Compressed training adaptive equalization

Abstract

We introduce compressed training adaptive equalization as a novel approach for reducing number of training symbols in a communication packet. The proposed semi-blind approach is based on the exploitation of the special magnitude bounded-ness of communication symbols. The algorithms are derived from a special convex optimization setting based on l∞ norm. The corresponding framework has a direct link with the com-pressive sensing literature established by invoking the duality between l1 and l∞ norms. Through this Link, it is possible to adapt various research results in sparse signal processing literature to adaptive equalization problem. In fact, through utilization of such a link, we show that the amount of training data needed is in the order of the logarithm of the channel spread (or equalizer length) in the fractionally spaced equalization scenario. The numerical experiments provided validates the analytical results and the potentials of the proposed approach.