… designing sensing policies by using an automated approach based on deep reinforcement
… Guided by an uncertainty-aware prediction model, the sensors learn sensing behaviors …

… Eventually, the sensing error is represented in Fig. 3, where … As depicted, the sensing error
in both cases for the proposed … sensing error and lowest fluctuations in the sensing error by …

… In order to close to the reality communication environment, the spectrum access problem
with sensing error is considered in this paper. The sensing result’s accuracy of SV i on channel …

… Next, we examine the impact of sensing errors on the performance of our novel DDQSA …
sensing (only a 2% decrease). We conclude that the DDQSA is quite robust to sensing errors, …

… We assume that this (downlink) transmission from gateway to sensor works without errors. …
, resulting in an exponentially reduced error probability. Error performance can be further …

… of mobile robotic sensors in an EOI. The robotic sensors are assumed to be point sensor,
which … quality of the robotic sensors is determined by the field estimation error as calculated by …

… Variable errors, on the other hand, try to emulate the sensing errors that come, for example,
from the vibration of the arm or local odometry errors describing its orientation and position. …

… (1) is adopted in the simulations presented in Section VI, the framework presented in this
work is not restricted to the sensing evaluation error model in (1). More specifically, the …

… to use deep reinforcement learning (DRL) for the task of cooperative spectrum sensing (CSS)
in a … All methods have similar performance in terms of sensing errors where sensing errors …

… use of deep reinforcement learning in the creation of sensing policies for … error under a
threshold value. Most similar to our work are those that use Gaussian processes in each sensor …