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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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Energy Efficiency Optimization in Massive MIMO Secure Multicast Transmission.

Bin Jiang1, Linbo Qu1, Yufei Huang1

  • 1National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China.

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|December 8, 2020
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Summary
This summary is machine-generated.

This study optimizes energy efficiency for secure multicast transmissions using massive MIMO and statistical CSI. The proposed iterative algorithm enhances secure data rates while minimizing power consumption against eavesdroppers.

Keywords:
beam domain power allocationenergy efficiency optimizationmassive MIMOprivacy engineeringstatistical CSIutility–privacy trade-off

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Area of Science:

  • Wireless Communication Engineering
  • Information Security
  • Signal Processing

Background:

  • Massive MIMO systems enable high data rates but face energy efficiency challenges.
  • Secure communication is crucial, especially in multicast scenarios where eavesdropping is a risk.
  • Balancing energy efficiency and security (utility-privacy trade-off) is a key research area.

Purpose of the Study:

  • To maximize energy efficiency in massive MIMO downlink secure multicast transmission.
  • To address the utility-privacy trade-off by optimizing secure transmit rate versus power consumption.
  • To develop an efficient algorithm for this complex optimization problem.

Main Methods:

  • Formulated the problem as maximizing energy efficiency (secure rate/power consumption).
  • Used a lower bound for the secure multicast rate to simplify the non-convex problem.
  • Developed an iterative algorithm using Minorize-Maximize and Dinkelbach's transform for convex subproblems.
  • Obtained a closed-form eigenvector for the transmit covariance matrix, simplifying the strategy to power allocation.

Main Results:

  • The proposed iterative algorithm converges and effectively solves the secure energy efficiency optimization problem.
  • Reduced computational complexity by using a deterministic equivalent of the objective function.
  • The approach demonstrates superior performance compared to conventional methods in numerical simulations.

Conclusions:

  • The developed method provides an effective solution for energy efficiency optimization in massive MIMO secure multicast systems.
  • The approach successfully balances energy efficiency and security requirements.
  • The algorithm offers a practical and computationally efficient solution for secure wireless communication design.