Dr Rahul Pandya 6G Vision, Potential technologies, and Challenges - Animated 17.11.2022.pptx

Dr. Rahul J. Pandya,
Assistant Professor,
Department of Electrical Engineering,
Indian Institute of Technology (IIT), Dharwad
Email: rpandya@iitdh.ac.in 1
6G Vision, Potential technologies, Challenges,
and Role of AI in 6G
https://youtu.be/JFCHXn6yvJU

Outline
RJEs: Remote job entry points
 Key possibilities of 6G wireless networks
 Design considerations and potential technologies 6G wireless networks
 Terahertz Communications: Challenges and Opportunities
 Architecture of 6G
 Role of Artificial Intelligence in 6G
 Emerging Trends in 6G
 Applications and Features of 6G
 Research Opportunities in 6G
2

Key Possibilities of 6G Wireless Networks (ITM-2030)
1. W. Jiang, B. Han, M. A. Habibi and H. D. Schotten, "The Road Towards 6G: A Comprehensive Survey," in IEEE Open Journal of the Communications Society, vol. 2, pp. 334-366, 2021
2. Z. Zhang et al., "6G Wireless Networks: Vision, Requirements, Architecture, and Key Technologies," in IEEE Vehicular Technology Magazine, vol. 14, no. 3, pp. 28-41, Sept. 2019.
3
 A peak data rate of ≥1 Tb/s (100 times of 5G)
 A user-experienced data rate of 1 Gb/s (10x of 5G)
 THz frequency range: 0.1 – 10 THz
 Spectrum efficiency of 5–10 times of 5G
 High mobility
 Latency of 10–100 µs
 Ten times the connectivity density of 5G
 Energy efficiency of 10–100 times of 5G
 High throughput, network capacity
 Enhanced data security
 Ubiquitous connectivity
 AI integrated communication
IMT: International Mobile Telecommunications

Comparison of 6G with 4G and 5G Communication Systems
4
P. N. Srinivasu, M. F. Ijaz, J. Shafi, M. Woźniak and R. Sujatha, "6G Driven Fast Computational Networking Framework for Healthcare Applications," in IEEE Access, vol. 10, pp. 94235-94248, 2022

Design considerations and potential technologies 6G networks
L. U. Khan, I. Yaqoob, M. Imran, Z. Han and C. S. Hong, "6G Wireless Systems: A Vision, Architectural Elements, and Future Directions," in IEEE Access, vol. 8, pp. 147029-147044, 2020.
5

The electromagnetic spectrum
RJEs: Remote job entry points Ref
T. S. Rappaport, Y. Xing, O. Kanhere, S. Ju, A. Madanayake, S. Mandal, A. Alkhateeb, and G. C. Trichopoulos, “Wireless communications and
applications above 100 GHz: Opportunities and challenges for 6G and beyond,” IEEE Access, vol. 7, pp. 78 729–78 757, 2019.
7

Terahertz Communications: Challenges and Opportunities
 THz waves, submillimeter radiation, 0.1 to 10 THz, 0.03–3 mm range
 Lower THz band, 275 GHz–3 THz band is the immediate target for 6G
 The key properties of THz communications
(i) widely available bandwidth to support very high data rates
(ii) high path loss arising from the high frequency
 6G services are likely to run on ultra-high spectrum bands (0.1-10 THz), which travel shorter
distances compared with lower bands used in 4G and 5G
 Higher frequencies could be blocked by buildings and they lose intensity over longer distances. That
means, offering wider coverage would be a challenge. Needs more number of towers
 The narrow beam widths generated by the highly directional (pencil) antennas are required
 Spectral efficiency can be increased by increasing the bandwidth by using sub-THz communication
with wide bandwidths and by applying Supper Massive-MIMO
8
T. S. Rappaport et al., “Wireless communications and applications above 100 GHz: Opportunities and challenges for 6G and beyond,” IEEE Access, vol. 7, pp. 78 729–78 757, 2019.

Atmospheric absorption for Terahertz Communications
 Atmospheric absorption in addition to
the natural Friis free space loss of
electromagnetic waves at sea level
versus frequency under different
humidity conditions
 At lower frequencies (below 6 GHz),
the attenuation of a propagating wave
is mainly caused by molecular
absorption in free space, which is
minimal, but at higher frequencies, as
the wavelength approaches the size of
dust, rain, snow, or hail, the effects of
Mie scattering become more severe
1. T. S. Rappaport et al., “Wireless communications and applications above 100 GHz: Opportunities and challenges for 6G and beyond,” IEEE Access, vol. 7, pp. 78 729–
78 757, 2019.
2. Y. Xing and T. S. Rappaport, "Propagation Measurement System and Approach at 140 GHz-Moving to 6G and Above 100 GHz,“ 2018 IEEE Global Communications
Conference (GLOBECOM), Abu Dhabi, United Arab Emirates, 2018, pp. 1-6.
9

Rain attenuation for Terahertz Communications
RJEs: Remote job entry points Ref
 Rain attenuation flattens out from 100 GHz to 500 GHz,
meaning that rain will not cause any additional attenuation
at operating frequencies above 100 GHz
 The rain attenuation at 1 THz is 10 dB/km for moderate
rainfall of 25 mm/h, only 4 dB/km more than the rain
attenuation experienced at 28 GHz
 For all mmWave frequencies where the urban cell sizes
are in the order of 200 m, rain or snow attenuation can
clearly be overcome with additional antenna gain
(obtainable by switching in more antenna array elements
and adaptive beam steering)
 A higher transmitted power is required during a snowfall to
maintain the same data rate
10

Surface scattering for Terahertz Communications
 Surface scattering across the EM spectrum
 a) Most surfaces appear smooth at microwave frequencies (specular scattering)
 b) Same surfaces exhibit significant considerable roughness in optical spectrum
(diffuse scattering)
 c) in THz regime most building surfaces exhibit significant diffuse scattering and strong specular
reflections
11

Partition and Penetration losses
12

Partition and Penetration losses
13

Measured path losses
14

Large-scale Path Loss Model
 The close-in free space reference distance (CI) path loss model with a 1 m reference distance
 Extra attenuation term due to various atmospheric conditions
 where d is the 3-D T-R separation distance in meter, and n is the path loss exponent (n = 2 for free
space)
 χσ is the shadow fading (SF) modeled as a log-normal random variable with zero mean and standard
deviation in dB
 AT is a total atmospheric absorption (~1-3 dB, 200-300GHz)
 FSPL(f;1m) is the free space path loss in dB at a T-R separation distance of 1 m
 c is a speed of light
15

Human Blockage Model
 A human blockage event causes a temporal shadowing loss
 A typical blockage event can be divided into four stages, unshadow, decay, shadowed, rising
 Field human blockage measurements using three sets of antennas (7°, 15°, and 60° half power beam
width (HPBW)) that the transition rates in the Markov model and the mean attenuation of blockage
events depend on antenna HPBW
 More and deeper blockages will occur if a narrower antenna is equipped
 Simulated and measured CDF of user SNR values (in dB) with and without human blockage events at
73 GHz. 1000 simulations of directional channels were run in Monte Carlo fashion
16

Channel modeling and capacity analysis
 Channel modeling and capacity analysis for THz communication 0.1-10 THz
 Absorption and Rain attenuation
 Path loss
 Partition and Penetration losses
 Human Blockage
 Channel capacity of the THz needs to be investigated by using the novel path loss
model for different power allocation and Radio access schemes
 For the short propagation distance, the THz channel supports very large bit-rates (up
to few terabits per second), which enables a radically different communication
paradigm for nano-networks
 Diffraction effect at THz frequencies will not be as prominent as it is at microwave
frequencies. Scattering, instead, is expected to be more notable at the wavelength at
THz frequencies. 17

Architecture of 6G
18

Architecture of 6G
 Space–Air–Ground–Underwater Networks
• extremely broad coverage and ubiquitous connectivity
 Three-tier network descriptions
• Satellite network
• Air network
• Terrestrial and Underwater networks
19

6G Network architecture
20
 Three-tier network
descriptions
• Space network
• Airborne-network
• Terrestrial-network
M. Höyhtyä et al., "Sustainable Satellite Communications in the 6G
Era: A European View for Multilayer Systems and Space Safety,"
in IEEE Access, vol. 10, pp. 99973-100005, 2022

Proposed Architecture of 6G
21
F. Liu et al., "Integrated Sensing and Communications: Toward Dual-Functional Wireless Networks for 6G and Beyond," IEEE Journal on Selected Areas in Communications, vol. 40, no. 6, pp. 1728-1767, June 2022
 Vision of 6G:
 To fully support
the development
of a Ubiquitous
Intelligent Mobile
Society
 To meet the
mobile
communication
demands of 2030
and beyond

Potential Technologies
22

Potential Technologies
 Terahertz Communications – 6G Test Bed
 Artificial Intelligence and Big Data Analytics
 Novel Radio Access Technology for 6G: Hybrid-NOMA-OFDM
 Super Massive Multiple Input Multiple Output (SM-MIMO)
 Laser Communications and Visible Light Communication (VLC)
 Quantum Communications and Computing
 Orbital Angular Momentum Multiplexing (OAM)
 Energy and Spectrum Efficient Hardware and Resource Allocation
 Holographic Beamforming
 Reconfigurable Intelligent Surface (RIS)
 Dynamic Network Slicing
 Molecular Communications and the Internet of Nano-Things
 Semantic Communication
 Unmanned Aerial Vehicle
 Integration of Access-Backhaul Networks
23

Applications and Features of 6G
24

Applications and Features of 6G
25
L. U. Khan, I. Yaqoob, M. Imran, Z. Han and C. S. Hong, "6G Wireless Systems: A Vision, Architectural Elements, and Future Directions," in IEEE Access, vol. 8, pp. 147029-147044, 2020
 AI/ML/DL
 AR/VR (Virtual /Augmented Reality,
Metaverse)
 Internet of Nano Things (IoNT)
 Tactile Internet and Haptic Communication
 Deep Connectivity, Holographic
Connectivity and Ubiquitous Connectivity.
 Smart healthcare and Telemedicine
 Connected intelligence and autonomous
systems
 Cell-free communication
 Blockchain

Emerging Trends in 6G
26

Technology Trends and Challenges
27
• Challenges of 6G: system coverage and capacity, user data rate and movement speed, spectrum and energy
efficiency.
• 6G can be represented using “6” key aspects - three “new” resources and methods and three “ing”-related key
technologies
arXiv:2002.04929v1 [cs.IT] 12 Feb 2020 - https://doi.org/10.48550/arXiv.2002.04929

Expending Coverage: The Integration of Terrestrial and
Satellite Mobile Communication
28
• Solve global coverage and
higher speed user movement
problem:
• Adopt a satellite mobile
communication system - achieve
wider coverage at low cost.
• 6G: integrate land wireless
mobile communication,
MEO/LEO satellite mobile
communication, and short range
direct communication
technologies into one mobile
system
M. Höyhtyä et al., "Sustainable Satellite Communications in the 6G Era: A European View for Multilayer Systems and Space Safety," in IEEE Access, vol. 10, pp. 99973-100005, 2022

Expending Coverage: The Integration of Terrestrial and
Satellite Mobile Communication
29
• Challenges - design a new core
network architecture and mobility
management.
• Major challenges:
Doppler Shift and Doppler
Variation
Large Transmission Delay
Transmission Technology for
Inter-Satellite Link (ISL)
M. Höyhtyä et al., "Sustainable Satellite Communications in the 6G Era: A European View for Multilayer Systems and Space Safety," in IEEE Access, vol. 10, pp. 99973-100005, 2022

New Core Network: A 3D Architecture and
Intelligent Mobility Management
30
• Past - terrestrial mobile system and satellite mobile system are independent - both are 2D network
architectures.
• 6G: integrate terrestrial wireless mobile communication, MEO/LEO satellite mobile communication
and short distance direct communication technologies.
• 6G: integrate communication and computing, navigation, perception, and other new technologies.
• 6G: integrate systems - support global ubiquitous coverage of high-speed mobile communications
Challenges:
• Establish a new 3D core network architecture – AI network being composed of network nodes of
a terrestrial system and network nodes of a satellite system.
• Intelligent Mobility Management
• Efficient Network Resource Utilization
• Protocol Interoperability

New Resource and Utilization Method of Spectrum
31
• Terahertz and Visible Light Band: use of (still-unregulated) high-frequency band - overcome spectrum
scarcity and capacity limitations of current wireless communication systems.
• Two ultra-wide spectral bands - enable ultra-high-rate communications - promising technologies for 6G
and beyond!
 Terahertz (THz) band: 0.1 THz to 10 THz
 Visible light band: 400 THz to 800 THz
• THz communication - rely on advanced THz devices - transfer information at THz band.
• Two kinds of THz communication systems:
 Solid state THz communication system - based on a frequency mixing mechanism
 Spatial direct modulation THz communication system - modulates baseband signals directly into a
continuous THz carrier wave.

Principle of metamaterial lens antenna
32
J. Lee, H. Kim and J. Oh, "Large-Aperture Metamaterial Lens Antenna for Multi-Layer MIMO Transmission for 6G," in IEEE Access, vol. 10, pp. 20486-20495, 2022.

New Resource and Utilization Method of Spectrum
33
• Meta-surface lens as a phase shifting
structure is applied to the signal
radiated from an antenna array
• It can adjust a beam direction by
applying DC bias to its constituting
elements
• Metamaterial antenna acts as a
resonant antenna to radiate directive
beams by itself
• In contrast to meta-surface lens, it
does not require a separate antenna
array with phase shifters
J. Lee, H. Kim and J. Oh, "Large-Aperture Metamaterial Lens Antenna for Multi-Layer MIMO
Transmission for 6G," in IEEE Access, vol. 10, pp. 20486-20495, 2022.

Research Opportunities and Challenges
35
• High-Frequency Hardware Components
• THz Communication Channel Modelling and Estimation
• Directional Networking for THz Wireless Communication
• Bandwidth Enhancement
• MIMO Techniques: MIMO is tough to realize in VLC
• Intelligent Dynamic Spectrum Access
• Cognitive Radio (CR) and Intelligent Spectrum Sharing
CR - allow multiple wireless systems to share same spectrum by utilizing spectrum
sensing and interference management techniques
• Intelligent Symbiotic Radio (SR): new promising technique for 6G
SR - multiple subsystems of a heterogeneous wireless communication system
intelligently cooperate with each other to realize mutually beneficial transmission and
efficient resource sharing
SR - aims to enhance performance of communication system via intelligent inter-
subsystem cooperation.

Integration of Wireless Information and Energy Transfer (WIET)
 WIET is a promising technology for lengthening the lifetime of the battery-charging
wireless systems in 6G
 Uses the same fields and waves as wireless communication systems
 Sensors and smartphones will be charged by using wireless power transfer during
communication
 Devices without batteries will be supported in 6G communications
 Seamless integration of wireless information and energy transfer
 The 6G wireless networks will also transfer power to charge battery devices, such
as smartphones and sensors. Hence, wireless information and energy transfer
(WIET) will be integrated
36

Reconfigurable Intelligent Surfaces (RIS)
37
 An RIS is an artificial surface made of
electromagnetic (EM) materials that can
change the properties of the EM waves
 It is significantly different from other
traditional technologies such as
massive MIMO and amplify-and-forward
relay (AF-relay)
 Large intelligent surface (RIS) is a
promising energy-efficient and cost-
effective
B. -S. Shin, et al., "Limited Channel Feedback Scheme for Reconfigurable Intelligent Surface Assisted MU-MIMO Wireless Communication Systems," in IEEE Access, vol. 10, pp. 50288-50297, 2022

Super Massive Multiple Input Multiple Output (SM-MIMO)
 For 6G, SM-MIMO with more than 10,000 antenna elements is expected to be deployed, providing
the following benefits
38
 Super-high spectrum efficiency can be
achieved through spatial multiplexing, which
transmits hundreds of parallel data streams on
the same frequency channel
 Improve energy efficiency, network throughput
and reduce latency
 Combination of SM-MIMO and nonorthogonal
multiple-access techniques will be an enabler
for massive access communications to
support SM connectivity
T. Kebede et al., "Precoding and Beamforming Techniques in mmWave-Massive MIMO: Performance Assessment," in IEEE Access, vol. 10, pp. 16365-16387, 2022

RF and non-RF Link Integration
 Challenges
• Different physical nature of RF/non-RF interfaces
 Open Problems
• Design of joint RF/non-RF hardware
• System-level analysis of joint RF/non-RF systems
• Use of RF/Optical systems for various 6G services
39

New Resource for Modulation: OAM
40
• Foundation for current wireless
communications - based on traditional plane.
• Electromagnetic (PE) waves - not only have
linear momentum, but also have angular
momentum.
• Orbital Angular Momentum (OAM) - kind of
wave-front with helical phase - can be used as a
new resource for modulation in wireless
communications.
• Spacing OAM has a great number of topological charges – OAM modes.
• Different OAM-modes are orthogonal with each other - can be multiplexed/demultiplexed together - new way for
capacity enhancement in 6G and Beyond wireless communications networks.
• Critical problems need to be solved before its practical implementation.
 Beam-Divergence
 Transmission-Optical
 Transceiver-Misalignment: OAM is phase sensitive
S. K. Noor et al., "A Review of Orbital Angular Momentum Vortex Waves for the Next Generation Wireless Communications," in IEEE Access, vol. 10, pp. 89465-89484, 2022.

Multiplexing and Demultiplexing of OAM-modes.
41
• Specific design of the OAM multiplexing for wireless backhaul channels - LoS path is
dominant.
• Through OAM, high-order spatial multiplexing can be achieved in the environment
where it is impossible with conventional MIMO technologies, i.e., LoS channel.

Mode Domain Multiple Access (MDMA) scheme
42

Optical Wireless Communication
 Conventional wireless
communications based on
electromagnetic-wave signals cannot
provide high-speed data transmission
for these scenarios
 Laser communications have ultrahigh
bandwidth and can achieve high-
speed data transmission
 VLC employs ultrahigh bandwidth to
achieve high-speed data transmission
and is widely available, making it
suitable for such scenarios as an
indoor hotspots
43
 6G will integrate space/air networks and underwater networks with terrestrial networks to provide
superior coverage
M. Z. Chowdhury, M. K. Hasan, M. Shahjalal, M. T. Hossan and Y. M. Jang, "Optical Wireless Hybrid Networks:
Trends, Opportunities, Challenges, and Research Directions," in IEEE Communications Surveys & Tutorials, vol.
22, no. 2, pp. 930-966, Secondquarter 2020

Relay-Assisted Technology in Optical Wireless Communications
44
W. Liu, J. Ding, J. Zheng, X. Chen and C. -L. I, "Relay-Assisted Technology in Optical Wireless Communications: A Survey," in IEEE Access, vol. 8, pp. 194384-194409, 2020

Quantum Communication (QC)
 The fundamental difference between quantum
communication and classical binary based communication
is that the information is encoded in quantum state using
photons or quantum particles by superimposing both the
bits into a Q-bit and cannot be accessed or cloned without
tampering it due to quantum principles such as correlation
of entangled particles and inalienable law
 High security
 Furthermore, QC can improve data rates due to the
superposition nature of qubits
 Branches of QC:
 Quantum key distribution
 Quantum teleportation
 Quantum secret sharing and quantum secure direct communication
 Huge potential in long-distance communication using Quantum repeaters 45
1
0
Traditional bits
1
0
|1 +|0
2
Qubit
|1
|0

Role of Machine Learning in 6G
46

AI Inspired Intelligent Resource Management
in Future Wireless Network
S. Fu, F. Yang, and Y. Xiao, “AI inspired intelligent resource management in future wireless network,” IEEE Access, vol. 8, pp. 22 425–22 433, 2020.
47

Types of Machine Learning
48
O. Elijah et al., "Intelligent Massive MIMO Systems for Beyond 5G Networks: An Overview and Future Trends," in IEEE Access, vol. 10, pp. 102532-102563, 2022

AI for Resource Allocation Problem
 Supervised Learning & Unsupervised Learning:
 The high-quality training dataset in wireless network environment is difficult to obtain,
which constraints the application of supervised learning, and unsupervised learning in
the wireless network
 Deep Learning (DL):
 DL is usually used for classification or regression. However, the performance of deep
learning is highly related to model training tricks and experiences
 Reinforcement Learning (RL):
 RL algorithms is suitable for making decisions on resource allocation. However, some
tabular based RL algorithms such as Q-learning, stores experiences in a Q-table.
However, the size of Q-table grows exponentially with the levels of state space, and the
convergence rate to the optimal policy is relatively low
 Deep Reinforcement Learning (DRL):
 DRL combines the advantages of deep learning and reinforcement learning.
S. Fu, F. Yang, and Y. Xiao, “AI inspired intelligent resource management in future wireless network,” IEEE Access, vol. 8, pp. 22 425–22 433, 2020.
49

Distributed / Transferred Learning
Ref
Traditional Learning Transferred Learning
50

Transferred Learning (TL) in 6G wireless communications
51
M. Wang, Y. Lin, Q. Tian and G. Si, "Transfer Learning Promotes 6G Wireless Communications: Recent Advances and Future Challenges," in IEEE Transactions on Reliability, vol. 70, no. 2, pp. 790-807,

Distributed / Transferred Learning
 Training needs high computation capability
 Distributed machine learning reduces the computational complexity by means of data parallelism, so that
different machines have multiple copies of the same model, each machine is assigned to different data,
and then the results of all the machines are merged
 Transferred learning can model the target domain better using knowledge from the source domain, when
the data distribution, feature dimension and model output change. At the same time, transferred learning
can also make a huge difference when we need to apply the well-trained model to different network
environments
 The future network will be a large decentralized system, where intelligent decisions are made at different
granular levels. To accelerate the learning through parallel training process that requires splitting the data
and model in an appropriate manner
 Instead of training a deep network form the scratch for a task
 Take a network trained on a different domain for a different source task
 Adapt it for the main domain and target task
52

Edge AI empowered 6G networks
53
K. B. Letaief, Y. Shi, J. Lu and J. Lu, "Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications," in IEEE Journal on Selected Areas in Communications, vol. 40, no. 1, pp. 5-36, Jan. 2022

Edge learning models and architectures
54

Edge learning modes in dynamic and adversarial environments
55

Enabling wireless techniques for edge training
56

Deep Q-Network (DQN) Based Joint Resource Allocation
 In Q-learning, there is an interaction between environment
and agent during the process
 First, the environment state is sensed by agents. Then,
the agent decides to choose an action based on a proper
policy and reward function under the environment state
 Action-state value function is an evaluation of the policy,
and usually a policy with higher state-action value is a
better policy
 Then, we can adjust the parameters so that the action
with high action-state value is more likely to be selected
 The learning process of Q-learning is the process of
continuous iteration of the value function
Ref
S. Fu, F. Yang, and Y. Xiao, “AI inspired intelligent resource management in future
wireless network,” IEEE Access, vol. 8, pp. 22 425–22 433, 2020. 57

Network slice functionality in B5G/6G network
58
M. Gupta, R. K. Jha and S. Jain, "Tactile based Intelligence Touch Technology in IoT configured WCN in B5G/6G-A Survey," in IEEE Access, doi: 10.1109/ACCESS.2022.3148473.

Machine Learning based Resource Allocation
 Resource Allocation:
 Channels, power level, computing ability, and time slots, are unevenly distributed in networks, and
the user requirements are different. How to allocate suitable resources in a wireless network?
 Greedy algorithm, auction theory, and game theory are widely proposed to deal with the resource
allocation problem
 high-mobility nodes, heterogeneous structure, and high QoS requirements
 The radio resources in power, spectrum, and time domain should be jointly considered to fit the
mutative scenario
 Rapid Response:
 Accurate Environment Modelling: For various kinds of criteria and requirements in the
heterogeneous vehicular network, creating an accurate mathematical model to measure all
resources in the complex environment is challenging
 Self-Adaptivity: The environment is spatial–temporal changing, the resource allocation scheme
should be evolved with proactive exploration and self-adaptive ability
59

Research opportunities in 6G
60

6G wireless systems: Future directions
61
L. U. Khan, I. Yaqoob, M. Imran, Z. Han and C. S. Hong, "6G Wireless Systems: A Vision, Architectural Elements, and Future Directions," in IEEE Access, vol. 8, pp. 147029-147044, 2020
• Terahertz Communication Modeling
and Resource Allocation
• Radio Multiplexing Technologies
• Efficient Spectrum Usage Techniques
• Energy Saving Mechanisms
• Artificial Intelligence and Blockchain
• Routing schemes
• Intelligent Network slicing (NasS)
• CapEx/OpEx Reduction Techniques
• Application Specific Improvements

Thank you
62

Stanford CS229: Machine Learning Lecture Series
• Lecture 1 - Welcome | Stanford CS229: Machine Learning
• Lecture 2 - Linear Regression and Gradient Descent
• Lecture 3 - Locally Weighted & Logistic Regression
• Lecture 4 - Perceptron & Generalized Linear Model
• Lecture 5 - GDA & Naive Bayes
• Lecture 6 - Support Vector Machines
• Lecture 7 – Kernels
• Lecture 8 - Data Splits, Models & Cross-Validation
• Lecture 9 - Approx/Estimation Error & ERM
• Lecture 10 - Decision Trees and Ensemble Methods
• Lecture 11 - Introduction to Neural Networks
• Lecture 12 - Backprop & Improving Neural Networks 63
• Lecture 13 - Debugging ML Models and Error Analysis
• Lecture 14 - Expectation-Maximization Algorithms
• Lecture 15 - EM Algorithm & Factor Analysis
• Lecture 16 - Independent Component Analysis & RL
• Lecture 17 - MDPs & Value/Policy Iteration
• Lecture 18 - Continuous State MDP & Model Simulation
• Lecture 19 - Reward Model & Linear Dynamical System
• Lecture 20 - RL Debugging and Diagnostics

Stanford CS230: Deep Learning Lecture Series
• Lecture 1 - Class Introduction and Logistics
• Lecture 2 - Deep Learning Intuition
• Lecture 3 - Full-Cycle Deep Learning Projects
• Lecture 4 - Adversarial Attacks / GANs
• Lecture 5 - AI + Healthcare
• Lecture 6 - Deep Learning Project Strategy
• Lecture 7 - Interpretability of Neural Network
• Lecture 8 - Career Advice / Reading Research Papers
• Lecture 9 - Deep Reinforcement Learning
• Lecture 10 - Chatbots / Closing Remarks
64

Stanford online
65
https://www.youtube.com/user/stanfordonline/videos?view=0&sort=p&flow=grid

Machine Learning in Warless Communication
66
https://github.com/IIT-Lab/Paper-with-Code-of-Wireless-communication-Based-on-DL
• Research articles with python codes
• Git Hub – IIT Lab

Architecture of the heterogeneous vehicular
F. Tang et al., “Future intelligent and secure vehicular network toward 6G: Machine-learning approaches,” Proceedings of the IEEE, vol. 108, no. 2, pp. 292–307, 2020.
68

Machine Learning for Vehicular network in 6G
F. Tang et al., “Future intelligent and secure vehicular network toward 6G: Machine-learning approaches,” Proceedings of the IEEE, vol. 108, no. 2, pp. 292–307, 2020.
69

Dynamic radio resource allocation
F. Tang et al., “Future intelligent and secure vehicular network toward 6G: Machine-learning approaches,” Proceedings of the IEEE, vol.
108, no. 2, pp. 292–307, Feb. 2020.
70

Machine Learning based Radio Access Technology for 6G
 Rapid, precise, and advanced channel division and multiple-radio access are essential for the high
mobility users
 time, power, and frequency-based channel division technology, such as TDD, OFDM, and
NOMA, H-NOMA-OFDM
 SM-MIMO
 The mentioned multiradio access technologies provide intelligent multiple access to a future 6G
network and are mainly based on the channel state information (CSI), which can be predicted with
probed feedback from environment
 Channel estimation and signal detection are widely used to improve the spectrum utilization,
signal detection accuracy
 However, because of the high mobility of vehicles, the high Doppler spread causes extremely fast
channel fading and hinders the accuracy of channel estimation
 The simple CSI estimation based only on linear models is not fulfilled in the high dynamic
environment. Conventional multiradio access methods cannot satisfy the ultralow-delay and high
throughput requirement of future 6G communication
 How to intelligently schedule the multiradio access for dynamic 6G communication is an immediate
challenge
71

Machine Learning based Radio Access Technology for 6G
 Radio Configuration Challenge:
 Intelligently allocate resources with low latency should be an emerging topic in the future
6G vehicular network
 Beamforming Challenge:
 Both the beamforming and massive MIMO technologies for specific high-dynamic vehicular
communications are desired
 The mutative vehicular location and high overhead of frequent beam training are the critical
drawback to develop the mmWave in high-mobility vehicular communication systems
 The conventional beamforming methods provide high overload for the mmWave in the
dynamic vehicular communication and are not intelligent to adapt to frequently changing
vehicular scenario. In order to make the mmWave and the corresponding beamforming
strategy intelligent, the novel intelligent ML technology might be a potential solution
72

Machine Learning based Network Traffic Control
 Machine Learning Oriented Routing Protocols
 Network routing, congestion avoidance, and traffic offloading, has been widely
researched in conventional wireless networks
 Dynamics of network topology, the traffic control strategy becomes more
complex
 Routing protocols, such as open shortest path first (OSPF)
 Intermediate system-to-intermediate system (IS-IS)
 Routing information protocol (RIP) are not sufficient
 Destination-sequenced distance vector routing (DSDV)
 Stability-based adaptive routing (SSA)
 Dynamic source routing (DSR)
 Proactive source routing (PSR)
 Optimized link state routing protocol (OLSR)
73

Cell Free Cellular Communications
74

Macro, Pico, and Femto Cells for Cellular Communications
RJEs: Remote job entry points https://www.qorvo.com/design-hub/blog/small-cell-networks-and-the-evolution-of-5g
75

Cell Free Cellular Communications in 6G
https://www.youtube.com/watch?v=11nOVG3N6ug
 Resource allocation in Cell Free environment
 Role of machine learning and artificial intelligence for the optimized resource allocation
M. Z. Chowdhury et al., “6G Wireless Communication Systems: Applications, Requirements, Technologies, Challenges, and Research Directions”, arXiv:1909.11315, Sep. 2019.
76

Cell-free communications
M. Z. Chowdhury et al., “6G Wireless Communication Systems: Applications, Requirements, Technologies, Challenges, and Research Directions”, arXiv:1909.11315, Sep. 2019.
 The tight integration of multiple frequencies and heterogeneous communication technologies will
be crucial in 6G systems
 The user will move seamlessly from one network to another network without the need for
making any manual configurations in the device
 The best network will be automatically selected from the available communication technology.
This will break the limits of the concept of cells in wireless communications
 Currently, the user movement from one cell to another cell causes too many handovers in dense
networks, and also causes handover failures, handover delays, data losses, and the ping-pong
effect
 The 6G cell-free communications will overcome all these and provide better QoS. Cell-free
communication will be achieved through multi-connectivity and muti-tier hybrid techniques and
by different and heterogeneous radios in the devices
77

Applications of artificial intelligence at different layers
of wireless systems
78
I. F. Akyildiz, A. Kak and S. Nie, "6G and Beyond: The Future of Wireless Communications Systems," in IEEE Access, vol. 8, pp. 133995-134030, 2020.

capacity and data rate as well as 6D (3 spatial, 3
rotational dimensions) high-resolution
localization and sensing explored in the Hexa-X
project.
79
6G Vision, Value, Use Cases and Technologies

General architecture of existing wireless and IoT based
scenario.
80
Tactile based Intelligence Touch Technology in IoT configured WCN in B5G/6G-A Survey

Mil-Drone: A BC and FL based UAV enabled scheme for
region surveillance and region demarcation for IoMT
operations.
81

important aspects of 6G-V2X, namely, communication,
computing, and
security.
82

Multi-layer network architecture for 6G.
83

Summary of various ML techniques
84

ISAC technology for future wireless networks.
85

Generic system model for the considered amplifying
RIS-assisted scheme.
86

Generic system model for a passive RIS-assisted
scheme.
87

Dr Rahul Pandya 6G Vision, Potential technologies, and Challenges - Animated 17.11.2022.pptx

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