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Application scenario

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:

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Application scenario

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  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)

  2. The envisioned Active Mesh Net architecture Cluster 2 • Formedbyinterconnectingseveral cluster ofmobileMeshClients (MCs) via a wireless backbonecomposedbystaticMesh Router (MRs) • Dowlink and uplinktraffic • Each MR actsasaccesspoint • Frequencies • {fi ,i=1,2,3} are usedbothtoreceive data from the MC(j) by MR(i+1) f2 Internet MR23 f2 f2 Gateway C1.2 C1.3 C3.3 C3.1 f3 C1.1 C3.2 C2.2 C2.1 C1.4 f1 MR33 f3 f3 f1 MR13 f1 f3 f1 f1 Cluster 3 Cluster 1

  3. Cognitive functionality MC i,2 fi MC i,1 MR(i+1) (access point) fi Cluster i-th fi • MC problem: • Optimalaccess rate and flow-control • MR problem: • Optimal set of the accesstimes fi MR(i) (access point) MC i,3 • 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

  4. 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 Intra-cluster slot structure LD LB LF LE LS LP LA vv Belief Propagation Soft Data Fusion Channel Estination ResourceAllocation and Client’s scheduling Clients’ Payload ACK Channel Detection Channel Learning

  5. MCs are listening to the channel Channel Detection LD LE LP vv Channel Estimation Clients’ Payload Channel Detection MC functionalities State of primary user’s activity Sample (deterministic or aleatory) generated by MR(i+1) in the minislot k-th Channel Coefficient MR(i+1)-MC(j) in the minislot k-th

  6. MR(i) transmits a known pilot’s sequence MC(j) known this sequence MC(j) calculates the channel estimation based on: Noise sequence Pilot sequence a priori know ChannelEstimation LD LE LP vv Channel Estimation Clients’ Payload Channel Detection MC functionalities

  7. MR(i) transmits a known pilot’s sequence MC(j) known this sequence MC(j) calculates the channel estimation based on: Each MC use these minislots to transmit data to MR Noise sequence Pilot sequence a priori know ChannelEstimation LD LE LP vv Channel Estimation Clients’ Payload Channel Detection MC functionalities

  8. BeliefPropagation LB LF LA Belief Propagation 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

  9. BeliefPropagation • Definition: • At the beginning of each slot, each access point MR(i) estimates and/or updating the following conditional probability LB LF LA Belief Propagation Soft Data Fusion ACK MR functionalities Set of the informations about the MR(i+1) activity in the previous slot (t-1)

  10. Set of the informations about the MR(i+1) activity in the previous slot (t-1) BeliefPropagation • Definition: • At the beginning of each slot, each access point MR(i) estimates and/or updating the following conditional probability • Noncooperative: when is empty set or contains informations about only the MR(i+1) of the cluster i-th • Cooperative: when is nonempty and it contains informations about the previous activities all primary users LB LF LA Belief Propagation Soft Data Fusion ACK MR functionalities

  11. Data Fusion (1/3) LB LF LA BeliefPropagation Soft Data Fusion ACK MR functionalities • Each MR(i) knows the primary’s activity only at the end of the slot t-th but MR(i) must know the state of MR(i+1) at the beginning of the phase Resource Allocation MR(i) merges (Data Fusion) decisions already calculated by MC(j) in the first part of Channel Detection MR(i) calculates a posteriori probabilities that the i-th channel is transmission free

  12. Data Fusion (2/3) LB LF LA BeliefPropagation Soft Data Fusion ACK MR functionalities • Definition: • Algorithm that computes the conditional probability. This last is computed by each MR(i) as in

  13. Definition: Algorithm that computes the conditional probability. This last is computed by each MR(i) as in Data Fusion (2/3) LB LF LA BeliefPropagation Soft Data Fusion ACK MR functionalities Set of the informations about the MR(i+1) activity. This informations are available at the end of the Channel Detection phase

  14. Data Fusion(3/3) Number of clusters Set of the MCs belonging to i-th cluster Optimal Soft Data Fusion

  15. represents the conditional probability that the i-th channel is available • MR(i) knows probability from the Belief Propagation phase Data Fusion(3/3) Number of clusters Set of the MCs belonging to i-th cluster Optimal Soft Data Fusion

  16. 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 theMR • 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)

  17. LB LF LA BeliefPropagation Soft Data Fusion ACK MR functionalities ACK • MR(i) sent an Ack message defined in the following as: • 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 Binary variable that defines Ack message

  18. Work in Progress • Develop in closed-formexpressionsfor the optimalaccess rate and the optimalaccesstime • Unconditionaloptimizationproblem • Performance evaluationof the overallActiveMesharchitecture

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