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Found 8 papers, 0 papers with code
Spectrum Coexistence of Satellite-borne Passive Radiometry and Terrestrial Next-G Networks
In this study, we develop a framework based on stochastic geometry to evaluate the statistical characteristics of radio frequency interference (RFI) originating from a large-scale terrestrial Next-G network operating in the same frequency band as an RS satellite.
Coexistence of Satellite-borne Passive Radiometry and Terrestrial NextG Wireless Networks in the 1400-1427 MHz Restricted L-Band
Second, leveraging stochastic geometry, we assess the feasibility of using this passive band within a large-scale network in LoS of SMAP while ensuring that the error induced on SMAP's measurements due to RFI is below a given threshold.
minimizing estimation error variance using a weighted sum of samples from the soil moisture active passive (SMAP) satellite
The National Aeronautics and Space Administration's (NASA) Soil Moisture Active Passive (SMAP) is the latest passive remote sensing satellite operating in the protected L-band spectrum from 1. 400 to 1. 427 GHz.
What is Interpretable? Using Machine Learning to Design Interpretable Decision-Support Systems
Our results demonstrate that ML experts cannot accurately predict which system outputs will maximize clinicians' confidence in the underlying neural network model, and suggest additional findings that have broad implications to the future of research into ML interpretability and the use of ML in medicine.
Estimation of Individual Treatment Effect in Latent Confounder Models via Adversarial Learning
Estimating the individual treatment effect (ITE) from observational data is essential in medicine.
Accelerated Structure-Aware Reinforcement Learning for Delay-Sensitive Energy Harvesting Wireless Sensors
Our experiments demonstrate that the proposed algorithm closely approximates the performance of an optimal offline solution that requires a priori knowledge of the channel, captured data, and harvested energy dynamics.
Structural Properties of Optimal Transmission Policies for Delay-Sensitive Energy Harvesting Wireless Sensors
We consider an energy harvesting sensor transmitting latency-sensitive data over a fading channel.
Networking and Internet Architecture
Fast Reinforcement Learning for Energy-Efficient Wireless Communications
The advantages of the proposed online method are that (i) it does not require a priori knowledge of the traffic arrival and channel statistics to determine the jointly optimal power-control, AMC, and DPM policies; (ii) it exploits partial information about the system so that less information needs to be learned than when using conventional reinforcement learning algorithms; and (iii) it obviates the need for action exploration, which severely limits the adaptation speed and run-time performance of conventional reinforcement learning algorithms.
