Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications Outside Coverage

Radio resources in vehicle-to-vehicle (V2V) communication can be scheduled either by a centralized scheduler residing in the network (e.g., a base station in case of cellular systems) or a distributed scheduler, where the resources are autonomously selected by the vehicles. The former approach yields a considerably higher resource utilization in case the network coverage is uninterrupted. However, in case of intermittent or out-of-coverage, due to not having input from centralized scheduler, vehicles need to revert to distributed scheduling. Motivated by recent advances in reinforcement learning (RL), we investigate whether a centralized learning scheduler can be taught to efficiently pre-assign the resources to vehicles for out-of-coverage V2V communication. Specifically, we use the actor-critic RL algorithm to train the centralized scheduler to provide non-interfering resources to vehicles before they enter the out-of-coverage area. Our initial results show that a RL-based scheduler can achieve performance as good as or better than the state-of-art distributed scheduler, often outperforming it. Furthermore, the learning process completes within a reasonable time (ranging from a few hundred to a few thousand epochs), thus making the RL-based scheduler a promising solution for V2V communications with intermittent network coverage.Comment: Article published in IEEE VNC 201

Comparison of Radio Frequency and Visible Light Propagation Channels for Vehicular Communications

Recent research has shown that both radio and visible light waves can be used to enable communications in highly dynamic vehicular environments. However, the roles of these two technologies and how they interact with each other in future vehicular communication systems remain unclear. Understanding the propagation characteristics is an essential step in investigating the benefits and shortcomings of each technology. To this end, we discuss salient properties of radio and visible light propagation channels, including radiation pattern, path loss modeling, noise and interference, and channel time variation. Comparison of these properties provides an important insight that the two communication channels can complement each other’s capabilities in terms of coverage and reliability, thus better satisfying the diverse requirements of future cooperative intelligent transportation systems

Bioelectrical Impedance Analyzes Offers Clinically Relevant Appraisal of Body Composition, but Fails to Recognize Nutritional Risk or Differences between Surgery and Percutaneous Coronary Interventions Treatments – A Non-Randomized Cohort

Our aim was to evaluate the adipose tissue percentage content appraised with BIA in patients recently treated for cardiovascular disorders by means of surgery or percutaneous coronary interventions. Study included 208 consecutive patients, in age range 25–84 years, 176 male and 32 female. There were 108 (51.9%) percutaneous coronary interventions and 100 (48.1%) operations. Adipose tissue share appraised by BIA in our settings was 28.6±6.7% with significant differences in relation with gender (p<0.001) and no relations with the age of patients. Intermediate levels of correlations were found in relation to the body mass index (Rho: 0.521, p<0.001), waist-circumference (Rho: 0.450; p<0.001) and hip-circumference (Rho: 0.393; p<0.001). ROC-analyzes revealed diagnostic cutoff point of BIA at 29.5% for predicting the obesity (AUC=0.761; p<0.001) and 27% for metabolic syndrome (AUC=0.715; p<0.001). There were no relations of BIA to nutritional status, laboratory or echocardiography diagnostic. BIA offered clinically relevant appraisal of anthropometrically and metabolic related risks from cardiovascular continuum. Diagnostic yields solely on impedance analyze bases seem limited, particularly in investigational settings with composited endpoints