Machine Learning-Enabled Joint Antenna Selection and Precoding Design: From Offline Complexity to Online Performance

We investigate the performance of multi-user multiple-antenna downlink systems in which a BS serves multiple users via a shared wireless medium. In order to fully exploit the spatial diversity while minimizing the passive energy consumed by radio frequency (RF) components, the BS is equipped with M RF chains and N antennas, where M < N. Upon receiving pilot sequences to obtain the channel state information, the BS determines the best subset of M antennas for serving the users. We propose a joint antenna selection and precoding design (JASPD) algorithm to maximize the system sum rate subject to a transmit power constraint and QoS requirements. The JASPD overcomes the non-convexity of the formulated problem via a doubly iterative algorithm, in which an inner loop successively optimizes the precoding vectors, followed by an outer loop that tries all valid antenna subsets. Although approaching the (near) global optimality, the JASPD suffers from a combinatorial complexity, which may limit its application in real-time network operations. To overcome this limitation, we propose a learning-based antenna selection and precoding design algorithm (L-ASPA), which employs a DNN to establish underlaying relations between the key system parameters and the selected antennas. The proposed L-ASPD is robust against the number of users and their locations, BS's transmit power, as well as the small-scale channel fading. With a well-trained learning model, it is shown that the L-ASPD significantly outperforms baseline schemes based on the block diagonalization and a learning-assisted solution for broadcasting systems and achieves higher effective sum rate than that of the JASPA under limited processing time. In addition, we observed that the proposed L-ASPD can reduce the computation complexity by 95% while retaining more than 95% of the optimal performance.Comment: accepted to the IEEE Transactions on Wireless Communication

Assistance for parents with unsettled infants in Central Vietnam: a qualitative investigation of health professionals' perspectives

Background:Unsettled infant behaviours are a common concern for parents internationally, and have been associated with maternal stress, reduced parenting confidence, and postnatal mental health problems among parents. Little information currently exists regarding available support for the parents of unsettled infants in low-and-middle income countries such as Vietnam. We aimed to describe how unsettled infant behaviour was understood and investigated by Vietnamese health professionals, and what health education was provided to parents regarding infant sleep and settling.Methods:This qualitative study elicited the perspectives of Vietnamese health professionals working in Thua Thien Hue Province, Vietnam. A semi-structured interview guide included participant demographics, and questions about providing assistance to the parents of unsettled infants, understandings of unsettled infant behaviour, management of unsettled infant behaviour and health education. Individual interviews or small-group discussions were undertaken in Vietnamese, data were translated and analysed in English. The authors used a thematic approach to analysis, supported by Nvivo software.Results:Nine health professionals (four primary care doctors, one paediatrician and four nurses/midwives) working in urban and rural areas of Thua Thien Hue were interviewed. Four themes were created that reflected the responses to the literature-based interview questions. Health professionals described having received little formal training about infant sleep and settling, thus based their advice on personal experience. Information on infant sleep and settling was not included in health education for new mothers, which focused on breastfeeding and preventing malnutrition. Where advice was given, it was generally based on settling strategies involving high levels of caregiver intervention (holding, rocking, breastfeeding on demand and tolerating frequent overnight wakings) rather than behaviour management style strategies. Participants emphasised the importance of recognising and responding to infant behavioural cues (e.g infants cry when hungry).Conclusions:There is an unmet need for information on infant sleep and settling for new parents and health professionals in Vietnam. Our findings suggest information for caregivers on how to respond sensitively to infant tired signs should be formally included in the training of health professionals in LALMI settings. Sleep and settling information should also be part of culturally appropriate multi-component maternal and child health interventions aimed at promoting early childhood development

Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework

To enable an intelligent, programmable and multi-vendor radio access network (RAN) for 6G networks, considerable efforts have been made in standardization and development of open RAN (O-RAN). So far, however, the applicability of O-RAN in controlling and optimizing RAN functions has not been widely investigated. In this paper, we jointly optimize the flow-split distribution, congestion control and scheduling (JFCS) to enable an intelligent traffic steering application in O-RAN. Combining tools from network utility maximization and stochastic optimization, we introduce a multi-layer optimization framework that provides fast convergence, long-term utility-optimality and significant delay reduction compared to the state-of-the-art and baseline RAN approaches. Our main contributions are three-fold: i) we propose the novel JFCS framework to efficiently and adaptively direct traffic to appropriate radio units; ii) we develop low-complexity algorithms based on the reinforcement learning, inner approximation and bisection search methods to effectively solve the JFCS problem in different time scales; and iii) the rigorous theoretical performance results are analyzed to show that there exists a scaling factor to improve the tradeoff between delay and utility-optimization. Collectively, the insights in this work will open the door towards fully automated networks with enhanced control and flexibility. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the convergence rate, long-term utility-optimality and delay reduction.Comment: 15 pages, 10 figures. A short version will be submitted to IEEE GLOBECOM 202

Knowledge of Antiretroviral Treatment and Associated Factors in HIV-Infected Patients

This study aimed to assess the knowledge of antiretroviral (ARV) treatment and the associated factors in HIV-infected patients in Vietnam. We conducted a cross-sectional descriptive study of 350 human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) patients being treated with ARV at outpatient clinics at Soc Trang, Vietnam, from June 2019 to December 2019. Using an interview questionnaire, patients who answered at least eight out of nine questions correctly, including some required questions, were considered to have a general knowledge of ARV treatment. Using multivariate logistic regression to identify factors associated with knowledge of ARV treatment, we found that 62% of HIV-infected patients had a general knowledge of ARV treatment, with a mean score of 8.2 (SD 1.4) out of 9 correct. A higher education level (p < 0.001); working away from home (p = 0.013); getting HIV transmitted by injecting drugs or from mother-to-child contact (p = 0.023); the presence of tension, anxiety, or stress (p = 0.005); self-reminding to take medication (p = 0.024); and a high self-evaluated adherence (p < 0.001) were found to be significantly associated with an adequate knowledge of ARV treatment. In conclusion, education programs for patients, as well as the quality of medical services and support, should be strengthened

Risk analysis procedure for woodframe roof sheathing panel debris impact to windows in hurricanes

The assessment of losses during extreme events such as hurricanes is important for performance-based design of residential buildings. In this paper, a methodology for estimating the risk of debris impact, specifically roof sheathing panels, to windows as a result of hurricanes is introduced and applied to an illustrative example. The method is a combination of approaches on flat plate trajectories, numerical hurricane modeling, and statistical analysis of structural capacity. Within this methodology, one can estimate the risk of impact for one or more windows in a certain house group as a hurricane approaches and passes on a deterministic track as defined by the center of its eye. The impact risk is analyzed for the each hour making up the full hurricane duration rather than a single analysis using the blended (total) hurricane statistics. An illustration of the method is presented through a risk assessment of windborne debris impacts to windows in a house group located near the U.S. Gulf coast using a hurricane having the same track as hurricane Katrina in 2005. As a result, the probability of each window being hit by a roof sheathing panel (RSP) during each hour of the hurricane as well as during each hurricane is presented. The results quantify the risk from hour to hour during a hurricane and may serve to better orient houses in planned communities in hurricane prone regions as well as provide a better understanding of the interaction of hurricanes and structures.KEYWORDS: fragility, wind force, windborne debris, light-frame wood, hurrican

Probabilistic procedure for wood-frame roof sheathing panel debris impact to windows in hurricanes

The assessment of losses during extreme events such as hurricanes is important for performance-based design of residential buildings. In this paper, a methodology for estimating the probability of debris impact, specifically roof sheathing panels, to windows as a result of hurricanes is introduced and applied to a series of illustrative examples. The methodology is a combination of approaches on flat plate trajectories, numerical hurricane modeling, and statistical analysis of structural capacity. Within this methodology, one can estimate the probability of impact for one or more windows in a certain house group as a hurricane approaches and passes on a deterministic track as defined by the center of its eye. The impact probability is analyzed for each hour making up the full hurricane duration rather than a single analysis using the blended (total) hurricane statistics. An illustration of the method is presented through an assessment of windborne debris impacts to windows in a house group located near the US Gulf coast using a hurricane having the same track as hurricane Katrina in 2005. As a result, the probability of each window being hit by a roof sheathing panel (RSP) during each hour of the hurricane as well as during each of the example hurricanes is presented. The results quantify the probability from hour to hour during a hurricane and will provide a more accurate estimate of the probability and timing of pressurization of buildings for total loss estimation including rainwater intrusion volumes

Defeating Super-Reactive Jammers WithDeception Strategy: Modeling, SignalDetection, and Performance Analysis

This paper aims to develop a novel framework to defeat a super-reactive jammer, one of the mostdifficult jamming attacks to deal with in practice. Specifically, the jammer has an unlimited power budgetand is equipped with the self-interference suppression capability to simultaneously attack and listen tothe transmitter’s activities. Consequently, dealing with super-reactive jammers is very challenging. Thus,we introduce a smart deception mechanism to attract the jammer to continuously attack the channel andthen leverage jamming signals to transmit data based on the ambient backscatter communication whichis resilient to radio interference/jamming. To decode the backscattered signals, the maximum likelihood(ML) detector can be adopted. However, the method is notorious for its high computational complexityand require a specific mathematical model for the communication system. Hence, we propose a deeplearning-based detector that can dynamically adapt to any channel and noise distributions. With the LongShort-Term Memory network, our detector can learn the received signals’ dependencies to achieve theperformance close to that of the optimal ML detector. Through simulation and theoretical results, wedemonstrate that with proposed approaches, the more power the jammer uses to attack the channel, thebetter bit error rate performance we can achiev

Defeating Super-Reactive Jammers WithDeception Strategy: Modeling, SignalDetection, and Performance Analysis

This paper aims to develop a novel framework to defeat a super-reactive jammer, one of the mostdifficult jamming attacks to deal with in practice. Specifically, the jammer has an unlimited power budgetand is equipped with the self-interference suppression capability to simultaneously attack and listen tothe transmitter’s activities. Consequently, dealing with super-reactive jammers is very challenging. Thus,we introduce a smart deception mechanism to attract the jammer to continuously attack the channel andthen leverage jamming signals to transmit data based on the ambient backscatter communication whichis resilient to radio interference/jamming. To decode the backscattered signals, the maximum likelihood(ML) detector can be adopted. However, the method is notorious for its high computational complexityand require a specific mathematical model for the communication system. Hence, we propose a deeplearning-based detector that can dynamically adapt to any channel and noise distributions. With the LongShort-Term Memory network, our detector can learn the received signals’ dependencies to achieve theperformance close to that of the optimal ML detector. Through simulation and theoretical results, wedemonstrate that with proposed approaches, the more power the jammer uses to attack the channel, thebetter bit error rate performance we can achiev

Social Determinants of Long Lasting Insecticidal Hammock-Use Among the Ra-Glai Ethnic Minority in Vietnam: Implications for Forest Malaria Control

BACKGROUND: Long-lasting insecticidal hammocks (LLIHs) are being evaluated as an additional malaria prevention tool in settings where standard control strategies have a limited impact. This is the case among the Ra-glai ethnic minority communities of Ninh Thuan, one of the forested and mountainous provinces of Central Vietnam where malaria morbidity persist due to the sylvatic nature of the main malaria vector An. dirus and the dependence of the population on the forest for subsistence - as is the case for many impoverished ethnic minorities in Southeast Asia. METHODS: A social science study was carried out ancillary to a community-based cluster randomized trial on the effectiveness of LLIHs to control forest malaria. The social science research strategy consisted of a mixed methods study triangulating qualitative data from focused ethnography and quantitative data collected during a malariometric cross-sectional survey on a random sample of 2,045 study participants. RESULTS: To meet work requirements during the labor intensive malaria transmission and rainy season, Ra-glai slash and burn farmers combine living in government supported villages along the road with a second home at their fields located in the forest. LLIH use was evaluated in both locations. During daytime, LLIH use at village level was reported by 69.3% of all respondents, and in forest fields this was 73.2%. In the evening, 54.1% used the LLIHs in the villages, while at the fields this was 20.7%. At night, LLIH use was minimal, regardless of the location (village 4.4%; forest 6.4%). DISCUSSION: Despite the free distribution of insecticide-treated nets (ITNs) and LLIHs, around half the local population remains largely unprotected when sleeping in their forest plot huts. In order to tackle forest malaria more effectively, control policies should explicitly target forest fields where ethnic minority farmers are more vulnerable to malaria