Cross-Layer Optimization in 6G Networks

 Abstract:

An integration of 6G network intelligence technologies and the F-RAN architecture, the 6G smart fog radio access network (F-RAN) is proposed to aim for low-latency, high-performance services in terms of access devices. Nevertheless, the level of integration with the 6G network intelligence technologies in their current form, and with the system-level requirements related to the number of access devices and constraints on energy consumption, has slowed the progress of improvement for the smart F-RAN 6G. To better analyze the root causes of the network problems and promote the practical development of the network, this study used structured methods such as segmentation to conduct a review of the topic. The research results show that there are still many problems in the current 6G smart F-RAN. Future research directions and difficulties are also discussed.

 

6G Smart F-RANS:

The smart F-RAN-based 6G network can facilitate the low-latency services over a massive number of access devices. In this chapter, the first step will be to present the 6G smart F-RAN network architecture followed by discussing the related key issues. Finally, the chapter discusses solutions for the key issues of 6G smart F-RANs based on the techniques of current AI and network slicing.

6G Emerging Technologies:

Many innovative new technologies that push the boundaries on connectivity, data rates, latency, and applications are what 6G wireless communications will bring. In their collective forms, they represent the landscape of 6G wireless communications but as of now are yet to be decided on the form and eventual deployment. Since 6G is still in its conceptual/early research stage, though, several emerging technologies have already been proposed for potential inclusion in its ecosystem:

Challenges and Future Trends in 6G AI-Enabled Wireless Communications:

This 6G network will generate and process very large volumes of information and, therefore, requires the proper management of storage, processing, and bandwidth to survive in today's globalized society. Data handling could change significantly as more devices contribute their information. Artificially intelligent plays a great part in managing this inflow,High-speed 6G networks can transfer IoT equipment or vehicle-generated information volumes on such a scale that quickly yet reliably. Network management may benefit greatly from the power of AI predictive capability through the combination of AI with 6G as very disruptive across industries and all fields of life by adequately providing volumetric, speed as well as security-related demand.

Abbreviations List:

Symbol	Description
4G	Fourth generation
5G	Fifth generation
6G	Sixth generation
AI	Artificial intelligence
AR	Augmented reality
AV	Autonomous vehicle
BS	Base station
CEM	Customer experience management
CNN	Convolutional neural network
CS	Communication system
CSI	Characteristic stability index
D2D	Device to device
DAI	Distributed AI
DL	Deep learning
DNN	Deep neural network
DRL	Deep reinforcement learning
eMBB	Enhanced mobile broadband
FC	Fog computing
FL	Federated learning
FNN	Feedforward neural network
GAN	Generative adversarial network
GRU	Gated recurrent units
GPU	Graphics processing unit
HMIMO	Holographic multiple-input and multiple-output
IoT	Internet of things
IRS	Intelligent reflecting surface
ISTN	Integrated satellite and terrestrial network
ITNT	Integrated terrestrial and nonterrestrial network
LEO	Low earth orbits
LSTM	Long short-term memory
LTE	Long-term evolution
MaEC	Multiaccess edge computing
MARL	Multiagent reinforcement learning
MDP	Markov decision process
MEC	Mobile edge computing
MIMO	Multiple-input and multiple-output
ML	Machine learning
mMTC	Massive machine-type communications
MSE	Mean square error
MU-HMIMO	Multiuser holographic multiple-input multiple-output
NWDA	Network data analytics
QCN	Quantum communication network
QoS	Quality of service
RAN	Radio access network
RELU	Rectified linear unit
RKHS	Reproducing kernel Hilbert space
RL	Reinforcement learning
RNN	Recurrent neural network
RSMA	Rate-splitting multiple access
SNR	Signal-to-noise ratio
SON	Self-organizing networks
SVM	Support vector machine
THz	Terahertz
TL	Transfer learning
UAV	Unmanned aerial vehicle
UM-MIMO	Ultramassive multiple-input multiple-output
URLLC	Ultrareliable low latency service
V2X	Vehicle-to-everything
VNE	Virtual network embedding
VNR	Virtual network requests
VR	Virtual reality

Conclusion:

Recently, AI has depicted its advanced advantages over the wireless technologies. This technique has received engineers' interest in utilizing these concepts within their designs. In this research, evolution and classification of AI, as well as requirement of various constituent parts in order to achieve AI-based wireless communication, are being discussed. Details regarding different AI-enabled future technologies in wireless communications are discussed below. The present study discusses AI-enabled applications to address various aspects of 6G mobile communication, including intelligent mobility and network management, channel coding, massive MIMO, and beamforming. It has been studied that, enabled by AI techniques, the 6G system can automatically control network structure and various resources, including slices, computing, caching, energy, and communication, to fulfill changing demands.