Stochastic geometry based dynamic fractional frequency reuse for OFDMA systems

Fractional Frequency Reuse (FFR) has been acknowledged as an efficient Interference Management (IM) technique, which offers significant capacity enhancement and improves cell edge coverage with low complexity of implementation. The performance of cellular system greatly depends on the spatial configuration of base stations (BSs). In literature, FFR has been analyzed mostly with cellular networks described by Hexagon Grid Model (HGM). HGM is neither tractable nor scalable to the dense deployment of next generation wireless networks. Moreover, the perfect geometry based HGM tends to overestimate the system's performance and not able to reflect the reality. In this paper, we use the stochastic geometry approach; FFR is analyzed with cellular network modeled by homogeneous Poisson Point Process (PPP). PPP model provides complete randomness in terms of BS deployment, which captures the real network scenario. A dynamic FFR scheme is proposed in this article, which take into account the randomness of the cell coverage area described by Voronoi tessellation. It is shown that the proposed scheme outperforms the traditional fixed frequency allocation schemes in terms of capacity and capacity density

Enhanced label noise filtering with multiple voting

© 2019 by the authors. Label noises exist in many applications, and their presence can degrade learning performance. Researchers usually use filters to identify and eliminate them prior to training. The ensemble learning based filter (EnFilter) is the most widely used filter. According to the voting mechanism, EnFilter is mainly divided into two types: single-voting based (SVFilter) and multiple-voting based (MVFilter). In general, MVFilter is more often preferred because multiple-voting could address the intrinsic limitations of single-voting. However, the most important unsolved issue in MVFilter is how to determine the optimal decision point (ODP). Conceptually, the decision point is a threshold value, which determines the noise detection performance. To maximize the performance of MVFilter, we propose a novel approach to compute the optimal decision point. Our approach is data driven and cost sensitive, which determines the ODP based on the given noisy training dataset and noise misrecognition cost matrix. The core idea of our approach is to estimate the mislabeled data probability distributions, based on which the expected cost of each possible decision point could be inferred. Experimental results on a set of benchmark datasets illustrate the utility of our proposed approach

Characterization of End-to-end Path Selection for Cognitive Radio Wireless Mesh Networks

Abstract: The Cognitive Radio (CR) can delivers the environment to Secondary Users (SUs) of Wireless Mesh Network (WMN) to utilize unused spectrum of Primary Users (PUs) opportunistically. The CR can improve the spectrum usage of the WMN. However, this rises the some additional complexities for the SUs such as spectrum heterogeneity, unpredictable PU activity and interference constraints. In this paper an analytical model has been developed to analyse these complexities for each SU node and link characteristics for end-to-end optimal Path and channel assignment. Numerical results show that the analytical model is an effective tool to investigate the effects of the PU activities and channel heterogeneity on the network performance

Voronoi Cell Geometry Based Dynamic Fractional Frequency Reuse for OFDMA Cellular Networks

Interference Management (1M) is one of the major challenges of next generation wireless communication. Fractional Frequency Reuse (FFR) has been acknowledged as an efficient 1M technique, which offers significant capacity enhancement and improve cell edge coverage with low complexity. In literature, FFR has been analyzed mostly with cellular networks described by Hexagon Grid Model, which is neither tractable nor scalable to the dense deployment of next generation wireless networks. Moreover, the perfect geometry based grid model tends to overestimate the system performance and not able to reflect the reality. In this paper, we use the stochastic geometry approach, FFR is analyzed with cellular network modeled by homogeneous Poisson Point Process (PPP). A dynamic frequency allocation scheme is proposed which take into account the randomness of the cell coverage area describe by Voronoi tessellation. It is shown that the proposed scheme outperforms the traditional fixed frequency allocation schemes in terms of per user capacity and capacity density

Resource Allocation for Uplink M2M Communication: A Game Theory Approach

Machine-to-Machine (M2M) communication in cellular network is the driver for the future Internet of Things (IoT). The main challenge of M2M communication is the possibility of huge traffic in the uplink network that can cause problem in the network. This paper considers the problem of resource allocation among machines connecting in uplink to different femto base stations (FBSs). Resource allocation problem is analyzed through both non-cooperative and cooperative game to maximize their data rate and minimize utilization of power. Numerical result shows that by adapting non-cooperative game, all machines are getting data rate as per Nash Equilibrium (NE) or either they can set their strategy to maximize their data rate selfishly. On the other hand for coalitional game theory approach machines who participate in game are getting fair resource allocations

Correlating health and wellness analytics for personalized decision making

© 2015 IEEE. Personalized healthcare envisions providing customized treatment and management plans to individuals at their doorstep. Key factors to ensure personalized healthcare is to involve with the individual in their daily life activities and process the gathered information to provide recommendations. We identified the mostly exposed domains for gathering chronic disease patients information that includes: clinical, social media, and daily life activities. Clinical data is related to the health-care of the patients while social media, sensory, and wearables data is related to the wellness data of the patients. A framework is required to monitor the health and wellness information of the patients for health and wellness analytics provisioning to the physicians for better decision making. We propose Personalized, Ubiquitous Life-care Decision Support System (PULSE); a state of the art decision support system that helps physicians and patients in life-style management of chronic disease patients such as Diabetes. The proposed approach not only utilizes clinical information but also personalized information by correlation to find hidden information using big data health analytic for improvement of life-care. PULSE provides health analytics by utilizing and processing clinical information of the patient. In the same way, it provides wellness analytics to the patients by using their social, activities, emotions and daily life information. The co-relation between clinical and personalized analytics is performed for better recommendations to the patients. This eventually results in improved life-care and healthy living of the individuals

Acquiring Guideline-enabled data driven clinical knowledge model using formally verified refined knowledge acquisition method

Background and Objective: Validation and verification are the critical requirements for the knowledge acquisition method of the clinical decision support system (CDSS). After acquiring the medical knowledge from diverse sources, the rigorous validation and formal verification process are required before creating the final knowledge model. Previously, we have proposed a hybrid knowledge acquisition method with the support of a rigorous validation process for acquiring medical knowledge from clinical practice guidelines (CPGs) and patient data for the treatment of oral cavity cancer. However, due to lack of formal verification process, it involves various inconsistencies in knowledge relevant to the formalism of knowledge, conformance to CPGs, quality of knowledge, and complexities of knowledge acquisition artifacts.Methods: This paper presents the refined knowledge acquisition (ReKA) method, which uses the Z formal verification process. The ReKA method adopts the verification method and explores the mechanism of theorem proving using the Z notation. It enhances a hybrid knowledge acquisition method to thwart the inconsistencies using formal verification.Results: ReKA adds a set of nine additional criteria to be used to have a final valid refined clinical knowledge model. These criteria ensure the validity of the final knowledge model concerning formalism of knowledge, conformance to GPGs, quality of the knowledge, usage of stringent conditions and treatment plans, and inconsistencies possibly resulting from the complexities. Evaluation, using four medical knowledge acquisition scenarios, shows that newly added knowledge in CDSS due to the additional criteria by the ReKA method always produces a valid knowledge model. The final knowledge model was also evaluated with 1229 oral cavity patient cases, which outperformed with an accuracy of 72.57% compared to a similar approach with an accuracy of 69.7%. Furthermore, the ReKA method identified a set of decision paths (about 47.8%) in the existing approach, which results in a final knowledge model with low quality, non-conformed from standard CPGs.Conclusion: ReKA refined the hybrid knowledge acquisition method by discovering the missing steps in the current validation process at the acquisition stage. As a formally proven method, it always yields a valid knowledge model having high quality, supporting local practices, and influenced by standard CPGs. Furthermore, the final knowledge model obtained from ReKA also preserves the performance such as the accuracy of the individual source knowledge models.N/

Effects of a high-dose 24-h infusion of tranexamic acid on death and thromboembolic events in patients with acute gastrointestinal bleeding (HALT-IT): an international randomised, double-blind, placebo-controlled trial

Background: Tranexamic acid reduces surgical bleeding and reduces death due to bleeding in patients with trauma. Meta-analyses of small trials show that tranexamic acid might decrease deaths from gastrointestinal bleeding. We aimed to assess the effects of tranexamic acid in patients with gastrointestinal bleeding. Methods: We did an international, multicentre, randomised, placebo-controlled trial in 164 hospitals in 15 countries. Patients were enrolled if the responsible clinician was uncertain whether to use tranexamic acid, were aged above the minimum age considered an adult in their country (either aged 16 years and older or aged 18 years and older), and had significant (defined as at risk of bleeding to death) upper or lower gastrointestinal bleeding. Patients were randomly assigned by selection of a numbered treatment pack from a box containing eight packs that were identical apart from the pack number. Patients received either a loading dose of 1 g tranexamic acid, which was added to 100 mL infusion bag of 0·9% sodium chloride and infused by slow intravenous injection over 10 min, followed by a maintenance dose of 3 g tranexamic acid added to 1 L of any isotonic intravenous solution and infused at 125 mg/h for 24 h, or placebo (sodium chloride 0·9%). Patients, caregivers, and those assessing outcomes were masked to allocation. The primary outcome was death due to bleeding within 5 days of randomisation; analysis excluded patients who received neither dose of the allocated treatment and those for whom outcome data on death were unavailable. This trial was registered with Current Controlled Trials, ISRCTN11225767, and ClinicalTrials.gov, NCT01658124. Findings: Between July 4, 2013, and June 21, 2019, we randomly allocated 12 009 patients to receive tranexamic acid (5994, 49·9%) or matching placebo (6015, 50·1%), of whom 11 952 (99·5%) received the first dose of the allocated treatment. Death due to bleeding within 5 days of randomisation occurred in 222 (4%) of 5956 patients in the tranexamic acid group and in 226 (4%) of 5981 patients in the placebo group (risk ratio [RR] 0·99, 95% CI 0·82–1·18). Arterial thromboembolic events (myocardial infarction or stroke) were similar in the tranexamic acid group and placebo group (42 [0·7%] of 5952 vs 46 [0·8%] of 5977; 0·92; 0·60 to 1·39). Venous thromboembolic events (deep vein thrombosis or pulmonary embolism) were higher in tranexamic acid group than in the placebo group (48 [0·8%] of 5952 vs 26 [0·4%] of 5977; RR 1·85; 95% CI 1·15 to 2·98). Interpretation: We found that tranexamic acid did not reduce death from gastrointestinal bleeding. On the basis of our results, tranexamic acid should not be used for the treatment of gastrointestinal bleeding outside the context of a randomised trial

Resource allocation for uplink M2M communication in multi-tier network

Machine-to-Machine (M2M) communication in heterogeneous cellular networks (HCNs) is “ONE OF THE DRIVERS” for the future Internet of Things (IoT). Coverage areas of HCNs cells may vary and the capabilities to handle users may vary also. To support massive numbers of machines connected in uplink in HCNs, one of the challenging issues of M2M communication is the possibility of huge traffic that can cause overload problem for specific tier/tiers. Increase the capacity of the network and avoid overload condition for BSs, machines will need to be pushed to the less loaded BSs even they offered a lower instantaneous SINR than the nearest BS. To push the machine to less loaded BS, biasing is introduced to enhance the coverage of the machine or group of machines. This paper proposes the solution of resource allocation in uplink by using cooperative game theory approach by introducing a biasing factor to enhance the overall system performance with fair utilization of radio resources