Balancing Available Energy Distribution in mMIMO SWIPT Sensor Networks With Low Resolution ADC/DAC
This paper addresses the fair distribution of available energy (AE) in a massive multiple-input multiple-output (mMIMO) simultaneous wireless information and power transfer (SWIPT) sensor network, where sensor nodes are equipped with low-resolution analog-to-digital converters (ADCs) and digital-to-...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10670407/ |
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| author | Suho Shin Prince Anokye Hoon Lee Jihwan Moon Kyoung-Jae Lee |
| author_facet | Suho Shin Prince Anokye Hoon Lee Jihwan Moon Kyoung-Jae Lee |
| author_sort | Suho Shin |
| collection | DOAJ |
| description | This paper addresses the fair distribution of available energy (AE) in a massive multiple-input multiple-output (mMIMO) simultaneous wireless information and power transfer (SWIPT) sensor network, where sensor nodes are equipped with low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). We define the system model and estimate the channel using linear minimum mean square error (LMMSE) estimation. To analyze system performance, we derive closed-form expressions for the signal-to-interference-plus-noise ratio (SINR), and harvested energy. These expressions allow us to evaluate the impact of low-resolution ADCs/DACs on both the achievable rate and energy harvesting. Based on these expressions, we formulate an optimization problem fair AE distribution. To solve this non-convex problem, we propose an alternating optimization (AO) algorithm that optimizes the downlink/uplink (DL/UL) powers and the power splitting (PS) ratio. Monte Carlo simulations confirm that our closed-form analysis accurately represents the system performance and that the optimization algorithm distributes AE more fairly compared to the equal power and power splitting case. |
| format | Article |
| id | doaj-art-af432d6920db4ac8a6fa71f1df7bd9cb |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-af432d6920db4ac8a6fa71f1df7bd9cb2025-08-20T01:54:33ZengIEEEIEEE Access2169-35362024-01-011212870212871610.1109/ACCESS.2024.345680010670407Balancing Available Energy Distribution in mMIMO SWIPT Sensor Networks With Low Resolution ADC/DACSuho Shin0https://orcid.org/0000-0002-3485-5635Prince Anokye1https://orcid.org/0000-0002-4014-4207Hoon Lee2https://orcid.org/0000-0003-0753-8324Jihwan Moon3https://orcid.org/0000-0002-9812-7768Kyoung-Jae Lee4https://orcid.org/0000-0001-9579-6600Department of Electronic Engineering, Hanbat National University, Daejeon, South KoreaIMT-Atlantique, Lab-STICC, Brest, FranceDepartment of Electrical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South KoreaDepartment of Mobile Convergence Engineering, Hanbat National University, Daejeon, South KoreaDepartment of Electronic Engineering, Hanbat National University, Daejeon, South KoreaThis paper addresses the fair distribution of available energy (AE) in a massive multiple-input multiple-output (mMIMO) simultaneous wireless information and power transfer (SWIPT) sensor network, where sensor nodes are equipped with low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). We define the system model and estimate the channel using linear minimum mean square error (LMMSE) estimation. To analyze system performance, we derive closed-form expressions for the signal-to-interference-plus-noise ratio (SINR), and harvested energy. These expressions allow us to evaluate the impact of low-resolution ADCs/DACs on both the achievable rate and energy harvesting. Based on these expressions, we formulate an optimization problem fair AE distribution. To solve this non-convex problem, we propose an alternating optimization (AO) algorithm that optimizes the downlink/uplink (DL/UL) powers and the power splitting (PS) ratio. Monte Carlo simulations confirm that our closed-form analysis accurately represents the system performance and that the optimization algorithm distributes AE more fairly compared to the equal power and power splitting case.https://ieeexplore.ieee.org/document/10670407/Available energylow-resolution analog-to-digital converters/digital-to-analog convertersmassive multiple-input multiple-outputoptimizationsensor network |
| spellingShingle | Suho Shin Prince Anokye Hoon Lee Jihwan Moon Kyoung-Jae Lee Balancing Available Energy Distribution in mMIMO SWIPT Sensor Networks With Low Resolution ADC/DAC IEEE Access Available energy low-resolution analog-to-digital converters/digital-to-analog converters massive multiple-input multiple-output optimization sensor network |
| title | Balancing Available Energy Distribution in mMIMO SWIPT Sensor Networks With Low Resolution ADC/DAC |
| title_full | Balancing Available Energy Distribution in mMIMO SWIPT Sensor Networks With Low Resolution ADC/DAC |
| title_fullStr | Balancing Available Energy Distribution in mMIMO SWIPT Sensor Networks With Low Resolution ADC/DAC |
| title_full_unstemmed | Balancing Available Energy Distribution in mMIMO SWIPT Sensor Networks With Low Resolution ADC/DAC |
| title_short | Balancing Available Energy Distribution in mMIMO SWIPT Sensor Networks With Low Resolution ADC/DAC |
| title_sort | balancing available energy distribution in mmimo swipt sensor networks with low resolution adc dac |
| topic | Available energy low-resolution analog-to-digital converters/digital-to-analog converters massive multiple-input multiple-output optimization sensor network |
| url | https://ieeexplore.ieee.org/document/10670407/ |
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