Are we ready for 5G vision? What is next?

Flexible and Cognitive RAT for 5G and Beyond

HUSEYIN ARSLAN

huseyinarslan@medipol.edu.tr

http://cosinc.medipol.edu.tr

arslan@usf.edu

http://wcsp.eng.usf.edu

Istanbul Medipol University University of South Florida

Bs

Middle East Technical University, Turkey

1987-1992 1992-1993 1993-1998

1998-2002 2002-present

Ms and PhD

Southern Methodist University, Dallas, Tx

ERICSSONResearch Engineer

MsMiddle East Technical University,

Turkey

University of South Florida, Tampa, Florida

2014: Prof.

2014-present

Medipol University,Istanbul, Turkey

ANRITSU

Consulting Engineer

TubitakConsulting Engineer

OUTLINEp Evolution of wireless p 5G and Beyond (Flexibility and adaptability)p Flexibility framework

n Sensing and radio environment monitoringn Cognitive radio and networks (AI and ML)n PHY security and wireless security

p Flexible RAT (PHY and MAC features)n Intelligent Surfacesn Flexible waveforms and frames (hybrid and joint waveforms)

p Integrated sensing and communication

n Flexibility in modulationn Flexible interference management (co-existence and coordination)n Flexible coverage and base stations (aerial base stations) n Flexible band and spectrum usage

Evolution of wirelessp BW

n Increasing (from 30kHz, 200kHz, 1.25MHz, 5MHz, 20MHz, 100MHz, …)

n Getting flexible (fixed earlier, aggregation is now real, dynamic spectrum access (DSA) further along the line). Fractional BW

p Spectrum (started below 1 GHz, later included above 1GHz, now mm-wave frequencies, possibly terahertz and visible light communication in the future)

p Spectrum Efficiencyn Increased number of bits per Hertz

n Re-use (cell size is getting smaller, re-use factor is getting smaller)

n HetNets and Co-existence

p Adaptation and cognition capabilities (ML and AI)p Awareness capabilities

Wireless Evolution p 1G: Cellularp 2G: Digital

p 3G: Data (first attempt to MBB)p 4G: High Speed Data (true MBB)

p 5G: Applications (verticals)p 6G: Flexibility (AI and sensing)=

CR and CN

VOİCE

VOİCE&

DATA

CAPACITY (re-use)

CAPACITY&

SPEED

Diverse applicationclasses

Diverse requirementsand metrics

Single Layer (cells)Single connection pointHomogeneous Nets.MobilityCoveragePersonal Com.

1G(1980s)

Analog

3 G(2000s)

2 G(1990s)

Digital

4 G(2010s)

5 G(2020s)

1) Extreme high traffic2) Extreme variable traffic

Adaptation and flexibilityp 1G and 2G

n Tx : heavily standard defined, not much flexibilityn Channel and Interference: random n Rx: flexible (channel adaptive) - adaptive equalizer, adaptive

channel trackers, etc.p 3G and 4G

n Tx : heavily standard defined but some flexibility (adaptive scheduling, adaptive modulation and coding, MIMO and BF, etc.)

n Channel : random Interference: random (unsuccessful CoMP)n Rx: more flexible

p 5Gn Tx :flexible, standard defines general and essential featuresn Channel: random Interference: controlled (CRAN, BF, mMIMO) n Rx: even more flexible

p 6Gn Tx : extremely flexiblen Channel: controlled (IRS) Interference: controlled (CRAN, IRS,

BF, mMIMO)n Rx: extremely flexible

Standard for dummies Standard on the fly

1G through 4G5G and Beyond

5G is not a standard like we know …

5G is a vision …5G is a dream …5G is the beginning of a new era

5G is aimed to provide service for wide variety of applications

with some Flexibility and Adaptation

5G

BANPANLANWANLP-WANIn-vivoSatelliteUnderwaterUnderground

Applications (diverse and with a range of requirements)

Smart transportation Remote Health

Remote education

Smart Grid

11

IoT

5G AND BEYOND RADIO ACCESS TECHNOLOGIES

LATENCY RELIABILITY CAPACITY SPECTRUM EFFICIENCY

URLLC mMTC eMBB

RE

QU

IRE

ME

NT

SM

ET

RIC

SA

PP

LIC

AT

ION

SR

ES

OU

RC

ES

C

ON

ST

RA

INT

S

SPECTRUMPROCESSING POWER

COST ENERGY

HARDWARE

ENVİRONMENT AND CHANNEL

MIMO & BEAM-FORMINGMASSIVE MIMO

ADVANCED WAVEFORMSENSİNG SMALL CELLS

MM-WAVE COGNITIVE RADIODSA

CRAN

q High data rate & capacity

q Better coverage

q Improved spectral efficiency

q High reliability

q Low latency

q Strong security

q Very large number of devices

q High energy efficiency

q Low device complexity

A flexible air interface is required to meet these diverse requirements!

Metrics and technical expectationsp Capacityp Spectral efficiencyp Energy Efficiency, Power (PAPR, CCDF)p Complexity (computational, hardware)p Cost (device and reduced bit cost)p Accessibility (call blocking, service blocking)p Improved QoS p Data rate (peak rate, average rate, cell edge rate, min. rate)p Coverage (cell edge)p Latency – (no perceived delay)p Reliability (99.999 percent)p Flexibility (Support of variety of services )p Mobilityp SECURITY (Communication security in PHY, MAC, NET)

6G ProjectionsINTELLIGENCE + COMMUNICATION

AI and machine learning for cognitive radio and cognitive networks

Sensing & Communication

Flexible Adaptive Dynamical

Scalable Cognitive Intelligent

Softwarization Degree of Freedom Tunable

Configurable

Adjustable

Programmable

flexibility

6G RAT vision

• Security and Privacy• Co-existence and interference management (and dynamic

spectrum access)• Sensing, radar, joint sensing and communications (precision

sensing and actuation)• Channel control through IRS, backscatter communication, etc• Cell-less networks (edge-less networks), coordination, C-RAN

(multi-connectivity)• Massive MIMO, holographic MIMO • Flexible PHY, MAC, and networks (flexible waveforms,

modulations, radios, channel…) • Edge AI (distributed intelligence), smart environments• THz (beyond mmwave), and VLC• Quantum communication• Energy efficiency and energy harvesting

6G keywords (technical)

Expectation from 6G (applications)

• Society and human centric networks and systems (serving society and well-being of people, including disaster, serving remote area)

• Human-machine and human-environment interaction (interconnection of physical, biological, and digital worlds)

• Expansion of communication environment:High altitude platforms, beyond terrestrial coverage – integrated terrestrial and space

• Communication everywhere with everything (IIoT, education, health, agriculture, grids, traffic, city, Industrial internet … more verticals)

• Fully automated driving, internet of bio-things

• Virtual reality, extended reality, augmented reality (holographic verticals and society)

My Perspectives

SENSINGIntegratedsensing and

communications

AI and ML

Applications Requirements Requirement SetsService Types

FLEXIBLE RAT PLATFORM

• Flexible multi-band utilization

• Super flexible PHY and MAC

• Super flexible heterogeneous networks

• Co-existence

• Intelligent transmission

• Green communications

• Channel Control (IRS)

PHY Security&

Securecommunication

Learning and reasoning

Adaptation

Cognitive Radio &

Networks

PHY Security&

Secure communication

SENSINGIntegratedsensing and

communications

Adaptation

FLEXIBLE RAT PLATFORM

• Flexible multi-band utilization

• Super flexible PHY and MAC

• Super flexible heterogeneous networks

• Co-existence

• Intelligent transmission

• Green communications

• Channel Control (IRS)

AI and ML

Learning and reasoning

Cognitive Radio &

Networks

Cognitive Radio&

Networks

AI and ML

Cognitive Radio &

Networks

Cognitive and Software Defined RadioCognitive radio (highly intelligent radio systems)

Requiring a flexible platform: Flexibility

COGNITIVE CYCLE

Awareness

Sensing

Learning

Soft. Def. Radio

Cog. Radio

Cog. Net.Response

Adaptation

Input (sensing, awareness)Decision making (reasoning, interpretation, learning) Action (implementation of decision, adaptation, parameter change)

•CR requires a flexible radio device. SDRs are ideal platform for CRs.•SDR might not be necessary for CR, but, highly desirable•SCR might possibly do the job for most interpretation of CR•Even multiband and multimode radio devices can do the job for some CR interpretations

Two aspects of CRp Dynamic spectrum utilizationp Fully adaptive and reconfigurable RAN

p Both are requiring user/channel/interference/context awareness

NETWORK LAYER

Demodulator

RF Front End

RSSI

SNRSIR

SINRChannel

Noise Power

Channel Decoder

BER

FERCRC

Packet LossRouting Table Change Rate

Congestion LevelPositions of Nodes

Power Level of Nodes

TRANSPORT LAYER RTT

PHYS

ICAL

& D

ATA

LIN

K LA

YERS

UPPERLAYERS Perceptual Quality

MARKOV MODELSNEURAL

NETWORKSGENETIC

ALGORITHMS

...TO

OLS

LAYE

R IN

PUT

MEMORY

COGNITIVE INTERFACE

DESCRIPTIVE LANGUAGE

EXTE

RNAL

SE

NSI

NG

COGNITIVE ENGINE

ANTENNA

§ Antenna Powers§ Dynamic Range§ Pre–distortion

Parameter§ Pre–equalization

Parameter

RF Front End

§ Transmit Power§ Digital Modulation

Order§ Carrier Frequency§ Operation Bandwidth§ Processing Gain§ Duty Cycle§ Waveform§ Pulse Shaping Filter

Type§ FFT Size (for OFDM)§ Cyclic Prefix Size (for

OFDM)

PHYSICAL LAYER

§ Channel Coding Rate§ Channel Coding Type§ Packet Size§ Packet Type§ Data Rate§ Interleaving Depth§ Channel/Slot Allocation§ Carrier Allocation (in

multi–carrier systems)§ MAC Scheduling

Algorithm§ Hand-off (Handover)§ Number of Slots

DATA LINK LAYER

§ Routing Algorithm§ Routing Metric§ Clustering Parameters§ Network Scheduling

Algorithm

NETWORK LAYER

§ Congestion Control Parameters

§ Rate Control Parameters

TRANSPORT LAYER

§ Communication Modes (Simplex, Duplex, etc.)

§ Source Coding§ Encryption§ Service Personalization

UPPER LAYERS

LAYE

R O

UTP

UT

ADJU

STABLE PARAMETERS

OBSERVABLE PARAM

ETERS

Con

cept

ual M

odel

of C

hann

el A

war

e Ra

dios

Adjustable parameters - FLEXIBILITYControllable parameters:-Modulation options (types and orders) -Power options, transmitted power-Coding options (types and orders)-Waveform options (lattice, shape, etc)-Multiple accessing options-Antenna usage (MIMO, beamforming, precoding)-Scheduling options (types, criteria)-User-cell association options-Carrier Frequency options (mmwave, microwave, THz …)-Bandwidth options-Frequency use options (dynamic, secondary, primary, unlisenced, shared lisenced, etc…)-Access points (like CoMP, how many and which one)-Processing gain-Data rate

Adjustable parameters define: - what we transmit, how we transmit in different layers of the protocol stack- how many different ways we can transmit and receive- how we use the spectrum, power, hardware- how we generate the digital baseband signal- various ways of multiplexing, multiple accessing- how to use resources, how to share resources- how to build the physical signal- how to control the medium (LIS, RIS)- how to use the hardware (antennas, RF)- how often to adjust

COGNITIVE CYCLE

Awareness

Sensing

Learning

Soft. Def. Radio

Cog. Radio

Cog. Net.Response

Adaptation

Input (sensing, awareness)Decision making (reasoning, interpretation, learning)Action (implementation of decision, adaptation, parameter change)

•CR requires a flexible radio device. SDRs are ideal platform for CRs.•SDR might not be necessary for CR, but, highly desirable•SCR might possibly do the job for most interpretation of CR•Even multiband and multimode radio devices can do the job for some CR interpretations

Machine Learning

p 5G is somewhat flexible and adaptivep But, still not Cognitive

p 5G + Machine Learning = Cognitive Radio = maybe 6G

Are We Ready for Machine Learning?

Starting of5G Research Starting of

5G Standardization Starting of6G Research

Data: Google Trends, worldwide interest over time

When to use Model and AI-based Approaches?

Hybrid Approach

Model-Based Approach AI-Based Approach

Model is already well-known

Simple problem to solve

Not enough data

Noisy data

Power and computational complexity is important

To merge and avail advantage of both

approaches

System is completely blind

Problem is too complex to solve

Big data is available

Clean data

Complexity is not critical

Applications of AI and Wireless Communication

Physical Layer

Blind signal identification

Modulation identification

Dynamic spectrum access

RF-impairments detection,

compensation

Channel estimation and prediction

Channel modelling

Channel coding

Symbol detection

End-to-end learning

NOMA techniques

User detection

Signal classification

Blind signal analysis

MAC Layer

Channel access mechanism

Quality of service optimization

Scheduling

Mobility management

Hand-off/handover techniques

Routing

BS Switching

Resource management

User association

Cell-sectorization

Energy optimization

Network Layer

Channel Estimation with AI

M. A. Aygül, M. Nazzal, A. Görcin, H. Arslan, “Sparsifying Dictionary Learning for Beamspace Channel Representation and Estimation in Millimeter-Wave Massive MIMO,” submitted to IEEE Transactions on Vehicular Technology.

M. Nazzal, M. A. Aygül, A. Görcin, H. Arslan, “Estimating the Unknown Sparsity in Multiple Dimensions to Realize Compressive Channel Estimation: AMachine Learning Approach,” submitted to IEEE Transactions on Vehicular Technology.

M. Nazzal, M. A. Aygül, A. Görcin, H. Arslan, “Dictionary Learning-Based Beamspace Channel Estimation in Millimeter-Wave Massive MIMO Systemswith a Lens Antenna Array,” IEEE International Wireless Communications and Mobile Computing Conference (IWCMC), Jun. 24-28, 2019.

M. Nazzal, M. A. Aygül, A. Görcin, H. Arslan, “Sparse Coding for Transform Domain-Based Sparse OFDM Channel Estimation,” IEEE SignalProcessing and Communications Applications Conference (SIU), Apr. 24-26, 2019.

Spectrum Sensing and Prediction with AI

M. A. Aygül, M. Nazzal, M. İ. Sağlam, A. R. Ekti, A. Görçin, D. B. da Costa, H. F. Ateş, H. Arslan “Spectrum Occupancy Prediction Exploiting Multi-Dimensional Correlations Through Composite LSTMs,” submitted to Sensors.

M. A. Aygül, M. Nazzal, A. R. Ekti, A. Görçin, D. B. da Costa, H. F. Ateş, H. Arslan “Spectrum Occupancy Prediction Exploiting Time and FrequencyCorrelations Through 2D-LSTM,” IEEE Vehicular Technology Conference (VTC-Spring), May 25-28, 2020.

M. Nazzal, A. R. Ekti, A. Görçin, H. Arslan, “Exploiting Sparsity Recovery for Compressive Spectrum Sensing: A Machine Learning Approach,” IEEEAccess, 7(1), 126098-126110, Sep. 2019.

M. Nazzal, O. Hasekioglu, A. R. Ekti, A. Gorcin, H. Arslan, “Compressed Spectrum Sensing Using Sparse Recovery Convergence Patterns ThroughMachine Learning Classification,” IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Sep. 8-11, 2019.

Physical Layer Security with AI

M. A. Aygül, S. B. Edibali, D. B.da Costa, H. F. Ateş, H. Arslan “Signal Relation-Based Physical Layer Authentication,” IEEE International Conferenceon Communications (ICC), June 7-11, 2020.

H. M. Furqan, M. A. Aygül, M. Nazzal, H. Arslan, “Primary User Emulation and Jamming Attack Detection in Cognitive Radio via Sparse Coding,”EURASIP Journal on Wireless Communications and Networking, 2020(141), 1-19, July 2020.

M. A. Aygül, H. M. Furqan, M. Nazzal, H. Arslan, “Deep Learning-Assisted Detection of PUE and Jamming Attacks in Cognitive Radio Systems,” IEEEVehicular Technology Conference (VTC-Fall), Oct. 4-7, 2020.

Adversarial Attack

Eavesdropping Spoofing Jamming and PUEA

Waveform Parameter Selection with AI

A. Yazar, H. Arslan, “A Waveform Parameter Assignment Framework for 6G with the Role of Machine Learning,” IEEE Open Journal of VehicularTechnology, 1(1), 156-172, May 2020.

A. Yazar, H. Arslan, “Selection of Waveform Parameters Using Machine Learning for 5G and Beyond,” IEEE International Symposium on Personal,Indoor, and Mobile Radio Communications (PIMRC), Sep. 8-11, 2019.

Implementation of AI and Wireless Communication?

What are the considerations?

PowerCost

StorageLatency

ReliabilitySecurity

Where to use AI?

User Equipment

Base Station

Cloud