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 Internet Gateway MR3 3 C3.3 C3.2 C3.1 f3 MR2 3 C2.2 C2.1 f2 MR1 3 C1.1 C1.2 C1.3 C1.4 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 {f i,i=1,2,3} are used both to receive data from the MC(j) by MR(i+1) Cluster 3 Cluster 1 Cluster 2

Cognitive functionality 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 MC i,1 MC i,2 MC i,3 MR(i) (access point) Cluster i-th MR(i+1) (access point) MC problem: ▫Optimal access rate and flow-control MR problem: ▫Optimal set of the access times fifi fifi fifi fifi

Clients’ Payload ACKvv Belief Propagation Soft Data Fusion Channel Estination Resource Allocation and Client’s scheduling Channel Detection Intra-cluster slot structure Slot-duration of T S (sec.) It is split into L t minislot Each MC(j) uses: ▫L D minislot for Channel Detection phase ▫L E minislot for Channel Estimation phase ▫L P minislot to transmit data to MR(i) ▫L A minislot to receive Ack message Each MR(i) uses: ▫L B minislot for Belief Propagation phase ▫L F minislot for Soft Data Fusion phase ▫L A minislot to sent Ack message MR(i) and MC(j) use: ▫L S minislot for Resource Allocation and Clients’ Scheduling LDLD LBLB LFLF LELE LSLS LPLP LALA Channel Learning

MCs are listening to the channel Channel Detection Clients’ Payload vv Channel Estimation Channel Detection MC functionalities 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 State of primary user’s activity LDLD LELE LPLP

Channel Estimation MR(i) transmits a known pilot’s sequence MC(j) known this sequence MC(j) calculates the channel estimation based on: Pilot sequence a priori know Noise sequence Clients’ Payload vv Channel Estimation Channel Detection MC functionalities LDLD LELE LPLP

Channel Estimation 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 Pilot sequence a priori know Noise sequence Clients’ Payload vv Channel Estimation Channel Detection MC functionalities LDLD LELE LPLP

Belief Propagation Definition: ▫At the beginning of each slot, each access point MR(i) estimates and/or updating the following conditional probability ACK Belief Propagation Soft Data Fusion MR functionalities LBLB LFLF LALA

Belief Propagation Definition: ▫At the beginning of each slot, each access point MR(i) estimates and/or updating the following conditional probability ACK Belief Propagation Soft Data Fusion Set of the informations about the MR(i+1) activity in the previous slot (t-1) MR functionalities LBLB LFLF LALA

Belief Propagation 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 ACK Belief Propagation Soft Data Fusion Set of the informations about the MR(i+1) activity in the previous slot (t-1) MR functionalities LBLB LFLF LALA

Data Fusion (1/3) 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 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 ACK Belief Propagation Soft Data Fusion MR functionalities LBLB LFLF LALA

Data Fusion (2/3) Definition: ▫Algorithm that computes the conditional probability. This last is computed by each MR(i) as in MR functionalities ACK Belief Propagation Soft Data Fusion LBLB LFLF LALA

Data Fusion (2/3) Definition: ▫Algorithm that computes the conditional probability. This last is computed by each MR(i) as in Set of the informations about the MR(i+1) activity. This informations are available at the end of the Channel Detection phase MR functionalities ACK Belief Propagation Soft Data Fusion LBLB LFLF LALA

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

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

Hard or Soft Data Fusion? 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) P.K.Varshney, ‘Distributed Detection and Data Fusion’, Springer, 1997

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 MR functionalities ACK Belief Propagation Soft Data Fusion LBLB LALA LFLF Binary variable that defines Ack message

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