It is well known that Maximum Likelihood (ML) detection for multiantenna and/or multiuser systems has complexity that grows exponentially with the number of antennas and/or users. A number of suboptimal algorithms has been developed in the past that present an acceptable computational complexity and good approximations of the optimal solution. In this paper it is proposed a tree-search algorithm that provides the exact ML solution with lower computational complexity than that required by an exhaustive search of minimum distance. Also a two-stage tree-search algorithm is presented based on the idea that the ML solution is in the set of equilibrium points of a Hopfield Neural Networks (HNN). The two algorithms work without any modification both in underloaded and overloaded (underdetermined) systems.

A tree-search algorithm for ML decoding in underdetermined MIMO systems

MATTERA, DAVIDE
2009

Abstract

It is well known that Maximum Likelihood (ML) detection for multiantenna and/or multiuser systems has complexity that grows exponentially with the number of antennas and/or users. A number of suboptimal algorithms has been developed in the past that present an acceptable computational complexity and good approximations of the optimal solution. In this paper it is proposed a tree-search algorithm that provides the exact ML solution with lower computational complexity than that required by an exhaustive search of minimum distance. Also a two-stage tree-search algorithm is presented based on the idea that the ML solution is in the set of equilibrium points of a Hopfield Neural Networks (HNN). The two algorithms work without any modification both in underloaded and overloaded (underdetermined) systems.
9781424435845
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11588/368152
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