GEOMETRIC DEEP LEARNING FOR LATTICE REDUCTION

Abstract: certain aspects of the present disclosure provide techniques for wireless communications by an apparatus. certain techniques include receiving signals corresponding to a mimo channel matrix; generating a first gram matrix from a basis for a first lattice corresponding to a first signal of the received signals; providing the first gram matrix to a neural lattice reduction model comprising an equivariant neural network configured to generate a current extended gauss move; generating, with the neural lattice reduction model, a current partial changed basis based on the current extended gauss move and the basis; executing one or more additional iterations of the neural lattice reduction model; and demapping the mimo channel matrix based on combining the current partial changed basis and each of the additional partial changed basis generated by each of the one or more additional iterations of the neural lattice reduction model.

Inventor(s): Giovanni Luca MARCHETTI, Gabriele CESA, Kumar PRATIK, Arash BEHBOODI

CPC Classification: H04B7/0413 (MIMO systems)

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