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The monotony properties of the optimal policy have been inferred to simplify and solve the optimization problem using the backward induction algorithm.
The optimization problem in (4) can be formulated into a finite horizon Markov decision process framework and the optimal transmission schedule can be found using the backward induction algorithm, which is a dynamic programming approach.
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We use the backward induction on k.
The backward induction method [23] can be used to solve (25).
The optimal transmission power of each SU can be obtain by using dynamic programming (DP) algorithm and immediate optimization solved by the backward induction method and the Lagrange dual method, respectively.
Based on the backward induction principle, the NTGO scheme can derive the near-optimal strategy for all the mobile devices using a convex optimization approach.
This dynamic game is analysed with the backward induction approach.
This proof follows by the backward induction principle [10, 19].
Let \(BI\) denote the resulting backward induction profile (where each player is following the strategy given by the backward induction algorithm).
We propose to use forward simulation to approximate the integral expressions, and a reduction of the allowable action space to avoid problems related to an increasing number of possible trajectories in the backward induction.
The employed computational method is a modification of the backward induction algorithm applied to a Bayesian decision problem.
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