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Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  2/2021 - 8

Multiobjective Optimization for Resource Allocation in Full-duplex Large Distributed MIMO Systems

SHARMA, S. See more information about SHARMA, S. on SCOPUS See more information about SHARMA, S. on IEEExplore See more information about SHARMA, S. on Web of Science, YOON, W. See more information about YOON, W. on SCOPUS See more information about YOON, W. on SCOPUS See more information about YOON, W. on Web of Science
 
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Download PDF pdficon (1,380 KB) | Citation | Downloads: 301 | Views: 297

Author keywords
antenna, convergence, energy efficiency, optimization, uplink

References keywords
energy(34), efficiency(23), systems(20), mimo(14), efficient(14), networks(13), multi(12), distributed(12), optimization(11), spectral(10)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2021-05-31
Volume 21, Issue 2, Year 2021, On page(s): 67 - 74
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2021.02008
Web of Science Accession Number: 000657126200008
SCOPUS ID: 85112806037

Abstract
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The most conflicting key variables in wireless networks are energy efficiency (EE) and spectral efficiency (SE). In this paper, we propose an energy-efficient allocation algorithm of network resources for multi-input multi-output networks distributed with large-scale antenna systems. We formulate a multiobjective optimization problem (MOOP) to maximize the EE of each distinct user and to show the EESE trade-off as a MOOP. To find the Pareto optimal solution, we transform this MOOP into single-objective optimization problem (SOOP) through Tchebycheff scalarization and by exploiting it with Dinkelbach's method. To solve the SOOP, we apply a joint antenna selection and user scheduling (JASUS) algorithm for the joint allocation of antenna scheduled users solved through an iterative approach. The power allocations are applied distinctly for individual cell users by a subgradient iterative method to simplify the SOOP further and improve the EE. The simulation results reveal that our proposed MOOP has a fast convergence, achieving maximum EE after a few iterations. Additionally, our proposed methods unveil an interesting trade-off between EE and SE at a faster speed and demonstrate that an important performance gain is achieved by using the proposed algorithm.


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References Weight

Web of Science® Citations for all references: 8,621 TCR
SCOPUS® Citations for all references: 10,588 TCR

Web of Science® Average Citations per reference: 160 ACR
SCOPUS® Average Citations per reference: 196 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-11-27 01:41 in 257 seconds.




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