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Related Concept Videos

Maximum Power Transfer01:16

Maximum Power Transfer

464
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...
464
Power Factor Correction01:20

Power Factor Correction

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The power transmission to a factory involves the transfer of apparent power, a combination of active and reactive power. The power factor measures how effectively electrical power is converted into useful work output. The ratio of the real power (KW) that does the work to the apparent power (KVA) supplied to the circuit.
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The Power Superposition Principle01:19

The Power Superposition Principle

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Consider a circuit with two sinusoidal voltage sources. Each one influences the circuit independently, and the superposition principle helps us understand the combined effect by adding up the responses from each source.
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Gain01:15

Gain

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Gain and phase shift are properties of linear circuits that describe the effect a circuit has on a sinusoidal input voltage or current. The circuit's behavior that contains reactive elements will depend on the frequency of the input sinusoid. As a result, it is observed that the gain and phase shift will all be frequency functions.
Gain:
Suppose Vin is the input and Vout is the output signal to a circuit.
259
The Maximum Power Transfer Theorem01:20

The Maximum Power Transfer Theorem

798
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.
798
Multimachine Stability01:25

Multimachine Stability

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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
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Updated: Sep 27, 2025

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
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Closed-Form Power Normalization Methods for a Satellite MIMO System.

Andrea Segneri1, Alejandro Baldominos1, George Goussetis1

  • 1Institute of Sensors Signals and Systems, Heriot-Watt University, Edinburgh EH14 4AS, UK.

Sensors (Basel, Switzerland)
|April 12, 2022
PubMed
Summary
This summary is machine-generated.

New normalization techniques improve signal quality in satellite communications by addressing signal loss for users at the coverage edge. These methods ensure uniform signal-to-noise ratios without impacting overall system capacity.

Keywords:
array-fed reflectorslinear precodingmultiple-input multiple-outputpower normalization techniquessatellite communications

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Area of Science:

  • Satellite Communications Engineering
  • Signal Processing
  • Antenna Theory

Background:

  • Broadband multiuser multiple-input multiple-output (MIMO) satellite systems face challenges with signal attenuation for users at the coverage edge.
  • Array-fed reflector (AFR) antennas in geosynchronous orbit (GEO) offer benefits but suffer from scan losses impacting global coverage.
  • Existing normalization methods require adaptation to account for these specific satellite system signal degradations.

Purpose of the Study:

  • To propose novel normalization techniques for precoding/beamforming matrices in broadband multiuser MIMO satellite systems.
  • To adapt existing normalization methods to mitigate signal attenuation and scan losses.
  • To achieve uniform signal-to-noise ratio (SNR) and signal-to-noise plus interference ratio (SNIR) across users.

Main Methods:

  • Development of low-complexity power normalization techniques applied digitally after beamforming.
  • Adaptation of zero forcing (ZF), minimum mean squared error (MMSE), and matched filtering (MF) precoding/beamforming techniques.
  • Formulation of closed-form expressions for normalization, avoiding iterative algorithms.

Main Results:

  • The proposed techniques achieve iso-flux-like characteristics and satisfy per-feed power constraints in the absence of atmospheric effects.
  • Uniform SNR and SNIR distribution across users is achieved without significant impact on payload sum rate.
  • Numerical results demonstrate comparable or superior performance in total capacity and SNR/SNIR distribution versus existing methods.

Conclusions:

  • The novel normalization techniques effectively address signal attenuation in satellite MIMO systems.
  • Closed-form solutions offer a computationally efficient alternative to iterative methods for improving user signal quality.
  • The proposed methods enhance the performance of broadband multiuser MIMO satellite systems, particularly for global coverage applications.