Low complexity M-hypotheses detection: M vectors case

Abstract

Low complexity algorithms are essential in many applications which require low power implementation. We present a low complexity technique for solving M-hypotheses detection problems, that involve vector observations. This technique works in these cases where the number of vectors is equal to or smaller than the dimensionality of the vectors. It attempts to optimally trade off complexity with probability of error through solving the problem in a lower dimension.