Industry Research : Engineering Bites, October 2020

Engineering Bites, October 2020

Serving you four recent portions of brilliance at the cutting edge of technology that you may have missed this month.

Serves You Right?

The fixed-line broadband world has historically served customers with a specific level of service to a location such as a home or business, with pricing based on the capacity offered. A patent from US-based CableLabs shows the possibility of radical change in how services are provided.

“Providing broadband communication service on a location basis may result in suboptimal performance and/or suboptimal resource allocation,” the patent explained. “For example, some clients at a location may receive(…) insufficient bandwidth and/or unacceptable latency, while other less-demanding clients at the location may receive a higher-level of broadband communication service than needed. Additionally, one client’s use of shared broadband communication service may interfere with another client’s.”

The patent depends upon pairing each individual connected device to a service profile outlining its resource requirements and identity. The network can use this to provide the appropriate levels of e.g. bandwidth, reliability, security, etc. to the device. By tracking the device identity the patent also supports the possibility of moving a device to another location and having the network maintain service levels, potentially enabling new kinds of commercial relationships between network providers.

ITS Green and Reliable

Managing urban transport in the coming decade has already gained a great deal of interest. V2V and V2X communications are not new concepts and a good deal of work has been done on low-latency and high-bandwidth communications. Coordinating and managing the data is a resource-intensive activity, though. Edge nodes tend to be resource-constrained and struggle with the high mobility of vehicles and dynamic wireless channels.

An international collaboration of researchers has proposed and trialled a 5G-based method to coordinate and manage an Intelligent Transport System (ITS) with a focus on lowering the energy requirements of the edge nodes involved and improving reliability. Their energy-efficient algorithm coordinates the application, physical, network, and Media Access Control (MAC) layers to minimise overall energy consumption, while a reliability algorithm was produced based on real-life ITS data sets. By combining these, the researchers were able to produce drastic reductions in energy consumption while boosting the reliability of the system.

Probabilistic Computing in Practice

The wait for high-performance, reliable quantum computers may be a long one, according to this review of existing progress from Aberdeen University and NTT Research.

However, a team led by Kerem Cayseri at Purdue University is exploring other non-traditional computing techniques to solve the kinds of optimisation and machine learning problems to which ultimately quantum computers may be suited. In this lecture, Cayseri discusses not just the concept of probabilistic computing and its uses, but also demonstrates both a physical 8-bit “p-computer” and a functional simulator built on an Arduino board, highlighting the practicality of p-computing as a bridge between classical and quantum computing.

Micro-Operator Networks

5G deployments beyond consumer use cases have been discussed extensively for several years. This recent contribution to the IEEE by the University of Oulu addresses a significant business and technical issue: “Case specific and localised requirements are expanding beyond the current capabilities of the traditional [Mobile Network Operators; MNOs] whose services are often designed to serve masses.” Micro-operators may be a solution, running dedicated 5G networks in a particular factory, hospital, campus, etc. with specific use cases and service-level agreements.

The paper outlines the network architecture for a local 5G micro-operator to be 3GPP compliant, as well as delivering the tailored performance required for the location. It includes network deployment models for:

  • A self-contained local core and access network,
  • An MNO-provided network and
  • A hybrid model where core network functions are split between the micro-operator and national MNO.

The paper then provides data from a factory testbed comparing latency and throughput in all three network scenarios, assessing how well they would support applications of AR, mobile robots, and massive wireless sensor networks. They conclude that the more network functions that are deployed on or close to the location, the lower the latency; and that a local network keeping all data on-premises has a security advantage.




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