Interference reduced routing for sensor networks

Construction of interference reduced routes is an all-important problem in sensor network. We propose a model for extracting a small size backbone network from a given background network. The extracted network possesses the property of reduced static interference. A backbone structure, constructed on the top of a planar sensor network can be used to route message with lower interference. We propose two centralized algorithms for constructing the backbone network. The first algorithm is based on the spanning tree construction of inner holes of sensor network. The second algorithm builds the backbone network by using the Delaunay triangulation of the center of gravity of holes in the network, which runs in O(n2) time. We also present a distributed localized implementation of the proposed algorithm by using the quasi Voronoi diagram and medial axis formed by the distribution of network holes. We describe an experimental investigation of the proposed algorithm. The results of the simulation show that the routing guided by the proposed backbone network is effective in reducing interference

Joint User Pairing and Beamforming Design of Multi-STAR-RISs-Aided NOMA in the Indoor Environment via Multi-Agent Reinforcement Learning

The development of sixth-generation (6G)/Beyond Fifth-Generation (B5G) wireless networks, which have requirements that go beyond current 5G networks, is gaining interest from academia and industry. However, to increase 6G/B5G network quality, conventional cellular networks that rely on terrestrial base stations are constrained geographically and economically. Meanwhile, Non-Orthogonal Multiple Access (NOMA) allows multiple users to share the same resources, which improves the spectral efficiency of the system and has the advantage of supporting a larger number of users. Additionally, by intelligently manipulating the phase and amplitude of both the reflected and transmitted signals, Simultaneously Transmitting and Reflecting RISs (STAR-RISs)can achieve improved coverage, increased spectral efficiency,and enhanced communication reliability. However, STAR-RISsmust simultaneously optimize the amplitude and phase shiftcorresponding to reflection and transmission, which makes theexisting terrestrial networks more complicated and is considereda major challenging issue. Motivated by the above, we studythe joint user pairing for NOMA and beamforming design ofMulti-STAR-RISs in an indoor environment. Then, we formulatethe optimization problem with the objective of maximizing thetotal throughput of mobile users (MUs) by jointly optimizingthe decoding order, user pairing, active beamforming, andpassive beamforming. However, the formulated problem is amixed-integer non-linear programming (MINLP). To addressthis challenge, we first introduce the decoding order for NOMAnetworks. Next, we decompose the original problem into twosubproblems, namely: 1) MU pairing and 2) Beamformingoptimization under the optimal decoding order. For the firstsubproblem, we employ correlation-based K-means clusteringto solve the user pairing problem. Then, to jointly deal withbeamforming vector optimizations, we propose Multi-AgentProximal Policy Optimization (MAPPO), which can make quickdecisions in the given environment owing to its low complexity.Finally, simulation results prove that our proposed MAPPOalgorithm is superior to Proximal Policy Optimization (PPO)and Advanced Actor-Critic (A2C) by a maximum of 1% and6%, respectively.<br/

Joint User Pairing and Beamforming Design of Multi-STAR-RISs-Aided NOMA in the Indoor Environment via Multi-Agent Reinforcement Learning

The development of 6G/B5G wireless networks, which have requirements that go beyond current 5G networks, is gaining interest from academia and industry. However, to increase 6G/B5G network quality, conventional cellular networks that rely on terrestrial base stations are constrained geographically and economically. Meanwhile, NOMA allows multiple users to share the same resources, which improves the spectral efficiency of the system and has the advantage of supporting a larger number of users. Additionally, by intelligently manipulating the phase and amplitude of both the reflected and transmitted signals, STAR-RISs can achieve improved coverage, increased spectral efficiency, and enhanced communication reliability. However, STAR-RISs must simultaneously optimize the amplitude and phase shift corresponding to reflection and transmission, which makes the existing terrestrial networks more complicated and is considered a major challenging issue. Motivated by the above, we study the joint user pairing for NOMA and beamforming design of Multi-STAR-RISs in an indoor environment. Then, we formulate the optimization problem with the objective of maximizing the total throughput of MUs by jointly optimizing the decoding order, user pairing, active beamforming, and passive beamforming. However, the formulated problem is a MINLP. To address this challenge, we first introduce the decoding order for NOMA networks. Next, we decompose the original problem into two subproblems, namely: 1) MU pairing and 2) Beamforming optimization under the optimal decoding order. For the first subproblem, we employ correlation-based K-means clustering to solve the user pairing problem. Then, to jointly deal with beamforming vector optimizations, we propose MAPPO, which can make quick decisions in the given environment owing to its low complexity.Comment: 8 pages, 9 figures, IEEE/IFIP Network Operations and Management Symposium (NOMS) 2024 submitte

Trajectory Optimization and Phase-Shift Design in IRS Assisted UAV Network for High Speed Trains

The recent trend towards the high-speed transportation system has spurred the development of high-speed trains (HSTs). However, enabling HST users with seamless wireless connectivity using the roadside units (RSUs) is extremely challenging, mostly due to the lack of line of sight link. To address this issue, we propose a novel framework that uses intelligent reflecting surfaces (IRS)-enabled unmanned aerial vehicles (UAVs) to provide line of sight communication to HST users. First, we formulate the optimization problem where the objective is to maximize the minimum achievable rate of HSTs by jointly optimizing the trajectory of UAV and the phase-shift of IRS. Due to the non-convex nature of the formulated problem, it is decomposed into two subproblems: IRS phase-shift problem and UAV trajectory optimization problem. Next, a Binary Integer Linear Programming (BILP) and a Soft Actor-Critic (SAC) are constructed in order to solve our decomposed problems. Finally, comprehensive numerical results are provided in order to show the effectiveness of our proposed framework.Comment: This paper has been submitted to IEEE Wireless Communications Letter

Joint Trajectory and Resource Optimization of MEC-Assisted UAVs in Sub-THz Networks: A Resources-based Multi-Agent Proximal Policy Optimization DRL with Attention Mechanism

THz band communication technology will be used in the 6G networks to enable high-speed and high-capacity data service demands. However, THz-communication losses arise owing to limitations, i.e., molecular absorption, rain attenuation, and coverage range. Furthermore, to maintain steady THz-communications and overcome coverage distances in rural and suburban regions, the required number of BSs is very high. Consequently, a new communication platform that enables aerial communication services is required. Furthermore, the airborne platform supports LoS communications rather than NLoS communications, which helps overcome these losses. Therefore, in this work, we investigate the deployment and resource optimization for MEC-enabled UAVs, which can provide THz-based communications in remote regions. To this end, we formulate an optimization problem to minimize the sum of the energy consumption of both MEC-UAV and MUs and the delay incurred by MUs under the given task information. The formulated problem is a MINLP problem, which is NP-hard. We decompose the main problem into two subproblems to address the formulated problem. We solve the first subproblem with a standard optimization solver, i.e., CVXPY, due to its convex nature. To solve the second subproblem, we design a RMAPPO DRL algorithm with an attention mechanism. The considered attention mechanism is utilized for encoding a diverse number of observations. This is designed by the network coordinator to provide a differentiated fit reward to each agent in the network. The simulation results show that the proposed algorithm outperforms the benchmark and yields a network utility which is $2.22\%$, $15.55\%$, and $17.77\%$ more than the benchmarks.Comment: 13 pages, 12 figure

SpaceRIS: LEO Satellite Coverage Maximization in 6G Sub-THz Networks by MAPPO DRL and Whale Optimization

Satellite systems face a significant challenge in effectively utilizing limited communication resources to meet the demands of ground network traffic, characterized by asymmetrical spatial distribution and time-varying characteristics. Moreover, the coverage range and signal transmission distance of low Earth orbit (LEO) satellites are restricted by notable propagation attenuation, molecular absorption, and space losses in sub-terahertz (THz) frequencies. This paper introduces a novel approach to maximize LEO satellite coverage by leveraging reconfigurable intelligent surfaces (RISs) within 6G sub-THz networks. The optimization objectives encompass enhancing the end-to-end data rate, optimizing satellite-remote user equipment (RUE) associations, data packet routing within satellite constellations, RIS phase shift, and ground base station (GBS) transmit power (i.e., active beamforming). The formulated joint optimization problem poses significant challenges owing to its time-varying environment, non-convex characteristics, and NP-hard complexity. To address these challenges, we propose a block coordinate descent (BCD) algorithm that integrates balanced K-means clustering, multi-agent proximal policy optimization (MAPPO) deep reinforcement learning (DRL), and whale optimization (WOA) techniques. The performance of the proposed approach is demonstrated through comprehensive simulation results, exhibiting its superiority over existing baseline methods in the literature

Factors Associated with Improved Outcome of Inhaled Corticosteroid use in COVID-19: A Single Institutional Study

Asthmatics seem less prone to adverse outcomes in coronavirus disease 2019 (COVID-19) and some data shows that inhaled corticosteroids (ICS) are protective. We gathered data on anecdotal ICS and outcomes of patients hospitalized with COVID-19, given there is literature supporting ICS may reduce risk of severe infection. In addition, we fill gaps in current literature evaluating Charlson Comorbidity Index (CCI) as a risk assessment tool for COVID-19. This was a single-center, retrospective study designed and conducted to identify factors associated intubation and inpatient mortality. A multivariate logistic regression model was fit to generate adjusted odds ratios (OR). Intubation was associated with male gender (OR, 2.815; 95% confidence interval [CI], 1.348– 5.881; P = .006) and increasing body mass index (BMI) (OR, 1.053; 95% CI, 1.009–1.099; P = .019). Asthma was associated with lower odds for intubation (OR, 0.283; 95% CI, 0.108–0.74; P = .01). 80% of patients taking pre-hospital ICS were not intubated (n = 8). In-patient mortality was associated with male gender (OR, 2.44; 95% CI, 1.167–5.1; P = .018), older age (OR, 1.096; 95% CI, 1.052–1.142; P = \u3c. 001), and increasing BMI (OR, 1.079; 95% CI, 1.033–1.127; P = .001). Asthma was associated with lower in-patient mortality (OR, 0.221; 95% CI, 0.057–0.854; P = .029). CCI did not correlate with intubation (OR, 1.262; 95% CI, 0.923–1.724; P = .145) or inpatient mortality (OR, 0.896; 95% CI, 0.665–1.206; P = .468). Asthmatics hospitalized for COVID-19 had less adverse outcomes, and most patients taking pre-hospital ICS were not intubated. CCI score was not associated with intubation or inpatient mortality

Presence of Burkholderia pseudomallei in the 'Granary of Myanmar'.

Melioidosis is a frequently fatal infectious disease caused by the Gram negative bacillus Burkholderia pseudomallei. Although it was originally discovered in Myanmar, the disease disappeared from sight for many decades. This study focuses on detection of B. pseudomallei in soil in selected sampling sites in an attempt to start to fill the gaps in the current status of our knowledge of the geographical distribution of B. pseudomallei in soil in Myanmar. This cross-sectional study consists of 400 soil samples from 10 selected study townships from two major paddy growing regions. Bacterial isolation was done using a simplified method for the isolation of Burkholderia pseudomallei from soil. In this study, only 1% (4/400) of soil samples were found to be positive; two of four were found at 90 cm depth and another two positive samples were found at 30 cm and 60 cm. This survey has confirmed the presence of environmental B. pseudomallei in Myanmar indicating that the conditions are in place for melioidosis acquisition

A Systematic Review of the Efficacy and Safety of Fecal Microbiota Transplantation in the Treatment of Hepatic Encephalopathy and Clostridioides difficile Infection in Patients With Cirrhosis

The microbiome of the human gut and liver coexists by influencing the health and disease state of each system. Fecal microbiota transplantation (FMT) has recently emerged as a potential treatment for conditions associated with cirrhosis, such as hepatic encephalopathy and recurrent/refractory Clostridioides difficile infection (rCDI). We have conducted a systematic review of the safety and efficacy of FMT in treating hepatic encephalopathy and rCDI. A literature search was performed using variations of the keywords “fecal microbiota transplant” and “cirrhosis” on PubMed/MEDLINE from inception to October 3, 2021. The resulting 116 articles were independently reviewed by two authors. Eight qualifying studies were included in the systematic review. A total of 127 cirrhotic patients received FMT. Hepatic encephalopathy was evaluated by cognitive tests, such as the Psychometric Hepatic Encephalopathy Score (PHES) and EncephalApp Stroop test. Not only was there an improvement in the cognitive performance in the FMT cohort, but the improvement was also maintained throughout long-term follow-up. In the treatment of rCDI, the FMT success rate is similar between cirrhotic patients and the general population, although more than one dose may be needed in the former. The rate of serious adverse events and adverse events in the cirrhotic cohort was slightly higher than that in the general population but was low overall. We found evidence that supports the therapeutic potential and safety profile of FMT to treat hepatic encephalopathy and rCDI in cirrhotic patients. Further research will be beneficial to better understand the role of FMT in cirrhosis