Learning Optimal Lattice Codes for MIMO Communications
Laia Amorós, Mikko Juhani Pitkänen · 2021
We propose a novel reinforcement learning approach to learning lattice codes for MIMO channels. We use the block error rate as a loss function to be minimized and compare the learnt lattices with those obtained from algebraic design methods for different SNR ranges. Our results indicate that our learnt lattices achieve close to optimal performance in some cases.