An Adaptive Limited Fractional Guard Channel Policy Using Continuous Action Learning Automata
نویسنده
چکیده
In this paper, we rst propose a new continuous action learning automaton which uses a stochastic estimator learning algorithm to increase the speed of convergence. Then we introduce an adaptive and autonomous call admission algorithm, which uses the proposed learning automaton. The proposed algorithm minimizes blocking probability of new calls subject to constraint on dropping probability of hando calls. The simulation results show that the performance of the proposed algorithm is close to the performance of limited fractional guard channel policy for which we need to know all tra c parameters in advance. Two advantages of the proposed policy are that it is fully autonomous and adaptive.
منابع مشابه
Limited Fractional Guard Channel Scheme Using Learning Automata
In this paper, we propose a new continuous action learning automaton and an adaptive and autonomous call admission algorithm, which uses the proposed learning automaton. The proposed algorithm minimizes blocking probability of new calls subject to constraint on dropping probability of handoff calls. The simulation results show that the performance of this algorithm is close to the performance o...
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