Cognitive Networks Dasilva

8/13/2019 Cognitive Networks Dasilva

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10/ 05/ 2012

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Cognitive Networks

Prof. Luiz DaSilva

Theory and Practice of Cognitive Radio

Aalborg University

May 9-11, 2012

Cognitive Networks:

Architectures and Principles

Opportunistic Channel Access

and Rendezvous

MACs for Cognitive Networks

Cross‐Layer Design



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Bringing the network into the picture

• In a network of autonomous, adaptive radios,

do individual optimizations result in network‐

wide optimal performance?

• How much information about network

conditions must independent radios have to

make effective adaptations?

• Learning and reasoning are needed to manage

complex cross‐layer optimizations

A definition

• A cognitive network has a cognitive process that can perceive

current network conditions, and then plan, decide and act on

those conditions. The network can learn from these

adaptations and use them to make future decisions, all while

taking into account end‐to‐end goals

• Critical components: – Cognition (as opposed to reactive, localized schemes)

– End ‐to‐end goals (as opposed to layer level goals)

• Does not specify application or mechanism

R. W. Thomas, L. A. DaSilva, and A. B. MacKenzie, “Cognitive

Networks,” IEEE DySPAN, 2005.



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Node

Link

Network

Dynamic

frequency

selection

Beam

forming/

nulling

Power

control

MIMO

Topology

control

Spectrum

negotiation

Cooperative

Comms.

Waveform

selection

Adaptive

routing

In a cognitive radio network, adaptations can occur at

multiple layers, and they interact with one another

Network

coding



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Cognitive Network Architectures: Example 1

Cognitive

element

element goal

cognitive element

element goal

end-to-end goalend-to-end goal end-to-end goal

cognitive element

element goal

cognitive

element

element goal(s)

cognitive

element

element goal

cognitive

element

element goal

CSL

RequirementsLayer

CognitiveProcess

Software

Adaptab leNetwork

configurable

elementconfigurable

element

configurable

element

network

statussensor

network

statussensor

cognitive

element

element goal(s)

element

status

transfer

SAN API

R. W. Thomas, D. H. Friend, L. A. DaSilva, and A. B. MacKenzie,

“Cognitive Networks: Adaptation and Learning to Achieve

End‐to‐end Performance Objectives,” IEEE Communications

Magazine, Dec. 2006


P. Sutton, L. Doyle, and K. Nolan, “A Reconfigurable Platform

for Cognitive Networks,” Proc. CROWNCOM, 2006



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P. Mahonen, M. Petrova, J. Riihijarvi, and M. Wellens,

“Cognitive Wireless Networks: Your Network Just Became a

Teenager,” INFOCOM (poster), 2006

Opportunistic channel access: cognitive

radio perspective

• Scenario: a set of channels {1, 2, …, N} is

allocated to an incumbent/primary user

(PU) and can be opportunistically utilized,

on a non‐interfering basis, by a secondary

user (SU)

• Problem: the SU must select the ‘best’

channel in which to operate

• Challenges:

• Accurate, reliable sensing

• Learning patterns of utilization of

channels by PU

• Abiding by interference constraints

1

2

3

N

channels

primary

user

1

secondary

user



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Opportunistic channel access: cognitive

network perspective

• Scenario: a set of M cognitive radios now

compete for opportunistic access to the N

channels that are allocated to a PU

• Problem: the SUs must select a good

channel but also to coexist efficiently

(and peacefully) with other SUs

• Additional challenges:

• Intra‐SU competition and/or

cooperation

• Fairness and efficiency in resource

allocation

• Topology control, routing

1

2

3

N

channels

primary

user

1

secondary

users

M…

Extended example: channel

selection by autonomous,

frequency ‐agile radios

• Problem: M cognitive radio

pairs sense from a set of N

channels. When a CR finds a

free channel, it transmits for

the remainder of the time

slot.

• A CR’s throughput is affected

by other CRs’ success in

finding a vacant channel, as

well as by PU activity.

• There is the potential to learn

from past choices and

observations.

Z. Khan, J. J. Lehtomaki, L. A. DaSilva, and M. Latva‐aho,

“Autonomous Sensing Order Selection Strategies

Exploiting Channel Access Information ,” IEEE Trans. on

Mobile Computing (in press, 2012)



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Extended example: channel

selection by

autonomous,

frequency ‐agile radios

Objective – A mechanism to

enable CRs to autonomously

arrive at collision‐free sensing

orders

• Problem is complicated by the

presence of the PU and by the

possibility of false alarms

Extended example: channel selection

by autonomous, frequency ‐agile

radios

• Sensing orders selected from a Latin

square.

• Initially, each CR selects any sensing

order with equal probability.

• Whenever successful, or if it finds

all channels busy, the CR selects the

same sensing order in the next slot.

• In the case of a collision, the CR

multiplicatively decreases the

probability of picking the same

sensing order, by a factor γ, with all

other sensing orders equally likely.

all permutations

of 4 channels

a Latin square



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by autonomous,

frequency

‐agile

radios

[Thm] When N = M, and 0 ≤P[false

alarm] < 0.5, for any 0 < γ <1 the

network converges to collision‐free

sensing orders

When N > M, convergence to collision‐

free sensing orders is even faster (N =

M is the ‘worst case’ for convergence)

[Proposition] When P[PU present] = 0,

the expected number of slots until

collision‐free sensing orders are

obtained is O(N)

N = M = 10

TTC = time to

arrive at

collision-free

sensing orders

Extended example: channel selection by

autonomous, frequency ‐agile radios

Analytical results

• An (ugly) analytical expression for M=2

• A bound for M > 2



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by autonomous,

frequency

‐agile

radios

Comparison:

RPS: random sensing order selection

from all permutations of channels

LS: random sensing order selection

from a Latin square

rand‐AP/LS: randomize after collision

MxP[N,M,θ]: average # of successful

transmissions in a time slot

Radio Rendezvous

• A meeting at an established time and place

– From the French “rendez vous,” for “present yourself”

• The ability of two or more radios to meet and establish a link

on a common channel, bootstrapping communication

– A requirement of any multi‐channel system

– In opportunistic spectrum access, particularly challenging

due to large number of potentially usable channels and

presence of incumbents

• Rendezvous also includes link maintenance as channel

availability changes



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Rendezvous Taxonomy

Rendezvous

Unaided

Singlecontrolchannel

Multiplecontrol

channels

No controlchannel

Aided

Dedicatedcontrolchannel

Aided vs. Unaided Rendezvous

• Aided (infrastructure‐based)

– Accomplished with the help of a server

– Server periodically broadcasts channel availability

information

– Server may also serve as a clearinghouse for link

establishment and scheduling of transmissions

– Typically uses a well‐known control channel

• Unaided (infrastructure‐less)

– Radios are on their own

– May or may not use dedicated control channels



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Control Channel Tradeoffs

• Use of a control channel for rendezvous and channel

reservation

– Simplifies the rendezvous and negotiation processes

– But creates a bottleneck and single point of failure

• Use of locally‐selected control channels

– Improves scalability and reduces bottlenecks

– But imposes the overhead of cluster formation and control

channel selection

• Control messages exchanged over data channels

– Scalable, flexible, distributed solution

– But adds complexity to rendezvous

Dedicated Control Channel:

Infrastructure Networks

• Spectrum information

channels

• Clients dedicate an interface

to scan these channels

• Base station broadcasts info

about channel availability,

interference conditions

• Clients use the same channel

to request the use of an

available data channel for

their traffic

M. Buddhikot et al., “DIMSUMNet: New Directions in

Wireless Networking Using Coordinated Dynamic

Spectrum Access ,” IEEE WOWMOM, 2005.



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Dedicated Control Channel: Ad Hoc Networks

• Radios periodically monitor a known control channel

– Or, if multiple transceivers are available, one is dedicated

to that purpose

– Channel occupancy announcements are periodically

broadcast by all nodes on this channel

• Negotiation of data channels

– For example, through the exchange of RTS/CTS

• Once negotiation is completed, transmitter and receiver move

to the reserved data channel

– And start broadcasting channel occupancy announcements

on the control channel

Dynamically Changing Control Channel

• Example: radios are programmed to always attempt to

rendezvous on lowest numbered channel not currently

occupied by an incumbent

• Robustness issue

– Transmitter/receiver may not both be able to sense

presence of incumbents due to differences in location,

range, and sensing capabilities

PU

Channel 1

SU A

SUB



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No Control Channel

• All channels {1, 2, …, N} potentially available for rendezvous

• Radios visit these channels in random or prescribed order,

alternatively transmitting beacons and listening for responses

until rendezvous is successful

– Blind rendezvous

• Time to rendezvous (TTR) is one metric of interest

– Increases with N

Blind Rendezvous Process

Sensemedi um

Tr ansmi tbeacon

Li st en

Sensemedi um

Sensemedi um

Radio A

Radio B

Radio C

Time Slot

Requestr endez-

vous



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Random vs. Sequence‐based Blind Rendezvous

• Random: radios wanting to establish a link visit the N potential

channels in random order

– E[TTR] = N

– Rendezvous is equally likely to happen in any of the N

channels

• Sequence‐based: radios follow a pre‐established sequence in

visiting channels

– Possible to construct a sequence that minimizes Max(TTR)

or E[TTR] – Possible to provide deterministic guarantees

– Prioritizes certain channels for rendezvous

Sequence‐based Rendezvous Example

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

Permutation P(N)

Permutation P(N)

Permutation P(N)Permutation P(N)

… ……

…

)1(3

362][

24

+

−++=

N N

N N N TTR E

A family of sequences

L. DaSilva and I. Guerreiro, “Sequence‐based Rendezvous

for Dynamic Spectrum Access ,” IEEE DySPAN, 2008.



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Sequence‐based Rendezvous Example (cont’d)

N Sample sequence (one period) Max TTR E[TTR]

3 1 1 2 3 2 2 1 3 3 3 1 2 8 2.75

4 1 1 1 2 3 4 2 2 2 1 3 4 3 3 3 1 2 4 4 4 4 1 2 3 13 3.96

5 2 3 5 4 1 1 2 5 4 3 4 5 3 2 1 4 2 5 3 1 3 4 5 1 2

3 4 2 5 1

11 4.23

Modular Clock Algorithm

• Cryptography‐inspired

• τ = starting channel index

• r = number of channels radio

“skips” in a time slot

• Guarantees that rendezvous

will occur even if radios sense

different sets of available

channels (as long as sets are

not disjoint)

N. Theis, R. W. Thomas, and L. A. DaSilva, “Rendezvous for

Cognitive Radios ,” IEEE Trans. Mobile Computing, vol. 10,

no. 2, Feb. 2011.



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Link Maintenance

• Even after successful rendezvous, the appearance of an

incumbent may require the link to be reestablished in an

alternate channel

• Alternatives

– Return to rendezvous phase

– If there is enough time, first radio to detect the incumbent

informs the other of new channel

– Negotiate a fallback dictionary while the link is still active

(prior to the appearance of the incumbent)

Cognitive Medium Access Control (MAC)

• Arbitrates among CRs co‐existing in a shared/broadcast medium (similar to the traditional MAC function)

– Competition and/or cooperation among CRs• Multi‐channel operation: opportunistic selection of which

channel to transmit on – Channel availability may vary temporally and spatially

• Interference constraints: a CR must vacate the medium when

the PU becomes active

• Control information – In‐band – Out‐of ‐band (a control channel) – Hybrid: a base channel for control and data, with

additional data‐only channels



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Common Control Channel

• Channel availability information and handshake for

opportunistic access can be exchanged over this channel

• Cognitive pilot channel

– Regulatory issues: What channel to dedicate for this? How

is access managed?

– The common control channel may vary spatially

• One transceiver may be tuned to the CCC at all times

A. De Domenico, E. Strinati, and M. Di Benedetto, “A

Survey on MAC Strategies for Cognitive Radio Networks ,”

IEEE Comm. Surveys and Tutorials, vol. 14, no. 1, 2012.

Split Phase Approach

• Channel

reservation

phase,

followed

by

the

transmission

phase

– Can be adopted with a single control channel, or on every

channel

• Some portion of the bandwidth is ‘wasted’ during the control

phase



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No Dedicated Control Channel

• Rendezvous: radios that want to communicate select a

channel (possibly using a pre‐defined sequence in which

channels are visited) and then perform a handshake for

channel reservation

• Hybrid approaches are possible

– Control information exchanged on one channel, indicating

what additional channels can be exploited in parallel

(Carvalho, DaSilva, 2012)

Example 1: Cognitive MAC (C ‐MAC)

• Rendezvous channel (RC) and a backup channel in case of the

appearance of a PU

• Superframes: beacon period and data transfer period

• Beacon periods are slotted

– SU periodically tunes to the RC and issues a beacon

– Beaconing is used for synchronization, exchange of

neighborhood topology information, establishment of

communications in another channel

– Superframe structure is then adopted in the new channel

C. Cordeiro and K. Challapali, “C‐MAC: A Cognitive MAC

Protocol for Multi‐Channel Wireless Networks ,” IEEE

DySPAN, 2007.



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C ‐MAC, cont’d

• Beacons carry traffic

reservation information for

current super‐frame

• Load balancing possible

• Scalability issues

• Assumes all SUs will converge

on the choice of the same RC,

which is not always possible

Example 2: Stochastic Multi ‐channel Load Balancing

• Probabilistic channel selection with multi‐channel exponential

backoff

• Radios maintain estimated probability of appearance of a PU

within each of the channels (pi)

• Channel selection algorithm

– Radio initializes a counter for each channel w/ a randomly

selected integer

– In each slot, radio counts down for a channel w/

probability pi

– First channel to count down to 0 is selected, triggering an

RTS/CTS exchange on a common control channel

K. Ghaboosi, A. B. MacKenzie, L.A. DaSilva, A. S. Abdallah, and M.

Latva‐aho, “A Channel Selection Mechanism Based on

Incumbent Appearance Expectation for Cognitive Networks ,”

IEEE WCNC, 2009.



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SMLB, cont’d

Cognitive Networks and Cross‐

Layer Optimization

• Cognitive network

architectures pre‐suppose

cross layer optimization





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Challenges

• Preserving modularity

– Development of APIs that enable reusability

– Abstraction from underlying technology

• Information dissemination

– Knowledge representation language

– Context for interpretation of this information at the

different layers of the protocol stack

– Indication of imprecision and uncertainty

• Complexity and scalability

Example 1: Fuzzy Logic

• Variables and parameters identified at each layer and

represented using fuzzy variables

– Fuzzification by the layer that exports the variable

– Interpretation of fuzzy information (“SNR is good”)

requires less context

– Modularity is preserved

• All layers perform adaptations based on exported variables

• Example: link, routing, and transport layers characterized by

reliability, congestion, bandwidth and delay



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Example 1, cont’d

N. Baldo and M. Zorzi, “Fuzzy Logic for Cross Layer Optimization

in Cognitive Radio Networks ,” IEEE Communications Magazine,

2008.

Example 2: ARQ‐based

• Network layer exports ACK/NACK information for each packet

• Decision variables

– PHY: transmission power and modulation order for each

subcarrier

– MAC: frame size, min and max contention window size

– NET: variable transmission range (controls number of hops

in the route), index of AP to associate with (assumes

multiple APs within range)

• Multi‐objective function: weighted sum (min power

consumption, max throughput, min bit/packet error rate, min

transmission delay)

• Approach: genetic algorithm



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Example 2, cont’d

A. De Baynast et al., “ARQ ‐based Cross Layer Optimization for

Wireless Multicarrier Transmission on Cognitive Radio

Networks ,” Computer Networks, 2008.

• It’s important to consider cognitive radios

not only from a single‐link perspective but in

the context of a broader network

• Opportunistic channel selection and

rendezvous are elements of a cognitive radio

medium access scheme

• The control channel (fixed, dynamic,

exclusive or shared with data traffic) plays an

important role in the design of cognitive

MACs

• Gradually, cognitive network research is

starting to take into account decision making

at layers 3 and above

Documents

Cognitive Networks Dasilva