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Related Concept Videos

Power and Energy01:12

Power and Energy

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The power and energy delivered to an element are subjects of great significance in the field of electrical engineering. It is a well-known fact that a 100-watt light bulb emits more light than a 60-watt one. Therefore, power and energy calculations play a crucial role in the analysis of electrical circuits.
Power, defined as the time rate of expending or absorbing energy, is quantified in units called watts (W). The relation between power and energy is mathematically given as
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In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
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The total energy associated with a wavelength is the sum of the potential energy and the kinetic energy. The average rate of energy transfer associated with a wave is called its power, which is total energy divided by the time it takes to transfer the energy. For a sinusoidal wave, energy and power are proportional to the square of both the amplitude and the angular frequency.
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Control of Power Flow01:30

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Effects of Temperature on Free Energy02:11

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The spontaneity of a process depends upon the temperature of the system. Phase transitions, for example, will proceed spontaneously in one direction or the other depending upon the temperature of the substance in question. Likewise, some chemical reactions can also exhibit temperature-dependent spontaneities. To illustrate this concept, the equation relating free energy change to the enthalpy and entropy changes for the process is considered:
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Protein Networks02:26

Protein Networks

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Related Experiment Video

Updated: Feb 5, 2026

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
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Energy-Effective Power Control Algorithm with Mobility Prediction for 5G Heterogeneous Cloud Radio Access Network.

Hyebin Park1, Yujin Lim2

  • 1Department of IT Engineering, Sookmyung Women's University, Seoul 04310, Korea. hb0390@sookmyung.ac.kr.

Sensors (Basel, Switzerland)
|September 12, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a power control algorithm with mobility prediction for 5G heterogeneous cloud radio access networks (H-CRAN). The algorithm enhances energy efficiency by predicting user mobility and optimizing remote radio head switching operations.

Keywords:
5Gheterogeneous cloud radio access networkremote radio head switching operationvehicular mobility

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Area of Science:

  • Telecommunications Engineering
  • Wireless Communication Systems
  • Network Resource Management

Background:

  • 5G networks increasingly utilize heterogeneous cloud radio access networks (H-CRAN) for energy efficiency and resource allocation.
  • Growing user numbers in 5G H-CRAN necessitate advanced solutions to manage energy consumption challenges.

Purpose of the Study:

  • To propose a novel power control algorithm incorporating mobility prediction for enhanced energy efficiency in 5G H-CRAN.
  • To address energy consumption issues in 5G H-CRAN, particularly in vehicular mobility scenarios.

Main Methods:

  • Developed a power control algorithm that predicts User Equipment (UE) mobility in vehicular scenarios.
  • Implemented remote radio head (RRH) switching operations based on mobility prediction outcomes.
  • Formulated an optimization problem to maximize energy efficiency under outage probability constraints.
  • Utilized Markov mobility prediction and a gradient method for transmission power optimization.

Main Results:

  • The proposed algorithm demonstrated significant improvements in energy efficiency compared to existing RRH switching algorithms.
  • Simulation results validated the effectiveness of the mobility prediction-driven RRH switching strategy.

Conclusions:

  • The proposed power control algorithm with mobility prediction offers a viable solution for improving energy efficiency in 5G H-CRAN.
  • This approach is particularly effective in dynamic vehicular mobility environments, optimizing resource allocation and power usage.