Coherent Distributed Detection Using Inhomogeneous Sensors Under Local Power Constraint

2015 ◽  
Vol 743 ◽  
pp. 792-795
Author(s):  
Z.H. Xu ◽  
J.Y. Li ◽  
J. Wang
Keyword(s):  
Signal To Noise Ratio ◽  
Closed Form Solution ◽  
Form Solution ◽  
Distributed Detection ◽  
Fusion System ◽  
Local Power ◽  
Power Constraint ◽  
Deflection Coefficient ◽  
Access Channel ◽  
Multi Access

In this paper, we consider the problem of local power constrained coherent distributed detection over fading multi-access channel (MAC). The deflection coefficient maximization (DCM) is used to optimize the performance of fusion system under local power constraint of sensors. We use the statistical information of channel gain to obtain the deflection coefficient of the detection statistics at the fusion center. We derive the closed-form solution to the considered optimization problem. Monte-Carlo simulations are carried out to verify the performance of the proposed method. Simulation results show that the proposed method could significantly improve the detection performance of the fusion system under local power constraint and low signal-to-noise ratio (SNR).

scholarly journals Optimal Power Constrained Distributed Detection over a Noisy Multiaccess Channel

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Zhiwen Hu ◽  
Zhenhua Xu
Keyword(s):  
Signal To Noise Ratio ◽  
Closed Form Solution ◽  
Form Solution ◽  
Detection Performance ◽  
Distributed Detection ◽  
New Method ◽  
Fusion System ◽  
Robust Detection ◽  
Optimal Power ◽  
Multiaccess Channel

The problem of optimal power constrained distributed detection over a noisy multiaccess channel (MAC) is addressed. Under local power constraints, we define the transformation function for sensor to realize the mapping from local decision to transmitted waveform. The deflection coefficient maximization (DCM) is used to optimize the performance of power constrained fusion system. Using optimality conditions, we derive the closed-form solution to the considered problem. Monte Carlo simulations are carried out to evaluate the performance of the proposed new method. Simulation results show that the proposed method could significantly improve the detection performance of the fusion system with low signal-to-noise ratio (SNR). We also show that the proposed new method has a robust detection performance for broad SNR region.

scholarly journals Noncoherent Decision Fusion over Fading Hybrid MACs in Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 120 ◽  
Author(s):  
Shoujun Liu ◽  
Kehao Wang ◽  
Kezhong Liu ◽  
Wei Chen
Keyword(s):  
Sensor Networks ◽  
Rayleigh Fading ◽  
Signal To Noise Ratio ◽  
Fusion Rule ◽  
Low Complexity ◽  
Decision Fusion ◽  
Wireless Sensor ◽  
Deflection Coefficient ◽  
Access Channel ◽  
Multi Access

In this paper, we consider the problem of decision fusion for noncoherent detection in a wireless sensor network. Novel to the current work is the integration of the hybrid multi-access channel (MAC) in the fusion rule design. We assume that sensors transmit their local binary decisions over a hybrid MAC which is a composite of conventional orthogonal and nonorthogonal MACs. Under Rayleigh fading scenario, we present a likelihood ratio (LR)-based fusion rule, which has been shown to be optimal through theoretical analysis and simulation. However, it requires a large amount of computation, which is not easily implemented in resource-constrained sensor networks. Therefore, three sub-optimal alternatives with low-complexity are proposed, namely the weighed energy detector (WED), the deflection-coefficient-maximization (DCM), and the two-step (TS) rules. We show that when the channel signal-to-noise ratio (SNR) is low, the LR-based fusion rule reduces to the WED rule; at high-channel SNR, it is equivalent to the TS rule; and at moderate-channel SNR, it can be approached closely by the DCM rule. Compared with the conventional orthogonal and nonorthogonal MACs, numerical results show that the hybrid MAC with the proposed fusion rules can improve the detection performance when the channel SNR is medium.

scholarly journals Deep learning for a space-variant deconvolution in galaxy surveys

2020 ◽  
Vol 641 ◽  
pp. A67
Author(s):  
F. Sureau ◽  
A. Lechat ◽  
J.-L. Starck
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Signal To Noise Ratio ◽  
Closed Form Solution ◽  
Computation Time ◽  
Form Solution ◽  
High Signal ◽  
Galaxy Surveys ◽  
Point Spread ◽  
Space Variant

The deconvolution of large survey images with millions of galaxies requires developing a new generation of methods that can take a space-variant point spread function into account. These methods have also to be accurate and fast. We investigate how deep learning might be used to perform this task. We employed a U-net deep neural network architecture to learn parameters that were adapted for galaxy image processing in a supervised setting and studied two deconvolution strategies. The first approach is a post-processing of a mere Tikhonov deconvolution with closed-form solution, and the second approach is an iterative deconvolution framework based on the alternating direction method of multipliers (ADMM). Our numerical results based on GREAT3 simulations with realistic galaxy images and point spread functions show that our two approaches outperform standard techniques that are based on convex optimization, whether assessed in galaxy image reconstruction or shape recovery. The approach based on a Tikhonov deconvolution leads to the most accurate results, except for ellipticity errors at high signal-to-noise ratio. The ADMM approach performs slightly better in this case. Considering that the Tikhonov approach is also more computation-time efficient in processing a large number of galaxies, we recommend this approach in this scenario.

EEG SOURCE LOCALIZATION: A COMPARATIVE STUDY OF CLASSICAL AND NEURAL NETWORK METHODS

2001 ◽  
Vol 11 (04) ◽  
pp. 349-359 ◽  
Author(s):  
UDANTHA R. ABEYRATNE ◽  
G. ZHANG ◽  
P. SARATCHANDRAN
Keyword(s):  
Neural Network ◽  
Inverse Problem ◽  
Signal To Noise Ratio ◽  
Closed Form Solution ◽  
Form Solution ◽  
Hybrid Technique ◽  
Eeg Signals ◽  
Rbf Network ◽  
Network Methods ◽  
Lm Algorithm

We address the problem of estimating biopotential sources within the brain, based on EEG signals observed on the scalp. This problem, known as the inverse problem of electrophysiology, has no closed-form solution, and requires iterative techniques such as the Levenberg – Marquardt (LM) algorithm. Considering the nonlinear nature of the inverse problem, and the low signal to noise ratio inherent in EEG signals, a backpropagation neural network (BPN) has been recently proposed as a solution. The technique has not been properly compared with classical techniques such as the LM method, or with more recent neural network techniques such as the Radial Basis Function (RBF) network. In this paper, we provide improved strategies based on BPN and consider RBF networks in solving the inverse problem. We compare the performances of BPN, RBF and a hybrid technique with that of the classical LM method.

scholarly journals DRL-Assisted Resource Allocation for NOMA-MEC Offloading with Hybrid SIC

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 613
Author(s):  
Haodong Li ◽  
Fang Fang ◽  
Zhiguo Ding
Keyword(s):  
Resource Allocation ◽  
Interference Cancellation ◽  
Multiple Access ◽  
Closed Form Solution ◽  
Form Solution ◽  
Time Duration ◽  
User Grouping ◽  
Sixth Generation ◽  
Multi Access ◽  
Delay Tolerant

Multi-access edge computing (MEC) and non-orthogonal multiple access (NOMA) are regarded as promising technologies to improve the computation capability and offloading efficiency of mobile devices in the sixth-generation (6G) mobile system. This paper mainly focused on the hybrid NOMA-MEC system, where multiple users were first grouped into pairs, and users in each pair offloaded their tasks simultaneously by NOMA, then a dedicated time duration was scheduled to the more delay-tolerant user for uploading the remaining data by orthogonal multiple access (OMA). For the conventional NOMA uplink transmission, successive interference cancellation (SIC) was applied to decode the superposed signals successively according to the channel state information (CSI) or the quality of service (QoS) requirement. In this work, we integrated the hybrid SIC scheme, which dynamically adapts the SIC decoding order among all NOMA groups. To solve the user grouping problem, a deep reinforcement learning (DRL)-based algorithm was proposed to obtain a close-to-optimal user grouping policy. Moreover, we optimally minimized the offloading energy consumption by obtaining the closed-form solution to the resource allocation problem. Simulation results showed that the proposed algorithm converged fast, and the NOMA-MEC scheme outperformed the existing orthogonal multiple access (OMA) scheme.

scholarly journals Network Enhancement: a general method to denoise weighted biological networks

2018 ◽  
Author(s):  
Bo Wang ◽  
Armin Pourshafeie ◽  
Marinka Zitnik ◽  
Junjie Zhu ◽  
Carlos D. Bustamante ◽  
...  
Keyword(s):  
Biological Networks ◽  
Signal To Noise Ratio ◽  
Closed Form Solution ◽  
Form Solution ◽  
Fine Grained ◽  
Contact Maps ◽  
Novel Method ◽  
Alternative Approaches ◽  
Downstream Analysis ◽  
General Method

Networks are ubiquitous in biology where they encode connectivity patterns at all scales of organization, from molecular to the biome. However, biological networks are noisy due to the limitations of technology used to generate them as well as inherent variation within samples. The presence of high levels of noise can hamper discovery of patterns and dynamics encapsulated by these networks. Here we propose Network Enhancement (NE), a novel method for improving the signal-to-noise ratio of undirected, weighted networks, and thereby improving the performance of downstream analysis. NE applies a novel operator that induces sparsity and leverages higher-order network structures to remove weak edges and enhance real connections. This iterative approach has a closed-form solution at convergence with desirable performance properties. We demonstrate the effectiveness of NE in denoising biological networks for several challenging yet important problems. Our experiments show that NE improves gene function prediction by denoising interaction networks from 22 human tissues. Further, we use NE to interpret noisy Hi-C contact maps from the human genome and demonstrate its utility across varying degrees of data quality. Finally, when applied to fine-grained species identification, NE outperforms alternative approaches by a significant margin. Taken together, our results indicate that NE is widely applicable for denoising weighted biological networks, especially when they contain high levels of noise.

scholarly journals An Adiabatic Approximation to the Tissue Homogeneity Model for Water Exchange in the Brain: I. Theoretical Derivation

1998 ◽  
Vol 18 (12) ◽  
pp. 1365-1377 ◽  
Author(s):  
Keith S. St. Lawrence ◽  
Ting-Yim Lee
Keyword(s):  
Adiabatic Approximation ◽  
Signal To Noise Ratio ◽  
Closed Form Solution ◽  
Form Solution ◽  
Model Parameters ◽  
Theoretical Derivation ◽  
Extraction Fraction ◽  
Tissue Changes ◽  
Clearance Rate Constant ◽  
The Brain

Using the adiabatic approximation, which assumes that the tracer concentration in parenchymal tissue changes slowly relative to that in capillaries, we derived a time-domain, closed-form solution of the tissue homogeneity model. This solution, which is called the adiabatic solution, is similar in form to those of two-compartment models, Owing to its simplicity, the adiabatic solution can be used in CBF experiments in which kinetic data with only limited time resolution or signal-to-noise ratio, or both, are obtained. Using computer simulations, we investigated the accuracy and the precision of the parameters in the adiabatic solution for values that reflect 2H-labeled water (D2O) clearance from the brain (see Part II). It was determined that of the three model parameters, (1) the vascular volume ( Vi), (2) the product of extraction fraction and blood flow ( EF), and (3) the clearance rate constant ( kadb), only the last one could be determined accurately, and therefore CBF must be determined from this parameter only. From the error analysis of the adiabatic solution, it was concluded that for the D2O clearance experiments described in Part II, the coefficient of variation of CBF was approximately 7% in gray matter and 22% in white matter.

scholarly journals Distributed Beamforming Design for Nonregenerative Two-Way Relay Networks with Simultaneous Wireless Information and Power Transfer

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Keyun Liao ◽  
Sai Zhao ◽  
Yusi Long ◽  
Gaofei Huang ◽  
Dong Tang
Keyword(s):  
Energy Harvesting ◽  
Upper Bound ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Closed Form Solution ◽  
Form Solution ◽  
Relay Network ◽  
Power Transfer ◽  
Distributed Beamforming ◽  
Relay Nodes

This paper considers the distributed beamforming design for a simultaneous wireless information and power transfer (SWIPT) in two-way relay network, which consists of two sources, K relay nodes and one energy harvesting (EH) node. For such a network, assuming perfect channel state information (CSI) is available, and we study two different beamforming design schemes. As the first scheme, we design the beamformer through minimization of the average mean squared error (MSE) subject to the total transmit power constraint at the relays and the energy harvesting constraint at the EH receiver. Due to the intractable expression of the objective function, an upper bound of MSE is derived via the approximation of the signal-to-noise ratio (SNR). Based on the minimization of this upper bound, this problem can be turned into a convex feasibility semidefinite programming (SDP) and, therefore, can be efficiently solved using interior point method. To reduce the computational complexity, a suboptimal beamforming scheme is proposed in the second scheme, for which the optimization problem could be recast to the form of the Rayleigh–Ritz ratio and a closed-form solution is obtained. Numerical results are provided and analyzed to demonstrate the efficiency of our proposed beamforming schemes.

scholarly journals Closed Form Solution for Outage and Average Bit Error Rate of Asymmetric Relaying System

2019 ◽  
Vol 8 (11) ◽  
pp. 3304-3310
Keyword(s):  
Closed Form ◽  
Fading Channels ◽  
Bit Error Rate ◽  
Error Rate ◽  
Signal To Noise Ratio ◽  
Closed Form Solution ◽  
Form Solution ◽  
Amplify And Forward ◽  
Severity Factor ◽  
Special Case

In this paper, closed form solution of outage and bit error rate (BER) is evaluated for the purpose of performance analysis of the amplify and forward relaying scheme under the asymmetric fading environment. In this dual hop system, we have used Rayleigh fading along source S to relay R and Mixture Gamma fading along relay R to destination D. First, we have derived closed form solution of outage and then we have used this derived expression for getting closed form solution of bit error rate (BER) for different kinds of modulation. Since Mixture Gamma fading channel represents many fading channels as its special case, so proposed closed form solution of outage and bit error rate may be used for analysis of outage and bit error rate under various fading scenarios. Specifically, in this paper, for analysis of outage and bit error rate (BER), we have taken Nakagami-m fading as a particular case of Mixture Gamma fading and analyzed the performance of proposed system after observing effect of fading severity factor and signal to noise ratio (SNR) on outage probability and bit error rate (BER)

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