Artificial Intelligence (AI) & Fifth Generation (5G) Networks

Choong Seon Hong

Department of Computer Science and EngineeringKyung Hee University

• Introduction• Background

• 5G deliverables

• AI deliverables

• Motivation of AI in 5G

• Artificial Intelligence (AI) • Introduction

• Evolution of AI

• Machine Learning

• Artificial Neural Networks

• Deep Learning

• Deep Learning implementation using open source: Use-Cases

• Applications of AI in 5G• Network Slicing Enablers

• Wireless Network Virtualization

• Evolution of Cellular Networks

• Network slicing Deliverables

• Network Slicing Industrial Efforts

• AI and Network Slicing for 5G Networks: Use-Cases

• Conclusions

Outline 2

Introduction• Background• 5G deliverables• AI deliverables• Motivation of AI in 5G

3

Background

• Traffic growth due to:• Tsunami of heterogeneous connections:

• Smartphones

• Connected vehicles

• Wearables devices

• IoT sensors

• And so on…

• Novel bandwidth hungry applications:

• Real time HD streaming

• Online Gaming

• Ultra-reliable and low-latency

communication

• Virtual reality services

• Enhanced mobile broadband

• And so on…

Source: Cisco Visual Networking Index (VNI), Feb. 2017.

4

Bottleneck and New Paradigms in 5G

• Network capacity is a

bottleneck due to:• Radio Access Networks (RANs)

• Mostly wireless and highly dynamic

• New paradigms to support:• Small cell (SC) deployment

• Device-to-device(D2D)

• Network virtualization

• LTE-unlicenced

• And so on…

.

Source: http://www.eurescom.eu/news-and- events/eurescommessage/eurescom-message-1-2014/3gpp-system-standards-heading-into-the-5g-era.html.

5

Introduction: 5G?

Broadband Back bone

6

A. Agarwal, G. Misra and K. Agarwal, "The 5th Generation Mobile Wireless Networks- Key Concepts, Network Architecture and Challenges", American Journal of Electrical and Electronic Engineering, Vol.3, No.2, pp.22-28, 2015.

5G Deliverables

5G Deliverables: • Higher data rates

• Reduced end-to-end latency

• Higher energy efficiency

• Better network coverage

• Enhanced security

• Ultra reliability

• and so on…

Source: “5G Use Cases and Requirements,” a white paper from Nokia.

7

• KT, SK-Telecom, LGU+, Korea:

• Successfully collaborated with Samsung Electronics and Huawei to commertialize a 5G end-to-end network that includes:

• 5G virtualized core

• Virtualized RAN

• Distributed Unit

(baseband unit and radio unit)

• Devices - based on the 3GPP

5G New Radio (5G NR)

5G Networks: Industrial efforts 8

• Is it 20G? (LTE 1G)

Current Commencialized Speed 9

• Nokia, Finland:

• Actively focusing for providing 5G services such as:

• 5G mobility service supporting enhanced mobile broadband (eMBB)

• 5G mobility service supporting ultra-reliable and ultra-low latency communications (URLLC)

• Huawei, China:

• Huawei have actively worked to enhance the antenna capabilities for 5G networks

• It has released its new FDD antenna and FDD/TDD converged antenna platforms

• And it developed Switching systems and Base Station

5G Networks: Industrial efforts 10

• Ericsson, Sweden:

• Ericsson’s is actively participating in the development of 5G networks

• It has recently developed the Ericsson’s 5G radio test-bed that comprises of Massive MIMO, multi-user MIMO, and beamforming technologies

• Ericsson has introduced a new radio product, AIR 3246, for Massive Multiple Input Multiple Output (Massive MIMO).

• This launch will enable operators – especially in metropolitan areas – to bring 5G to subscribers using today’s mid-band spectrum and boost capacity in their LTE networks.

5G Networks: Industrial efforts 11

• Air Latency 1ms

• Transmission Latency << 1ms

• Core Latency 1ms

• App./Server E2E Latency 10ms

Low Latency in 5G 12

• Total E2E Latency 13ms

• How low latency is needed?

Massive Connectivity 13

Multi-RAT Aggregation 14

• Spectrum : 28GHZ

5G of Verizon 15

Introduction: Artificial Intelligence

• Artificial Intelligence (AI) is the science and engineering of making

machines as intelligent as humans.

• AI Deliverables:• Create systems that can perform:

• Perception.

• Reasoning.

• Learning.

• Problem Solving.

16

• To unleash the true potential of 5G networks:

• Intelligent functions using AI across both the edge and core of the network are required along with the novel enabling technologies.

• AI functions must be able to:

• Adaptively exploit the wireless system resources.

• Generated data to optimize network operation.

• Guarantee the QoS in real time.

• Mobile edge and core intelligence can only be realized by integrating fundamental notions of artificial intelligence (AI) across the wireless infrastructure and end-user devices.

Introduction: Motivation of AI in 5G 17

• Role of AI in 5G networks: Exploit big data analytics to enhance situational awareness and overall network operation such as:

• Fault Monitoring

• User Tracking

• Cell Association

• Radio Resource Management

• Cache Resource Management

• Mobility Management

• Management and Orchestration

• Service Provisioning Management

• And so on..

Introduction: Motivation of AI in 5G 18

Artificial Intelligence (AI) • Introduction• Evolution of AI• Machine Learning • Artificial Neural Networks• Deep Learning• Deep Learning implementation using open source: Use-Cases

19

Artificial Intelligence, Machine Learning and Deep Learning 20

http://www.deeplearningbook.org/contents/intro.html

Machine Learning

AI

Deep Learning

AI – Any technique which enables computers to mimic human behavior.

ML – Subset of AI techniques which use statistical methods to enable machines to improve with experiences.

DL – Subset of ML which make the computation of multi-layer neural networks feasible.

• In the early days of AI, researchers were very interested in machines that could learn from data.

• But ML’s increased focus on a logical, knowledge-based approach occasioned a split from AI in 1980.

• Statistical-based research, probabilistic reasoning, pattern recognition and information retrieval came into the fold of ML; by 1990s, ML became a separate field altogether, and began to flourish by shifting its goal from achieving AI to tackling practical problems.

• Machine Learning grew out a branch of artificial intelligence that studies pattern recognition and computational learning. It is a subfield of computer science.

History 21

The Evolution of Artificial Intelligence (AI) 22

1950Alan Turing

Proposes the

Turing Test

1950Isaac Asimov

proposes the Three

Laws of Robotics

1951First AI based

Program was

written

1955First self learning

game playing

program is written

1959MIT AI Lab

is setup

1961First Robot is inducted

into GM’s assembly

production line

1963First Machine

Learning program

is written

1964First demonstration of

an AI program which

understand Natural

Language

1965First AI based

Chat-bot

(ELLZA) was

created

1969Stanford Research

Institute (SRI)

demonstrates the first

locomotive and intelligent

robot (Shakey)

1969First autonomous

vehicle is created at the

Stanford AI LAB

1974First rule based AI

expert system for

medical diagnostics

1980LISP based machines

are developed and

marketed

1986Learning

representations by

back-propagating error

(Backpropagation)

1997IBMs Deep Blue beats

Gary Kasparov at

Chess

1999Sony introduces the

first artificially

intelligent domestic

robot, AIBO

1999First Emotional AI

machines

demonstrated at

MIT AI Lab

2004DAPRA introduces

the first challenge

for Autonomous

Vehicles

2005AI based

recommendation

engines

2009Google builds

Self Driving Car

2010Narrative Science’s

AI demonstrates

ability to write

reports

2011IBM Watson beats

Jeopardy

champions

2011Personal

Assistants like Siri,

Google Now and

Cortana become

mainstream

2015Elon Musk and others

announce a $1B non

profit open source

initiative, OPEN AI to

develop friendly AI

2016Google’s

Deepmind AlphaGo

defeats Go’s

champions

2016NVIDIA announces

supercomputer for

Deep Learning and

AI

Modified from Source: https://twitter.com/mikequindazzi/status/835589969909424130

2017AlphaGo Zero which

learns from scratch

2018OpenAI 5

(Dota 2)

AI Revenue, Top 10 Use Cases, World Markets, 2016-2025 23

Source :https://twitter.com/MikeQuindazzi

Reasoning/Prediction: It is the set of processes that enables us to provide basis for making decisions, and prediction.

Problem Solving: It is the process in which one perceives and tries to arrive at a desired solution from a current situation.

Perception: The process of acquiring, interpreting, selecting, and organizing sensory information.

Learning: It is the process of knowledge acquisition by experiencing.

Overview of AI architecture 24

Artificial Intelligence

Reasoning

Problem Solving

Perception

Learning

• Perception: Perception presumes sensing. In humans, perception is aided

by sensory organs.

• In the domain of AI, perception mechanism puts the data acquired by the artificial sensors together to analyze the environment into objects and their features and relationships.

• Example

• Artificial perception is sufficiently well advanced to enable optical sensors to identify individuals such as autonomous vehicles to drive at moderate speeds on the open road, and robots to roam through buildings collecting trash.

Components of Artificial Intelligence: Perception 25

• Learning: It is the activity of gaining knowledge or skill by studying, practicing, being taught, or experiencing something.

• The simplest learning is by trial-and-error method.

• Learning is categorized as

• Supervised learning

• Unsupervised learning

• Reinforcement learning

• Example

• A simple program for solving mate-in-one chess problems might try out moves at random until one is found that achieves mate.

Components of Artificial Intelligence: Learning 26

• Reasoning/Prediction: It is the set of processes that enables us to provide basis for making decisions, and prediction.

• To reason is to draw inferences appropriate to the situation.

• Deductive Reasoning

• Inductive Reasoning

• Case-based reasoning

• Rule-based reasoning

• Example:

• Previous accidents of these types were caused by instrument failure; therefore, we can reason and predict that this accident was also caused by instrument failure.

Components of Artificial Intelligence: Reasoning 27

• Problem Solving: It is the process in which one perceives and tries to arrive at a desired solution from a current situation.

• Problem solving also includes decision making, which is the process of selecting the best suitable alternative out of multiple alternatives to reach the desired goal are available.

• Some problem solving AI techniques are:

• Genetic algorithms

• Fuzzy models

• Swarm intelligence

• Example:

• Some examples are finding the winning move (or sequence of moves) in a board game, devising mathematical proofs, and manipulating “virtual objects” in a computer-generated world.

Components of Artificial Intelligence: Problem Solving 28

• Machine learning (ML) deals with systems and algorithms that can learn from various data and make predictions.

• Example: Predicting traffic patterns at a busy intersection

• A program can run a machine learning algorithm containing past data with traffic patterns and, having “learned” previous data, it can devise better predictions of future traffic patterns

Machine Learning (ML) 29

https://futurism.com/images/understanding-machine-learning-infographic/

• The importance of ML is that, since it’s data-driven, it can be trained to create valuable predictive models that can guide proper decision and smart actions

• With today’s processing, and cheaper data storage, it’s possible to build models that can analyze large, complex data to deliver faster and more precise results

Applications 30

https://futurism.com/images/understanding-machine-learning-infographic/

Machine Learning Taxonomy 31

Machine Learning Types

No target variableCategorical

target variable

Continuoustarget variable

Categorical target variable

No target variable

LogisticLinear

Regression

Housing price prediction

SVM kNN

Classification

Medical Imaging

K-means Hierarchical

Clustering

Customer Segmentation

AprioriDBSCAN

Association

Market Basket Analysis

Hybrid RL+SVM RL+NN

Classification

Optimized Marketing

Deep Q-learning Actor Critic learning

Control

Self-Driving Cars

K-means, HMM, CRF, MEMM, GMM

Unsupervised learning

MDP, Markov approximation, Q-learning

Reinforcement learning

SVM, kNN, Naïve Bayes, Random Forest

Supervised learning

• Supervised ML – relies on data where the true label is indicated. Example: teaching a computer to distinguish between pictures of cats and dogs, with each image tagged “cat” or “dog”. Labeling is normally performed by humans to guarantee high data quality. Having learned the difference, the ML algorithm can now classify new data and predict labels (“cat” or “dog”) on previously unseen images.

Supervised ML 32

• Supervised learning:

• Supervised learning algorithms are trained using labeled data.

• When dealing with labeled data, both the input data and its desired output data are known to the system.

• Supervised learning is commonly used in applications that have enough historical data.

• Applications:

• Classification

• Regression

Machine Learning: Supervised learning

Machine Learning

Algorithm

Predictive Model

features vectorNew

Text, Document,

Image, Sound

Training Text,

Documents, Images,

Sounds…

features vector

33

Expected Label

Labels

Machine Learning Model (Supervised) 34

Training

Model

Output

Evaluation

Features

Extraction

Weight update

Raw

DATA

Labels

New

DATA

ModelFeatures

ExtractionOutput

Predicted

Outputs

Save Train Model

Training phase

Testing phase

• Model Selection(in Training Model) - Convolutional Neural Network, Recurrent Neural

Network, etc..

• Performance Metrics – Accuracy

• Feature Extraction :Scaling (normalized

inputs) (Dimensionality Reduction

• Feature Selection : Selecting important

features

• Algorithms: • Decision tree

• Random forest

• Neural networks

• Support vector machines

• Ensemble learning

• Bayesian learning

• And so on..

• Examples:

• Speech recognition used in smart devices is based on supervised learning techniques.

Machine Learning: Supervised learning 35

• Supervised Learning is being used to detect spam emails, i.e., Naive Bayes spam filtering. Particular words have specific probabilities of occurring in spam email and in legitimate email, e.g., “refinance”, “Viagra”

• Probabilities are not known in advance. A filter is trained by users manually indicating if email is spam or not through which the filter adjusts the probabilities of each word and save in its database

• After training, the word probabilities are used to compute the probability that an email with a particular set of words belongs to either spam or not spam category

Example: Supervised learning for spam filtering 36

!!!!$$$!!!!

Spam filtering

Input Output

Spam

or

Not Spam

• Unsupervised ML- deprives a learning algorithm of the labels used in supervised learning. Usually involves providing the ML algorithm with a large amount of data on every aspect of an object. Example: presented with images of cats and dogs that have not been labeled, unsupervised ML can separate the images into two groups based on some inherent characteristics of the images.

Unsupervised ML 37

Machine Learning: Unsupervised learning

• Unsupervised learning:

• Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data.

• No labels are given to the learning algorithm, leaving it on its own to find structure in its input.

• Unsupervised learning can be a goal in itself to discover hidden patterns in data.

• Applications:

• Clustering

• Associations

• Anomaly detection

38

Machine Learning: Unsupervised learning

• Algorithms: • Kmeans clustering• Hierarchical clustering• DBScans• Apriori Associations• Principal component analysis• Independent component analysis • Non-negative matrix factorization• And so on..

• Examples:• Market segmentation uses clustering to identify subgroups of people who might be

more receptive to a specific form of advertising, or more likely to purchase a particular product.

• In medicine, clustering diseases, cures for diseases, or symptoms of diseases can lead to very useful taxonomies.

39

Machine Learning: Reinforcement learning

• Reinforcement learning:

• Reinforcement learning is a type of learning in which an agent learns its best action through trial-and-error by interactions with a dynamic environment.

• Reinforcement Learning is learning how to act in order to maximize a numerical reward.

• Close to human learning.

• Every action has some impact in the environment, and the environment provides rewards that guides the learning algorithm.

• Applications:

• Delivery Management

• Supply chain inventory management

• Stock market trading

40

• Reinforcement Learning – Example: learning to play chess. ML receives information about whether a game played was won or lost. The program does not have every move in the game tagged as successful or not, but only knows the result of the whole game. The ML algorithm can then play a number of games, each time giving importance to those moves that result in a winning combination.

Reinforcement Learning 41

Machine Learning: Reinforcement learning

• Algorithms: • Q-Learning

• Double Q-Learning

• Actor critic learning

• State–action–reward–state–action (SARSA)

• Expected SARSA

• Temporal-Difference Learning

• And so on..

• Examples:• A robot uses deep reinforcement learning to pick a device from one box and

putting it in a container. Whether it succeeds or fails, it memorizes the object and gains knowledge and train’s itself to do this job with great speed and precision.

42

• Artificial Neural Networks – a learning algorithm, inspired by biological neurons, that uses statistical data modeling tools to find patterns in data.

• ANNs are capable of applying the human intelligence to learn complex patterns and to model relationships that are too difficult to learn by traditional techniques

• The key technique of ANNs is multiple processing elements called neurons working in parallel to solve a specific problem

Artificial Neural Networks (ANN)

x1

x2

x3

xn

Activation Function

Output

.

.

.

43

• The perceptron is a mathematical model of a biological neuron.

Perceptron 45

https://cs.stanford.edu/people/eroberts/courses/soco/projects/neural-networks/Neuron/index.html

Output

w1

w2

w3

x1

x2

x3

Activation Function Equation Example Graph

Unit Step

𝜙 𝑧 = ቐ0, 𝑧 < 00.5, 𝑧 = 01, 𝑧 > 0

Perceptron

Linear 𝜙 𝑧 = zAdaline(adaptive linear neuron), Linear regression

Sigmoid𝜙 𝑧 =

1

1 + 𝑒−𝑧Perceptron,Logistic Regression,Multi layer Neural Network

Rectified Linear Unit (ReLU) 𝜙 𝑧 = max(0, z) Perceptron,Multi layer Neural Network

Examples of Activation functions in Perceptron 46

Perceptron and ANN 47

The main components of a ANN are as follows • Input Layer• Output Layer• Hidden Layer• Input Weight Matrix• Output Weight Matrix

A neuron or perceptron A neural network

mainly consists of Input/output and an activation function

consists of connecting these neurons through multiple weights .

Main types of Artificial Neural Networks are

• Feed Forward Neural Networks:

Connections between neurons come only from the previous layer with no feedback involved.

• Recurrent Neural Networks:

Connection between neurons forms a directed cycle so as to use the internal memory of each unit for information processing.

Artificial Neural Networks (ANN): Classifications 48

Input layer (x) Hidden layer (h)Output layer (y)

Feed-forward networks have the following characteristics:

1. Perceptrons are arranged in layers, with the first layer taking in inputs and the last layer producing outputs. The middle layers have no connection with the external world, and hence are called hidden layers.

2. Each perceptron in one layer is connected to every perceptron on the next layer. There is no connection among perceptrons in the same layer.

3. Drawback of FNN is that it is not capable of handling the order of input samples, i.e., sequence of time.

Feed Forward Neural Network (FNN)

Input HiddenOutput

49

Recurrent Neural Networks (RNNs) 50

RNNs have the following characteristics:

1. RNNs, are designed for modeling sequences and are capable of remembering past information and processing new events accordingly.

2. In RNNs, the hidden layer gets its input from both the input layer and the hidden layer from the previous time step.

3. RNNs have multiple categories:

• One to many model

• Many to one model

• Many to many model

• Learning requires adjustment of weights between neurons which is called training

• The objective is to minimize the error (𝐸) between desired and actual output which is:

𝐸 𝑾, 𝒃 =1

2(||𝒚 𝑾, 𝒃, 𝒙 − 𝒚𝐷||)

• Gradient descent is commonly used to minimize 𝐸 𝑾,𝒃

Training in ANNs 51

𝑾 is weight matrix

𝒚 𝑾, 𝒃, 𝒙 is desired output

𝒚𝐷 is the actual output

𝒙 is input vector𝒃 is bias vector

𝜸 is learning rate

represents the partial derivative

Shallow and Deep ANNs 52

𝑥1

𝑥2

𝑥3

ℎ1

ℎ2

ℎ3

ℎ4

ො𝑦1

ො𝑦2

Input LayerHidden Layer

Output Layer

Shallow Artificial Neural Network

𝑤11

𝑤14

𝑤21

𝑤42

𝑥1

𝑥1

𝑥1

ℎ11

ℎ12

ℎ13

ℎ14

ℎ21

ℎ22

ො𝑦1

ො𝑦2

ො𝑦3

Input Layer Hidden Layers Output Layer

Deep Artificial Neural Network

𝑤1,11

𝑤1,14

𝑤11,21

𝑤14,22

𝑤21,1

𝑤22,3

• ANNs with one hidden layer are typically called shallow neural networks

• Deep Neural Networks (DNNs) have many hidden layers for learning multiple levels of representation and abstraction

• Initially DNN could not be used to model the problems due to high number of parameters and its computation in real-time.

• Deep learning can now be realized because of the followings:

• Improved computing capacity

• Improved datasets, i.e., Big Data

• Improved training algorithms and network architectures

Motivation for Deep Neural Network 53

Milestones in the Development of Deep Neural Networks 54

https://beamandrew.github.io/deeplearning/2017/02/23/deep_learning_101_part1.htmlhttps://deepmind.com/research/alphago/

Geoffrey Hinton

Deep Learning Taxonomy 55

Modified from source: Fadlullah, Zubair, et al. "State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems." IEEE Communications Surveys & Tutorials (2017).

Deep Learning

Reinforcement learning

Unsupervised Learning

Supervised learning

• Deep Q-learning• Double Q-learning• Prioritized experience replay

Reinforcement Learning

Advertising and business intelligence (Google ads, etc.), Weather forecasting, Market forecasting, Political campaigns

Real-time decisions, Game Artificial Intelligence, Learning tasks, Skill acquisition, Personal assistants (Google Now, Microsoft Cortana, Apple Siri, etc.), Autonomous (“Self-driving”) cars

Big data visualization, Feature elicitation, Structure discovery, Meaningful compression

Recommendation engines (Amazon web service, Netflix, etc.), Customer segmentation, Target marketing, Filter

Economics (risk prediction, etc.)

• Neural Network (NN)• Convolutional Neural Network (CNN)• Deep Belief Networks (DBN)• Recurrent Neural Network (RNN)

Classification

• Neural Network (NN)

Regression

• Stacked Auto-Encoders (SAE)• Auto-Associative Neural Network

Dimensionality Reduction

• Convolutional Neural Network (CNN)• Deep Belief Networks (DBN)

• Deep Boltzmann Machine (DBM)

Clustering

Density Estimation

Applications

Image classification, Character recognition, Facial recognition, Surveillance systems

• The “Hey Siri” uses a Deep Neural Network (DNN) to convert the acoustic pattern of your voice into a probability distribution over speech sounds.

• It uses a temporal integration process to compute a confidence score (alpha values) that you uttered “Hey Siri”.

• If the score is high enough, Siri wakes up.

Example: Apple (The Detector: Listening for “Hey Siri”) 56

https://machinelearning.apple.com/2017/10/01/hey-siri.html

• Two major types of deep learning models

• Convolutional Neural Network (CNN)

• Recurrent Neural Network (RNN)

Deep Learning 57

• One to many: image captioning (ex, House, Dog, Trees)

• Many to one : Weather/Stock price forecasting• Many to many: video processing by frames to

caption, Language translation

• Usually applied for image recognition

• Regression : The output variable takes continuous values

• Classification : The output variable takes class labels

• Underneath it may still produce continuous values such as probability of belonging to a particular class.

Convolutional Neural Network (CNN) 58

http://cs231n.github.io/convolutional-networks/

Recurrent Neural Network (RNN)

• Similar to shallow RNNs, the deep RNNs are designed for modeling sequences and are capable of remembering past information and processing new events accordingly which was not possible with CNNs.

• Incredible success applying RNNs to a variety of problems:

• speech recognition

• language modeling

• translation

• image captioning

Recurrent Neural Network 60

<EOS> = end-of-sentence tag<BOS> = begin-of-sentence<pad> = Zeros are used, when there is no input at the time step

• Reinforcement learning is a general-purpose frame work for decision-making:

• An agent operates in an environment: Atari Breakout

• An agent has the capacity to act. Each action influences the agent’s future state

• Success is measured by a reward signal. Goal is to select action to maximize future reward

Deep Reinforcement Learning 61

https://deepmind.com/research/dqn/

Reward Discount factor State ActionNext Q-value

Mean Square

Popular Neural Network Architectures 62

Convolution Pooling Convolution Pooling Fully Connected Output Predictions

Dog(0.01)cat(0.04)

boat(0.94)bird(0.02)

Convolutional Neural Network

Recurrent Neural Network

LSTM LSTM

𝑦𝑡−1 𝑦𝑡 𝑦𝑡+1

• Deep Learning in Wireless Sensor Networks

• Network Traffic Classification

• Network Flow Prediction

• Deep Learning in Social Networks

• Mobility Prediction with Deep Learning

• Deep Learning in Cognitive Radio and Self-Organized Networks

• Deep Learning Based Routing

• Deep Learning in Internet-of-Things

• Deep Learning Approaches to Mobile Edge Computing

• Deep Learning for Network Security

Applications of Deep Learning in Network Related Areas 63

• TensorFlow is an open source software library for numerical computation using data flow graphs

• TensorFlow supports popular programming languages such as Python, C++, Java

• TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well

Deep Learning implementation using open source: Use-Case 64

• Installing TensorFlow with native pip

Installing TensorFlow 65

If one of the following versions of Python is not installed on your machine, install using:Python 3.5.x 64-bit from python.orgPython 3.6.x 64-bit from python.org

C:\> pip3 install --upgrade tensorflow

C:\> pip3 install --upgrade tensorflow-gpu

https://www.tensorflow.org

Pip is a package management system used to install and manage software packages, such as those found in the Python Package Index.

TensorFlow visualization Tool: TensorBoard 66

• TensorFlow for training a massive deep neural network can be complex and confusing

• TensorBoard (visualization tools)• To make it easier to

• Understand

• Debug

• Optimize TensorFlow programs

• To visualize

• TensorFlow graph

• Plot quantitative metrics about the execution of graph

• Show additional data like images that pass through it

https://www.tensorflow.org

https://www.tensorflow.org/get_started/graph_viz

Supervised learning in Tensorflow 67

Training

Model

Output

Evaluation

Features

Extraction

Weight update

New

DATA

Labels

Raw

DATA

ModelFeatures

ExtractionOutput

Predicted

Outputs

Save Train Model

CNN,RNN, etc..

Training phase

Testing phase

• Build a convolutional neural network model to recognize the handwritten digits in the MNIST (Modified National Institute of Standards and Technology database) data set

1. Input Layers : monochrome 28x28 pixel images

2. Convolutional Layer 1: Applies 32 5x5 filters (extracting 5x5-pixel subregions), with ReLU activation function

3. Pooling Layer 1: Performs max pooling with a 2x2 filter and stride of 2 (which specifies that pooled regions do not overlap)

4. Convolutional Layer 2: Applies 64 5x5 filters, with ReLU activation function

5. Pooling Layer 2: Again, performs max pooling with a 2x2 filter and stride of 2

6. Dense Layer 1: 1,024 neurons, with dropout regularization rate of 0.4 (probability of 0.4 that any given element will be dropped during training)

7. Dense Layer 2 (Logits Layer): 10 neurons, one for each digit target class (0–9)

Example use-case: recognizing the handwritten digits 68

http://yann.lecun.com/exdb/mnist/ https://www.tensorflow.org/tutorials/layers

Convolutional Neural Network model for the use-case 69

InputOutput

• Input Layer

• batch_size : Size of the subset of example to use when performing gradient descent during training

• image_width : Width of the example images

• image_height : Height of the example images

• Channels : Number of color channels in the example images. For color images, the number of channels is 3 (red, green, blue). For monochrome images, there is just 1 channel (black)

• Here, MNIST dataset is composed of monochrome 28x28 pixel images, so the desired shape for our input layer is

• To convert our input feature map (features) to this shape, we can perform the following reshape operation:

• -1is used to reshape the input matrix into sequences

Input Layer: Convolutional Neural Network 70

TensorFLow function for Input layer:

input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])

Feature

[batch_size, 28, 28, 1]

28 X28 784

• Convolutional layers, apply a specified number of convolution filters to the image

• In our use-case, we apply 32 5x5 filters to the input layer, with a ReLU activation function

• Kernel size is same as the filter size (5x5)

Training Model: Convolutional Layer 1 71

TensorFLow function for convolution:

conv1 = tf.layers.conv2d(

inputs=input_layer,

filters=32,

kernel_size=[5, 5],

padding="same",

activation=tf.nn.relu)

5X5X32 28 X28X32

Cov1 Output

output tensor produced by conv2d() has a shape of [batch_size, 28, 28, 32]

• Pooling layers downsample the image data extracted by the convolutional layers to reduce the dimensionality of the feature

• We apply the max_pooling2d() method in layers to construct a layer that performs max pooling with a 2x2 filter and stride of 2:

Training Model: Pooling Layer 1 72

TensorFLow function for Pooling:

pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2],

strides=2)

Output tensor produced by max_pooling2d() (pool1) has a shape of [batch_size, 14, 14, 32]

Pool1

14X14X32

Outputs ofPool1

2X2X32

1 2 5 6

3 4 7 8

strides=2

Training Model: Convolutional Layer 2 and Pooling Layer 2 73

Output pool2 has shape [batch_size, 7, 7, 64]

Cov2

14X14X64

Outputs of Cov2

5X5X64 2X2X64 7X7X64

Pool2Outputs of Pool2

• We connect a second convolutional and pooling layer to CNN using

• conv2d()

• max_pooling2d()

• For convolutional layer 2

• 64 5x5 filters with ReLU activation,

• For pooling layer 2,

• Same as pooling layer 1

• 2x2 max pooling filter with stride of 2

TensorFLow function for Convolution and Pooling:

conv2 = tf.layers.conv2d(

inputs=pool1,

filters=64,

kernel_size=[5, 5],

padding="same",

activation=tf.nn.relu)

pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2],

strides=2)

• Add a dense layer (1,024 neurons and ReLU activation)

• to perform classification on the features extracted by the convolution/pooling layers

• How to find number of neurons ? : Trials and errors

Training Model: Dense Layer 74

TensorFLow function for connecting Dense layer:

dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)

Output tensor dropout has shape [batch_size, 1024]dense1024

ReLU activation

• The final layer is the logits layer, which will return the raw values for predictions

• Create a dense layer with 10 neurons (one for each target class 0–9), with linear activation

Training Model: Logits Layer 75

TensorFLow function for logits layer:

logits = tf.layers.dense(inputs= dense, units=10)

Final output tensor of the CNN, logits, has shape [batch_size, 10].Logits 10

0

9

• The logits layer of model returns predictions as raw values in a [batch_size, 10]-dimensional tensor

• The predicted class for each example: a digit from 0–9

Output: Generate Predictions 76

TensorFLow function for generating output

tf.argmax(input=logits, axis=1)

Training Loss Evaluation 77

Iterations

Trai

nin

g Lo

ss (

%)

Open source Machine Learning Library 78

https://www.svds.com/getting-started-deep-learning/

Applications of AI in 5G• Network Slicing Enablers• Wireless Network Virtualization• Evolution of Cellular Networks• Network slicing Deliverables • Network Slicing Industrial Efforts • AI and Network Slicing for 5G Networks: Use-Cases

79

5G cellular networks were assumed to be the key enabler and infrastructure provider in the ICT industry, by offering three types of services:

• Enhanced mobile broadband (eMBB)

• Ultra-reliable low latency service (URLLC)

• Massive machine-type communications (mMTC)

5G Promises

http://www.itu.int/dms_pubrec/itu-r/rec/m/R-REC-M.2083-0-201509-I!!PDF-E.pdf

80

• The existing mobile network architecture was designed to meet requirements for voice and conventional mobile broadband (MBB) services

• To meet the requirements of novel bandwidth hungry services, there is a need to deploy smarter 5G networks

• Note that, all novel services have very diverse requirements, thus having traditional RAN and core solutions for every service cannot guarantee the QoS

Challenges to realize 5G Networks 81

How to fulfil the diverse 5G networks requirements?

• Network slicing can fulfil the diverse requirements of these novel network services

• Network slicing enables one physical network into multiple, virtual, end-to-end (E2E) networks, each logically isolated including device, access, transport and core network

• A slice is dedicated for different types of service with different characteristics and requirements given to a Service End-point Agent (SEA)

• Enforce strong isolation between slices, i.e., actions in one slice do not affect another

Network Slicing

Physical Resource Pool

WindowsWindows

Virtualization

Slice 1WindowsWindowsSlice X

WindowsWindowsSlice Z

Network Slicing

82

• Network Slicing enablers: How to do it ?• Software-defined networking (SDN)

• Network Functions Virtualization (NFV)

• A single physical network will be sliced into multiple virtual networks:

• Different service types running across each virtual network, i.e., URLLC, EMBB, etc.

• Support different radio access networks (RANs), i.e., LTE, Wi-Fi, etc.

• It is envisaged that network slicing will be used to partition the core network and radio access networks

Network Slicing and 5G Networks 83

Network slicing enablers: Software defined network (SDN) 84

API to the data plane

(e.g., OpenFlow , ONOS)

Decentralized control plane

(which is closely tied to data

planes)

API to SDN application programmer

(who can now program the network as a system and not as a collection of

individual boxes)

Logically-centralized DP-

decoupled control

SDN Controller

At the highest level, the SDN movement is an effort to build networks you can

program at a higher level of abstraction— just as you can program a computer.

SDN enables programmability 85

Vertically integrated

Closed, proprietary

Slow innovation

Horizontal

Open interfaces

Rapid innovation

Specialized

Control

Plane

Specialized

Hardware

Specialized

Features

AppAppAppAppAppAppAppAppAppAppApp

Control

Plane

Control

Plane

Control

Plane or or

Open Interface

Merchant

Switching Chips

Open Interface

SDN enables programmability which is important for network slicing

• A network architecture concept that uses the technologies of IT virtualization to virtualize entire classes of network node functions that may connect, or chain together, to create communication services

• NFV is envisioned to play a crucial role in network slicing as it will be responsible to build isolated slices based on user service requirements

Network slicing enablers: Network function virtualization (NFV) 86

WindowsWindows

Physical Hardware

Virtualization

Virtual

Compute

WindowsWindowsVirtual

Storage

WindowsWindowsVirtual

Network

Mobility

Management

Entity (MME)

Serving Gateway

(S-GW)

Policy and

Charging Rules

Function (PCRF)

Man

agem

ent an

d O

rchestratio

n

• Due to massive success of NFV and SDN in wired domain, a number of studies are being conducted to adopt them both in the core and radio access networks (RANs) for future cellular networks such as:

• CORD (Central Office Re-architected as a Datacenter) [1]

• Radisys M-CORD [2]

• Wireless network virtualization (WNV) is a novel concept for virtualizing the RANs of future cellular networks

• WNV has a very broad scope ranging from spectrum sharing, infrastructure virtualization, to air interface virtualization

Wireless Network Virtualization 87

[1] https://opencord.org/[2] http://www.radisys.com/radisys-m-cord-open-platform-emerging-5g-applications

• WNV abstracts the physical wireless infrastructure and radio resources

• These resources are then isolated to a number of virtual resources (slices)

• The goal is to assign slices to different mobile virtual network operators (MVNOs) such that the network utility is maximized.

WNV: Slice Allocation 88

S.M Ahsan Kazmi, Choong Seon Hong, "A matching game approach for resource allocation in wireless network virtualization", The International Conference on Ubiquitous Information Management and Communication (IMCOM 2017), Jan. 05-07, 2017, Beppu, Japan

MVNO 1 MVNO 2 MVNO V

UE 1 UE 5UE 2UE 3 UE 4

UE k

Infrastructure Provider (InP)

2

FrequencyRad

io E

lem

ent

1

FrequencyRad

io E

lem

ent

C

FrequencyRad

io E

lem

ent

Mobile virtual network operators (MVNOs)

UE NUE n

UE l

User Equipment (UEs)

Slice (InP 1)

MVNO 1 MVNO 2 MVNO 3

UE 1 UE 4UE 2 UE 3 UE 5 UE 6

1 𝐶12 3

321 𝑆1

𝐶22 31

321

Slice (InP 2)

𝑆2

Physical Resource of InP 1

UE 7

Physical Resource of InP 2

• A practical deployment of a WNVinvolves a multi-cell scenario

• the coverage area will be servicedby a set of InPs

• The goal is efficient allocation of theslices such that the total performanceof WNV is improved.

• To solve this problem, we propose:

• Hierarchical matching game whichenables distributed implementationwhile satisfying efficient resourceallocation and strict isolation.

WNV: Service Selection and Resource Purchasing 89

S. M. Ahsan Kazmi, Nguyen H. Tran, Tai Manh Ho, Choong Seon Hong, "Hierarchical Matching Game for Service Selection and Resource Purchasing in Wireless Network Virtualization," IEEE Communications Letters (online)

Evolution of Cellular Networks

http://www.5gsummit.org/berlin/docs/slides/HansEinsiedler.pdf

90

• Ultra high bandwidth for enhanced mobile broadband (eMBB) through customized slice both at RAN and core.

• The core addresses this by placing the contents near to UE, i.e., mobile edge computing or smart caching schemes

• Ultra low delay/reliability for URLLC through customized slice both at RAN and CN

• Dedicated bandwidth allocation at core routers

Prospects of Network Slicing 91

• 3GPP working group on architecture (SA2) has already defined the basis for building an evolved core network

• The 5G infrastructure is expected to manage multiple slices on the same network infrastructure

• The envisioned architecture clearly differentiates between control plane (C-Plane) and user plane (U-Plane)

Role of 3GPP in Network Slicing

Third Generation Partnership Project (3GPP), “System Architecture for the 5G System,” 3GPP TS 23.501 v0.3.1, Mar. 2017.

92

PCFSMFs

eNB

gNB

Slice Manager

ng-RAN

NG2 Interface

UPF

AMF

NG15NG4

NG7 NG11

WindowsWindowsNetwork Slices

• In the control plane, new components are introduced to • Manage user authentication and registration (AMF)

• Support multiple connection sessions (SMF)

• Instruct different routing policies (PCF)

• The user plane is unified into a generic function (UPF) managing distinct data networks (DNs) through the next-generation-Radio Access Network (ngRAN)

• This new architecture allows for an easier network functions virtualization and enables flexible multitenant deployments

• RAN nodes (and functions) are virtualized and flexibly chained to provision end-to-end RAN slices with a dedicated SMF

Role of 3GPP in Network Slicing 93

Slicing: Next Generation Mobile Networks (NGMN)

https://www.ngmn.org/uploads/media/NGMN_5G_White_Paper_V1_0.pdf

94

CP: Control Plane

UP: User Plane

Vertical AP: Vertical Application

RAT: Radio Access Technology

Slicing: Huawei Technologies

http://www.huawei.com/minisite/5g/img/5G_Nework_Architecture_A_High_Level_View_en.pdf

95

RAN-RT: Radio Access Network-Real Time

RAN-NRT: Radio Access Network-non Real Time

AC: Access Cloud

CP: Control Plane

UP: User Plane

MCE: Mobile Cloud Engine

DC: Data Center

• Network Slicing certainly is one of the most discussed technologies these days. Network operators like KT, SK Telecom, China Mobile, DT, KDDI and NTT, and also vendors like Ericsson, Nokia and Huawei are all recognizing it as an ideal network architecture for the coming 5G era

• Ericsson has been working on network slicing with NTT DOCOMO since 2014. In 2016 the two announced a successful proof of concept of dynamic network slicing technology for 5G core networks

• They created a slice management function and network slices based on requirements such as latency, security or capacity

Network Slicing: Industrial Efforts 96

• In 2015 Ericsson and South Korea’s SK Telecom joined hands to develop and deploy network slicing technology optimized for 5G services.

• The two companies demonstrated the creation of virtual network slices optimized for services including super multi-view and augmented reality/virtual reality, massive Internet of Things and enterprise solutions.

• In November 2016, Huawei and Deutsche Telekom demonstrated the world’s first 5G end-to-end autonomous network slicing.

• The demo showed how different network slices can be created automatically and in an optimized way on a shared RAN, core and transport network.

Network Slicing: Industrial Efforts 97

What is missing?

• The basic goal of an AI in 5G is its ability to extract, predict, and characterize specific patterns from datasets

• To unleash the true potential of 5G networks:• Intelligent functions using AI across both the edge and core of the network are

required along with the novel enabling technologies

• AI functions must be able to:• Adaptively exploit the wireless system resources • Generated data to optimize network operation• Guarantee the QoS in real time

• Such mobile edge and core intelligence can only be realized by integrating fundamental notions of artificial intelligence (AI) across the wireless infrastructure and end-user devices

AI for 5G Networks 98

• Role of AI in 5G networks: Exploit big data analytics to enhance situational awareness and overall network operation such as:

• Fault Monitoring

• User Tracking

• Cell Association

• Radio Resource Management

• Cache Resource Management

• Mobility Management

• Management and Orchestration

• Service Provisioning Management

• And so on..

AI for 5G Networks

AI-based system operation is no longer a privilege, but rather a necessity for 5G and beyond networks.

99

AI for Networking Slicing in 5G Networks

Perception

LearningProblem Solving

Reasoning

Core Network

Gateway Switch

Radio Access Network

RBsCache

Slice Manager: AI Enabled

mMTCeMBB URLLC

100

• AI engine can act as an application on top of slicing manager or run as an independent network entity, and communicate with RAN and CN

• AI enabled Slice manager related information:

• The slice manager will read service-level agreements of Users, e.g., requirements on rate, coverage, failure duration, redundancy, etc.

• UE-level information such as receiver category, battery limitation, power, mobility and etc.

• Network-level information such as resource spectrum, number of serving users, QoS (quality of service), key performance indicators of network functions, scheduled maintenance period, and etc.

• Infrastructure-level information such as server type, CPU, memory, storage, and etc.

AI enabled 5G networks 101

Information sent to the AI enabled Slice Manager

Perception

LearningProblem Solving

Reasoning

RBs CacheSlice Manager: AI Enabled

102

RAN Agent

Controller API

Radio Access Network

Core Network

Gateway Switch

Core Network Agent

Spectrum, Requirements on rate, coverage, etc.

Service-level agreements

Moving speed, power, etc.

UE level information

CPU, memory, network capabilities, etc.

Infrastructure level information

Number of serving users, etc.

Network level information

• These information can be acquired via SDN controllers or open API

• Then, the slice manager will utilize its embedded modules of problem solving to process the obtained information, and feedback learning results

• These results can include:• Traffic characteristic analysis reports such as service provisioning suggestion to CN

or RAN

• User-specific controlling information such as serving priority, bandwidth allocation, mobility tracking command to RAN

• Network configuration notification such as parameter adjustment, access method, network error alert to the controllers of RAN or CN

AI enabled 5G networks 103

Perception

LearningProblem Solving

Reasoning

Physical Resource Blocks (PRBs)Cache

Slice Manager: AI Enabled

104

RAN Agent

Radio Access Network

Core Network

Gateway Switch

Core Network Agent

PRBs adjustment, cache adjustment, etc.

Service Provisioning

serving priority, PRB allocation, etc.

UE specific information

Adjust CPU and Storage, assign network capabilities , etc.

Infrastructure level information

Adjustment in number of serving users, etc.

Network level service provisioning

Controller API

Information sent by the AI enabled Slice Manager

• Currently network slices are created and modified based on pre-defined stimuli, i.e., time of day, scheduled maintenance, social event, and etc.

• Through AI enabled slice manger, we can perform prediction of the UEs requirements based on service-level agreements of UEs, density of UEs, and QoS of UEs

• These prediction results can then be used in deciding new creation or deletion of network slices. Moreover, we can also determine the types of slices required in the network

• Thus, the slice manager can decide and inform the RAN and CN to create or delete slices of specific types

Use-Case: Slice Creation 105

• Goal : Maximize the cache hit, in order to reduce access latency for URLLC service

• Potential Benefits : Enhanced cache hit, low access latency, bandwidth saving for backhaul

• Approach: Deep Learning using the Recurrent Neural Network

• Input : Sequences of content access information

• Output : Content popularity values and cache decision

• Learning Model :

• Long Short Term Memory (LSTM)

• Gated Recurrent Unit (GRU)

• Simple RNN

Use-Case: Content’s Popularity prediction and Cache decision 106

Content’s Popularity prediction and Cache decision 107

Feed Collected Data

Learning Content’s

Popularity with Deep Recurrent Neural Network

Cache Decision

Performance Measurement

AI enabled Slice Manager

Cache

a b c d

Cache

e f g a

𝑥1 𝑥2 𝑥𝑛

RNN

Cache Decision

Performance Measurement

Up

dat

e W

eigh

t

Sequences Inputs : Features

Output: Popularity Score

Long Short Term Memory (LSTM)

Gated Recurrent Unit (GRU)

Simple Recurrent Neural Network

• Type of content• Rating (IMDB)• Time Zone• Hit count• User ID• movID• Quality of video

Sequence Inputs: Features

RNNRNN

𝑦

Training

Prediction

• Dataset : MovieLens is a popular dataset for video content recommendation, i.e., https://grouplens.org/datasets/movielens/. We divide the dataset into training (70%) and test data (30%) sets.

• We build the RNN model to predict the Content’s Popularity in TensorFlow. We have two phases.

• Training: • Batch size =200, i.e., movie context. This is the input of the RNN model.• Hidden layers = 200, we use the Long Short Term Memory (LSTM).• Time frame = 3, i.e., after every three sequences of input, the model will predict.• Loss = root mean squared error (RMSE), once the model predicts, we calculate the loss

using RMSE.• Optimizer = adam, we use the adam optimizer to minimize the loss.• Accuracy = We define the accuracy threshold for stopping the learning phase.

• Testing: • We evaluate the accuracy of the test data from the trained model.

Dataset Preprocessing and Model Description 108

Implementing our model using TensorFlow 109

RNN RNN RNN

RNN RNN RNN

RNN RNN RNN

𝑌1

𝑋𝑡1

𝑋𝑡2

𝑋𝑡3

Inputs Layer Hidden 1 Hidden N

𝑌1

𝑅𝑀𝑆𝐸

Movie ID, Number of requests, Region,

Type of movie.

Context Info

Pop

ularity Sco

res

True Label

Output: Popularity Score

Trained Model

𝑋𝑡

𝑋𝑡+1

Trai

n D

ata

Test

Dat

a

Dat

a se

t

𝑌1 𝑌1

𝑅𝑀𝑆𝐸 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦

𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦

Cache Decision

Hidden layers = 200Batch size =200 (number of Contents)Time frame = 3Activation = sigmoidOptimizer = adamLoss = mean square error

𝑋𝑡+2

Prediction Results 110

• Mobility Management

• Fault Monitoring

• User Tracking

• Cell Association

• Load Balancing

• Power Management

• Radio Resource Management

• Cache Resource Management

• And many more benefits..

What more can be achieved via enabling AI? 111

• The use of artificial intelligence will play a vital role for enabling a variety of applications in 5G and beyond wireless networks.

• AI definitively provides precious opportunities to analyze trends and recognize patterns. However, it is difficult to perfectly predict the desired results by using traditional simple models such as shallow ANNs.

• Deep Neural Networks are envisioned to fill this gap and serve as key predicting enabler to support the 5G networks

• Network slicing coupled with AI will be defining the future of wireless networks.

Conclusion 112