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Related Concept Videos

Maximum Power Transfer01:16

Maximum Power Transfer

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Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
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Related Experiment Video

Updated: Sep 10, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

657

Hybrid optimization-based deep learning for energy efficiency resource allocation in MIMO-enabled wireless networks.

Mian Muhammad Kamal1, Ijaz Khan2, M A Al-Khasawneh3,4

  • 1School of Electronics and Communication Engineering, Quanzhou University of Information Engineering, Quanzhou, 362000, China. mianmuhammadkamal@qzuie.edu.cn.

Scientific Reports
|August 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces HGGO_XCovNet for optimizing resource allocation in 5G wireless networks, enhancing energy efficiency and data rates for multiple users. The novel deep learning approach improves system performance and reliability.

Keywords:
5G networkDeep learningEnergy efficiencyMultiple-input multiple-output systemResource allocation

Related Experiment Videos

Last Updated: Sep 10, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

657

Area of Science:

  • Wireless Communication
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Resource allocation in multiple-input multiple-output (MIMO)-enabled wireless networks is crucial for optimizing network performance and energy efficiency.
  • Existing methods face challenges in meeting the high resource demands of MIMO users, necessitating advanced techniques.
  • Deep learning (DL) offers potential for improved reliability and accuracy in 5G network resource allocation.

Purpose of the Study:

  • To introduce a novel hybrid optimization technique, HGGO_XCovNet, for efficient resource allocation in MIMO-enabled wireless networks.
  • To enhance system performance by maximizing energy efficiency, data rate, and throughput.
  • To leverage deep learning for accurate and reliable resource distribution.

Main Methods:

  • A base station (BS) scenario with multiple users was considered for resource allocation.
  • The Xception convolutional neural network (XCovNet) was employed for resource allocation, trained by a hybrid optimization algorithm.
  • The Hippo Graylag Goose Optimization (HGGO) algorithm, combining Greylag Goose Optimization (GGO) and Hippopotamus Optimization (HO), was developed to train XCovNet.

Main Results:

  • The HGGO_XCovNet technique achieved a maximum energy efficiency of 74.943 kbits/joule.
  • The system demonstrated a sum data rate of 269.93 Mbps.
  • A maximum throughput of 551.262 Mbps was recorded.

Conclusions:

  • The proposed HGGO_XCovNet technique effectively optimizes resource allocation in 5G MIMO networks.
  • The hybrid deep learning approach significantly improves energy efficiency, sum rate, and throughput.
  • This method provides a reliable and accurate solution for resource management in advanced wireless systems.