Robust FIR equalization for time-varying communication channels with intermittent observations via an LMI approach

The optimal design of finite impulse response (FIR) filters for equalization/deconvolution is investigated in this paper. Two practical yet challenging constraints are incorporated into the modeling of the equalization system: (1) The parameters of the communication channel model are arbitrarily time-varying within a polytope with finite known vertices; (2) at the received end, the received signal is usually intermittent due to network-induced packet dropouts which are modeled by a stochastic Bernoulli distribution. Under the stochastic theory framework, a robust design method for the FIR equalizer is proposed such that the equalization system can achieve the prescribed energy-to-peak performance even it is subject to uncertainties, external noise, and data missing. Sufficient conditions for the existence of the equalizer are derived by a set of linear matrix inequalities (LMIs). An illustrative design example demonstrates the design procedure and the effectiveness of the proposed method.