology. There have been successful tests using hydrogen fuel cells to power trains and airplanes. Most car companies have experimented with making hydrogen-fueled cars. Several countries are experimenting with hydrogen power in submarines because of the silent operation and the lack of heat.

Hydrogen fuel cells have a potential place in telecom. In 2020, Microsoft was able to operate a data center continuously for two days with hydrogen fuel cell generators. Tele2 and Telia are using hydrogen fuel cell generators for telecom sites in Estonia.

https://www.popularmechanics.com/science/a33499249/microsoft-hydrogen-generator-test/

There are practical downsides to using hydrogen on a commercial basis, although cities with fleets of hydrogen buses have solved the biggest problems. Hydrogen has a low volumetric energy density, which requires storing it in large quantities. Bus fleets have solved this issue by storing hydrogen in vehicles at high pressure, which carries a different set of risks. Hydrogen is flammable, but so are fossil fuels used for combustion generators. The solution to the widespread use of hydrogen as a fuel would be to develop hydrogen depots, which would be the equivalent of gas stations, where hydrogen canisters could be refilled or swapped.

For now, the biggest downside is probably the upfront cost of the generators and the infrastructure that is needed to store the gas to support them. However, cities say that ongoing costs compare favorably to diesel generators. The number one way to get costs down would be widespread adoption, which would bring economies of scale to manufacturing the units.

This seems like a technology that data center operators should be interested in. The public is increasingly pushing back against the noise and pollution created by data centers, and hydrogen generators would help to lessen the negative impacts on those living close to a data center.