Two-Way Training for Discriminatory Channel Estimation in Wireless MIMO Systems

This work examines the use of two-way training to efficiently discriminate the channel estimation performances at a legitimate receiver (LR) and an unauthorized receiver (UR) in a multiple-input multiple-output (MIMO) wireless system. This work improves upon the original discriminatory channel estimation (DCE) scheme proposed by Chang et al where multiple stages of feedback and retraining were used. While most studies on physical layer secrecy are under the information-theoretic framework and focus directly on the data transmission phase, studies on DCE focus on the training phase and aim to provide a practical signal processing technique to discriminate between the channel estimation performances at LR and UR. A key feature of DCE designs is the insertion of artificial noise (AN) in the training signal to degrade the channel estimation performance at UR. To do so, AN must be placed in a carefully chosen subspace based on the transmitter's knowledge of LR's channel in order to minimize its effect on LR. In this paper, we adopt the idea of two-way training that allows both the transmitter and LR to send training signals to facilitate channel estimation at both ends. Both reciprocal and non-reciprocal channels are considered and a two-way DCE scheme is proposed for each scenario. {For mathematical tractability, we assume that all terminals employ the linear minimum mean square error criterion for channel estimation. Based on the mean square error (MSE) of the channel estimates at all terminals,} we formulate and solve an optimization problem where the optimal power allocation between the training signal and AN is found by minimizing the MSE of LR's channel estimate subject to a constraint on the MSE achievable at UR. Numerical results show that the proposed DCE schemes can effectively discriminate between the channel estimation and hence the data detection performances at LR and UR.Comment: 1

Two-way training for discriminatory channel estimation in wireless MIMO systems

This work examines the use of two-way training to efficiently discriminate the channel estimation performances at a legitimate receiver (LR) and an unauthorized receiver (UR) in a multiple-input multiple-output (MIMO) wireless system. This work improves upon the original discriminatory channel estimation (DCE) scheme proposed by Chang where multiple stages of feedback and retraining were used. While most studies on physical layer secrecy are under the information-theoretic framework and focus directly on the data transmission phase, studies on DCE focus on the training phase and aim to provide a practical signal processing technique to discriminate between the channel estimation performances (and, thus, the effective received signal qualities) at LR and UR. A key feature of DCE designs is the insertion of artificial noise (AN) in the training signal to degrade the channel estimation performance at UR. To do so, AN must be placed in a carefully chosen subspace, based on the transmitter's knowledge of LR's channel, in order to minimize its effect on LR. In this paper, we adopt the idea of two-way training that allows both the transmitter and LR to send training signals to facilitate channel estimation at both ends. Both reciprocal and nonreciprocal channels are considered and a two-way DCE scheme is proposed for each scenario. For mathematical tractability, we assume that all terminals employ the linear minimum mean square error criterion for channel estimation. Based on the mean square error (MSE) of the channel estimates at all terminals, we formulate and solve an optimization problem where the optimal power allocation between the training signal and AN is found by minimizing the MSE of LR's channel estimate subject to a constraint on the MSE achievable at UR. Numerical results show that the proposed DCE schemes can effectively discriminate between the channel estimation and, hence, the data detection performances at LR and UR.This work was supported in part by the National Science Council, Taiwan, by Grant NSC 100-2628-E-007-025-MY3 and Grant NSC 101-2218-E-011-043, and in part by the Australian Research Council's Discovery Projects Funding Scheme (Project no.DP110102548)

Analysis of clinical outcomes in pediatric bacterial meningitis focusing on patients without cerebrospinal fluid pleocytosis

BackgroundCerebrospinal fluid (CSF) cell count and biochemical examinations and cultures form the basis for the diagnosis of bacterial meningitis. However, some patients do not have typical findings and are at a higher risk of being missed or having delayed treatment. To better understand the correlation between CSF results and outcomes, we evaluated CSF data focusing on the patients with atypical findings.MethodsThis study enrolled CSF culture-proven bacterial meningitis patients aged from 1 month to 18 years in a medical center. The patients were divided into “normal” and “abnormal” groups for each laboratory result and in combination. The correlations between the laboratory results and the outcomes were analyzed.ResultsA total of 175 children with confirmed bacterial meningitis were enrolled. In CSF examinations, 16.2% of patients had normal white blood cell counts, 29.5% had normal glucose levels, 24.5% had normal protein levels, 10.2% had normal results in two items, and 8.6% had normal results in all three items. In logistic regression analysis, a normal CSF leukocyte count and increased CSF protein level were related to poor outcomes. Patients with meningitis caused by Streptococcus pneumoniae and hyponatremia were at a higher risk of mortality and the development of sequelae.ConclusionsIn children with bacterial meningitis, nontypical CSF findings and, in particular, normal CSF leukocyte count and increased protein level may indicate a worse prognosis

Application-Based Online Traffic Classification with Deep Learning Models on SDN Networks

The traffic classification based on the network applications is one important issue for network management. In this paper, we propose an application-based online and offline traffic classification, based on deep learning mechanisms, over software-defined network (SDN) testbed. The designed deep learning model, resigned in the SDN controller, consists of multilayer perceptron (MLP), convolutional neural network (CNN), and Stacked Auto-Encoder (SAE), in the SDN testbed. We employ an open network traffic dataset with seven most popular applications as the deep learning training and testing datasets. By using the TCPreplay tool, the dataset traffic samples are re-produced and analyzed in our SDN testbed to emulate the online traffic service. The performance analyses, in terms of accuracy, precision, recall, and F1 indicators, are conducted and compared with three deep learning models

Modeling of Location Estimation for Object Tracking in WSN

Location estimation for object tracking is one of the important topics in the research of wireless sensor networks (WSNs). Recently, many location estimation or position schemes in WSN have been proposed. In this paper, we will propose the procedure and modeling of location estimation for object tracking in WSN. The designed modeling is a simple scheme without complex processing. We will use Matlab to conduct the simulation and numerical analyses to find the optimal modeling variables. The analyses with different variables will include object moving model, sensing radius, model weighting value α, and power-level increasing ratio k of neighboring sensor nodes. For practical consideration, we will also carry out the shadowing model for analysis

Multiplexed capillary electrophoresis system

The use of capillary electrophoresis (CE) has greatly improved DNA sequencing rates compared to conventional slab gel electrophoresis. Part of the improvement in speed, however, has been offset by the loss of the ability (inherent in slab gels) to accommodate multiple lanes in a single run. Highly multiplexed capillary electrophoresis, by making possible hundreds or even thousands of parallel sequencing runs, represents an attractive approach to overcoming the current throughput limitations of existing DNA sequencing instrumentation

Multiplex capillary electrophoresis system

The use of capillary electrophoresis (CE) has greatly improved DNA sequencing rates compared to conventional slab gel electrophoresis. Part of the improvement in speed, however, has been offset by the loss of the ability (inherent in slab gels) to accommodate multiple lanes in a single run. Highly multiplexed capillary electrophoresis, by making possible hundreds or even thousands of parallel sequencing runs, represents an attractive approach to overcoming the current throughput limitations of existing DNA sequencing instrumentation

Analgesic Effects and the Mechanisms of Anti-Inflammation of Hispolon in Mice

Hispolon, an active ingredient in the fungi Phellinus linteus was evaluated with analgesic and anti-inflammatory effects. Treatment of male ICR mice with hispolon (10 and 20 mg/kg) significantly inhibited the numbers of acetic acid-induced writhing response. Also, our result showed that hispolon (20 mg/kg) significantly inhibited the formalin-induced pain in the later phase (P<.01). In the anti-inflammatory test, hispolon (20 mg/kg) decreased the paw edema at the fourth and fifth hour after λ-carrageenin (Carr) administration, and increased the activities of superoxide dismutase (SOD), glutathione peroxidase (GPx) and glutathione reductase (GRx) in the liver tissue. We also demonstrated that hispolon significantly attenuated the malondialdehyde (MDA) level in the edema paw at the fifth hour after Carr injection. Hispolon (10 and 20 mg/kg) decreased the nitric oxide (NO) levels on both the edema paw and serum level at the fifth hour after Carr injection. Also, hispolon (10 and 20 mg/kg) diminished the serum TNF-α at the fifth hour after Carr injection. The anti-inflammatory mechanisms of hispolon might be related to the decrease in the level of MDA in the edema paw by increasing the activities of SOD, GPx and GRx in the liver. It probably exerts anti-inflammatory effects through the suppression of TNF-α and NO