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A novel approximate technique is proposed for the estimation of call blocking probabilities in cellular mobile telephony networks where call blocking triggers customer retrials. The approximate analysis technique is based on Markovian models with state spaces whose cardinalities are proportional to the maximum number of calls that can be simultaneously in progress within cells. The accuracy of the approximate technique is assessed by comparison against results of detailed simulation experiments, results of a previously proposed Markovian analysis approach, and upper and lower bounds to the call blocking probability. Numerical results show that the proposed approximate technique is very accurate, in spite of the remarkably small state spaces of the Markovian models.

Summary In this paper, a novel data‐driven approach is proposed to obtain output regulation for linear systems in finite time, requiring only limited information about the plant and the exosystem. The resulting regulator is robust because it does not rely on the knowledge of perturbed or even nominal plant parameters. Finite‐time regulation is achieved, combining the use of the external‐model technique with the deadbeat controller design. The core of the method lies in an error‐feedback reset logic for the state of the external model to compensate for the contribution of the exosystem on the plant output response, then an ancillary control law is introduced to steer the error to zero in a preassigned time interval, trading‐off between convergence rate, and control effort.

In this paper, we propose an external model based, data-driven approach to robust output regulation. No a priori knowledge of the plant and the exosystem is required, apart from their state dimension and an upper bound on the exosystem frequencies. The core of the method lies in an error-feedback reset logic for the state of the external model

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