Internet of Things
Cognitive Radio Technologies
Torino, 29 aprile 2010
Roberto GARELLO, Politecnico di Torino, Italy
Speaker:
Roberto GARELLO, Ph.D.
Associate Professor in Communication Engineering
Dipartimento di Elettronica
Politecnico di Torino, Italy
email: [email protected]
sito web: www.tlc.polito.it/garello
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European Commission Report, May 2009 [1]
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Contents
Introduction to cognitive radio
Spectrum sensing
Sensing network structure: fusion centre/fully distributed
Resource allocation/Cross layer design
Standard
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Introduction to Cognitive Radio
new systems
increasing bandwidth need
static frequency allocation
Spectrum congestion
idea = opportunistic usage of some frequency bands
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Cognitive Radio paradigm
opportunistic usage
Primary users (licensed) can transmit whenever they need
Secondary users can transmit only when the primary users are silent
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Primary users are transmitting Secondary users cannot transmit
Primary user
Primary user
Secondary users
Primary users are not transmitting Secondary users can transmit
Primary user
Primary user
Secondary users
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COGNITIVE RADIO
Must:
• Sense the frequency bands and understand the channel status
[SPECTRUM SENSING]
• Dynamically adapt its radio parameters for
o maximize its throughput
o produce zero (very limited) interference to primary users
[RESOURCE ALLOCATION]
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SPECTRUM SENSING
In its simplest form:
• must understand if a channel is free or busy
More features:
• multi-dimensional sensing: determine frequency, temporal, geographical
occupancy
• modulation detection: infer modulation type & parameters, channel access,
protocol
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SPECTRUM SENSING: typical performance measures
False alarm probability
The primary users are not transmitting
Primary user
Primary user
Secondary users
The secondary user spectrum sensing makes a mistake and reveals its presence
The secondary users could transmit but they don’t They are wasting bandwidth
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SPECTRUM SENSING: typical performance measures
Missed detection probability
The primary users are transmitting
Primary user
Primary user
Secondary users
The secondary user spectrum sensing makes a mistake and does not reveal its presence
The secondary users should not transmit but they do They are producing interference
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Multi-dimensional spectrum sensing
Frequency/time:
The spectrum bandwidth is divided in smaller channels
Channel occupation is not continuous
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Multi-dimensional spectrum sensing
Geographical:
the spectrum can be available to a subset of the cognitive networks
(example: users at larger distance)
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SPECTRUM SENSING: requirements
• Good performance
o Low False alarm probability: maximize secondary users throughput
o Low Missed detection probability: minimize primary users interference
• Very reactive (must take a decision in a limited amount of time)
• Efficient secondary user network structure (central fusion centre, all/some/few
cognitive users, completely distributed)
•Good integration with upper layers (sensing/allocation schedule for a single node,
sensing distribution between nodes)
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SPECTRUM SENSING: algorithms
• Non-parametric detection:
(energy detection/eigenvalue-based detection)
no assumption on primary
signals
•Search for known patterns
•Cyclo-stationarity sensing
•Matched filter
completely known primary
signals
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Energy detection
H0 ONLY NOISE
y(n) = v(n)
H1 SIGNAL + NOISE
y(n) = s(n)+v(n)
N samples for slot
y = (y(1) … y(n) … y(N))
N
Test statistics
T=
E
σ2
E = ∑ y ( n)
2
Estimated energy
n =1
σ2
Estimated noise variance
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Energy detection: algorithm decision
Set a threshold λ
if T =
E
σ
2
≥λ
DECISION = signal is present
if T =
E
σ
2
<λ
DECISION = signal is absent
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Energy detection: performance
FALSE-ALARM PROBABILITY: signal is absent but detector erroneously detects it
H0 ONLY NOISE y(n) = v(n)
T ≥λ

 λ

Pfa = P(T ≥ λ | H 0 ) = Q  N  2 − 1 
σ


Q function decreases with its argument
Pfa decreases if threshold λ increases
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Energy detection: performance
MISSED-DETECTION: signal is present but detector erroneously does not detect it
H1 SIGNAL + NOISE y(n) = s(n)+v(n)
T <λ

 λ


 σ 2 − 1 − SNR )  
Pmd = P(T < λ | H1 ) = 1 − Q  N 


 1 + 2 SNR  


 
Q function decreases with its argument
Pmd decreases if threshold λ decreases
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Energy detection: ROC performance
ROC (Receiver Operating Characteristic): Pmd vs. Pfa
Taken from [3]
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Eigenvalue-based detection
With respect to energy detection, this class of detectors is characterized by:
• Better performance
• Higher complexity
Samples are collected by K independent receivers (or antennas), forming a matrix
 y11 ... y1N 
Y =  ... ... ... 
 yk1 ... ykN 
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Eigenvalue-based detection
Given the K x K sample covariance matrix
R = YY h
λ1...λK
Its K eigenvalues are computed:
The most popular test is the GLRT (Generalized Likelihood Ratio Test):
T=
λ1
K
∑λ
i =1
i
which strongly improves the Energy Detection performance
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Non-parametric detection algorithms:
…
performance comparison
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Spectrum sensing network structure
• Device-centric (a single device senses and takes the decision)
• Cooperative sensing (K device sense and merge their information to take the decision)
o Fusion center
o Fully distributed
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Fusion center
All the sensing devices transmit some information (local decision or more information) to a
central privileged device
The fusion center merge the information, takes the information and communicate it to the network
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Fully distributed sensing
•Each sensing user sends some information to its neighbors
•Sensing information is propagated through the network
•After some iterations the algorithm can be used to
-compute multiple, location-dependent, sensing estimation relative to each single node
- make a global decision about the presence of primary users in the overall area
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Fully distributed sensing by factor graph
Example: factor graph modelling
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Network message passing
Belief propagation (on loopy networks)
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Fully distributed sensing by factor graph
After few iteration, each node is helped by its neighbors to converge to the true sensing
estimation
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Fully distributed sensing by factor graph
Interesting feature:
Some nodes can obtain the estimation without sensing, by using the neighbors
information
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Resource allocation: cross layer design
Towards a unified framework for designing:
•Spectrum sensing parameters
•MAC (medium access control)
with the aim of optimizing the secondary network average throughput
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Resource allocation: cross layer design
Opportunistic spectrum usage requires to take decisions on some key events.
Some examples:
- When a channel is declared free and secondary users begin to transmit over it, is the
spectrum sensing performed in parallel to data transmission?
- How often must the secondary users negotiate their transmission parameters?
- Can multiple channels be used together?
- What happens when Secondary Users data transfer is interrupted by Primary Users?
•It is being buffered for further transmission
•It tries to switch to a free channel
- Is it better to divide the secondary network in clusters? How to do this?
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Enabling technology
Software radio
Represents an “optimal solution”: re-configurable, re-programmable, adaptive, reactive,
multi-standard, etc.
Node computational capacity and power consumption
Key issue: some operations can be rather complex
Nodes synchronization, and coordination
Low data-rate control channel?
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International standards
Cognitive radio WRAN 802.22
WiFi 802.11k
Bluetooth
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Cognitive Radio Networks (802.22)
First international “cognitive radio standard” :
WRAN (Wireless Regional Area Networks) 802.22
USA TV bands (54/862 MHz)
Stringent requirements (Pfa < 0.1, Pmd < 0.1)
Positioning information based on some GPS-equipped base stations can be used to
improve geographical information
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WLAN (802.11k)
WLAN devices usually connect to the Access Point with the strongest signal level
802.11k:
•The Access Point senses each channel and collects sense information from users
•Non 802.11 utilization of each band is estimated
•This information is used for channel allocation
•If an Access Point is already loaded at his maximum capacity (also taking into
account interference), new users are assigned to underutilized AP
•Overall system throughput increases
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Bluetooth Adaptive Frequency Hopping
ISM band: frequency hopping to reduce interference with other devices
Adaptive Frequency Hopping:
• Channel quality is sensed
• Bad channels are avoided
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Example of application: WiFi vs. WSN [5]
The concept of “adaptive resource allocation” can already be applied to existing
systems Spectrum sensing + optimal frequency allocation
Example: WSN, Bluetooth
PTX=20 dBm
PTX=0 dBm
The WiFi and WSN channels partially overlap in the 2.4 GHz ISM
Asymetric situation: WiFi interferers over WSN
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Some references
[1] Maarten Botterman – European Commission, “Internet of Things: an early reality of the Future
Internet”, Workshop Report, May 2009
[2] Ekram Hossain, Vijay Bhargava, “Cognitive Wireless Communication Networks”, Springer 2007.
[3] Tevfik Yucek and Huseyin Arslan, “A Survey of Spectrum Sensing Algorithms for Cognitive Radio
Applications”, IEEE Communications surveys and tutorials, vol. 11, no. 1, first quarter 2009.
[4] Jihoon Park, Przemysław Pawełczak, and Danijela Cabric, “Performance of Joint Spectrum
Sensing and MAC Algorithms for Multichannel Opportunistic Spectrum Access Ad Hoc Networks”,
submitted to IEEE Transactions, 2010.
[5] Federico Penna, Roberto Garello, Maurizio A. Spirito, “Distributed Inference of Channel
Occupation
Probabilities in Cognitive Networks via Message Passing”, Dyspan 2010, Singapore, April 2010.
[6] Istituto Superiore Mario Boella and Politecnico di Torino, “Impact of Wi-Fi Traffic on the IEEE
802.15.4 Channels Occupation in Indoor Environments”, ICEAA 2009.
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