663 research outputs found
Fusion Rules for Distributed Detection in Clustered Wireless Sensor Networks with Imperfect Channels
In this paper we investigate fusion rules for distributed detection in large random clustered-wireless sensor networks (WSNs) with a three-tier hierarchy; the sensor nodes (SNs), the cluster heads (CHs) and the fusion center (FC). The CHs collect the SNs' local decisions and relay them to the FC that then fuses them to reach the ultimate decision. The SN-CH and the CH-FC channels suffer from additive white Gaussian noise (AWGN). In this context, we derive the optimal log-likelihood ratio (LLR) fusion rule, which turns out to be intractable. So, we develop a sub-optimal linear fusion rule (LFR) that weighs the cluster's data according to both its local detection performance and the quality of the communication channels. In order to implement it, we propose an approximate maximum likelihood based LFR (LFR-aML), which estimates the required parameters for the LFR. We also derive Gaussian-tail upper bounds for the detection and false alarms probabilities for the LFR. Furthermore, an optimal CH transmission power allocation strategy is developed by solving the Karush-Kuhn-Tucker (KKT) conditions for the related optimization problem. Extensive simulations show that the LFR attains a detection performance near to that of the optimal LLR and confirms the validity of the proposed upper bounds. Moreover, when compared to equal power allocation, simulations show that our proposed power allocation strategy achieves a significant power saving at the expense of a small reduction in the detection performance
Distributed M-ary hypothesis testing for decision fusion in multiple-input multiple output wireless sensor networks
In this study, the authors study binary decision fusion over a shared Rayleigh fading channel with multiple antennas at
the decision fusion centre (DFC) in wireless sensor networks. Three fusion rules are derived for the DFC in the case of
distributed M-ary hypothesis testing, where M is the number of hypothesis to be classified. Namely, the optimum maximum a
posteriori (MAP) rule, the augmented quadratic discriminant analysis (A-QDA) rule and MAP observation bound. A comparative
simulation study is carried out between the proposed fusion rules in-terms of detection performance and receiver operating
characteristic (ROC) curves, where several parameters are taken into account such as the number of antennas, number of local
detectors, number of hypothesis and signal-to-noise ratio. Simulation results show that the optimum (MAP) rule has better
detection performance than A-QDA rule. In addition, increasing the number of antennas will improve the detection performance
up to a saturation level, while increasing the number of the hypothesis will deteriorate the detection performance
Distributed Detection over Gaussian Multiple Access Channels with Constant Modulus Signaling
A distributed detection scheme where the sensors transmit with constant
modulus signals over a Gaussian multiple access channel is considered. The
deflection coefficient of the proposed scheme is shown to depend on the
characteristic function of the sensing noise and the error exponent for the
system is derived using large deviation theory. Optimization of the deflection
coefficient and error exponent are considered with respect to a transmission
phase parameter for a variety of sensing noise distributions including
impulsive ones. The proposed scheme is also favorably compared with existing
amplify-and-forward and detect-and-forward schemes. The effect of fading is
shown to be detrimental to the detection performance through a reduction in the
deflection coefficient depending on the fading statistics. Simulations
corroborate that the deflection coefficient and error exponent can be
effectively used to optimize the error probability for a wide variety of
sensing noise distributions.Comment: 30 pages, 12 figure
Distributed Combining Techniques for Distributed Detection in Fading Wireless Sensor Networks
We investigate distributed combining techniques for distributed detection in wireless sensor networks (WSNs) over Rayleigh fading multiple access channel (MAC). The MAC also suffers from with path loss and additive noise. The WSN is modelled as a Poisson point process (PPP). Two distributed transmit combining techniques are proposed to mitigate fading; distributed equal gain transmit combining (ddEGTC) and distributed maximum ratio transmit combining (dMRTC). The performance of the previous methods is analysed using stochastic geometry tools, where the mean and variance of the detector’s test statistic are found thus enabling the fitting of the received signal distribution by a log-normal distribution. Surprisingly, simulation results show a that ddEGTC outperforms dMRTC
Combined Soft Hard Cooperative Spectrum Sensing in Cognitive Radio Networks
Providing some techniques to enhance the performance of spectrum sensing in cognitive radio systems while accounting for the cost and bandwidth limitations in practical scenarios is the main objective of this thesis. We focus on an essential element of cooperative spectrum sensing (CSS) which is the data fusion that combines the sensing results to make the final decision. Exploiting the advantage of the superior performance of the soft schemes and the low bandwidth of the hard schemes by incorporating them in cluster based CSS networks is achieved in two different ways. First, a soft-hard combination is employed to propose a hierarchical cluster based spectrum sensing algorithm. The proposed algorithm maximizes the detection performances while satisfying the probability of false alarm constraint. Simulation results of the proposed algorithm are presented and compared with existing algorithms over the Nakagami fading channel. Moreover, the results show that the proposed algorithm outperforms the existing algorithms. In the second part, a low complexity soft-hard combination scheme is suggested by utilizing both one-bit and two-bit schemes to balance between the required bandwidth and the detection performance by taking into account that different clusters undergo different conditions. The scheme allocates a reliability factor proportional to the detection rate to each cluster to combine the results at the Fusion center (FC) by extracting the results of the reliable clusters. Numerical results obtained have shown that a superior detection performance and a minimum overhead can be achieved simultaneously by combining one bit and two schemes at the intra-cluster level while assigning a reliability factor at the inter-cluster level
Distributed M-ary hypothesis testing for decision fusion in multiple-input multipleoutput wireless sensor networks
In this study, the authors study binary decision fusion over a shared Rayleigh fading channel with multiple antennas at
the decision fusion centre (DFC) in wireless sensor networks. Three fusion rules are derived for the DFC in the case of
distributed M-ary hypothesis testing, where M is the number of hypothesis to be classified. Namely, the optimum maximum a
posteriori (MAP) rule, the augmented quadratic discriminant analysis (A-QDA) rule and MAP observation bound. A comparative
simulation study is carried out between the proposed fusion rules in-terms of detection performance and receiver operating
characteristic (ROC) curves, where several parameters are taken into account such as the number of antennas, number of local
detectors, number of hypothesis and signal-to-noise ratio. Simulation results show that the optimum (MAP) rule has better
detection performance than A-QDA rule. In addition, increasing the number of antennas will improve the detection performance
up to a saturation level, while increasing the number of the hypothesis will deteriorate the detection performance
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