PAC vs. MAC for Decentralized Detection Using Noncoherent Modulation

Berger, Christian; Guerriero, Marco; Zhou, Shengli; Willett, Peter

doi:10.1109/tsp.2009.2021691

Cited by 60 publications

(67 citation statements)

References 26 publications

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“…In the same figure we also report the (upper) "observation bound" [1], i.e. the optimum performances over noiseless channel, given by P…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The typical communication protocol between 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].…”

Section: Introductionmentioning

confidence: 99%

“…DF rules over a matrix channel model, with only channel statistics knowledge and non-coherent modulation were studied in [1]. Distributed detection over MIMO with instantaneous CSI at the fusion center is tackled with the use of amplify-and-forward sensors in [2]; the optimum (data) fusion rule is derived and performance improvement is demonstrated when using multiple antennas at the fusion center.…”

Section: Introductionmentioning

confidence: 99%

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Decision fusion in MIMO wireless sensor networks with channel state information

Ciuonzo

Romano

Rossi

2012

2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM)

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Abstract-We study channel-aware binary decision fusion over a Rayleigh flat fading shared channel with multiple antennas at the Decision Fusion Center (DFC). We derive the optimum and three sub-optimal fusion rules, namely the Max-Log, the Chair-Varshney -Maximum Likelihood and the Maximum Ratio Combining rules. Simulation results for the performance are presented in terms of Probability of Detection (under a fixed false alarm rate) vs SNR and number of antennas. The effect of multiple antennas at the DFC for the presented rules is analyzed, showing benefits and limitations of the choice.

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“…In the same figure we also report the (upper) "observation bound" [1], i.e. the optimum performances over noiseless channel, given by P…”

Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Decision fusion in MIMO wireless sensor networks with channel state information

Ciuonzo

Romano

Rossi

2012

2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM)

View full text Add to dashboard Cite

show abstract

“…Recently, the intrinsic interfering nature of the wireless medium has been exploited in the context of wireless sensor networks for distributed detection. A multiple access channel has replaced the parallel access channel for improving bandwidth efficiency [46] and the advantage of multiple antennas at the fusion center has been analytically analyzed using the error exponent [47]. Looking at the network as a "virtual" multi-input multi-output (MIMO) system, array processing techniques at the fusion center have been investigated and compared in terms of performance, complexity, and system knowledge requirements [48].…”

Section: Collection Of Indicators From Sensorsmentioning

confidence: 99%

Real-Time Data for Risk Assessment in the Offshore Oil and Gas Industry

Paltrinieri

Landucci

Rossi

2017

Volume 3B: Structures, Safety and Reliability

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Recent major accidents in the offshore oil and gas (O&G) industry have showed inadequate assessment of system risk and demonstrated the need to improve risk analysis. While direct causes often differ, the failure to update risk evaluation on the basis of system changes/modifications has been a recurring problem. Risk is traditionally defined as a measure of the accident likelihood and the magnitude of loss, usually assessed as damage to people, to the environment, and/or economic loss. Recent revisions of such definition include also aspects of uncertainty. However, Quantitative Risk Assessment (QRA) in the offshore O&G industry is based on consolidated procedures and methods, where periodic evaluation and update of risk is not commonly carried out. Several methodologies were recently developed for dynamic risk analysis of the offshore O&G industry. Dynamic fault trees, Markov chain models for the lifecycle analysis, and Weibull failure analysis may be used for dynamic frequency evaluation and risk assessment update. Moreover, dynamic risk assessment methods were developed in order to evaluate the risk by updating initial failure probabilities of events (causes) and safety barriers as new information are made available. However, the mentioned techniques are not widely applied in the common O&G offshore practice due to several reasons, among which their complexity has a primary role. More intuitive approaches focusing on a selected number of critical factors have also been suggested, such as the Risk Barometer or the TEC2O. Such techniques are based on the evaluation of technical, operational and organizational factors. The methodology allows supporting periodic update of QRA by collecting and aggregating a set of indicators. However, their effectiveness relies on continuous monitoring activity and realtime data capturing. For this reason, this contribution focuses on the coupling of such methods with sensors of different nature located in or around and offshore O&G system. The inheritance from the Centre for Integrated Operations in the Petroleum Industries represents the basis of such study. Such approach may be beneficial for several cases in which (quasi) real-time risk evaluation may support critical operations. Two representative cases have been described: i) erosion and corrosion issues due to sand production; and ii) oil production in environmental sensitive areas. In both the cases, dynamic risk analysis may employ real-time data provided by sand, corrosion and leak detectors. A simulation of dynamic risk analysis has demonstrated how the variation of such data can affect the overall risk picture. In fact, this risk assessment approach has not only the capability to continuously iterate and outline improved system risk pictures, but it can also compare its results with sensor-measured data and allow for calibration. This can potentially guarantee progressive improvement of the method reliability for appropriate support to safety-critical decisions. Direct causes of such events often differed, but...

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“…On the other hand, transmissions of the SUs to the fusion center could be non-orthogonal, that is cooperating SUs transmit a function of their observation byusing the same channel. In the non-orthogonal channel model, it is assumed that SUs are synchronized so that the received signal in the fusion center is the coherent sumof transmitted signals by SUs [8], [9]. For orthogonal channels, the fusion center canuse various combining techniques of the received vector to obtain the final decision.…”

Section: Cooperative Spectrum Sensingmentioning

confidence: 99%