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Maximum Power Transfer01:16

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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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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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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 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:
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Resource Allocation and Computation Offloading for Wireless Powered Mobile Edge Computing.

Jun Chen1, Zheng Chang2,3, Wenlong Guo1

  • 1The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Colleage of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China.

Sensors (Basel, Switzerland)
|August 26, 2022
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Summary
This summary is machine-generated.

This study optimizes resource allocation in mobile edge computing (MEC) by managing task offloading and energy harvesting. The proposed methods minimize mobile device energy consumption effectively.

Keywords:
full-duplexhalf-duplexmobile edge computingoffloadingwireless power transfer

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Wireless Communications

Background:

  • Mobile edge computing (MEC) systems face challenges in resource allocation and energy management for mobile devices (MDs).
  • Heterogeneous MEC environments involve multiple base stations (BSs) with varying capabilities, including wireless power transfer (WPT).

Purpose of the Study:

  • To investigate and optimize resource allocation and computation offloading in a heterogeneous MEC system.
  • To minimize the energy consumption of mobile devices (MDs) by optimizing offloading decisions, energy harvesting modes, and time allocation.

Main Methods:

  • Formulated the problem as a non-convex mixed integer programming problem.
  • Employed quadratically constrained quadratic programming (QCQP) and semi-definite relaxation (SDR) techniques for solving the optimization problem.
  • Considered both full-duplex and half-duplex wireless energy transmission modes for WPT.

Main Results:

  • Developed an optimization scheme for resource allocation and computation offloading.
  • Demonstrated the effectiveness of the proposed scheme through simulation results.
  • Achieved minimization of energy consumption for mobile devices.

Conclusions:

  • The proposed optimization strategy effectively addresses resource allocation and computation offloading in MEC systems with WPT.
  • The integration of WPT and intelligent offloading decisions leads to significant energy savings for mobile devices.
  • The employed QCQP and SDR methods provide a viable solution for complex non-convex optimization problems in MEC.