Cognitive Radio Networks, and Spectrum Sensing

Ghazi AL SUKKAR

ghazi.alsukkar@ju.edu.jo

The University of Jordan

Bologna, 10th of November 2017 Secondary user

Primary user

Primary base station

Secondary base station

Myself • Dr. Ghazi AL SUKKAR

• Department of Electrical Engineering, the University of Jordan

• Associate professor of Electrical engineering

• Head of EE Dept. for 3 years

• Deputy Dean for Accreditation & Quality Assurance for 2 years

• SMIEEE

• Vice-chair, IEEE-Jordan Section

• Education • 2000 B.Sc. Electrical Engineering, Jordan University of Science and Technology,

Jordan • 2003 M.Sc. Telecommunications, The University of Jordan, Jordan • 2008 Ph.D. Wireless Communication Networks, Telecom SudParis, France

Myself • Teaching

• Signal and Systems

• Cellular Communications

• Digital Communications

• Communication Networks

• Stochastic Processes

• Digital Signal processing

• Supervision • Supervised more than 30 graduation projects

• Supervised more than 10 M.Sc. students

Myself • Research

• Wireless communication networks • Wireless sensors

• Mesh

• Vehicular

• P2P networks

• Cognitive Radio

• LTE-Advanced

• Traffic simulation

• Digital Signal Processing

Agenda

• Spectrum Scarcity • RF spectrum • Wireless traffic growth

• Cognitive Radio Networks (CRNs) • Definition and Concepts • Categories of CRNs

• Spectrum Sensing • Sensing Algorithms • Types of Spectrum Sensing

• Concluding Remarks

Spectrum Scarcity Challenges and Solutions

Radio Spectrum • RF spectrum typically refers to the full frequency range from 3 KHz to

300 GHz.

Cont..

• RF spectrum is a national resource that is typically considered as an exclusive property of the state.

• RF spectrum usage is regulated and optimized

• RF spectrum is allocated into different bands and is typically used for • Radio and TV broadcasting

• Government (defense and public safety) and industry

• Commercial services to the public (voice and data)

US Frequency Allocation Chart

What is wireless communications?

Joseph Henry Michael Faraday Hans Christian Ørsted James Clerk Maxwell

Heinrich Hertz Nikola Tesla Guglielmo Marconi

Any form of communication that does not require the transmitter and receiver to be in physical contact

Basic Wireless Communication System

Wireless channelTransmitter Receiver

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Examples (Depends on Country)

Technology Frequency

AM radio 535 kHz to 1.7 MHz

FM radio 88 MHz to 108 MHz

Television stations (VHF) 54 MHz to 88 MHz 174 MHz to 220 MHz

Television stations (UHF) 470 MHz to 806 MHz

Wi-Fi (we say 2.4 GHz) 2.412 GHz to 2.484 GHz

5.15 GHz to 5.725 GHz

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GSM band ƒ (MHz) Uplink (MHz)

(Mobile to Base)

Downlink (MHz)

(Base to Mobile)

Equivalent

LTE band

T-GSM-810 810 806.2 – 821.2 851.2 – 866.2 27

GSM-850 850 824.2 – 849.2 869.2 – 893.8 5

P-GSM-900 900 890.0 – 915.0 935.0 – 960.0

E-GSM-900 900 880.0 – 915.0 925.0 – 960.0 8

DCS-1800 1800 1710.2 – 1784.8 1805.2 – 1879.8 3

PCS-1900 1900 1850.2 – 1909.8 1930.2 – 1989.8 2

Wireless is Everywhere!

• Cellular Telephony: • 2G, 3G, 4G, 5G (coming soon).

• Wi-Fi wireless local area networks.

• Bluetooth, Zigbee and NFC.

• WiMAX metropolitan area networks.

• Radio broadcasting (AM, FM, DAB).

• TV broadcasting (NTSC, PAL, DVB-S, DVB-T, ATSC).

• More in the future: V2V, V2I, IoT, WSN, …

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Wireless Demand Growth (4G LTE)

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Growth of Mobile Phone Subscribers

Mobile internet traffic is pushing the capacity limits of wireless networks !

RF Spectrum “Crunch”

• Smartphone usage tripled in 2011.

• Between 2011 and 2016, global wireless data traffic is expected to increase 18 times more.

• Future generations of wireless communications are expected to provide up to 10 Gbps data speed. Smart Devices will grow up to 34 billion by 2020.

• Rapid increase in the use of wireless services has lead the problem of spectrum exhaustion.

• FCC predicts that the US is going to start experiencing a spectrum deficit for wireless communications in 2013.

Potential Solutions

• More efficient usage of the available spectrum: • Multiple antenna systems • Adaptive modulation and coding systems

• More aggressive temporal and spatial reuse of the available spectrum: • Cognitive radio systems • Femto cells & Offloading solutions

• Use of unregulated bandwidth in the upper portion of the spectrum: • Microwave and millimeter-wave such as 60 GHz & 90 GHz • THz carriers • Optical spectrum (FSO)

Spectrum Sharing Systems Cognitive Radio Networks

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Spectrum White Space!

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Can we use it?

• Cognitive radio is a promising wireless technology.

• Allows users to harness spectrum that is assigned to licensed users, but is not being fully utilized at a specific place or time.

• Devices in a cognitive radio network sense the spectrum around them for unused portions and then dynamically utilize empty spectrum bands they can find.

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Terminology

• Primary Users (PUs): The main license-holders of the spectrum. Such devices are typically not cognitive, and do not have any functionality for sharing the spectrum with others, as they have priority access to the spectrum by law.

• Secondary Users (SUs): Allowed opportunistic access to the licensed spectrum of the PUs, but only temporarily and with less priority. Also known as CR nodes.

• An SU uses its cognitive abilities to communicate over the available spectrum bands, while concurrently minimizing interference with PUs.

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Intelligent Operations

Secondary user

Primary user

Primary base station

Secondary base station

Kalil, M., (October 2011), “Cognitive Radio Networks Part II”.

Cognitive Cycle

“A really smart radio …” [Mitola’1999]

Applications of Cognitive Radio

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Cognitive Radio Modes

• 3 Modes 1. Overlay: Cooperative

communications

2. Underlay: Respect interference constraints

3. Interweave: Spectrum holes

1. Overlay: Cooperative communications

• Simultaneous communication between SUs and PUs as long as secondary users facilitate the PUs communication

• Relies on: • Cooperative communication

• Distributed space-time coding

2. Underlay

• SU co-exist with PU as long as the interference to PU remains tolerable

• Secondary performance: Minimize power or maximize capacity.

• Primary protection measure: Depends on the channel state information (CSI) at SU Transmitter.

Underlay Challenges:

• Measurement challenges • Measuring interference at primary receiver

• Measuring direction of primary node for beamsteering

• Policy challenges • Underlays typically coexist with licensed users

• Licensed users paid $$$ for their spectrum • Licensed users don’t want underlays

• Insist on very stringent interference constraints

• Severely limits underlay capabilities and applications

3. Interweave: Opportunistic Spectrum Access

Primary (PU) and secondary users (SU) communicate simultaneously only in the case of false spectral hole detection • Secondary transmission is only allowed when primary is

idle • Spectrum sensing has to be performed

• Problem modeled as binary hypothesis detection problem • Secondary performance: minimizing probability of false

alarm. • Primary protection measure: satisfy probability of

detection.

Interweave Challenges

• Spectral hole locations change dynamically • Need wideband agile receivers with fast sensing

• Compressed sensing can play a role here

• Spectrum must be sensed periodically • Tx and Rx must coordinate to find common holes • Hard to guarantee bandwidth

• Detecting and avoiding active users is challenging • Fading and shadowing cause false hole detection • Random interference can lead to false active user detection

• Policy challenges • Licensed users hate interweave even more than underlay

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CR General Challenges

• Channel allocation between secondary users (called spectrum access strategy).

• Centralized: All SUs forward their spectrum sensing measurements to a central authority, which performs spectrum assignment. Requires a CCC and a powerful central controller.

• Decentralized (ad-hoc): Each SU uses its local spectrum sensing information to make its own decisions about spectrum allocation, independent of others in the system. Fairness and throughput issues (collisions).

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Standardization Groups • IEEE 802.22 Wireless Regional Area Network (WRAN) standard to

exploit the UHF/VHF TV bands. • TV frequency bands have favorable wireless propagation characteristics,

allowing longer distance communications.

• IEEE 802.16h standard (now part of IEEE 802.16-2012 standard) adds cognitive radio to WiMAX networks.

• IEEE 802.11af standard allows Wi-Fi operation in TV white space in VHF and UHF bands using cognitive radio.

• European Computer Manufacturers Association ECMA-392 standard.

• European Telecommunications Standards Institute ETSI TS 102 946 standard for cognitive radio in TV whitespace.

802.22 Network Deployment Scenario

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WRAN Repeater

TV Transmitter

WRAN Base Station

Wireless MIC

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Wireless MIC

집

WRAN Base Station

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: CPE 집

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: WRAN Base Station

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Typical ~33km Max. 100km

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Spectrum Sensing

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Reliable Sensing

Sensing is important to detect the presence of PUs. • But there is noise, attenuation and fading (too much in a wireless channel).

• Wide range of spectrum sensing algorithms • Trade-offs: detection performance, complexity, computational cost, applicability.

• Applicability depends on available information: • Detailed knowledge(modulation type, pulse shaping, synchronization info, etc..) Matched filter • Certain features Feature detector (cyclostationary, pilots, others…) • Correlated signal (oversampling, multiple antennas) Covariance detector • No prior information Energy detector

• Ideal sensor: • Simple (low complexity and low computational cost) • General applicability (ability to detect any signal format) • High detection performance (high detection prob., low false alarm prob.)

MF

Covariance

Cyclo

ED

Complexity

Acc

ura

cy

Energy detection scheme

• The energy detection scheme is the most popular spectrum sensing technique in cognitive radio.

• Because it exhibits: • Low complexity

• Robust to unknown dispersed channels fading, and variation of the primary signal.

• It does not require any a priori knowledge of the primary signal under detection.

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( )2

Noise pre-filter Squaring device Integrator

Test

statistics Y(t) ∑ ADC

Analog-to-digital converter

Spectrum Sensing Problem

• Primary user has two states, idle or busy. Noise Noise + signal

• Formulated according to simple binary hypothesis test:

Where,

x(n) Rx baseband equivalent of nth sample

s(n) nth sample of primary user signal seen at Rx

w(n) Complex Gaussian noise independent of s(n), unknown noise variance

Performance metrics:

False alarm (Pf): efficiency

Missed-detection (Pm): reliability

Detection (Pd): 1-Pm

• Higher Pd (lower Pm) and lower Pf are preferred.

𝐴: To detect the existence of PU 𝐴 : To detect the absence of PU 𝐵: PU is Busy 𝐵 : PU is idle 𝑃 𝐴/𝐵 = 𝑃𝑑 𝑃 𝐴 /𝐵 = 1 − 𝑃𝑑 = 𝑃𝑚 𝑃 𝐴/𝐵 = 𝑃𝑓

𝑃 𝐴 /𝐵 = 1 − 𝑃𝑓

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Performance Measurements • Average Pd:

• Pd vs. SNR

• ROC (receiver operating characteristic) curve:

• Pd vs. Pf

(1, 1)

False alarm probability

De

tectio

n p

rob

ab

ility

(0, 0)

Thres

hold

0

8

Det

ectio

n ca

pability

AUC (area under ROC curve): probability that choosing correct

decision is more likely than choosing incorrect decision.

Types of Spectrum Sensing

Parallel vs. Sequential Sensing

• If there are N frequency channels

• Sense channels 1 to N at the same time (parallel): requires N sensing device

• Sequential: Sense channels one by one. Which order?: May take too long to find an empty channel.

Proactive vs. Reactive Sensing

• Proactive Sensing:

CR senses even if it will not transmit immediately, e.g. periodic sensing. • Trade-off: collected information about the channels vs. sensing cost

• Reactive Sensing:

CR senses only if it will transmit or receive. • Energy-efficient, time to find an idle channel may be longer than Proactive

Sensing.

Synchronous vs. Asynchronous Sensing • Synchronous

All CRs have the same sensing schedule to sense a channel. How to Synchronize?

Stop transmission and sense the medium.

• Asynchronous

Each CR has its own schedule to sense a channel. If other CRs are transmitting while this CR is sensing, how to distinguish

between SU and PU signal.

In-band vs. Out-of-band Sensing

• In-band

CR senses the channel that it is already transmitting.

• Out-of-band

CR senses channels other than the channel it is in. Multi-Antennas

Centralized vs. Distributed Sensing

• Centralized

A Central Manager (BS or AP) collects CR sensing data and makes a decision on channel state, i.e. idle or busy

Cost of transmission sensing data?

What if the Central Manager fails? Single Point of Failure.

• Distributed (Decentralized)

Each CR makes decision itself. Inaccurate decision

Cooperative vs. Non-cooperative

Non-cooperative sensing (using local-based sensing): Each SU relies only on its local observations to make sensing decisions. Reliability issues.

Cooperative sensing (system-wide sensing): spectrum sensing measurements made by one individual SU is shared among all other SUs in the network More reliable: to solve the problem of hidden node.

How to communicate? Common control channels (CCC)

Cooperative Spectrum Sensing

• The hidden node problem and need for cooperative spectrum sensing

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The hidden node problem in a CRN

Decision Fusion: How to decide?

• DECISION FUSION LOGIC:

AND

OR

MAJORITY

K-of-N

• Soft or Hard Decision Combining: Yes or No answers (0-1), or Received Signal Strength.

Cooperative Sensing

• Three step: Local sensing, reporting, decision/data fusion

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Basic configuration of centralized cooperative spectrum sensing

Challenges of Spectrum Sensing

• Hardware requirements: High speed processing units (DSPs or FPGAs) performing computationally

demanding signal processing tasks with relatively low delay.

Operation in a wide spectrum range.

• Sensing-Transmission Tradeoff

• Security: a selfish or malicious user can modify its air interface to mimic a primary user

Summary Concluding Remarks

Conclusion and Current Work

• Spectrum scarcity is becoming a reality

• This scarcity can be relieved through: • Cognitive radio networks • Extreme bandwidth communication systems

• The spectrum sensing can be designed considering various criteria at MAC and PHY layer

• The longer is the sensing duration, generally the higher is the sensing reliability

• Cooperation increases sensing performance but has higher overhead

• Analytical results can be used to perform initial system level trade-offs

Thank you Any Questions?