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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
Cognitive Network Architectures: Example 2
P. Sutton, L. Doyle, and K. Nolan, “A Reconfigurable Platform
for Cognitive Networks,” Proc. CROWNCOM, 2006
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Cognitive Network Architectures: Example 3
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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Extended example: channel selection
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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Extended example: channel selection
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