“LTE Unlicensed” Deployments Planned for 2016

In the last post in this series I reviewed several different points of view regarding the pros and cons of cellular carriers using “LTE Unlicensed” (LTE-U) to expand their network capacity. In this post I’ll take a closer look at movement in this direction among U.S. carriers.

[Note: The deployment of LTE in unlicensed bands is referred to by multiple names, including “LTE Advanced in unlicensed spectrum,” “LTE Unlicensed” (LTE-U) and, most recently “Licensed Assisted Access” LTE (LAA). In this post I’ll refer to it as LTE-U, though other names may appear in some excerpts included in the post.]

The two U.S. cellular providers that have so far expressed most enthusiasm for LTE-U are Verizon and T-Mobile.

>Reporting from the Mobile World Congress held March 2-5 in Barcelona, Spain, Mike Dano wrote the following in the March 3 FierceWireless online newsletter:

[B]ased on the discussions I’ve had this week, it appears that Verizon…, Vodafone and other carriers last year decided they wanted to make LTE-U a reality–and they decided they didn’t want to wait for the 3GPP to standardize the technology. So they teamed up with some network technology companies to design real-world tests of the technology…

Verizon clearly has high hopes for the tests and the technology–it has said that it plans to commercially deploy it in the 5 GHz and 3.5 GHz bands in 2016. Verizon is not the only carrier that supports LTE-U/LAA. T-Mobile announced this year that it too will deploy what it calls LAA in the 5 GHz band in 2016. T-Mobile CTO Neville Ray said he believes the carrier can get LAA-capable handsets this year.

As Dano notes, “[h]owever, not all carriers are on board.” Specifically, he points to comments from Tom Keathley, senior VP of wireless network architecture and design for AT&T. As one might expect from a carrier that has invested in a network of more than 30,000 WiFi hotspots, AT&T’s concerns include the risk that LTE-U deployments will not share unlicensed spectrum fairly and efficiently with WiFi.

Keathley said that current approaches to LTE-U are vague about how exactly to check for existing users in unlicensed bands, and how long LTE users can occupy unlicensed spectrum.

Dano also cites comments from Eric Parsons, an executive at Ericsson, a leading wireless network equipment vendor, regarding how these spectrum sharing issues might be dealt with in different regions of the world. As Parsons explains, “there are very specific guidelines in Europe and Japan that cover these areas, but countries like the United States don’t have specific guidelines.”

T-Mobile, which has less licensed spectrum to work with than its competitors (see here for T-Mobile CEO John Legere’s perspective on this issue), seems particularly interested in LTE-U. In anticipation of commercial deployments in 2016, it has announced plans for multiple tests of the technology, in cooperation with Alcatel-Lucent and Qualcomm, Ericsson and Nokia.

In a January 5, 2015 blog post T-Mobile chief technology officer Neville R. Ray shed some light on the company’s plans:

Currently, there is approximately 550 MHz of underutilized spectrum in the 5 GHz Unlicensed National Information Infrastructure (UNII) band, which is available for any use within the FCC’s rules for the UNII band. LAA is a new and innovative approach that allows for licensed and unlicensed spectrum to work seamlessly together. And, we’ve already begun work with our various chipset, radio infrastructure and device partners to bring LAA production trials to life this year and bring the technology to our customers in the near-future.

During T-Mobile’s February 19, 2015 yearend earnings call, Ray provided an update on the company’s LTE-U plans:

[W]e’re looking early ’16 to potentially have the first commercial products in market…I think the first application that you’ll see on 5 gig and LAA will be in-building and it will be primarily in-building commercial, but potentially consumer, too. The great thing is we will move to outdoor. The performance of LTE in the 5 gig band is significantly better, the radio performance, than what’s seen with Wi-Fi.

While Verizon has also been working with vendors on LTE-U and is expected to begin deploying a version of the technology fairly soon, it’s strong spectrum position following the recent AWS auction (you can download Verizon’s post-auction presentation slides here), along with comments by company executives, suggest it feels less urgency than T-Mobile about its LTE-U deployment timetable.

As Network EVP Tony Melone explained during Verizon’s February 17, 2015 investor webcast following the AWS spectrum auction (you can download the webcast’s transcript here):

While we have consistently avoided building our own Wi-Fi networks outside of unique venues such as stadiums, we have always viewed Wi-Fi as complementary to our managed network. As such, we are very optimistic about LTE over unlicensed spectrum as a future capacity solution. With our key suppliers we are active in the standards process and will likely deploy a pre-standard version in the not-too-distant future…

[I]n terms of LTE unlicensed…[y]ou should think about that utilized in a supplemental downlink opportunity. Again, with small cells, utilizing unlicensed spectrum for LTE will be very similar to Wi-Fi in terms of power requirements, etc. So the advantage we will have is we will have centralized control and knowledge of the interference conditions, so we’ll be able to bring that unlicensed spectrum into play when it’s available, when it can provide a good experience for our customers. And again, use it as a supplemental downlink to augment capacity.

The prospect of T-Mobile and Verizon deploying LTE in unlicensed spectrum while cable operators deploy millions of in-home public access hotspots and begin using them to support unlimited-use WiFi-based services (see here, here, here and here), raises interesting and important questions about the future role and significance of unlicensed spectrum. These questions relate to technical standards development, spectrum policy, and difficult-to-predict impacts on competitive dynamics and, ultimately, the public interest. As such, it is an arena worth watching closely by those involved in analyzing and implementing communication policy.

Primary takeaways

Digital inequality shows larger impacts on youth academic performance as compared to time spent on screens.
Digital skills play a significant role in mediating unstructured online engagement (social media use, playing video games, browsing the web) and youth academic, social, and psychosocial development.
Unstructured online engagement and face-to-face social interaction are complementary and continuously interact to create and enhance youth capital outcomes.

A familiar story: concerns of screen time

Today’s discussions of adolescent well-being have coalesced around a clear narrative: teenagers spend too much time online, and their academic performance, mental health, and social lives are deteriorating as a result. A steady stream of academic papers, books, and op-eds, alongside a growing number of policy proposals––school phone bans, age-gated social media use, restrictive screen-time limits––rest on the same underlying claim, aligning with a contemporary, digitized version of the displacement hypothesis:

Screen time, particularly the unstructured, free-time spent on social media, gaming, watching video content, or browsing the web, is said to displace the productive face-to-face activities that build adolescents into capable adults.

The implied and often practiced solution is restriction. In response, this dissertation tested this claim directly, and placed it within the broader context of adolescence.

Across three years, I followed 653 Michigan adolescents from early through late adolescence: in grades 8 or 9 (survey one, 2019) to grades 11 or 12 (survey two, 2022). Notably, these students, studied over time, were part of a broader pooled sample of 5,825 students across the same eighteen highschools. The study window captured the year before and the year after the peak of the COVID-19 pandemic and related lockdown orders, functioning as an unprecedented stress test for theories of adolescent social, academic, and digital life and, importantly, as a benchmark to compare the effects of pandemic-related change and inequality to those effects from screen time alone.

Across four studies of adolescents, consisting of six cross-sectional and longitudinal analyses, findings are not consistent with the displacement narrative, nor the broader concerns about the time youth spend on screens.

Findings are, however, consistent with something the current public and (most) academic discussions have largely overlooked or ignored: the gaps and inequalities that determine whether adolescents can access and use the internet meaningfully in the first place.

What the displacement hypothesis overlooks

Displacement and related research and policy concerning the time young people spend online assumes a “zero-sum” model of adolescent day-to-day time. An hour online is an hour not spent studying, reading, sleeping, or interacting face-to-face (i.e., time spent on more productive or developmentally “better” activity).

Indeed, this makes sense logically. However, as an empirical claim, this model requires time spent online to behave differently from all other ways adolescents allocate time; it must produce uniquely negative outcomes and be inherently harmful across digital contexts, rather than the typical mix of trade-offs corresponding to, and often overlooked among any other social or developmental context.

Yet, online time does not differ from other youth activity. Instead, I find it has a mix of pros, cons, and even some “uniquely digital” benefits which youth utilize for social and academic gains. When I compared unstructured digital media use against traditional face-to-face interaction and activities, both produced similar patterns: some negative associations with academic outcomes, some null, and some positive.

Trade-offs within traditional face-to-face activity (for example, social time with friends and family, or time spent in after-school extracurriculars) are treated as ordinary developmental experiences that must be experienced for the betterment of development. The identical trade-offs involving digital time tend to be overlooked or ignored, and online engagement is perceived as altogether harmful.

A growing body of evidence, including this dissertation, do not support that distinction. Indeed, the developmental context is routinely misread, leaving out the context of the experiences and time spent on digital, as well as face-to-face activities, interactions, existing inequalities, and changes inherent to development. As such, I proposed a novel framework to understand these contexts:

Digital capital exchange

Rather than treating screen time as a unified harm, this dissertation advances an “exchange”-based framework, grounded in James Coleman’s theories of youth capital and digital inequality scholarship, particularly following Eszter Hargittai, Jan van Dijk, and Alexander van Deursen (see this list of all dissertation references for full works).

The core proposition is that adolescents’ online engagement is not an alternative to developmental activity but another, albiet modern domain through which young people accumulate and mobilize online resources––particularly digital skills––that work alongside existing social networks and experiences to be exchanged for human capital (measured as: academic achievement, aspirations, STEM interest) and social capital (peer networks, community participation, extracurricular involvement).

Online time is not the mechanism; instead, it is digital skills that I find to be the most vital component in youth capital exchange and enhancement. Unstructured online engagement contributes to online skills; those skills, accumulated and mobilized alongside existing peer, family, and community networks, translate into the outcomes researchers and parents care about, i.e., academic achievement, aspirations, and face-to-face interaction and social networks.

This digital capital framework treats online and in-person contexts as complementary rather than antagonistic, and it situates adolescents’ digital lives within the structural conditions––connectivity quality, device reliability, autonomy of use––that determine whether exchange can occur at all.

Methods (in brief)

Paper-and-pencil surveys were administered to students in classrooms at two time-points: spring 2019 (N=2,876) and spring 2022 (N=2,949), across the same eighteen predominantly rural Michigan schools, grades 8–12. Official, nationally-ranked standardized reading, writing, and math test scores (PSAT 8/9, PSAT 10, SAT; College Board) were then anonymously linked to students’ survey responses with the help of participating districts.

Cross-sectional path analyses modeled pooled and wave-specific samples (pooled N=5,825); two-wave cross-lagged panel models tested reciprocal, longitudinal relationships on the 653 students who completed both surveys. Multi-group analyses of the cross-lagged panel models compared relationships between girls (N=345) and boys (N=308). All longitudinal models included time-invariant socioeconomic covariates as well as time-varying covariates to reduce omitted-variable bias.

Key findings: an overview

To summarize, to the best of my ability, eight chapters across 376 pages, I present two primary findings:

First: digital inequality predicted larger and more consistent declines in human capital than screen time did.

Unreliable home internet and technology maintenance problems––experiencing and/or dealing with broken or outdated devices and software, restrictive school-issued hardware, issues with connecting to or maintaining internet access––decreased youth GPA and standardized test achievement. And, these effect sizes were substantially larger than any negative direct effect from unstructured digital media use.

Across all four empirical studies, digital inequality emerged as the most substantial predictor of academic and developmental decline.

Second: digital skills mediated the relationship between online time and adolescent academic and social outcomes.

Unstructured digital media use, particularly online gaming and web browsing, predicted higher internet and social media skills for adolescents, which in turn predicted stronger academic achievement and self-efficacy (human capital), and social interaction and extracurricular participation (social capital). The positive indirect effect of screen time through skills offset or exceeded any small negative direct effects across several outcomes (supporting our existing peer-reviewed work: Hales & Hampton, 2025, and which you can read more about here).

These exchange processes were amplified when peer and family networks were modeled alongside digital skills, consistent with the premise that online and offline contexts operate together rather than in competition. The effect was not universal: social media skills amplified rather than offset a negative association with consistency of interest, one of the two subscales of grit. The exchange framework describes a contextual and conditional, domain-specific mechanism, not a blanket defense of time spent online.

Implications

If digital inequality, and not screen time, is the primary predictor of adolescent academic and developmental decline, and still warrants concern regarding access quality and experience even with the broader adoption of digital devices across the United States, the current policy emphasis on restriction is pointed at the wrong target. The evidence supports a different set of priorities.

Stable, reliable home (fast) broadband should be treated as an educational prerequisite rather than a consumer amenity. Unreliable connectivity exerted larger downward pressure on human capital than any measure of screen time, and that pressure intensified during the pandemic-era reliance on digital infrastructure. Technology maintenance, device repair, replacement, technical support, and the flexibility to install software and explore the web autonomously, matters as much as initial access, and school-issued devices that restrict autonomous use appear to hinder skill accumulation rather than support it.

Restrictive parental mediation of internet use was negatively associated with grit and self-efficacy at magnitudes comparable to the positive contributions of face-to-face activity. This challenges the assumption that digital restriction functions protectively. Instructive mediation, teaching adolescents to verify information, navigate platforms critically, and mobilize online resources toward meaningful ends, is the posture the data supports.

Finally, the technical skill-building that occurs through gaming, self-directed exploration, and deep web use is skill-building, not wasted time. Closing the persistent gender gap in these domains likely requires legitimizing technical play for girls, rather than restricting it for everyone.

None of the above is an argument that screen time is benign. It is an argument that screen time is the wrong focus, particularly when studied mostly in isolation. Context matters substantially, whether that is time spent on other activities during adolescence, the period of adolescence itself, digital inequality, resources gained from such online use, and how all such factors interact. The factor that predicts whether a given adolescent can convert online engagement into capital outcomes is structural: access, infrastructure, skills, and the autonomy to use them. These factors are distributed unevenly, and its uneven distribution, not hours logged, is what separates adolescents who thrive from those who fall behind.

The full dissertation is available through Michigan State University’s ProQuest archive, or see the embedded full-text PDF below. I’m happy to share papers, preprints, or the underlying framework with anyone interested and working in this area––don’t hesitate to reach out via my contact form. Thanks for reading.

Tags: AT&T cable operators cellular carriers LTE T-Mobile unlicensed spectrum Verizon Wi-Fi

“LTE Unlicensed” Deployments Planned for 2016