US 12,293,292 B2
Multiple-input multiple-output (MIMO) detector selection using neural network
Hyukjoon Kwon, San Diego, CA (US); Shailesh Chaudhari, San Diego, CA (US); and Kee-Bong Song, San Diego, CA (US)
Assigned to Samsung Electronics Co., Ltd, (KR)
Filed by Samsung Electronics Co., Ltd., Gyeonggi-do (KR)
Filed on Jun. 3, 2019, as Appl. No. 16/429,856.
Claims priority of provisional application 62/817,372, filed on Mar. 12, 2019.
Prior Publication US 2020/0293894 A1, Sep. 17, 2020
Int. Cl. G06N 3/084 (2023.01); G06F 16/28 (2019.01); G06N 3/04 (2023.01)
CPC G06N 3/084 (2013.01) [G06F 16/285 (2019.01); G06N 3/04 (2013.01)] 14 Claims
OG exemplary drawing
 
1. A method of using a multi-layer perceptron (MLP) network to select a low-complexity, reliable detector, the method comprising:
receiving a signal vector and a multiple-input multiple-output (MIMO) channel matrix of resource elements (REs);
extracting channel features from the signal vector and the MIMO channel matrix;
generating a labelled dataset of the channel features and detector labels for each of the RE;
training the MLP network using the generated labelled dataset;
computing a margin associated with a maximum output value from the MLP network, wherein the computed margin is determined based on a conditional probability of detector error being less than or equal to a probability threshold value;
selecting, for an RE, a detector class from a plurality of detector classes based on a difference between the maximum output value from the MLP network and a second output value from the MLP network being less than the computed margin; and
detecting symbols in the RE using a MIMO detector corresponding to the selected detector class.