2025’s list of “most popular” 6G technologies will undergo change as further research, early development, and some rudimentary trials prove and, in some cases, disprove a technology’s viability.

In a subsequent article, I will look at trending 6G enabling technologies that have a higher commercialisation risk, while in this article I look at the list of technologies that currently have a high probability of not being culled from the 6G enabling technologies list, as follows:

7-16GHz Mobile Terrestrial Radio Systems

Wireless technology relies first and foremost on spectrum availability. The growth in data consumption and wireless connectivity has and will continue to put ever-increasing demand on spectrum. For a mobile operator, the ideal (in some cases, the only acceptable) scenario is to have exclusive use of spectrum in its geographic areas of operation over which they can transmit radio power levels high enough to maintain a high-capacity and high-reliability network.

The increased capacity demand has led to the exploration of repurposing the radio spectrum between 7 and 24GHz with special attention to frequencies between 7 and 16GHz. This spectrum has significant use in radio navigation, radio location, and satellite applications. This is complicated by heavy and exclusive use in this band by government agencies around the world (especially for Defense). In addition, these higher frequencies have increased radio propagation loss over frequencies between 3 and 5GHz. The latter is used in 5G but carries its own technology challenges due to higher loss than the lower frequencies that are so heavily used in 4G (most below 2.5GHz).

For mobile wireless to work from 7-16GHz, there is serious consideration for how some of the spectrum can be shared. Sharing mechanisms involve both complex policies and technologies, so both are getting attention. Even if some of this range is set aside for exclusive use in commercial wireless, the added propagation loss drives significant technology work. The most obvious solution to the issue of a lower signal-to-noise ratio at the receiver is to make the cell size smaller. However, this is not financially feasible for the mobile operators due to site acquisition costs and the challenge of adding a very dense backhaul interconnection to more cells. Therefore, investigations on how to overcome these problems with advanced integrated radio and antenna systems is critical (see below for “next generation MIMO”).

Artificial Intelligence (AI)

The form of AI known as Machine Learning (ML) is very popular given the advent of multiple powerful large-language models (LLM’s) available for public use. But telecommunications engineers are exploring very different types of models. Whereas LLM’s are trained in human language response given vast amounts of exchanges on the web, the mobile wireless industry is developing AI to optimise network performance, address the complexities of radio beam management, optimise circuit design, facilitate more efficient traffic flows, and reduce overall power consumption. None of this uses LLM’s but rather ML models trained on technical data from networks, circuits, and even synthesized data from simulation and emulation tools.

The key technical challenges are driven by the need to ensure a reliable model that is consistent in out-performing conventional means—these can be summed up in how to develop, refine, and train the model (this requires lots of data that developers can trust); and how to validate that the model works under the vast majority of circumstances.

Next Generation MIMO

Multiple in/multiple out (MIMO) was developed to take advantage of the fact that radio waves can follow multiple paths between the transmitter and the receiver (e.g., a direct path, one or more reflected paths). Before MIMO, multiple paths were a problem for radio communications and caused multi-path interference.

MIMO in cellular is now in its 4th generation. The latest manifestations were necessary to overcome the increased loss in the 3.5GHz spectrum allocated for 5G. The fundamental approach is to use many antenna elements and complex digital signal processing (DSP) so that the antenna elements work together to improve the effective signal-to-noise ratio at the receiver; and to constantly measure the state of the radio channel between the transmitter and receiver (mobile wireless channels are under a constant state of change) so that the DSP does continuous manipulation as to how the multiple antenna elements are used to overcome the constant change in the channel.

The move to 7-16 GHz while keeping the cell-size the same (e.g. keeping the maximum distance between transmit and receive the same as with 3.5GHz) means even more technical complexity in the MIMO system: more, and even distributed, antenna elements, and stronger DSP. This is an excellent place to leverage ML given the complexity of what is needed.

Open RAN

Radio Access Network” (RAN) is the term used for the network of radio base stations required to interface with mobile user equipment (e.g. smartphones). Before 5G, the RAN was a closed architecture, with each of a few large network equipment manufacturers having their own proprietary approaches. However, the idea of virtualising the digital parts of the RAN (software entities running on high-performance general-purpose servers) has driven the industry to work to standardise the resulting disaggregation (radio unit, digital unit, centralised unit) and to standardise the interfaces between these architectural components. This open RAN approach has led to new concepts including intelligent controllers of the RAN functionality (RAN Intelligent Controller or RIC) in which ML is already getting some degree of use. Open RAN (and other open standards) is seen by many as a necessary step for 6G and thus, further work in the space is happening to move the concepts to the next generation

By: DocMemory
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