Distributed Binary Detection over Fading Channels: Cooperative and Parallel Architectures

This paper considers the problem of binary distributed detection of a known signal in correlated Gaussian sensing noise in a wireless sensor network, where the sensors are restricted to use likelihood ratio test (LRT), and communicate with the fusion center (FC) over bandwidth-constrained channels that are subject to fading and noise. To mitigate the deteriorating effect of fading encountered in the conventional parallel fusion architecture, in which the sensors directly communicate with the FC, we propose new fusion architectures that enhance the detection performance, via harvesting cooperative gain (so-called decision diversity gain). In particular, we propose: (i) cooperative fusion architecture with Alamouti's space-time coding (STC) scheme at sensors, (ii) cooperative fusion architecture with signal fusion at sensors, and (iii) parallel fusion architecture with local threshold changing at sensors. For these schemes, we derive the LRT and majority fusion rules at the FC, and provide upper bounds on the average error probabilities for homogeneous sensors, subject to uncorrelated Gaussian sensing noise, in terms of signal-to-noise ratio (SNR) of communication and sensing channels. Our simulation results indicate that, when the FC employs the LRT rule, unless for low communication SNR and moderate/high sensing SNR, performance improvement is feasible with the new fusion architectures. When the FC utilizes the majority rule, such improvement is possible, unless for high sensing SNR

On Power Allocation for Distributed Detection with Correlated Observations and Linear Fusion

We consider a binary hypothesis testing problem in an inhomogeneous wireless sensor network, where a fusion center (FC) makes a global decision on the underlying hypothesis. We assume sensors observations are correlated Gaussian and sensors are unaware of this correlation when making decisions. Sensors send their modulated decisions over fading channels, subject to individual and/or total transmit power constraints. For parallel-access channel (PAC) and multiple-access channel (MAC) models, we derive modified deflection coefficient (MDC) of the test statistic at the FC with coherent reception.We propose a transmit power allocation scheme, which maximizes MDC of the test statistic, under three different sets of transmit power constraints: total power constraint, individual and total power constraints, individual power constraints only. When analytical solutions to our constrained optimization problems are elusive, we discuss how these problems can be converted to convex ones. We study how correlation among sensors observations, reliability of local decisions, communication channel model and channel qualities and transmit power constraints affect the reliability of the global decision and power allocation of inhomogeneous sensors

MXene-Integrated Silk Fibroin-Based Self-Assembly-Driven 3D-Printed Theragenerative Scaffolds for Remotely Photothermal Anti-Osteosarcoma Ablation and Bone Regeneration

Aiming to address the bone regeneration and cancer therapy functionalities in one single material, in this study, we developed a dual-functional theragenerative three-dimensional (3D) aerogel-based composite scaffold from hybridization of photo-cross-linked silk fibroin (SF) biopolymer with MXene (Ti3C2) two-dimensional (2D) nanosheets. To fabricate the scaffold, we first develop a dual-cross-linked SF-based aerogel scaffold through 3D printing and photo-cross-linking of the self-assembly-driven methacrylate-modified SF (SF-MA) gel with controlled pore size, macroscopic geometry, and mechanical stability. In the next step, to endow a remotely controlled photothermal antiosteosarcoma ablation function to fabricated aerogel scaffold, MXene 2D nanosheets with strong near-infrared (NIR) photon absorption properties were integrated into the 3D-printed scaffolds. While 3D-printed MXene-modified dual-cross-linked SF composite scaffolds can mediate the in vitro growth and proliferation of preosteoblastic cell lines, they also endow a strong photothermal effect upon remote irradiation with NIR laser but also significantly stimulate bone mineral deposition on the scaffold surface. Additionally, besides the local release of the anticancer model drug, the generated heat (45-53 ?) mediated the photothermal ablation of cancer cells. The developed aerogel-based composites and chosen therapeutic techniques are thought to render a significant breakthrough in biomaterials' future clinical applications

On Power Allocation For Distributed Detection With Correlated Observations And Linear Fusion

We consider a binary hypothesis testing problem in a wireless sensor network, where a fusion center (FC) makes a final decision about the underlying hypothesis. We assume under hypothesis H0, sensors\u27 observations are uncorrelated and identically distributed Gaussian, however, under H1 they are correlated and nonidentically distributed Gaussian and sensors are unaware of this correlation when making decisions. Sensors send their local binary decisions over power constrained fading channels. We consider both parallel access channel (PAC) and multiple access channel (MAC) models. To obtain the detection statistic in PAC, we assume that the FC utilizes a linear fusion rule, which linearly combines the signals received from all sensors, while in MAC, the signal received at the FC is naturally the coherent sum of the transmitted signals. For both PAC and MAC, we derive modified deflection coefficient (MDC) of the detection statistic at the FC with coherent reception. Choosing MDC as the detection performance metric, we formulate several constrained transmit power optimization problems. In these problems, MDC is the objective function to be maximized and there are three different sets of transmit power constraints: total power constraint, individual and total power constraints, and individual power constraints only. We refer to the solutions of these constrained optimization problems as MDC-based transmit power allocation. When analytical solutions to these constrained optimization problems are elusive, we discuss how these problems can be converted to convex ones. Our results show that, compared with equal power allocation, detection performance improvement provided by our proposed MDC-based power allocation is more significant in MAC. We quantify the improvement in terms of several factors, including the degree of correlation among sensors\u27 observations, reliability of local decisions (local detection performance indices), communication channel properties, and the type of transmit power constraint. We also study how the power allocation among sensors varies as these factors change

Channel-aware distributed detection in wireless networks with correlated observations

Thesis (Ph. D.)--University of Rochester. Department of Electrical and Computer Engineering, 2017.We study data fusion in a distributed detection system, consisting of several sensors and a fusion center (FC), that is tasked with solving an underlying binary hypothesis testing problem (e.g., detection of a signal source or a target in a field being monitored). Each sensor makes a binary local decision based on its local observation, where these local decisions are digital modulated and transmitted over wireless channels to neighboring sensors and/or the FC. A global binary decision is made at the FC by fusing the data received from sensors. But due to additive Gaussian noise and multipath fading, effect of wireless communication channel, the binary local decisions are corrupted, causing the global decision to be less reliable. Our goal is to maximize the reliability of the global decision. We ask the question: what is the optimal distributed detection system design in the presence of multipath fading and additive Gaussian noise in wireless communication channel? To address this question in this thesis, we identify and address three subproblems as the following: P1) We propose a new class of integrated distributed detection, which harvests cooperative gain (enabled by at most 1-bit information exchange among one-hop neighboring nodes) and improves the performance of the integrated distributed detection in the presence of fading, via pushing the communication bounds. In particular, we propose three schemes: (i) cooperative fusion architecture with Alamouti’s space-time coding (STC) scheme at sensors, (ii) cooperative fusion architecture with signal fusion at sensors, and (iii) parallel fusion architecture with local threshold changing at sensors. For these schemes, we derive the likelihood ratio test (LRT) and majority fusion rules at the FC, and provide upper bounds on the average error probabilities for homogeneous sensors, subject to uncorrelated Gaussian sensing noise, in terms of signal-to-noise ratio (SNR) of communication and sensing channels. Our numerical results show that, when the FC employs the LRT rule, unless for low communication SNR and moderate/high sensing SNR, performance improvement is feasible with the new cooperative and parallel fusion architectures, while scheme (iii) outperforms others. When the FC utilizes the majority rule, such improvement is possible, unless for high sensing SNR. In particular, for very high sensing SNR scheme (i) outperforms, whereas for moderate/low sensing SNR scheme (ii) outperforms others. P2) We consider a binary hypothesis testing problem in an inhomogeneous wireless sensor network, where a fusion center (FC) makes a global decision on the underlying hypothesis. We assume sensors’ observations are correlated Gaussian and sensors are oblivious of such correlation when making decisions. Sensors send their modulated decisions over nonideal fading channels, subject to individual and/or total transmit power constraints. Considering parallelaccess channel (PAC) and multiple-access channel (MAC) models, we derive modified deflection coefficient (MDC) of the test statistic at the FC for coherent and non-coherent reception. We propose a transmit power allocation scheme, which maximizes MDC of the test statistic, under three different sets of transmit power constraints. When analytical solutions to our constrained optimization problems are elusive, we discuss how these problems can be converted to convex ones. We study how correlation among sensors’ observations, reliability of local decisions, communication channel model and channel qualities, transmit power constraints, and reception at the FC, affect the reliability of the global decision and power allocation of inhomogeneous sensors. P3) We consider a wireless sensor network that is tasked with detecting a known signal in correlated Gaussian noise. We consider two system-level constrained optimization problems for three transmission approaches, which are deterministic, randomized (i), and randomized (ii) transmission approaches. The observation space at each sensor is divided to three regions R-1, R0, R1. In deterministic approach the symbol transmitted to the FC is determined only by the region that the observation at each sensor belongs to. In randomized approaches, the transmitted symbol is determined based on the region and the realizations of two independent Bernouli random vari131 ables with two different parameters. For deterministic transmission approach, we have shown that, similar to previous work for detecting a known signal in uncorrelated Gaussian noise, the optimal three regions R-1, R0, R1 are three continuous and disjoint intervals. We have shown that if the correlation coefficient is high enough such that the sensors observations are in two consecutive intervals, then using either of the two randomized transmission approaches leads into an enhanced detection performance, compared with using deterministic transmission approach. Moreover, if transmit power is low enough such that the smaller censoring threshold is negative, using randomized transmission approach (ii) provides a higher detection performance, compared with using deterministic transmission approac

Self-Assembly-Driven Bi2S3 Nanobelts Integrated a Silk-Fibroin-Based 3D-Printed Aerogel-Based Scaffold with a Dual-Network Structure for Photothermal Bone Cancer Therapy

: Multifunctional all-in-one biomaterial combining the therapeutic and regeneration functionalities for successive tumor therapy and tissue regeneration is in high demand in interdisciplinary research. In this study, a three-dimensional (3D) aerogel-based composite scaffold with a dual-network structure generated through self-assembly and photo-cross-linking with combined properties of photothermally triggered controlled anticancer drug release and photothermal cancer cell ablation was successfully fabricated. The fabrication of composites consists of self-assembly of a silk fibroin methacrylate (SF-MA) biopolymer incorporated with hydrothermally driven bismuth sulfide (Bi2S3) methacrylate nanobelts, followed by a photo-cross-linking-assisted 3D-printing process. The developed scaffolds presented hierarchically organized porosity and excellent photothermal conversion thanks to the strong near-infrared (NIR) photon absorption of incorporated Bi2S3 nanobelts inside the scaffold matrix. The heat generated in the scaffold mediated by laser irradiation has not only triggered controlled and prolonged release of the anticancer drug but also significantly ablated the bone cancer cells adhered on the scaffold. In addition, the developed 3D composite scaffolds have demonstrated excellent biodegradability for organic and inorganic network constituents at different media, enabling them as potential implants to be replaced by de novo tissue. In combination of chemotherapy and photothermal therapy, the multifunctional 3D-printed composite aerogel scaffold is expected to be an excellent implantable material in bone tissue engineering (BTE) for successive cancer therapy and tissue regeneration