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Related Concept Videos

Maximum Power Transfer01:16

Maximum Power Transfer

471
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.
By substituting the entire circuit with...
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Transmission-based Precautions II: Airborne and Protective Environment01:25

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Transmission-based precautions are for patients infected or suspected to be infected (or colonized) with organisms posing a significant risk to others. The transmission precautions include airborne and protective environment precautions.
Airborne precautions:
Use airborne precautions when treating patients known or suspected to have diseases that spread through the air—for example, tuberculosis or measles. These organisms are present in smaller droplets expelled by an infected person and...
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Transmission Line Design Considerations01:23

Transmission Line Design Considerations

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Aluminum has become the material of choice for overhead transmission lines, surpassing copper due to its abundance and cost-effectiveness. The most prevalent type is the aluminum conductor, steel-reinforced (ACSR), which combines aluminum strands around a steel core. Other variants include all-aluminum conductors (AAC), all-aluminum alloy conductors (AAAC), aluminum conductor alloy-reinforced (ACAR), and aluminum-clad steel conductors. Advanced designs, such as aluminum conductors with steel...
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Control of Power Flow01:30

Control of Power Flow

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There are several methods to control power flow in power systems:
324
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

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The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
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The Maximum Power Transfer Theorem01:20

The Maximum Power Transfer Theorem

807
Consider a linear AC Thevenin equivalent circuit connected to a load impedance.
The load connected draws the current, and the circuit delivers the power to the load. The alternating current flowing through the load is determined using the rectangular form of voltages, currents, network impedance, and load impedance. The average power delivered to the load is obtained from the product of the square of current and load resistance.
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Deep Q-Learning-Based Transmission Power Control of a High Altitude Platform Station with Spectrum Sharing.

Seongjun Jo1, Wooyeol Yang1, Haing Kun Choi2

  • 1Department of Electronic Engineering, Hanbat National University, Daejeon 34158, Korea.

Sensors (Basel, Switzerland)
|February 26, 2022
PubMed
Summary

A new Deep Q-learning algorithm optimizes High Altitude Platform Station (HAPS) transmission power to balance wide-area coverage and protect existing systems from interference. This AI approach minimizes service outages without compromising performance.

Keywords:
Deep Q-learning (DQL)Double Deep Q-learning (DDQL)High Altitude Platform Station (HAPS)cellular communicationsdynamic spectrum sharinginterference managementpower control

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Area of Science:

  • Wireless Communication
  • Artificial Intelligence
  • Signal Processing

Background:

  • High Altitude Platform Stations (HAPS) offer wide-area, high-speed data communication but pose interference risks to existing systems.
  • Spectrum sharing necessitates HAPS transmission power adjustment to protect incumbent users, yet excessive reduction degrades HAPS coverage.

Purpose of the Study:

  • To develop an intelligent transmission power control algorithm for HAPS.
  • To minimize HAPS downlink outage probability while ensuring incumbent system protection.

Main Methods:

  • A multi-agent Deep Q-learning (DQL) algorithm was proposed for HAPS transmission power control.
  • A Double Deep Q-learning (DDQL) variant was developed to mitigate action-value overestimation risks.
  • Agents were trained cooperatively with defined states and rewards to learn an optimal power control policy.

Main Results:

  • The DQL algorithm achieved performance equal to or near that of an optimal exhaustive search.
  • Performance was consistent across varying positions of the interfered system.
  • The DDQL approach demonstrated that action-value overestimation did not negatively impact the learned policy's quality.

Conclusions:

  • The proposed DQL and DDQL algorithms effectively manage HAPS transmission power for spectrum sharing.
  • These AI-driven methods provide a robust solution for balancing HAPS coverage and interference mitigation.
  • The findings validate the efficacy of multi-agent DQL in complex wireless communication scenarios.