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## Decision Fusion in MIMO Wireless Sensor Networks with Channel State Information

Citations: | 1 - 0 self |

### Citations

420 |
Distributed Detection and Data Fusion.
- Varshney
- 1997
(Show Context)
Citation Context ...s noise σ2 w, i.e. SNR � Es/σ2 w = KN/σ 2 w. Note that the corresponding channel SNR for the kth sensor is SNRk = N/σ 2 w. III. FUSION RULES Optimum Decision: The optimal test in Neyman-Pearson sense =-=[9]-=- for the considered problem can be formulated as Λopt � ln [ ] H=H1 ˆ p(y|H1) ≷ p(y|H0) ˆH=H0 γ (2) where ˆ H, Λopt and γ denote the estimated hypothesis, the LogLikelihood Ratio (LLR, i.e. the optima... |

370 |
A comparison of optimal and sub-optimal MAP decoding algorithms operating in the log domain,” in
- Robertson, Villebrun, et al.
- 1995
(Show Context)
Citation Context ...the CV-ML is the high-SNR approximation of the optimum of Eq. (3); the proof is reported in Appendix. Max-Log Rule: Let us first recall the Max-Log approximation ( known from ) turbo-codes literature =-=[12]-=-, and given by ∑L Aℓ ln Bℓe ≈ maxℓ∈{1,2,...,L}{Aℓ + ln(Bℓ)}, where ℓ=1 Ai ∈ R, Bi ∈ R +. This approximation is accurate when one of the terms in the sum ∑L ℓ=1 BℓeAℓ dominates over the remaining terms... |

199 |
Optimal data fusion in multiple sensor detection systems,”
- Chair, Varshney
- 1986
(Show Context)
Citation Context ...ly an estimate of x, denoted ˆx in the following, is computed with Maximum-Likelihood detector [10] from y. Then the global decision ˆ H is taken on the basis of ˆx using the Chair-Varshney (CV) rule =-=[11]-=-, i.e. the optimal fusion rule for noiseless channels. The expression of this two-stage fusion rule is given by ΛCV −ML = ˆx = arg min x∈X K ‖y − Hx‖ 2 K∑ ( ) PD,k ûk ln + (1 − ûk) ln k=1 PF,k ( 1−PD,... |

81 | Channel aware decision fusion in wireless sensor networks,”
- Chen, Jiang, et al.
- 2004
(Show Context)
Citation Context ...sion center (see Fig. 1). The stringent assumption is the instantaneous channel state information (CSI) at the fusion center, which provides high performances via design of channel-aware fusion rules =-=[4]-=-, [5], [6]. For this reason channel-aware fusion rules for coherent, non-coherent, and differential modulation were already proposed for PAC in [4], [5], [6], [7]. Unfortunately, the optimal DF rule o... |

46 | Fusion of decisions transmitted over Rayleigh fading channels in wireless sensor networks,”
- Chen, Varshney
- 2006
(Show Context)
Citation Context ...tigation of sub-optimal DF rules with simpler implementation and reduced system knowledge. Sub-optimal rules for PAC scenario, presenting only the issues (i) and (ii), were designed in [4], [5], [6], =-=[8]-=-. More specifically optimal rule was compared to Maximum Ratio Combining (MRC), Chair-Varshney - Maximum Likelihood (CV-ML), Equal Gain Combining (EGC) and Max-Log. MRC 1 IEEE Sensor Array and Multich... |

39 |
An efficient generalized sphere decoder for rank-deficient MIMO systems,”
- Cui, Tellambura
- 2005
(Show Context)
Citation Context .... Eq. (5)). Terms nj, j ∈ {1, 2}, are inserted to underline that the Expcomplexity of CV-ML and Max-Log can be mitigated by implementing them through the Generalized Sphere Decoder (GSD) presented in =-=[14]-=-. In fact for CV-ML the equivalent problem ˆx = arg minx∈X K ‖D(ρ − Hx)‖2 in place of Eq. (4) can be efficiently solved, with D denoting the uppertriangular matrix deriving from the Cholesky Factoriza... |

30 | Fusion of censored decisions in wireless sensor networks,” - Jiang, Chen - 2005 |

20 | Soft-input soft-output single tree-search sphere decoding.
- Studer, Bolcskei
- 2010
(Show Context)
Citation Context ...] ln P (xk|H1) k=1 The computation of Eq. (8) can be easily performed through a double search with GSD (one for each hypothesis) or with a more efficient single search, following the same approach in =-=[15]-=-. In both cases the complexity of Max-Log is always higher than CV-ML, that is n1 > n2. Detailed results on the complexity reduction deriving from the GSD implementations of minimum distance searches ... |

15 | Optimal Power Allocation for Distributed Detection Over MIMO Channels in Wireless Sensor Networks.
- Zhang, Poor, et al.
- 2008
(Show Context)
Citation Context ...sion schemes. However the broadcast nature of the wireless medium can be exploited for DF as in [1]. Multiple antennas are employed at the fusion center in order to combat deep fading effects in [2], =-=[3]-=-. The result is a communication over a “virtual” Multiple-Input MultipleOutput (MIMO) channel between the sensors and the fusion center (see Fig. 1). The stringent assumption is the instantaneous chan... |

14 |
PAC vs. MAC for decentralized detection using noncoherent modulation,”
- Berger, Guerriero, et al.
- 2009
(Show Context)
Citation Context ...een sensors and DFC is a parallel access channel (PAC), implemented through time, code or frequency division schemes. However the broadcast nature of the wireless medium can be exploited for DF as in =-=[1]-=-. Multiple antennas are employed at the fusion center in order to combat deep fading effects in [2], [3]. The result is a communication over a “virtual” Multiple-Input MultipleOutput (MIMO) channel be... |

12 |
Coherent Max-Log decision fusion in wireless sensor networks
- Lei, Schober
- 2010
(Show Context)
Citation Context ...r (see Fig. 1). The stringent assumption is the instantaneous channel state information (CSI) at the fusion center, which provides high performances via design of channel-aware fusion rules [4], [5], =-=[6]-=-. For this reason channel-aware fusion rules for coherent, non-coherent, and differential modulation were already proposed for PAC in [4], [5], [6], [7]. Unfortunately, the optimal DF rule over MIMO c... |

11 |
Nonorthogonal transmission and noncoherent fusion of censored decisions,”
- Yiu, Schober
- 2009
(Show Context)
Citation Context ...k=1 which can be interpreted as the difference between hypothesis prior-weighted minimum distance searches. MRC Rule: The LLR of Eq. (3) can be simplified under the assumption of perfect sensors [6], =-=[13]-=-, i.e. (PD,k, PF,k) = (1, 0), k ∈ K. In this case the transmitted vector x ∈ {1K, −1K} and Eq. (3) reduces to: ⎡ ΛMRC = ln ⎣ exp ( − ( exp − ‖y−H1K‖ 2 σ 2 w ‖y+H1K‖ 2 σ 2 w ) ⎤ (6) ) ⎦ ∝ ℜ(y † H1K) (7... |

9 | Distributed detection over fading MACs with multiple antennas at the fusion center
- Banavar, Smith, et al.
- 2010
(Show Context)
Citation Context ... division schemes. However the broadcast nature of the wireless medium can be exploited for DF as in [1]. Multiple antennas are employed at the fusion center in order to combat deep fading effects in =-=[2]-=-, [3]. The result is a communication over a “virtual” Multiple-Input MultipleOutput (MIMO) channel between the sensors and the fusion center (see Fig. 1). The stringent assumption is the instantaneous... |

4 | Multiple-symbol differential decision fusion for mobile wireless sensor networks
- Lei, Schober
(Show Context)
Citation Context ...te as it contains exponential functions that have a large dynamic range especially for moderate-high channel SNRs KN/σ 2 w ≫ 1; this becomes a quite severe requirement for fixed point implementations =-=[5]-=-, [6], [13]. All the proposed sub-optimal rules instead present numerical stability, however they require a different degree of system knowledge and they also differ in computational complexity. In Ta... |