Transcript Diapositiva 1

Application scenario
• WMNs offer a promising
networking architecture to provide
multimedia services to mobile
users
• WMNs represent an attractive
solution to extend the Internet
access over local areas and
metropolitan areas
• PROBLEM
▫ The spectrum resource
available
• POSSIBLE SOLUTION:
▫ Use the Cognitive Radio
paradigm
• FRAMEWORK:
▫ Consider Active Mesh
Networks (Content-aware
Cognitive Wireless Mesh
Network s)
The envisioned Active Mesh Net architecture
Cluster 2
Internet
MR2
3
f3
C3.3
f2
C2.2
f2
Gateway
f3
C2.1
f2
f1
MR3
3
f3
C3.2
C1.4
f3
C3.1
f1
C1.1
C1.2
MR1
3
f1
f1
C1.3
Cluster 3
Cluster 1
f1
• Formed by
interconnecting
several cluster of
mobile Mesh
Clients (MCs) via a
wireless backbone
composed by static
Mesh Router (MRs)
• Dowlink and uplink
traffic
• Each MR acts as
access point
• Frequencies
{fi ,i=1,2,3} are
used both to receive
data from the
MC(j) by MR(i+1)
Cognitive functionality
MC i,2
fi
Cluster i-th
fi
MC i,1
fi
fi
MR(i) (access point)
MC i,3
MR(i+1)
(access point)
• MC problem:
▫ Optimal access rate
and flow-control
• MR problem:
▫ Optimal set of the
access times
• MCs are battery-powered
• Fading affecting the wireless link, between MCs and MR, is assumed
constant over each slot (block fading)
• MC carry out Channel Detection and Channel Estimation
• MR carry out Belief propagation and Soft Data Fusion
LD
Intra-cluster slot structure
LB
Channel
Belief
vv
Detection Propagation
LF
LE
Soft
Data
Fusion
Channel
Estination
LS
Resource Allocation
and
Client’s scheduling
LP
LA
Clients’
Payload
ACK
Channel Learning
• Slot-duration of TS (sec.)
• It is split into Lt minislot
• Each MC(j) uses:
▫ LD minislot for Channel Detection phase
▫ LE minislot for Channel Estimation phase
▫ LP minislot to transmit data to MR(i)
▫ LA minislot to receive Ack message
• Each MR(i) uses:
▫ LB minislot for Belief Propagation phase
▫ LFminislot for Soft Data Fusion phase
▫ LA minislot to sent Ack message
• MR(i) and MC(j) use:
▫ LS minislot for Resource Allocation and Clients’ Scheduling
Channel Detection
•
LD
LE
LP
Channel
vv
Detection
Channel
Estimation
Clients’
Payload
MC functionalities
MCs are listening to the channel
 j (k ; t )  n j (k ; t )
State of
primary user’s
activity
if ai (t )  0
 j (k ; t )  g ji (k , t ) si 1 (k ; t )  n j (k ; t ) if ai (t )  1
Channel Coefficient MR(i+1)-MC(j) in the minislot k-th
Sample (deterministic or
aleatory) generated by
MR(i+1) in the minislot kth
Channel Estimation
LD
Channel
vv
Detection
LE
Channel
Estimation
LP
Clients’
Payload
MC functionalities
• MR(i) transmits a known pilot’s sequence
• MC(j) known this sequence
• MC(j) calculates the channel estimation based on:
 ji (t )  hji (t ) p  v j (k, t )
Noise sequence
Pilot sequence a priori know
Channel Estimation
LD
Channel
vv
Detection
LE
Channel
Estimation
LP
Clients’
Payload
MC functionalities
• MR(i) transmits a known pilot’s sequence
• MC(j) known this sequence
• MC(j) calculates the channel estimation based on:
 ji (t )  hji (t ) p  v j (k, t )
Noise sequence
Pilot sequence a priori know
• Each MC use these minislots to transmit data to MR
Belief Propagation
LB
Belief
Propagation
LF
LA
Soft
Data
Fusion
ACK
MR functionalities
• Definition:
▫ At the beginning of each slot, each access point MR(i) estimates
and/or updating the following conditional probability
Belief Propagation
LB
Belief
Propagation
LF
LA
Soft
Data
Fusion
ACK
MR functionalities
• Definition:
▫ At the beginning of each slot, each access point MR(i) estimates
and/or updating the following conditional probability
P(ai (t )  1| i (t ))
Set of the informations about
the MR(i+1) activity in the
previous slot (t-1)
Belief Propagation
LB
Belief
Propagation
LF
LA
Soft
Data
Fusion
ACK
MR functionalities
• Definition:
▫ At the beginning of each slot, each access point MR(i) estimates
and/or updating the following conditional probability
P(ai (t )  1| i (t ))
Set of the informations about
the MR(i+1) activity in the
previous slot (t-1)
• Noncooperative: when i (t ) is empty set or contains informations
about only the MR(i+1) of the cluster i-th
• Cooperative: when i (t ) is nonempty and it contains informations
about the previous activities all primary users
Data Fusion (1/3)
LB
Belief
Propagation
LF
Soft
Data
Fusion
LA
ACK
MR functionalities
• Each MR(i) knows the primary’s activity only at the end of the slot tth but MR(i) must know the state of MR(i+1) at the beginning of the
phase Resource Allocation
1. MR(i) merges (Data Fusion) decisions already calculated by MC(j)
in the first part of Channel Detection
2. MR(i) calculates a posteriori probabilities that the i-th channel is
transmission free
Data Fusion (2/3)
LB
Belief
Propagation
LF
Soft
Data
Fusion
LA
ACK
MR functionalities
• Definition:
▫ Algorithm that computes the conditional probability. This
last is computed by each MR(i) as in
Data Fusion (2/3)
LB
Belief
Propagation
LF
Soft
Data
Fusion
LA
ACK
MR functionalities
• Definition:
▫ Algorithm that computes the conditional probability. This
last is computed by each MR(i) as in
PL (t ) P(ai (t )  0| Vi (t ))
i
Set of the informations about the MR(i+1) activity. This informations are
available at the end of the Channel Detection phase
Data Fusion(3/3)
Number of
clusters
Vi (t ) {{a ji (t ), j Ci (t )}, i (t)}, i  I , t  1
Set of the MCs belonging to i-th cluster
Optimal Soft Data Fusion
P(ai (t )  0 | i (t ))[
PL i (t )
P(ai (t )  0 | i (t ))[

jCi ( t )

jCi ( t )
P( a ji (t ) | ai (t )  0)]
P( a ji (t ) | ai (t )  0)]  P( ai (t )  1|  i (t ))[

jCi ( t )
P( a ji (t ) | ai (t )  1)
Data Fusion(3/3)
Number of
clusters
Vi (t ) {{a ji (t ), j Ci (t )}, i (t)}, i  I , t  1
Set of the MCs belonging to i-th cluster
Optimal Soft Data Fusion
P(ai (t )  0 | i (t ))[
PL i (t )
P(ai (t )  0 | i (t ))[

jCi ( t )

jCi ( t )
P( a ji (t ) | ai (t )  0)]
P( a ji (t ) | ai (t )  0)]  P( ai (t )  1|  i (t ))[

jCi ( t )
P( a ji (t ) | ai (t )  1)
i
• PL (t ) represents the conditional probability that the i-th channel is
available
• MR(i) knows
P(ai (t ) | i (t )) probability from the Belief Propagation phase
Hard or Soft Data Fusion?
P.K.Varshney, ‘Distributed Detection and Data Fusion’, Springer, 1997
• Hard Data Fusion
▫ MCs provide hard informations (i.e., binary decisions) to the
corresponding MR
▫ MR provides hard informations
• Soft Data Fusion
▫ MCs provide the observations directly to the MR
▫ MR processes the set of the observations
▫ MR provides hard decisions
• My Data Fusion? Hard or Soft?
• Neither hard nor soft
▫ MCs provide the soft informations (in form of Probability) to the
MR
▫ MR processes the soft informations
▫ MR provides a soft information (in form of Probability)
ACK
LB
Belief
Propagation
LF
Soft Data
Fusion
LA
ACK
MR functionalities
• MR(i) sent an Ack message defined in the following as:
Binary variable
that defines Ack
message
Z Ai (t ) 1  ai (t )
• MC(j) receive ‘zero’, in that case:
▫ MR(i+1) was not active in that slot
▫ MC(j) removed from the queue the IUs that it has transmitted in
the slot t-th
• MC(j) receive ‘one’ , in that case:
▫ M(i+1) was active in that slot
▫ MC(j) not remove the IUs
Work in Progress
• Develop in closed-form expressions for the
optimal access rate and the optimal access time
• Unconditional optimization problem
• Performance evaluation of the overall Active
Mesh architecture