The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 23, 2026

Filed:

Mar. 25, 2024
Applicant:

Mitsubishi Electric Research Laboratories, Inc., Cambridge, MA (US);

Inventors:

Jianlin Guo, Cambridge, MA (US);

Zhiyang Wang, Philadelphia, PA (US);

Kieran Parsons, Cambridge, MA (US);

Philip Orlik, Cambridge, MA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
H04W 40/24 (2009.01); G06N 3/088 (2023.01);
U.S. Cl.
CPC ...
H04W 40/248 (2013.01); G06N 3/088 (2013.01); H04W 40/246 (2013.01);
Abstract

A heterogeneous multi-hop wireless network is provided. The network consists of single-link data nodes, multi-link data nodes and data centers. The single-link data node supports one communication interface, the multi-link data node supports two communication interfaces and the data center is considered as multi-link node. The routes from all data nodes to data centers are given. The network is configured to schedule data transmissions from all data nodes to data centers without transmission interference and channel access delay. The routing link scheduling problem is formulated as an optimization problem with constraints. Due to the NP-Hard complexity of the formulated optimization problem, the scheduling policies are then parameterized for the application of graph neural network techniques. The parameterized optimization problem is solved using primal-dual approach with zero duality gap. A heterogeneous graph neural network (HetGNN) algorithm is provided to train the primal-dual problems.


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