About Flex-5G

This project creates “Flex-5G”, a complete 5G StandAlone (SA) network. Flex-5G’s Open RAN compliance and resulting provider diversification significantly benefits the public by bringing down prices, improving equipment availability and scalability hence improving 5G deployments and their performances, efficiencies and reaches in the UK. Conversely, Flex-5G’s option to combine network modules into a “network-in-a-box”, as one of its instantiations, assists “private network” scenarios that leverage the maximal capabilities of the use cases unlocked by 5G’s high capacity, reliability, low latency and other characteristics. These use cases lead to public benefits in terms of health and social care, education and entertainment, among others.

The Flex-5G solution has a unique cutting-edge approach to “Software-Defined Radio” (SDR). This leads to further considerable flexibility in terms of the radio spectrum accessed, radio configuration or even the radio standards that are transmitted/received by the solution among other aspects. Flex-5G leverages this flexibility to further increase performance and efficiency of 5G networks as seen by the public in the UK, and enhance customisation to use cases, upgradability, deployment options, economies of scale, and robustness and security through software patches, among many other benefits. Such capabilities will also see economic yield for the UK and its technology base through associated international acceleration of the markets and revenues for the pioneering UK companies involved in Flex-5G.

Further key innovations of Flex-5G include, among others:

Creation and validation of a compact modular radio base station capable of supporting all 5G spectrum bands in the key 5G New Radio (NR) “Frequency Range 1”:

This is through the use of field-programmable RF in flexible radio chipsets and the SDR-basis of the Flex-5G solution in general.

Although wide frequency range support will be maintained, some specific radio bands will be focused on for testing, spectrum and equipment availability reasons. These include the widely used 3.4-3.8 GHz subset of the 5G NR n78 band, and the 3.8-4.2 GHz 5G NR upper-n77 band.

Enhancements to cutting-edge UK flexible radio chipsets and radio boards:

Specifically, changes will be made to support the further scaling of parallel operation of these chipsets in support of high order MIMO, particularly addressing phase and frequency synchronisation among the chipsets.

Key advances on “Massive MIMO” technology, improving the performance and practicality of high-end 5G networks:

The integration and refinement of Massive MIMO processing/detection algorithms developed at the University of Surrey, which can greatly increase the throughputs/performances of Massive MIMO links. Target improvements of tens of percentage points—as compared with MMSE detection—will be strived for. The project will also consider how the algorithms can alternatively maintain Massive MIMO performance with a reduced number of antenna elements, thereby greatly reducing the weight and improving the practicality and energy efficiency of Massive MIMO radio/antenna heads.

The integration and optimisation of this processing capability—and the Flex-5G solution in general—with the latest Massive MIMO antenna technology.

Integration and building on the capabilities of a custom in-house 5G core network:

Particularly, enhancements will be made around network slicing interacting with the RAN flexibility,managing multiple slices with different traffic characteristics concurrently at the same base station, compared with the baseline of current 5G network equipment which supports only fixed downlink-heavy communication.

Creation and integration of an AI-driven spectrum and network management framework. This framework aims to leverage the spatial/temporal flexibilities made possible by Flex-5G including in the frequency, spectrum bandwidth and MIMO domains. The objective is to achieve significant power/energy efficiency, spectral efficiency and performance advantages for the network:

The AI will take the multi-dimensional set of available parameters (frequencies, powers, bandwidths, basestation sleep modes, MIMO modes, among others) and continually update/implement its estimation of the “best choice” of parameters with the objective of satisfying current and predicted traffic demand while maximising efficiency. The AI automated decisions might result in, for example: (i) The better tuning to frequencies based on system/area traffic demands thereby achieving better propagation/coverage and allowing some base stations to be placed in sleep mode saving significant energy, (ii) the better coordination of transmission frequencies and spectrum bandwidths among nearby cells based on cell-served traffic demands, (iii) better Massive MIMO decision making, e.g., trading off Massive MIMO beamforming for high user densities with diversity/multiplexing gain for lower user densities, thereby improving coverage and performance.

The project will assess the overall performance of the framework in operational environments, showing performance and efficiency gains as outputs.

Integration and validation of packet-based synchronisation technology such that the Flex-5G solution can operate in areas without satellite coverage for synchronisation, e.g., private networks or base stations deep inside buildings:

This will be through building-in interfaces to PTP. The ultimate ambition is to achieve synchronisation of sufficient accuracy as defined in standards to support the most challenging 5G NR Massive MIMO scenarios.

Creation and validation of a rigorous security framework to address the security considerations that arise in such cases of “softwarisation” and extreme flexibility:

A wide range of solutions are intended to be incorporated, including automated parameter audits, software certification and verification for software reconfigurations, and the incorporation of trust models among others.

Please wait while flipbook is loading. For more related info, FAQs and issues please refer to DearFlip WordPress Flipbook Plugin Help documentation.