6th-generation Wireless (6G)

A communication protocol for wireless communications technologies that support cellular data networks, expected to be around 1,000 times faster than 5G.
Technology Life Cycle

Technology Life Cycle


Initial phase where new technologies are conceptualized and developed. During this stage, technical viability is explored and initial prototypes may be created.

Technology Readiness Level (TRL)

Technology Readiness Level (TRL)

Technology Concept

Research is scrutinized and correlations to practical applications are applied to initial scientific observations. No experimental proof of concept is available yet.

Technology Diffusion

Technology Diffusion

Early Adopters

Embrace new technologies soon after Innovators. They often have significant influence within their social circles and help validate the practicality of innovations.

6th-generation Wireless (6G)

The sixth-generation wireless (6G) is a communication protocol for wireless technologies that could provide higher capacity and lower latency compared to 5G, possibly reaching one microsecond-latency communication, which is 1,000 times faster than one-millisecond throughput (estimated 5G cellular technology latency). It is expected to be the successor to 5G mobile networks.

6G is still in the research stages. Although scientific advances are experimenting with devices capable of operating at higher frequencies, the required high transmission speeds, the energy consumption rates, and the acceptable proportions of the related heat development in the electronic circuits are just some examples of the challenges 6G networks will face in the future. Studies estimate that 6G networks would likely operate in frequencies from 100 GHz to 3 THz due to their wide swaths of the unexplored spectrum (unused frequency waves in the electromagnetic spectrum).

Future Perspectives

The scientific community commonly references 6G as the "wireless cognition," an allusion to wireless networks that could allow human thoughts to move freely over the air. In the future, through the electromagnetic frequencies of 6G, remote robots and AI applications could exchange data with excellent coverage at incredible speeds. Also, the 6G frequencies would be in the sub-Terahertz bands. Hence, researchers calculate that, based on these ample bands, it could transmit super-fast computations over a wide variety of frequencies, enabling future mobile devices to have vastly more extraordinary capacities, such as human-to-machine interactions.

Image generated by Envisioning using Midjourney

As 5G is deployed in the next several years, engineers and policymakers must start thinking about a 6G in the decade ahead.
In telecommunications, 6G is the sixth generation standard currently under development for wireless communications technologies supporting cellular data networks. It is the planned successor to 5G and will likely be significantly faster.[1] Like its predecessors, 6G networks will probably be broadband cellular networks, in which the service area is divided into small geographical areas called cells, a 6G network works in combination of 4G and 5G networks. Several companies (Nokia, Ericsson, Huawei, Samsung, LG, Apple, Xiaomi), as well as several countries (India, China, Japan and Singapore), have shown interest in 6G networks.[2][3][4][5][6]
User scheduling is a classical problem and key technology in wireless communication, which will still plays an important role in the prospective 6G. There are many sophisticated schedulers that are widely deployed in the base stations, such as Proportional Fairness (PF) and Round-Robin Fashion (RRF). It is known that the Opportunistic (OP) scheduling is the optimal scheduler for maximizing the average user data rate (AUDR) considering the full buffer traffic. But the optimal strategy achieving the highest fairness still remains largely unknown both in the full buffer traffic and the bursty traffic. In this work, we investigate the problem of fairness-oriented user scheduling, especially for the RBG allocation. We build a user scheduler using Multi-Agent Reinforcement Learning (MARL), which conducts distributional optimization to maximize the fairness of the communication system. The agents take the cross-layer information (e.g. RSRP, Buffer size) as state and the RBG allocation result as action, then explore the optimal solution following a well-defined reward function designed for maximizing fairness. Furthermore, we take the 5%-tile user data rate (5TUDR) as the key performance indicator (KPI) of fairness, and compare the performance of MARL scheduling with PF scheduling and RRF scheduling by conducting extensive simulations. And the simulation results show that the proposed MARL scheduling outperforms the traditional schedulers.
The ‘Internet of the Senses’ and a ‘cyber-physical continuum’ are on engineers’ horizon

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