US 12,355,518 B2
AI (artificial intelegence) MIMO (multiple input multiple output) detector
Zion Hadad, Rishon Lezion (IL); Eli Shasha, Rishon Lezion (IL); and Baruch Globen, Rishon Lezion (IL)
Assigned to RUNCOM Communication Ltd., Rishon Lezion (IL)
Filed by RUNCOM Communication Ltd., Rishon Lezion (IL)
Filed on Jan. 2, 2023, as Appl. No. 18/092,358.
Prior Publication US 2024/0223242 A1, Jul. 4, 2024
Int. Cl. H04L 5/12 (2006.01); H04B 7/0426 (2017.01); H04B 7/0456 (2017.01)
CPC H04B 7/043 (2013.01) [H04B 7/0456 (2013.01)] 5 Claims
OG exemplary drawing
 
1. A method for detecting transmitted data by a deep neural network, the method comprising:
receiving a plurality of complex data vectors (Y) and a plurality of MIMO-channel-transfer-matrixes-H, each complex data vector (Y) of said plurality of complex data vectors (Y) being associated with a MIMO-channel-transfer-matrix-H of said plurality of MIMO-channel-transfer-matrixes-H, each said MIMO-channel-transfer-matrix-H being associated with a certain Multiple Input Multiple Output (MIMO) channel;
decomposing each of said MIMO-channel-transfer-matrixes-H into a Q matrix and an R matrix;
transposing said Q matrix into QT;
multiplying each of said QT by an associated vector (Y) to thereby providing QT*Y;
training said deep neural network with a training set labeled with Log-Likelihood Ratio values; said training set comprising said plurality of said QT*Y and an upper part of said R matrix; and
utilizing said deep neural network for receiving second Log-Likelihood Ratio values from a second complex data vector (Y) and a second associated MIMO-channel-transfer-matrix-H and for detecting X from said second Log-Likelihood Ratio values; said X being a detected data vector detecting said transmitted data.