Energy Efficient Massive MIMO Array Configurations

Halbauer, Hardy; Weber, Andreas; Wiegner, Dirk; Wild, Thorsten

doi:10.1109/glocomw.2018.8644331

Cited by 13 publications

(8 citation statements)

References 13 publications

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“…We have ignored the influence of the transceiving circuit power consumption for SNs because we have assumed the SNs are the lower-powered device such as sensors deployed for the Internet of Things (IoT). It is also an essential issue for extending our system model under the consideration of the transceiving circuit power consumption as [9,40,41,42,43]. Moreover, the SNs have the same priority in our game for transmitting data to the PB, which may lead to the near-far problem.…”

Section: Discussionmentioning

confidence: 99%

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

Wang

Huang

Yang

et al. 2019

Sensors

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This paper considers the price-based resource allocation problem for wireless power transfer (WPT)-enabled massive multiple-input multiple-output (MIMO) networks. The power beacon (PB) can transmit energy to the sensor nodes (SNs) by pricing their harvested energy. Then, the SNs transmit their data to the base station (BS) with large scale antennas by the harvesting energy. The interaction between PB and SNs is modeled as a Stackelberg game. The revenue maximization problem of the PB is transformed into the non-convex optimization problem of the transmit power and the harvesting time of the PB by backward induction. Based on the equivalent convex optimization problem, an optimal resource allocation algorithm is proposed to find the optimal price, energy harvesting time, and power allocation for the PB to maximize its revenue. Finally, simulation results show the effectiveness of the proposed algorithm.

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Section: Discussionmentioning

confidence: 99%

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

Wang

Huang

Yang

et al. 2019

Sensors

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“…It can be easily extended towards hybrid and full digital architectures with N antenna elements by scaling the number of analog BB and conversion chains accordingly, as described e.g. in [6] and shown in TABLE II. For the hybrid architecture this results in M subpanels with N/M elements each.…”

Section: A Model Descriptionmentioning

confidence: 99%

“…Based on typical values of saturated output power Psat, power added efficiency PAE and gain values reasonable for devices operating at 90 GHz, 120 GHz and around 230 GHz we derive a more generic model covering the effect of reduced output power and decreasing PAE. The relation between Psat, PAE and gain is according to (6):…”

Section: B Power Consumption Data Of Devices and Building Blocksmentioning

confidence: 99%

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Towards Power Efficient 6G Sub-THz Transmission

Halbauer

Wild

2021

2021 Joint European Conference on Networks and Communications &Amp; 6G Summit (EuCNC/6G Summit)

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Extreme high rate demands for 6G can be addressed by communication technologies in the millimeter wave and Terahertz bands. In these high carrier frequencies, power consumption can become a key limiting factor and thus has to be assessed thoroughly. This paper considers the power consumption of the RF frontend of a transceiver for the use in a hot spot scenario. A power consumption model for the main functional blocks has been derived for different frequencies and array architectures, both at transmit and receive side. The impact of the EIRP reduction over the frequency range from 90 GHz up to 230 GHz due to increased path loss and reduced output power of the power amplifiers has been assessed and the related power consumption has been evaluated. Further, the impact of analog-todigital converters on the overall power consumption of the receive part have been carved out. Key dominating components have been identified indicating the number of antenna elements needed for different architectures and the related power consumption.

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“…The massive MIMO is a scalable version of multiuser MIMO. It is also called the extension of MIMO technology, which includes hundreds and even thousands of antennas at the BS to enhance the throughput and energy efficiency [8]. The underlying idea that allows for a considerable gain in energy efficiency is that energy can be absorbed with high intensity into smaller areas when a large number of antennas are used [9].…”

Section: Introductionmentioning

confidence: 99%

ResNet-Enabled cGAN Model for Channel Estimation in Massive MIMO System

Yadav

Dwivedi

Chaturvedi

2022

Wireless Communications and Mobile Computing

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Massive multiple-input multiple-output (MIMO), or large-scale MIMO, is one of the key technologies for future wireless networks to exhibit a large accessible spectrum and throughput. The performance of a massive MIMO system is strongly reliant on the nature of various channels and interference during multipath transmission. Therefore, it is important to compute accurate channel estimation. This paper considers a massive MIMO system with one-bit analog-to-digital converters (ADCs) on each receiver antenna of the base station. Deep learning (DL)-based channel estimation framework has been developed to reduce signal processing complexity. This DL framework uses conditional generative adversarial networks (cGANs) and various convolutional neural networks, namely reverse residual network (reverse ResNet), squeeze-and-excitation ResNet (SE ResNet), ResUNet++, and reverse SE ResNet, as the generator model of cGAN for extracting the features from the quantized received signals. The simulation results of this paper show that the trained residual block-based generator model of cGAN has better channel generation performance than the standard generator model in terms of mean square error.

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Energy Efficient Massive MIMO Array Configurations

Cited by 13 publications

References 13 publications

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

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

Towards Power Efficient 6G Sub-THz Transmission

ResNet-Enabled cGAN Model for Channel Estimation in Massive MIMO System

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