… Next, we examine the impact of sensingerrors on the performance of our novel DDQSA … sensing (only a 2% decrease). We conclude that the DDQSA is quite robust to sensingerrors, …
… the actual outcomes of Cooperative Spectrum Sensing (CSS). Existing … The present work explores the DeepReinforcement … sensed-energy values at FC and reduces the sensingerror …
… 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 …
J Yan, Y Meng, X Yang, X Luo… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
… deepreinforcement learning (DRL) based localization estimators are utilized to estimate the positions of sensor … In the following, we define H as the error tolerance threshold. If H(ˆxS,j,k+…
… 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 …
SQ Jalil, S Chalup, MH Rehmani - 2021 International Joint …, 2021 - ieeexplore.ieee.org
… to use deepreinforcement learning (DRL) for the task of cooperative spectrum sensing (CSS) in a … All methods have similar performance in terms of sensingerrors where sensingerrors …
… Variable errors, on the other hand, try to emulate the sensingerrors that come, for example, from the vibration of the arm or local odometry errors describing its orientation and position. …
L Xiao, Y Li, G Han, H Dai… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
… (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 estimate the observed process we apply a Linear Minimum Mean Square Error (LMMSE) estimator which is commonly used for such problems as demonstrated in [28]. One of the …