Stat of the art in cognitive radio

State of the Art in Cognitive Radio
By
Mohsen M. Tantawy
National Telecommunication Institute (NTI), Egypt.

Contents
 Introduction
 CR Network Architecture
 MAC Protocols for CRNs
 Routing in CRNs
 TCP in CRNs
 CRN Security
 CR Standards
 New Trends in CR
 CR in 5G
 New Applications in CR
 Green CR
 Cloud-based CR
 CRSN
 Regulatory Issues in CR
 CR Business Model
2

Introduction
Cognitive Radio Motivation
• Spectrum scarcity
– Increase in spectrum demand
– Spectrum is a scarce resource
– Static spectrum allocation policy
Source: http://ntra.gov.eg

Introduction
Cognitive Radio Definitions
• Mitola
• ITU
• FCC
• NTIA
• WWRF
• They talk about
– “radio”.
– “interaction with the environment”.
– “measuring”.
– “decision making”.
– “automaticity”.
– “adaptation”.
4
More Definitions

Dynamic Spectrum Access
• CR technology works on the principle of DSA, where CR users
utilize spectrum holes.
5
Source: Natarajan Meghanathan, “Cognitive Radio Technology Applications for Wireless and Mobile Ad Hoc Networks”, IGI Global Publisher, 2013. More

Cognitive Radio Network Architecture
6
Source: Geoffrey Ye Li, “Cognitive Radio Networks Project”, Georgia Institute of Technology, 2013.

Spectrum Allocation and Sharing Schemes
7
Spectrum Allocation and
Sharing Schemes
According to Three
Criteria
Network
Architecture
Centralized
Model
Distributed
Model
Spectrum Bands in
use by a CR User
Open Spectrum
Sharing Model
Hierarchical
spectrum
model
Spectrum
Underlay
Spectrum
Overlay
Access Behavior of
CR Users
Cooperative
Model
Non
Cooperative
Model
More about Underlay, Overlay, and Interweave

Underlay vs. Overlay
8
PU - Primary Users
SU - Overlay CR
SU - Underlay CR
Frequency
PSD
PU PU
PU
More about Underlay, Overlay, and Interweave

Cognitive Radio MAC Protocols - Example
9
More about Cognitive Radio MAC
Source: Mahfuzulhoq Chowdhury, Asaduzzaman, and M. Fazlul Kader, “Cognitive Radio MAC
Protocols: A Survey, Research Issues, and Challenges”, Smart Computing Review, vol. 5, no. 1, 2015.
• Time-slotted
• Random
Access
• Hybrid
CR MAC
• Single Radio
• Multiple Radio
Radio
• Centralized
• Distributed
Architecture
• Without CCC
• With CCC
• Global
• Local
CCC

Detailed Classifications of Distributed MAC Protocols - Example
Considering Common Control Channel (CCC)
10
Distributed MAC
Single Radio
Without CCC
(e.g., POMDP)
With CCC
Global Time
Synch.
(Network
Wide)
(e.g., C-MAC)
Local Time
Synch.
(only
neighboring
nodes)
(e.g., HC-MAC)
Multiple
Radio
Without CCC
(e.g., CA MAC)
With CCC
Dedicated
Global
(e., OP-MAC)
Dynamically
Configurable
Global
(e.g., DOSS)
Local
(e.g., Comesh)
Source: Mahfuzulhoq Chowdhury, Asaduzzaman, and M. Fazlul Kader, “Cognitive Radio MAC Protocols: A Survey, Research Issues, and
Challenges”, Smart Computing Review, vol. 5, no. 1, 2015.
C-MAC: Cognitive MAC
HC MAC: Hardware Constrained MAC
POMDP: partially observable Markov decision process
DOSS: Dynamic Open Spectrum Sharing
OP-MAC: Opportunistic MAC
CA-MAC: Concurrent MAC

Routing in CRNs
11Source: Natarajan Meghanathan, “A Critical Review of the Routing Protocols for Cognitive Radio Networks and a Proposal for Load Balancing
Local Spectrum Knowledge Based Routing”, Computer Science & Information Technology (CS & IT), pp. 17–26, 2013.
• Relay Nodes
• Spectrum for each
link
Routing Deciding
• Full Spectrum
Knowledge
• Local Spectrum
Knowledge
Routing
According to
• Minimum Delay
• Maximum Throughput
• Minimum Power
• Link Stability
Local Spectrum
Knowledge
Metrics

Transport Layer Issues in CRNs
TCP
in CR
PU
Behavior
Spectrum
Sensing
Spectrum
Changing
BW variation
due to
Spectrum
Availability
12
More about TCP in Cognitive Radio
In addition to traditional network congestion, Link
error, collision, mobility, …etc.
Source: X. Zhong, Y. Qin, and Li Li, “Transport Protocols in Cognitive Radio Networks: A Survey”,
KSII Transactions on Internet and Information Systems (TIIS), vol. 8, no. 11, pp. 3711-3730, 2014.

CR Attacks and Countermeasures
13
Attack Countermeasures
Attacks on CCC CCC Frequency hopping, CCC key distribution
PUE Attacks
(malicious or selfish SU)
priori known characteristics of PU signals, location determination
techniques, access to geo-location information about a priori known PUs
Spectrum Sensing Data
Falsification (SSDF) Attack
(Byzantine attack)
Mutual authentication, data integrity, and data encryption,
Deployment of dedicated trusty sensors, mechanisms to selectively forget
past information.
Jamming Attack
(trigger DoS)
Channel surfing, or frequency hopping, legitimate users change their
location to escape the interference range imposed by the attacker.
Objective Function Attack
(manipulate the values of the radio
parameters)
No good solution has been suggested, A simple suggestion is to define
threshold values for every updatable radio parameter, help from IDS.
More Cognitive Radio Attacks
Source: Marinho et al, “A survey on security attacks and countermeasures with primary user detection in cognitive radio networks”, EURASIP
Journal on Information Security, 2015.

Cognitive Radio Attacks by Layers
14
Physical
Layer Attacks
Jamming
PU
Emulation
Overlapping
SU
Objective
Function
Attacker
manipulates trans.
rate parameters so
calculated results
of the function are
biased towards the
attacker’s interests
Data Link
Layer Attacks
Byzantine
CCC
Jamming
CCC
Saturation
Network
Layer Attacks
HELLO flood nodes
send Hello loud
enough, all nodes
think it is a neighbor
Ripple
Wrong ch info intent to
passed hop by hop to
enter network to
confuse state
Sinkhole
Attacker advertise itself
as the best route
Sybil
Attacker send packets
as different identity
subverting trust system
Wormhole
Attacker tunnel
message to replay it
Transport
Layer Attacks
Key
depletion
With large
no. of
session keys,
keys may
repeated can
lead to break
cipher
system
Application
Layer Attacks
CR Virus
Policy
Attacks
Policy of the
radio is
changed or
not allowed to
be updated,
providing the
attacker unfair
spectrum
access
Cross Layer
Attacks
Jellyfish
(attacker
target closed
loop flow)
Lion
(use PUE to
disrupt TCP)
Small Back-
off Window
Routing
Information
Jamming
(force target
node to
handoff
before route
info exchange)
More Cognitive Radio Attacks
Source: Deanna Hlavacek, J. Morris Chang , “A layered approach to cognitive radio network security: A survey”, Journal of Computer
Networks, 2014. http://dx.doi.org/10.1016/j.comnet.2014.10.001

Major Standardization Organizations and Standards for
Cognitive Radio
• Institute of Electrical and Electronic Engineers (IEEE)
– IEEE 802.11af-2014: Wireless Local Area Network based on TVWS (White-Fi)
– IEEE 802.22-2011: Cognitive Wireless Regional Area Network (WRAN)
– IEEE 802.15.4m-2011: Wireless Personal Area Network (Low Rate PAN in TVWS)
– IEEE 802.19.1: Solves the coexistence problem by coordinating spectrum usage by several networks in the same area
– IEEE 1900.4a (SCC41 or DySPAN): Optimize the resource usage when many different types of networks are available.
• Internet engineering task Force (IETF)
– IETF Protocol to Access White Space (PAWS)- RFC 7545: Database access
• International Telecommunication Union (ITU)
– ITU-WP1B: International Telecommunication Union Working Party 1B – Spectrum Management Methodologies
• European Telecommunications Standards Institute (ETSI)
– ETSI BRAN: European Telecommunications Standards Institute Broadband Radio Access Networks
• European Association for Standardizing Information and Communication Systems.
– CEPT ECC SE43: European Conference of Postal and Telecommunications Administrations Electronics
Communications Committee Spectrum Engineering
15

Game Theory in Cognitive Radio
Game Theory in
CR has
3 Components
• Set of Players
• Set of Actions
• Utility Function
Game theory in CR has two types:
• Cooperative game theory: aims to maximize
total network performance by achieving Nash
Bargaining state. Individual user shares their
vital information like utility with other users in
network.
• Non cooperative game theory which considers
CR users as rational users and aims to maximize
their own utility function i.e., allocating
resources.
– This type of game converges at Nash
equilibrium state
– Stackelberg models : One CR user serves as
leader and implement its decision before
other CR users and anticipate the reacts to
its decision.
16Source: U. Sharma, P. Mittal and C. Nagpal, “ Implementing Game Theory in Cognitive Radio Network for Channel Allocation: An Overview”,
International Journal of Energy, Information and Communications vol. 6, no. 2, pp. 17-22, 2015.

New Trends in Cognitive Radio
Cognitive Radio in 5G
New Applications for CRNs
Green Cognitive Radio Networks
Cloud-based Cognitive Radio
Enhancement in CR MAC Protocols
Wireless Sensor Network-aided Cognitive Radio (CRSN)
Regulatory Aspects of CRNs
Cognitive Radio – Business Model
17

18

Cognitive Radio and 5G
(CR role in 5G)
• Transmission Adaption: CR can dynamically and
autonomously adjust the operating parameters to satisfy
the QoS requirement by offload delay tolerant data traffic
to different tiers and radio access technologies .
19
Massive growth in
Connected
Devices
“Communicating machines”
“50 billion devices in 2020”
More about 5G
UDN
D2D
MN
SON
CR help
5G in
femto 2 femto
femto 2 macro
Intercell
Interference
Mitigation

Cognitive Radio and 5G
(Challenges)
Spectrum
sensing with
focus on 5G
systems
Geo-location DB
for 5G CRNs
Cognitive Radio
with Massive
MIMO for 5G
communications
More …
IEEE Communication Society - SIG on Cognitive Radio for 5G
20
MAC for 5G-
CRNs
D2D comm.
In 5G CRNs
5G-CRNs
public safety
comm.
Cognitive
small cells in
5G cellular
systems

21

New Applications for Cognitive Radio
Cognitive
Radio
Applications
(sample)
Healthcare
Emergency
and Public
Safety
Smart Grid
Vehicular
Networks
Mobile
Networks
Unmanned
Aircraft
Systems
(UAS)
22

Cognitive Radio Architectures for
Unmanned Aircraft Systems (UAS)
23
Policy DB
Geolocation and DB With PU
Geolocation and DB Polling Without PU
Source: Timothy X. Brown, Mark McHenry, and Suppapol Jaroonvanichkul, “Cognitive Radio Architectures for Unmanned
Aircraft Systems”, Handbook of Unmanned Aerial Vehicles, Springer, 2013.

Cognitive Radio Architectures for
Smart Grid
24
A. Aijaz, S. Hongjia, A. Aghvami ,” CORPL: A Routing Protocol for Cognitive Radio Enabled AMI Networks”, IEEE Transactions in Smart Grid,
vol. 6, no. 1, pp. 477 – 485, 2015.
Advanced metering infrastructure
(AMI) or field area networks (FANs)
carry info between smart meters and
network gateway (power sub-station,
pole-mounted device, or a comm.
tower.
Home/building area networks (HANs)
connect smart meters with home
appliances.
WAN serves as the
backbone for
communication
between network
gateways and the
utility data center.
Cognitive
Radio

25

Green CRNs
• Green CRN requires
– Not only the optimization of dynamic spectrum access
– But also the optimal utilization of green energy.
26Source: X. Huang, T. Han, N. Ansari, “On Green Energy Powered Cognitive Radio Networks”, TR-ANL-2014-003,
New Jersy Institute of Technology, 2014.
• Energy
Minimization
• Performance
Maximization
• Utility
Maximization

Energy Efficiency via Cognitive Radio
• Green Relaying,
• Cooperative CRNs
– Between SUs.
– Between PUs and SUs.
• Green Cognitive Small Cells

Green Energy Utilization in CRN
(Challenges)
Simple decision policies are required to analytically
balance energy consumption and harvesting.
Environment-aware green topology management and
resource allocation schemes
(Artificial Intelligence for Green CR).
Mobile charging which relies on the wireless energy
transfer.
Sharing/Trading sensing results is a form of power
balancing.

29

Cloud-based Cognitive Radio and Challenges
By using its capabilities of memory and computational capacity, Cloud computing
platform can enhance spectrum management, reduce energy consumption and
frequent hardware upgrades.
30
Development of spectrum management
policies which describe the role of cloud data
centers in the cloud based CRN.
New protocols are required to manage the
flow of information between various cloud
data centers and CRN nodes.
Security: CRN nodes need to know which
data centers have access to the spectrum
info.
Compare the security and performance of
commercial cloud providers in their ability to
provide effective services to the CRN nodes.
Audit the security and performance of cloud
providers such that QoS for CRN nodes can
be met.

31

32
CR MAC
Challenges
MAC
Design
focusing on
energy.
Improve time
synchronization
and network
coordination for
SUs without
dedicated CCC.
A need to develop
more practical PU
activity models by
considering the
characteristics of
access technologies
as well as types of
traffic.
Ensuring a
reliable
network
coordination
and
reconfiguration
mechanism.
QoS-aware
MAC
protocols

33

Cognitive Radio Wireless Sensor Networks
(CRSN)
34
CRSN
Challenges
Low energy
consumption spectrum
sensing design
Dynamic spectrum aware
cluster formation and
maintenance techniques
Fault Tolerance
QoS
Security
• Clustered topology: Sensor nodes have a leader for a group called as cluster head
which may perform operation of spectrum sensing and local spectrum bargaining.
• Heterogeneous and Hierarchical CRSN: Actor node equipped with more power so
it can be works as relay node due to longer transmission range.
Source: Shailesh V. Kumbhar, Asha Durafe, “Cognitive Radio Sensor Network Future of Wireless Sensor Network”,
International Journal of Advanced Research in Computer and Communication Engineering vol. 4, no. 2, 2015.
Military and Public Security, Home Appliances and Indoor Applications, Bandwidth-Intensive Applications,
Real-Time Surveillance Applications, …
More about WSN

35

Regulatory Agencies Involved in CR and Activities
Federal Communications Commission
(FCC)
• Report in 2002, 2006 and 2008.
• FCC released the final rules for
“Unlicensed Operation in the TV
Broadcast Bands” in 2010/2011.
Office of Communications (Ofcom)
• In 2009 and 2015 reports, Ofcom
released a proposal which allowed
unlicensed cognitive access to the
spectrum.
• Ofcom suggested: (Master/Slave)
(another policy for power limits)
– Sensing
– Geolocation
– Beacon (not any more)
Conférence Européenne des
Administrations des Postes et des
Télécommunications (CEPT) – (CEPT’s SE43) 36
Fixed White Space Device
Operates from a specified stationary location
Suitable for commercial Wi-Fi Hot-Spots, rural broadband
distribution, or cellular-style installations.
Operate at comparatively higher power and with antennas
mounted on a tall building or mast.
Due to its stronger signal, fixed devices are more strict on where
and when the devices can operate.
Personal/Portable White Space Device
laptops, Wi-Fi access points, tablets, and smartphones.
Mode I: devices do not need geolocation capability or
access to a database.
Mode II: devices must have geolocation capability (±50
m ) and the means to access a database for list of
available channels.

Spectrum Database (sample)
37
Information gathered from all TVWS devices:
•The device’s geolocation
•Its device type (Fixed, Portable Mode I, Mode II)
•Its device identifier, which includes FCC ID and manufacturer serial no.
•Data kept (30 days for portable, indefinitely for fixed)

Regulatory and Standardization
(Challenges)
38
Roadmap for the time
phased transition to
the new spectrum
management
paradigm to consider
legacy issues and
special band issues.
Issues which need
to be addressed
by the regulatory
bodies be
identified.
Harmonizing
Terminology and
Reference
Models.
Regulatory
Dimensions to be
Considered
(aspects of equipment,
responsibility, cognitive
pilot channels,…)
Potential Risks and
Benefits that are
related to CR and
SDR technologies
(Part of the SWOT
analysis)
Regulatory Framework
to Encourage the
Research and the
Development of CR.
(for example, allocating
a block of spectrum for
CR control and enabling
secondary licensing, …)

39

Key Actors
40
Infrastructure
Vendors
Regulators
Challenger
Operators
Incumbent
Operators
Content
Providers
Equipment
Vendors

Business Model for TVWS Network
(SpectrumBridge®) – first FCC-certified geolocation database operator
41
Source: Yuan Luo, Lin Gao ; Jianwei Huang, “Business modeling for TV white space networks”, IEEE Communications Magazine, vol. 53, no. 5, pp. 82-88, 2015.
•For underutilized Licensed TV channels
•The database acts as a spectrum broker to facilitate the trading
process
Spectrum Market
(SpecEx)
•Unlicensed TV channels used by public and shared usage.
•Its quality are not guaranteed.
•The database has more information regarding the quality of TV
channels
•This information can be used by unlicensed WSDs to improve
their performance.
Information
Market
(White Space Plus)

Content Provider - Business Model (sample)
42
• Value propositions: Offered Products
/ services.
• Customer segments: Target customer
groups.
• Channels: Ch. for reaching the
customer.
• Customer relationships: Types of
relationships created with the different
customer segments.
• Revenue streams: How the company
earns money.
• Key resources: Assets depends on.
• Key activities: Most important
activities the company must perform in
order to operate successfully.
• Key partnerships: Key partners
needed to create and deliver the value
proposition.
• Cost structure: Describes the costs
and their implications for the business
model.

CR Promising Business Scenarios
Promising
business
scenarios to
be identified
Mobile operators use
TVWS to delay or
replace deployment of
a more dense network
Mobile operators
using TVWS in
countries with very
high spectrum prices
Use TVWS for indoor
systems provided by
local or mobile
operators.
43

More …
Algorithms and Protocols for Self-configuring CRNs.
Centralized/Distributed Algorithms for CRN Management.
CR Implementations, Test-beds and Spectrum Measurements.
Cross-layering in CR Systems.
CR Techniques for Offloading.
CRNs for IoT.
CR Satellite
44

Joseph Mitola and Cognitive Radio
The concept of CR was first proposed by Joseph Mitola III in a seminar at (the
Royal Institute of Technology in Stockholm) in 1998 and published in an article
by Mitola and Gerald Q. Maguire in 1999.
• Mitola later described CR as:
“The point in which wireless Personal digital assistants (PDAs) and the related
networks are sufficiently computationally intelligent about radio resources and related
computer-to-computer communications to detect user communications needs as a
function of use context, and to provide radio resources and wireless services most
appropriate to those needs”
Back
47

Cognitive Radio - Definition
• International Telecommunication Union (ITU)
– ITU: A radio or system that senses, and is aware of, its operational environment and can dynamically
and autonomously adjust its radio operating parameters accordingly.
• Federal Communications Commission
– FCC: CR is a radio that can change its transmitter parameters based on interaction with the
environment in which it operated.
• National Telecommunication and Information Administration (NTIA)
– NTIA: A radio or system that senses its operational electromagnetic environment and can
dynamically and autonomously adjust its operating parameters to modify system operation, such as
maximize throughput, mitigate interference, facilitate interoperability, access secondary markets.
• Wireless World Research Forum
– WWRF: Cognitive radio employs a dynamic time-frequency-power based radio measurement and
analysis of the RF environment, to make an optimum choice of carrier frequency and channel
bandwidth to guide the transceiver in its end-to-end communication, with quality of service being an
important design requirement.
48
Back

• Spectrum sensing: Detecting unused spectrum and
sharing the spectrum without harmful interference with
other users.
• Spectrum decision: CR networks need to decide which
suitable spectrum bands can be used by secondary user.
• Spectrum mobility: Maintaining seamless communication
requirements during the transition to better spectrum.
Furthermore, to implant this process, the spectrum
mobility in CR networks is divided into two parts,
spectrum handoff and connection management to
compensate handoff delay.
• Spectrum sharing: Providing the fair spectrum scheduling
method among coexisting CR users.
Cognitive Radio Cycle
Back

Overview of Spectrum Sensing Methods
50
Sp. sensing
Method
Disadvantages Advantages
Matched
filter
Requires a priori info on PU
transmissions, and extra
hardware on nodes for
synchronization with PUs.
Best in Gaussian noise.
Needs shorter sensing
duration (less power
consumption).
Energy
detection
Requires longer sensing
duration (high power
consumption). Accuracy
highly depends on noise
level variations.
Requires the least amount
of computational power on
nodes.
Feature
detection
Requires a priori knowledge
about PU transmissions.
Requires high
computational capability on
nodes.
Most resilient to variation
in noise levels.
Interference
Temperature
Requires knowledge of
location PU and imposes
polynomial calculations
based on these locations.
Recommended by FCC.
Guarantees a
predetermined
interference to PU is not
exceeded.
Spectrum
Sensing
Cooperative
Detection
Non
Cooperative
Detection
Energy
Detection
Matched Filter
Detection
Cyclostationary
Feature
Detection
Interference
based
Detection
Back
Source: Shailesh V. Kumbhar, Asha Durafe, “Cognitive Radio Sensor Network Future of Wireless Sensor Network”, International
Journal of Advanced Research in Computer and Communication Engineering vol. 4, no. 2, February 2015.

Comparison of Cognitive Radio Paradigms
51
Underlay Overlay Interweave
Network Side Information: SUs
know interference caused to PUs.
Network Side Information: SUs know
channel gains, encoding techniques
and possibly the transmitted data
sequences of the primary users.
Network Side Information: SUs
identify spectrum holes from which
the primary users are absent.
Simultaneous Transmission: SUs
can transmit simultaneously with
PUs as long as interference
caused is below an acceptable
limit.
Simultaneous Transmission: SUs can
transmit simultaneously with the PUs;
to relay the PUs’ data sequences.
Simultaneous Transmission: SUs
transmit simultaneously with a PU
only when there is missed detection
of the PU activity
Transmit Power Limits: Secondary
user’s transmit power is limited
by a constraint on the
interference caused to PUs.
Transmit Power Limits: SUs can
transmit at any power, the
interference to primary users can be
offset by relaying the PUs’ data
sequences.
Transmit Power Limits: Secondary
user’s transmit power is limited by
the range of PU activity it can
detect.
Hardware: SUs must measure the
interference they cause to PUs’
receivers by either sounding and
exploiting channel reciprocity or
via cooperative sensing.
Hardware: SUs must also listen to PU
transmissions. Encoding and decoding
complexity is also significantly higher
than other paradigms.
Hardware: Receiver must be
frequency agile or have a wideband
front end for spectrum hole
detection.
Back

Cognitive Radio MAC Protocols - Example
CR MAC
Centralized
CSMA-MAC
(Random Access –
Single Radio)
IEEE-802.22
(Time Slotted –
Single Radio)
DSA driven
(Hybrid MAC –
Single Radio)
Distributed
Single Radio
SRAC , HC-MAC
(Random Access )
OS-MAC
(Hybrid MAC)
Multiple
Radio
DOSS, DCA-MAC
(Random Access)
C-MAC
(Time Slotted)
SYN-MAC,
Opportunistic MAC
(Hybrid MAC)
52
• Time-slotted MAC protocols
–Require network-wide
synchronization and operate by
dividing time into discrete slots
for both the control channel and
data transmission.
• The random access protocols
–Do not require time
synchronization, and are based on
the CSMA principle.
• Hybrid MAC protocols
–The control signaling occurs in
synchronized time slots and the
data transmission follows random
access channel schemes, or
–Predefined durations for the
control/data frame – however,
the access to the channel within
each control or data transmission
duration is completely random.
Back
Source: Mahfuzulhoq Chowdhury, Asaduzzaman, and M. Fazlul Kader, “Cognitive Radio MAC
Protocols: A Survey, Research Issues, and Challenges”, Smart Computing Review, vol. 5, no. 1, 2015.
C-MAC: Cognitive MAC
HC MAC: Hardware Constrained MAC
POMDP: partially observable Markov decision process
OP-MAC: Opportunistic MAC
SRAC: Single-Radio adaptive Channel
DOSS: Dynamic Open Spectrum Sharing
DCA: Distributed Channel Assignment
DSA: Dynamic Spectrum Access

5G - Features
53
Source: http://parallelwireless/5GPP Vision - Radar diagram of 5G disruptive capabilities Back

WSN - Applications
54Source: Tifenn Rault, Abdelmadjid Bouabdallah, Yacine Challal. Energy Efficiency in Wireless Sensor Networks: a
top-down survey. Computer Networks, Elsevier, vol. 67, no. 4, pp. 104-122, 2014. Back

Classification of Energy-Efficient Mechanisms in
WSN
55Source: Tifenn Rault, Abdelmadjid Bouabdallah, Yacine Challal. Energy Efficiency in Wireless Sensor Networks: a top-down survey.
Computer Networks, Elsevier, vol. 67, no. 4, pp. 104-122, 2014.

Cognitive Radio Testbeds
CR Platforms
• Lyrtech’s small form factor SDR development platform
– Flexible, powerful, extremely expensive, too complex for research.
• Microsoft Research SORA
• Eurecom Open-Air-Interface
– Support LTE, limited support, expensive)
• Ettus/NI USRP
– Relatively cheap, Wide use, poor RF performance
CR Software
• GNU Radio (most common used, written in C++ and python, supported by Ettus)
• OSSIE (by Virginia Tech, written in C++, …)
• IRIS (by Trinity College Dublin, written in C++, configuration method based on XML)
• ASGARD (by Aalborg University in Denmark, written in C++, tackle the challenge
of next generation telecomm. systems using multi user MIMO and aggregated
spectrum techniques.
Testbeds in CRN
• Gain CR product credibility
• Check realistic limitations for CR algorithms
• Check the real performance of CRNs
56

Multiple Dimensions of Spectrum Space
• Legacy sensing algorithms monitor and supervise the
spectrum through three conventional dimensions:
frequency, time and space domains.
• However, other degrees of freedom such as the used
code and the angle of arrival may be used.
Challenges
• Hybrid and new dimensions to create new spectrum
opportunities and optimize the utilization of spectral
resources.
57

Comparison of Transport Protocols in CRNs (single hop)
Source: X. Zhong, Y. Qin, and Li Li, “Transport Protocols in Cognitive Radio Networks: A Survey”, KSII Transactions on Internet
and Information Systems (TIIS), vol. 8, no. 11, pp. 3711-3730, 2014.
58
More

Comparison of transport protocols in CRNs (Multiple hop)
BacktoTCPinCognitiveRadio
X.Zhong,Y.Qin,andLiLi,“TransportProtocolsinCognitiveRadioNetworks:ASurvey”,
KSIITransactionsonInternetandInformationSystems(TIIS),vol.8,no.11,pp.3711-
3730,2014.DOI:http://dx.doi.org/10.3837/tiis.2014.11.004

CR Attacks
(1/2)
60
Back to Cognitive Radio Main Attacks
Source:
D.Hlavacek,J.M.Chang,Alayeredapproachtocognitiveradionetworksecurity:Asurvey,
ComputerNetworks.,2014.http://dx.doi.org/10.1016/j.comnet.2014.10.001

CR Attacks
(2/2)
61
Source:
D. Hlavacek, J.M. Chang, A layered approach to cognitive radio network security: A survey, Computer Networks., 2014.
http://dx.doi.org/10.1016/j.comnet.2014.10.001
Back to Cognitive Radio Main Attacks

National Telecommunication Institute
Cognitive Radio Testbed (NTI-CRT)

NTI-CRT - Objective
• Objectives
– To create a real national cognitive radio test-
bed.
– Scientific and engineering understanding of
the technical constraints on the design and
regulation
– Proposal for future cognitive radio systems
operating on new bands.
Primary/Secondary
Transmitters
Primary Transmitter (PTx)
Secondary Transmitter (STx)
Primary/Secondary
Receivers
Primary Receiver (PRx)
Secondary Receiver (SRx)
Dispatcher
Spectrum Sensing USRP Spectrum Management USRP
National Telecommunication Institute (NTI) Cognitive Lab Architecture

NTI-CRT - Scenarios
Primary/Secondary
Transmitters
Primary Transmitter (PTx)
Secondary Transmitter (STx)
Primary/Secondary
Receivers
Primary Receiver (PRx)
Secondary Receiver (SRx)
Dispatcher
Spectrum Sensing USRP Spectrum Management USRP
National Telecommunication Institute (NTI) Cognitive Lab Architecture
• Sce 1: Prim Ch1 Sec Ch2 No Jamming
• Sce 2: Prim Ch1 Sec Ch2 Jam Ch0
• sce 3: Prim needs Ch1 Sec Ch2 Jam Ch1
– Dispatcher finds Jamming on the desired channel
– Dispatcher moves Primary to another Jam free channel (Ch0
as an example)
• Sce 4: Prim needs Ch2 Sec Ch2 Jam Ch1
– Dispatcher finds Secondary on the desired channel
– Dispatcher allocates Primary to the desired channel
– Dispatcher moves Secondary to another Jam free channel
(Ch3 as an example)
• Sce 5: Secondary needs Ch2 Prim Ch2 Jam Ch1
– Dispatcher finds Primary on the desired channel
– Dispatcher rejects the Secondary Required Channel
(Ch3 as an example)
• Sce 6: Jamming needs Ch0 Prim Ch1 Sec Ch0
– Dispatcher finds Primary on the desired channel
– Dispatcher rejects the Secondary Required Channel
(Ch2 as an example)
• Sce 7: Jamming needs Ch2 Prim Ch1 Sec Ch2
– Dispatcher finds Secondary on the desired channel
(Ch0 as an example)

NTI-CRT – Scenario (1) – No jamming

NTI-CRT – Scenario (2) – Ch1 jamming

NTI-CRT – Scenario (3)(1/2)
Prim needs Ch1 Sec Ch2 Jam Ch1

Jamming needs Ch0 Prim Ch1 Sec Ch0

Stat of the art in cognitive radio

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