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Maximum Power Transfer

Maximum Power Transfer

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...

The Maximum Power Transfer Theorem

The Maximum Power Transfer Theorem

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.

Short-distance Transport of Resources

Short-distance Transport of Resources

Short-distance transport refers to transport that occurs over a distance of just 2-3 cells, crossing the plasma membrane in the process. Small uncharged molecules, such as oxygen, carbon dioxide, and water, can diffuse across the plasma membrane on their own. In contrast, ions and larger molecules require the assistance of transport proteins due to their charge or size. Transport across membranes also occurs within individual cells, playing a variety of essential roles for the plant as a whole.

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Network Covalent Solids

Network Covalent Solids

Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...

Power

Power

The concept of work involves force and displacement; meanwhile, the work-energy theorem relates the net work done on a body to the difference in its kinetic energy, calculated between two points on its trajectory. While none of these quantities or relations involves time explicitly, we know that the time available to accomplish work is often just as important as the amount of work itself. For example, sprinters in a race may have achieved the same velocity at the finish, therefore,...

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Price-Based Resource Allocation in Wireless Power Transfer-Enabled Massive MIMO Networks.

Zhengqiang Wang¹, Kunhao Huang², Xiaona Yang²

¹School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China. wangzq@cqupt.edu.cn.

Sensors (Basel, Switzerland)

|July 31, 2019

Summary

This summary is machine-generated.

This study optimizes wireless power transfer (WPT) in massive MIMO networks by using a Stackelberg game to maximize revenue. An algorithm is proposed for optimal pricing and resource allocation, enhancing network efficiency.

Keywords:

Stackelberg game massive MIMO price resource allocation wireless power transfer

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Area of Science:

Wireless communication networks
Resource allocation algorithms
Game theory applications

Background:

Massive MIMO networks offer enhanced capacity and coverage.
Wireless Power Transfer (WPT) enables energy harvesting for sensor nodes.
Integrating WPT and massive MIMO presents unique resource allocation challenges.

Purpose of the Study:

To address the price-based resource allocation problem in WPT-enabled massive MIMO networks.
To maximize the revenue of the power beacon (PB) by optimizing energy pricing and transmission.
To develop an efficient algorithm for resource allocation in such networks.

Main Methods:

Modeling the interaction between the power beacon (PB) and sensor nodes (SNs) as a Stackelberg game.
Transforming the PB's revenue maximization problem into a non-convex optimization problem.
Utilizing backward induction to derive an equivalent convex optimization problem.

Main Results:

An optimal resource allocation algorithm is proposed.
The algorithm determines the optimal price, energy harvesting time, and power allocation for the PB.
Simulation results validate the effectiveness of the proposed algorithm in maximizing PB revenue.

Conclusions:

The proposed Stackelberg game model and optimization algorithm effectively solve the resource allocation problem in WPT-enabled massive MIMO networks.
The developed approach leads to maximized revenue for the power beacon.
This work provides a valuable framework for efficient resource management in future wireless networks.