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Related Experiment Video

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Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band
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Optimal Resource Allocation Policies for Multi-User Backscatter Communication Systems.

Bin Lyu1, Zhen Yang2, Guan Gui3

  • 1Key Laboratory of Ministry of Education in Broadband Wireless Communication and Senor Network Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China. 13010511@njupt.edu.cn.

Sensors (Basel, Switzerland)
|December 6, 2016
PubMed
Summary
This summary is machine-generated.

This study optimizes backscatter communication (BackCom) systems for maximum goodput. Novel resource allocation policies are derived for passive and semi-passive tags, enhancing system performance.

Keywords:
backscatter communicationresource allocationtotal goodput maximization

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Area of Science:

  • Wireless Communication
  • Information Theory
  • Optimization Theory

Background:

  • Backscatter communication (BackCom) systems enable energy-efficient data transmission.
  • Optimizing resource allocation is crucial for maximizing system performance in BackCom networks.
  • Existing methods may not fully address the complexities of joint power, time, and reflection ratio optimization.

Purpose of the Study:

  • To maximize the total system goodput in a BackCom system with passive and semi-passive tags.
  • To jointly optimize reader transmission power, time allocation, and reflection ratio.
  • To develop efficient algorithms for solving the resulting non-convex optimization problems.

Main Methods:

  • Formulation of non-convex optimization problems for passive and semi-passive BackCom tags.
  • Decomposition of problems into feasible sub-problems based on reader transmission power allocation.
  • Sequential solution of convex sub-problems for passive tags.
  • Application of a block coordinate descent (BCD)-based algorithm for biconvex sub-problems in semi-passive tags.

Main Results:

  • Derivation of an optimal resource allocation policy for passive tags.
  • A close-to-optimal solution achieved for semi-passive tags using the BCD algorithm.
  • Simulation results validating the superiority of the proposed resource allocation strategies.

Conclusions:

  • The proposed resource allocation policies significantly enhance system goodput in BackCom.
  • The developed optimization framework effectively handles both passive and semi-passive tag scenarios.
  • The BCD-based approach provides an efficient method for optimizing complex BackCom systems.