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Maximum Power Transfer01:16

Maximum Power Transfer

376
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...
376
Control of Power Flow01:30

Control of Power Flow

312
There are several methods to control power flow in power systems:
312
Network Function of a Circuit01:25

Network Function of a Circuit

364
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
364
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

174
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.
174
Current Growth And Decay In RL Circuits01:30

Current Growth And Decay In RL Circuits

4.0K
The current growth and decay in RL circuits can be understood by considering a series RL circuit consisting of a resistor, an inductor, a constant source of emf, and two switches. When the first switch is closed, the circuit is equivalent to a single-loop circuit consisting of a resistor and an inductor connected to a source of emf. In this case, the source of emf produces a current in the circuit. If there were no self-inductance in the circuit, the current would rise immediately to a steady...
4.0K
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

275
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
275

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A New Cache Update Scheme Using Reinforcement Learning for Coded Video Streaming Systems.

Sensors (Basel, Switzerland)ยท2021
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Updated: Sep 5, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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A Self-Regulating Power-Control Scheme Using Reinforcement Learning for D2D Communication Networks.

Tae-Won Ban1

  • 1Department of Intelligent Communication Engineering, Gyeongsang National University, Marine Science Bldg 807, Tongyeong-si 53064, Korea.

Sensors (Basel, Switzerland)
|July 9, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a deep deterministic policy gradient (DDPG) power control scheme for device-to-device (D2D) networks. The DDPG method enhances average sum-rate and energy efficiency, outperforming conventional approaches.

Keywords:
deep deterministic policy gradient (DDPG)deep reinforcement learning (DRL)device to device (D2D)power control

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Area of Science:

  • Wireless Communication
  • Network Engineering
  • Artificial Intelligence

Background:

  • Device-to-device (D2D) communication offers potential for enhanced mobile network capacity.
  • Power control is critical in D2D networks to manage interference and optimize performance.
  • Existing power control schemes often require complex network-wide information or lack adaptability.

Purpose of the Study:

  • To develop a self-regulating power control scheme for overlay D2D networks using a model-free algorithm.
  • To autonomously determine transmission power levels based on local channel information.
  • To evaluate the performance of the proposed scheme against conventional methods in terms of sum-rate and energy efficiency.

Main Methods:

  • Utilized a deep deterministic policy gradient (DDPG) algorithm, a model-free, off-policy reinforcement learning technique.
  • Implemented a DDPG-based self-regulating power control scheme where transmitters use local channel gains.
  • Analyzed performance through simulations comparing average sum-rate and energy efficiency.

Main Results:

  • The proposed DDPG-based scheme significantly increased the average sum-rate compared to conventional schemes.
  • The scheme demonstrated superior performance even under conditions of high interference from numerous D2D pairs or high transmission power.
  • The DDPG approach achieved the highest energy efficiency among the evaluated methods.

Conclusions:

  • The DDPG-based self-regulating power control is an effective strategy for overlay D2D networks.
  • This approach offers a robust solution for improving spectral efficiency and energy conservation in D2D communications.
  • Autonomous power control using DDPG provides a scalable and adaptive solution for future wireless networks.