The Telecommunications Technological Revolution: From Greenfield to Visionary

TL;DR:

5G is cool, B5G boosted by O-RAN is super-cool, and Quantum-fused 6G Networks will rule!

Background

I rarely post articles, but with the #BIG5GEvent just around the corner, an event close to my heart since my Data Science Ph.D. work revolves around Full Stack Quantum AI Pipelines for self-routing 6G networks, I wanted to take some time to write “a teaser” on what telecom looks like today, versus what it is projected to look like a decade from now. Hopefully, I’m still around in ten years to look back on this article and write a follow-up.?

And for those in the Austin area (you don’t need to be attending the BIG 5G Conference) interested in talking about AI, MLOps Pipelines, Quantum, O-RAN, or Telecom Evolution, Vertica will be hosting a private dinner on Tuesday night at Flemmings, space is limited since I requested an ‘intimate affair’, but it’ll be a night to remember for anyone able to make it!

Introduction

Within the next three years (2025), the world is expected to exceed 37 billion devices, with two-thirds of those devices comprising "things" (IoT devices).?This equates to approximately 4.3 devices for every man, woman, child, cat, and dog living on this planet.

Just focusing on the bandwidth problem (side-barring the energy, emissions, and skillset problems for later), the world's current state-of-the-art 5G cellular communication systems can only admit ~50,000 (narrowband) IoT devices per cell.?

So how is 5G expected to support 2025's massive IoT device load of 25 billion connected "things"?

Beyond 5G (B5G) Networks: Gap-Filling through new Open Radio Access Network (O-RAN) frameworks, Adaptive Full Stack AI Pipeline Development, and Federated MLOps

With user demand for bandwidth capacity outpacing 5G supply and current cellular base-stations physically incapable of meeting the High-Performance Computing (HPC) requirements of computationally intensive cooperative algorithms (which perform signal processing, encoding, and decoding), a radically new Radio Access Network (RAN) approach is needed.

B5G's emerging Open Radio Access Network (O-RAN) is a disaggregated virtual/physical network design, premised on Artificial Intelligence (AI) and collaborative open development. Unlike traditional RAN designs, O-RAN data is processed close to the mobile user, at the Multi-access Edge Computing (MEC) device.

To achieve B5G's Key Performance Indicator (KPI) targets of servicing 1 million IoT devices per 0.4mi2/1km2 with 1-millisecond latency and 20Gb/s peak data rates, O-RAN will necessitate a heavy dependence on automation using Machine Learning (ML) and Deep Learning (DL) techniques.

To directly address the burgeoning user demand placed on cellular base station computational capacity—predicted to increase 3.5X by 2024, largely due to advancements in video-streaming capabilities—O-RAN will require Full Stack AI pipelines driven by automated, secure, and operationalized ML techniques (ie., MLSecOps).?

However, O-RAN’s stringent KPIs will bear a new breed of MLOps Pipeline, since distributed, Massively Parallel Processing (MPP) techniques—innate to HPC,?will be necessary to support Federated-HPC ML/inference at the far-Edge (MEC device).

Still, software-driven AI pipelines can only get us so far ... and with hardware ultimately the key limiting factor for wireless capacity, this must eventually be addressed. While advances in hardware are occurring, such as massive MIMO (Multiple Input Multiple Output) wireless antenna technology breakthroughs, more innovation is needed to sustain the ITU’s (International Telecommunication Union) forecasted 120% IoT device growth rate by 2030.

Practical Industry Use Cases and KPIs

While Automotive has been active in 5G development for years, as evidenced by the V2X (Vehicle-to-Everything) cellular enhancements; Manufacturing is only just now getting started.?

By 2026, Manufacturing is expected to be the largest and fastest-growing B5G market with stringent KPIs that include 8-9s of reliability, sub-millisecond latency requirements, and positioning accuracy of ~7.87in/20cm.?

By 2030 practical use cases are forecasted for autonomous vehicles and swarm systems, intelligent automation, aerial and satellite networks, volumetric media streaming, as well as, multi-sensory (haptics), and immersive extended reality systems.

Quality of Service

To meet the Quality of Service (QoS) required for B5G use cases, workloads need to be placed as close to the user as possible. For B5G, workloads are run at the far-Edge (on the MEC device). For 6G, workloads will run where the user is, such as in the palm of the user’s hand (this is known as “the Fog”).

Supporting “Fog” implementations necessitates looking beyond cellular frameworks…beyond classical computing and modern AI capabilities…and beyond known physics.?

To meet 6G demands, a new method of thought and ingenuity is required.?

6G Networks: The Place Where Quantum, AI, and Efficiency Intersect

To achieve the ultra-low latency, incredibly high and reliable data rates, high energy efficiency, and broad frequency bands supporting 6G's massive and heterogeneous “Fog” device infrastructure, new physical layer technologies predicated on Full Stack Quantum Computing, (qubit-enabled) Quantum Networks, Quantum Machine Learning, and beaming (or teleporting) technologies are required.

The Sixth-Generation (6G) of wireless communication networks (ETA 2030-2035, depending on which visionary researcher you speak to) is projected to integrate communications for space, air, ground, and sea into a single robust network. It will connect billions of people to tens of billions of devices, creating an Internet of Everything (IoE).

Leveraging shiny-new millimeter-wave (mmWave) and Terahertz (THz) frequency bands, 6G will not only be reliable and fast, but it will possess seemingly limitless bandwidth.

Projected to support no fewer than 20X more devices (10,000,000) per 0.4mi/1km square than our newly minted 5G networks, 6G networks will sustain ultra-low, <=100 microsecond, latency requirements, and peak data rates of 1Tb/sec. This will lead to near-incomprehensible use case development.

Futuristic 6G Use Cases

6G will produce new holographic verticals along with an entirely new tactile internet which will support the next generation of advanced use cases. These new 6G use cases will include smart traffic, flying vehicles, environment monitoring and control, immersive reality systems, virtual navigation, digital sensing, and full HD video transmission in fully autonomous drones and robots.

Quality of Service

Quantum Machine Learning (QML) will drive 6G's intelligent decentralized network, stabilizing Quantum Network Communications (QNC) through a Full Stack Quantum Computing (FSQC) Pipeline and facilitating high performance and computational power for all 6G networks and end-devices, no matter their location (Edge/Fog/Cloud/etc.). 6G will possess self-regulated and self-adapting QoS, which will be based on use case-specific KPIs.

FSQC, QNC, and QML will instantly solve complex optimization problems for a fully connected IoE, automatically selecting the ideal route for data packets, load balancing transmissions, as well as estimating and coding channels. QML probability distributions will be measured in real-time, allowing the 6G network to choose the best solution to maintain its optimum state.

Conclusion

The telecommunications industry is undergoing a rapid technological revolution. With commercial 5G implementations barely two years old, telecom stakeholders are already working on greenfield projects for B5G network and O-RAN implementations for key industries, like automotive and manufacturing. Driven by IoT device growth forecasts over the next decade, visionary researchers are working in parallel on 6G, with many looking to Quantum to both satisfy and future-proof the world’s dynamic telecommunication needs.

References

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