Taxonomy of 6G Wireless systems

Taxonomy is defined as the process of naming and classifying things such as animals and plants into groups within a larger system, according to their similarities and differences. In the picture above, various technologies referred to 5G/6G are grouped together to form a taxonomy of 6G wireless systems.

6G Wireless Systems: A Vision, Architectural Elements, and Future Directions, a research paper from earlier this year, part of Special Section on Edge Intelligence for Internet of Things, IEEE Access (available here) contains details on this. 

Over the next few weeks/months, we will try and look at all the technologies one-by-one, if not already covered here.

The paper contains a lot of details but I am just extracting a few things related to 6G taxonomy below.


A 6G system will use a wide variety of computing, communication, networking, and sensing technologies to offer different novel smart applications. The key enablers of 6G wireless systems are edge intelligence, homomorphic encryption, blockchain, network slicing, AI, photonics-based cognitive radio, and space-air-ground-integrated network. Although network slicing was proposed in 5G as a key enabling networking technology, its true realization is expected in 6G. Network slicing based on software defined networking (SDN) and network function virtualization (NFV) employs shared physical resources to enable slices of different applications. The process of network slicing involves the optimization of a variety of network parameters.


Although 5G wireless networks were conceived to provide a wide variety of smart services, several services disrupt the vision of 5G design. Generally, 5G use cases have three main classes, such as URLLC, enhanced mobile broadband (eMBB), and massive machine-type communication (mMTC). However, several new applications are disrupting the vision of 5G use cases and we need new use cases. For instance, consider XR (i.e., combining mixed reality, augmented reality, and virtual reality) and braincomputer interaction that requires 5G-eMBB high data rates, low-latency, and high reliability. Therefore, we must define new use cases for these emerging applications. The novel 6G services are haptics, autonomous connected vehicles, massive URLLC (mURLLC), human-centric services, bio-Internet of things (B-IoT), nano-Internet of things (N-IoT), and mobile broadband reliable, low-latency communication.


Machine learning (ML) is considered one of the key drivers of 6G. ML recently elicited great attention in enabling numerous smart applications. In 6G, ML is expected to not only enable smart applications but also provide intelligent medium access control schemes and intelligent transceiver.Thus, ML can be one of the fundamental pillars of the 6G wireless network. Generally, we can divide ML into several types: traditional machine learning, federated learning, meta learning, and quantum machine learning.


A 6G system will use novel communication technologies to enable various smart applications. These communication technologies are terahertz communication, quantum communication, 3D wireless communication, visible light communication, nanoscale communication, and holographic communication. Recently, 3GPP has developed a new radio access technology; namely, 5G new radio using sub-6 GHz and mmWave bands for enabling high data rates. To enable further higher data rates, 6G will use terahertz bands in addition to mmWave bands. Generally, terahertz communication uses frequencies from 0:1 to 10 terahertz and is characterized by short-range, medium-power consumption, high security, and robustness to weather conditions.


Novel networking technologies for 6G are nano-networking, bio-networking, optical networking, and 3D networking. The operation of the N-IoT is based on molecular communication. Different materials, such as graphene and meta materials can be used to build nanometer-range devices. B-IoT using biological cells are used for communication using IoT. B-IoT and N-IoT are seemingly integral parts of future 6G smart services but have several implementation challenges.


A 6G system involves a wide variety of sources of different smart applications that generate an enormous amount of data. High-performance computing and quantum computing must be used to enable intelligent data analytics. Quantum computing is expected to revolutionize the field of computing by enabling higher speeds that users have never experienced until now. The key feature of quantum communication is secure channels, where every channel carries its distinct security protocols constructed into encrypted data. These features of security in addition to ultra-high speed make quantum computing preferable for secure 6G smart applications. Other than quantum computing, intelligent edge computing is required for 6G to provide intelligent on-demand computing and on-demand storage capabilities with extremely low latency to end nodes.

Complete details here.

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