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We will now introduce a reinforcement learning approach based on stochastic approximation to estimate (nu (G)).
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We also develop an analytical framework based on stochastic geometry tools, a number of simplifying assumptions, and a small number of judiciously chosen approximations.
NORMA is based on stochastic gradient descent.
Reliability analysis is based on stochastic discrete event models like stochastic Petri nets.
The solution method is based on stochastic successive linear programming.
The algorithm is developed based on stochastic finite-element method.
Our mechanism is based on stochastic datagram relocation.
KF technique makes predictions based on stochastic and dynamic structures.
Computational models, routinely based on stochastic optimization, support these decisions.
However, several simplifying assumptions were made in the calculations based on stochastic differential equations.
By exploiting the strong line-of-sight property of the 60 GHz channels, we propose an iterative antenna selection algorithm based on discrete stochastic approximation that can quickly lock onto a near-optimal antenna subset.
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