Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs with Graph Convolutional Networks

Last year, IEEE 802.11 Extremely High Throughput Study Group (EHT Study Group) was established to initiate discussions on new IEEE 802.11 features. Coordinated control methods of the access points (APs) in the wireless local area networks (WLANs) are discussed in EHT Study Group. The present study proposes a deep reinforcement learning-based channel allocation scheme using graph convolutional networks (GCNs). As a deep reinforcement learning method, we use a well-known method double deep Q-network. In densely deployed WLANs, the number of the available topologies of APs is extremely high, and thus we extract the features of the topological structures based on GCNs. We apply GCNs to a contention graph where APs within their carrier sensing ranges are connected to extract the features of carrier sensing relationships. Additionally, to improve the learning speed especially in an early stage of learning, we employ a game theory-based method to collect the training data independently of the neural network model. The simulation results indicate that the proposed method can appropriately control the channels when compared to extant methods

Proactive Received Power Prediction Using Machine Learning and Depth Images for mmWave Networks

This study demonstrates the feasibility of the proactive received power prediction by leveraging spatiotemporal visual sensing information toward the reliable millimeter-wave (mmWave) networks. Since the received power on a mmWave link can attenuate aperiodically due to a human blockage, the long-term series of the future received power cannot be predicted by analyzing the received signals before the blockage occurs. We propose a novel mechanism that predicts a time series of the received power from the next moment to even several hundred milliseconds ahead. The key idea is to leverage the camera imagery and machine learning (ML). The time-sequential images can involve the spatial geometry and the mobility of obstacles representing the mmWave signal propagation. ML is used to build the prediction model from the dataset of sequential images labeled with the received power in several hundred milliseconds ahead of when each image is obtained. The simulation and experimental evaluations using IEEE 802.11ad devices and a depth camera show that the proposed mechanism employing convolutional LSTM predicted a time series of the received power in up to 500 ms ahead at an inference time of less than 3 ms with a root-mean-square error of 3.5 dB

Impact of Gender on In-hospital Mortality in Patients with Acute Myocardial Infarction in Nagasaki

Acute myocardial infarction (AMI) is one of the leading causes of death in Japan. Immediate reperfusion therapy, includingcoronary intervention, improves patient prognosis. Despite this, females are said to be more prone to poor prognosis. A regional AMI registry in Nagasaki prefecture has been instituted recently that will evaluate whether female gender might predict short-term in-hospital death. Seventeen regional AMI centers enrolled all AMI patients from September 2014 through March 2016. A propensity score (PS) was derived using logistic regression to model the probability of females as a total function of the potential confounding covariates. Two types of PS techniques were used: PS matching and PS stratification. The consistency of in-hospital death was determined between PS matched patients of both genders. Based on PS, patients were ranked and stratified into five groups for the PS stratification. Out of 996 patients, 67 (6.7%) died during hospitalization: 31 (10.4%) out of 298 females and 36 (5.2%) out of 698 males (p < 0.0025). The proportion of cardiac and non-cardiac related death was almost same between genders (25 and 6 in female, 29 and 7 in male, respectively). Among 196 PS matched patients, there was a consistency between genders regarding in-hospital deaths (McNemar test, p = 0.6698). The 717 propensity scored patients had no significant differences between genders among propensity quintiles (Cochran-Mantel-Heanszel test, p = 0.7117). We found that gender alone is not an indicator of short-term in-hospital death in acute myocardial infarction patients

Cationic Host Defence Peptides:Potential as Antiviral Therapeutics

There is a pressing need to develop new antiviral treatments; of the 60 drugs currently available, half are aimed at HIV-1 and the remainder target only a further six viruses. This demand has led to the emergence of possible peptide therapies, with 15 currently in clinical trials. Advancements in understanding the antiviral potential of naturally occurring host defence peptides highlights the potential of a whole new class of molecules to be considered as antiviral therapeutics. Cationic host defence peptides, such as defensins and cathelicidins, are important components of innate immunity with antimicrobial and immunomodulatory capabilities. In recent years they have also been shown to be natural, broad-spectrum antivirals against both enveloped and non-enveloped viruses, including HIV-1, influenza virus, respiratory syncytial virus and herpes simplex virus. Here we review the antiviral properties of several families of these host peptides and their potential to inform the design of novel therapeutics

Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs With Graph Convolutional Networks

For densely deployed wireless local area networks (WLANs), this paper proposes a deep reinforcement learning-based channel allocation scheme that enables the efficient use of experience. The central idea is that an objective function is modeled relative to communication quality as a parametric function of a pair of observed topologies and channels. This is because communication quality in WLANs is significantly influenced by the carrier sensing relationship between access points. The features of the proposed scheme can be summarized by two points. First, we adopt graph convolutional layers in the model to extract the features of the channel vectors with topology information, which is the adjacency matrix of the graph dependent on the carrier sensing relationships. Second, we filter experiences to reduce the duplication of data for learning, which can often adversely influence the generalization performance. Because fixed experiences tend to be repeatedly observed in WLAN channel allocation problems, the duplication of experiences must be avoided. The simulation results demonstrate that the proposed method enables the allocation of channels in densely deployed WLANs such that the system throughput increases. Moreover, improved channel allocation, compared to other existing methods, is achieved in terms of the system throughput. Furthermore, compared to the immediate reward maximization method, the proposed method successfully achieves greater reward channel allocation or realizes the optimal channel allocation while reducing the number of changes