Ad-Hoc, Mobile and Wireless Networks: 8th International by Adnan Majeed, Saquib Razak, Nael B. Abu-Ghazaleh, Khaled A.

By Adnan Majeed, Saquib Razak, Nael B. Abu-Ghazaleh, Khaled A. Harras (auth.), Pedro M. Ruiz, Jose Joaquin Garcia-Luna-Aceves (eds.)

This booklet constitutes the refereed court cases of the eighth foreign convention on Ad-Hoc, cellular, and instant Networks, ADHOC-NOW 2009, held in Murcia, Spain, in September 2008.

The 24 revised complete papers and the ten brief papers have been conscientiously reviewed and chosen from ninety two submissions. The papers care for advances in Ad-Hoc networks, mesh networks, sensor networks and vehicular networks.

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Sλ } and q, v ∈ {0, 1}. The factor graph representing this Trellis diagram is then attached to the overall factor graph through variable nodes {Uk1 } and {Uk2 }. The marginalization of the joint conditional probability in expression (3) will be achieved by executing the message-passing Sum-Product Algorithm (SPA, [20]) over the overall factor graph describing the joint receiver. Notice that, since the resulting compound graph of the proposed receiver is cyclic, the SPA algorithm leads to an iterative message passing procedure which is performed, within a given iteration, in the order ZZ1→ZZ2→ZZR→MS.

Two binary sources S1 and S2 generate correlated information Sensor #1 S1 {u1k } Zigzag encoder BPSK h1k {c1k } n1k Fading Channels {yk1 } Relay node Correlation Decoder Relay Decoder P2P TDM NC Ideal noiseless links hR k BPSK {u2k } Zigzag encoder {ykR } {yk2 } Sensor #2 S2 nR k BPSK {c2k } h2k Iterative Joint Src-Chan-Ntw Decoding and Correlation Estimation {u1k } {u2k } n2k Fig. 1. Block diagram of the considered two-user multiple access communication scenario 32 J. Del Ser et al. modelled by the ergodic process {Uk1 , Uk2 }∞ k=1 .

Equiprobable binary random variables, while {Uk2 } is symbolwise built as Uk2 = Uk1 ⊕Ek where {Ek } is a binary stationary random process generated by a Hidden Markov Model (HMM, [18]). e. Sk ∈ {1, . . , Sλ } ∀k. – A is a Sλ × Sλ state transition probability matrix with entries as ,s , where Sλ as ,s PSk |Sk−1 (s|s ) (s, s ∈ {1, . . , Sλ }), satisfying s=1 as ,s = 1 ∀s ∈ {1, . . , Sλ }. 1 We implicitly assume that all encoded sequences are BPSK modulated with average energy per channel symbol Ec prior to transmission.

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