Patent application EP21172923A (EP), filed 2021-05-10
Publication number: EP3913816A1
Title: METHOD AND APPARATUS FOR OPTIMIZATION OF SIGNAL SHAPING FOR A MULTI-USER MULTIPLE INPUT MULTIPLE OUTPUT (MU-MIMO) COMMUNICATION SYSTEM
Abstract: A method and an apparatus for optimization of signal shaping for a multi-user multiple input multiple output (MU-MIMO) communication system (100) is described. A bit vector b<sub>u</sub> is received. A Geometric Shaping and Labeling Bock (GSLB) (204) is used for determining a constellation vector c<sub>u</sub>. The determined constellation vector c<sub>u</sub> is used for modulating the bit vector b<sub>u</sub>. The GSLB (204) is configured to implement an algorithm with one or more trainable parameters θ.
IPC codes: G06N  20/    00            A I
G06N   3/    02            A I
H04B   7/  0452            A I
H04L  27/    34            A I
Claims: number: 1
text: An apparatus for optimization of signal shaping for a multi-user multiple input multiple output, MU-MIMO, communication system (100), comprising:
number: 2
text: The apparatus of claim 1, further comprising:
number: 3
text: The apparatus of any one of the preceding claims, wherein the apparatus is configured to implement the algorithm with the one or more trainable parameters θ by a neural network.
number: 4
text: The apparatus of any one of the preceding claims, wherein the GSLB (204) comprises of one or more dense layers (302), a real-to-complex, R2C, conversion layer (304), and a normalization layer (306), and each one of the dense layers having one or more activation functions, and wherein the one or more dense layers (302) of the GSLB (204) comprise the one or more trainable parameters θ.
number: 5
text: The apparatus of any one of the preceding claims, further comprising:
number: 6
text: The apparatus of any one of the preceding claims, wherein the SGD operation is performed until a predefined stop criterion has been satisfied, the predefined stop criterion including a predefined number of iterations or the loss function has not decreased for a predefined number of iterations.
number: 7
text: A multi-user multiple input multiple output, MU-MIMO, communication system (100), the MU-MIMO communication system (100) comprising a plurality of transmitters (102), each transmitter (102) being implemented by an apparatus according to any one of claims 1 to 6.
number: 8
text: The MU-MIMO communication system (100) of claim 7, wherein the transmitters (102) of the plurality of transmitters (102) are configured to share one or more trainable parameters θ of the GSLB (204).
number: 9
text: The MU-MIMO communication system (100) of claim 7 or 8, further comprising a MIMO-channel (106) and a receiver (104), wherein the receiver (104) is configured to implement a demodulation and/or a demapping algorithm with one or more trainable parameters Φ.
number: 10
text: A method for optimization of signal shaping for a multi-user multiple input multiple output, MU-MIMO, communication system (100), the MU-MIMO communication system (100) comprising a plurality of transmitters (102), the method comprising at each transmitter (102):
number: 11
text: The method of claim 10, further comprising at each transmitter (102):
number: 12
text: The method of claim 10 or 11, wherein the algorithm is implemented by a neural network.
number: 13
text: The method of any one of claims 10 to 12, further comprising:
training the GSLB (204) by:
number: 14
text: The method of claim 13, wherein the MU-MIMO communication system (100) comprises a MIMO-channel (106) and a receiver (104), and wherein the initializing step comprises jointly initializing the one or more trainable parameters θ and one or more trainable parameters Φ; and
the updating step comprises jointly updating the one or more trainable parameters θ and the one or more trainable parameters Φ.
number: 15
text: A non-transitory computer-readable medium including instructions for causing a processor (1102) to perform functions for optimization of signal shaping for a multi-user multiple input multiple output, MU-MIMO, communication system (100), the functions including:
Office action: search_report, dated 2021-11-24
Rejection reasons: novelty_epc_54
Prior art cited: ref_id: - STARK MAXIMILIAN ET AL: "Joint Learning of Geometric and Probabilistic Constellation Shaping", 2019 IEEE GLOBECOM WORKSHOPS (GC WKSHPS), IEEE, 9 December 2019 (2019-12-09), pages 1 - 6, XP033735188, DOI: 10.1109/GCWKSHPS45667.2019.9024567
ref_type: npl
severity: background
ref_id: [XIA]     - RASMUS T JONES ET AL: "Geometric Constellation Shaping for Fiber Optic Communication Systems via End-to-end Learning", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 1 October 2018 (2018-10-01), XP080928702
ref_type: npl
severity: background
ref_id: - STARK MAXIMILIAN ET AL, "Joint Learning of Geometric and Probabilistic Constellation Shaping", 2019 IEEE GLOBECOM WORKSHOPS (GC WKSHPS), IEEE, (20191209), doi:10.1109/GCWKSHPS45667.2019.9024567, pages 1 - 6, XP033735188 [X] 1-3,5,15 * Sections I, II and III * * figures 1, 2 * [I] 4,6-14
ref_type: npl
severity: novelty_destroying
ref_id: - RASMUS T JONES ET AL, "Geometric Constellation Shaping for Fiber Optic Communication Systems via End-to-end Learning", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, (20181001), XP080928702 [X] 1,3-5,15 * Sections I and III * * figures 1, 2 * * equations (3), (4) and (5) * [I] 2,6 [A] 7-14
ref_type: npl
severity: novelty_destroying
Final disposition: pending (2021-11-24)