491 research outputs found
Control Aware Radio Resource Allocation in Low Latency Wireless Control Systems
We consider the problem of allocating radio resources over wireless
communication links to control a series of independent wireless control
systems. Low-latency transmissions are necessary in enabling time-sensitive
control systems to operate over wireless links with high reliability. Achieving
fast data rates over wireless links thus comes at the cost of reliability in
the form of high packet error rates compared to wired links due to channel
noise and interference. However, the effect of the communication link errors on
the control system performance depends dynamically on the control system state.
We propose a novel control-communication co-design approach to the low-latency
resource allocation problem. We incorporate control and channel state
information to make scheduling decisions over time on frequency, bandwidth and
data rates across the next-generation Wi-Fi based wireless communication links
that close the control loops. Control systems that are closer to instability or
further from a desired range in a given control cycle are given higher packet
delivery rate targets to meet. Rather than a simple priority ranking, we derive
precise packet error rate targets for each system needed to satisfy stability
targets and make scheduling decisions to meet such targets while reducing total
transmission time. The resulting Control-Aware Low Latency Scheduling (CALLS)
method is tested in numerous simulation experiments that demonstrate its
effectiveness in meeting control-based goals under tight latency constraints
relative to control-agnostic scheduling
Optimal WDM Power Allocation via Deep Learning for Radio on Free Space Optics Systems
Radio on Free Space Optics (RoFSO), as a universal platform for heterogeneous
wireless services, is able to transmit multiple radio frequency signals at high
rates in free space optical networks. This paper investigates the optimal
design of power allocation for Wavelength Division Multiplexing (WDM)
transmission in RoFSO systems. The proposed problem is a weighted total
capacity maximization problem with two constraints of total power limitation
and eye safety concern. The model-based Stochastic Dual Gradient algorithm is
presented first, which solves the problem exactly by exploiting the null
duality gap. The model-free Primal-Dual Deep Learning algorithm is then
developed to learn and optimize the power allocation policy with Deep Neural
Network (DNN) parametrization, which can be utilized without any knowledge of
system models. Numerical simulations are performed to exhibit significant
performance of our algorithms compared to the average equal power allocation
Learning Decentralized Wireless Resource Allocations with Graph Neural Networks
We consider the broad class of decentralized optimal resource allocation
problems in wireless networks, which can be formulated as a constrained
statistical learning problems with a localized information structure. We
develop the use of Aggregation Graph Neural Networks (Agg-GNNs), which process
a sequence of delayed and potentially asynchronous graph aggregated state
information obtained locally at each transmitter from multi-hop neighbors. We
further utilize model-free primal-dual learning methods to optimize performance
subject to constraints in the presence of delay and asynchrony inherent to
decentralized networks. We demonstrate a permutation equivariance property of
the resulting resource allocation policy that can be shown to facilitate
transference to dynamic network configurations. The proposed framework is
validated with numerical simulations that exhibit superior performance to
baseline strategies.Comment: 13 pages, 13 figure
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2019 Novel Coronavirus (COVID-19) Pandemic: Built Environment Considerations To Reduce Transmission.
With the rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that results in coronavirus disease 2019 (COVID-19), corporate entities, federal, state, county, and city governments, universities, school districts, places of worship, prisons, health care facilities, assisted living organizations, daycares, homeowners, and other building owners and occupants have an opportunity to reduce the potential for transmission through built environment (BE)-mediated pathways. Over the last decade, substantial research into the presence, abundance, diversity, function, and transmission of microbes in the BE has taken place and revealed common pathogen exchange pathways and mechanisms. In this paper, we synthesize this microbiology of the BE research and the known information about SARS-CoV-2 to provide actionable and achievable guidance to BE decision makers, building operators, and all indoor occupants attempting to minimize infectious disease transmission through environmentally mediated pathways. We believe this information is useful to corporate and public administrators and individuals responsible for building operations and environmental services in their decision-making process about the degree and duration of social-distancing measures during viral epidemics and pandemics
Particle Size Distribution in Aluminum Manufacturing Facilities.
As part of exposure assessment for an ongoing epidemiologic study of heart disease and fine particle exposures in aluminum industry, area particle samples were collected in production facilities to assess instrument reliability and particle size distribution at different process areas. Personal modular impactors (PMI) and Minimicro-orifice uniform deposition impactors (MiniMOUDI) were used. The coefficient of variation (CV) of co-located samples was used to evaluate the reproducibility of the samplers. PM2.5 measured by PMI was compared to PM2.5 calculated from MiniMOUDI data. Mass median aerodynamic diameter (MMAD) and concentrations of sub-micrometer (PM1.0) and quasi-ultrafine (PM0.56) particles were evaluated to characterize particle size distribution. Most of CVs were less than 30%. The slope of the linear regression of PMI_PM2.5 versus MiniMOUDI_PM2.5 was 1.03 mg/m3 per mg/m3 (± 0.05), with correlation coefficient of 0.97 (± 0.01). Particle size distribution varied substantively in smelters, whereas it was less variable in fabrication units with significantly smaller MMADs (arithmetic mean of MMADs: 2.59 μm in smelters vs. 1.31 μm in fabrication units, p = 0.001). Although the total particle concentration was more than two times higher in the smelters than in the fabrication units, the fraction of PM10 which was PM1.0 or PM0.56 was significantly lower in the smelters than in the fabrication units (p < 0.001). Consequently, the concentrations of sub-micrometer and quasi-ultrafine particles were similar in these two types of facilities. It would appear, studies evaluating ultrafine particle exposure in aluminum industry should focus on not only the smelters, but also the fabrication facilities
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