US 12,279,158 B2
Wireless communication network resource allocation method with dynamic adjustment on demand
Nan Cheng, Xi'an (CN); Changle Li, Xi'an (CN); Longfei Ma, Xi'an (CN); Xiucheng Wang, Xi'an (CN); and Ruijin Sun, Xi'an (CN)
Assigned to Xidian University, Xi'an (CN)
Filed by Xidian University, Xi'an (CN)
Filed on Jul. 4, 2022, as Appl. No. 17/857,114.
Claims priority of application No. 202111530124.7 (CN), filed on Dec. 14, 2021.
Prior Publication US 2023/0189075 A1, Jun. 15, 2023
Int. Cl. H04W 28/16 (2009.01); G06F 9/50 (2006.01); H04L 67/1008 (2022.01)
CPC H04W 28/16 (2013.01) [G06F 9/5044 (2013.01); G06F 9/505 (2013.01); H04L 67/1008 (2013.01)] 10 Claims
OG exemplary drawing
 
1. A wireless communication network resource allocation method implemented in a server in a wireless communication network comprising user devices, the method comprising:
sending task feature information of each of the user devices for each of a plurality of time slots to the server, wherein:
the task feature information of each of the user devices comprises: a task data volume, a task transmission distance, and a task importance weight;
a task data volume average value is based on the task data volumes of the user devices;
a target optimal resource allocation model matched with the task data volume average value and a CPU frequency of the server is based on a knowledge base;
the knowledge base comprises: a plurality of sample data groups composed of different task data volume average values, CPU frequencies of the server, and optimal resource allocation models corresponding to the plurality of sample data groups respectively;
the optimal resource allocation model corresponding to each of the plurality of sample data groups is based on a minimum of total delays in resource allocation as an optimization objective, optimal widths of dynamic neural networks in trained resource allocation models, the task feature information of the user devices of the sample data group, and the CPU frequency of the server of the sample data group;
a resource to be allocated in each time slot is one of a bandwidth and a power; and
transmitting task data to the user devices based on resource allocation results, wherein resource allocation results are based on the task feature information of the user devices in each time slot and the target optimal resource allocation model.