Energy efficient transmission techniques for LTE

Cardoso, Filipe D.; Petersson, Sven O.; Boldi, Mauro; Mizuta, Shinji; Dietl, Guido; Torrea-Duran, Rodolfo; Desset, Claude; Leinonen, Jouko; Correia, Luıs M.

doi:10.1109/mcom.2013.6619582

Cited by 10 publications

(6 citation statements)

References 6 publications

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“…VII. POWER SAVINGS WITH SLEEP MODES It is well-known that due to the recent advances in hardware technology, it has been made possible for wireless transceivers to consume varying power levels under different operational modes [33]. These include BS sleep, idle, transmit, and receive modes which can be accordingly adjusted based on the daily fluctuations in the traffic volume for the purpose of preserving energy.…”

Section: Optimal Deployment Solutionmentioning

confidence: 99%

Stochastic Geometric Analysis of Energy-Efficient Dense Cellular Networks

et al. 2017

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Dense cellular networks (DenseNets) are fast becoming a reality with the rapid deployment of base stations (BSs) aimed at meeting the explosive data traffic demand. In legacy systems however this comes with the penalties of higher network interference and energy consumption. In order to support network densification in a sustainable manner, the system behavior should be made 'load-proportional' thus allowing certain portions of the network to activate on-demand. In this work, we develop an analytical framework using tools from stochastic geometry theory for the performance analysis of DenseNets where load-awareness is explicitly embedded in the design. The model leverages on a flexible cellular network architecture where there is a complete separation of the data and signaling communication functionalities. Using the proposed model, we identify the most energyefficient deployment solution for meeting certain minimum service criteria and analyze the corresponding power savings through dynamic sleep modes. Based on state-of-the-art system parameters, a homogeneous pico deployment for the data plane with a separate layer of signaling macro-cells is revealed to be the most energy-efficient solution in future dense urban environments. Index TermsNetwork densification, load-proportionality, optimal deployment solution, daily traffic model, power savings, sleep modes, stochastic geometry theory, Monte-Carlo simulations. I. INTRODUCTIONUltra-dense deployment of base stations (BSs), relay nodes, and distributed antennas is considered a de facto solution for realizing the significant performance improvements needed to accommodate the overwhelming future mobile traffic demand [2]. While legacy wireless communication systems are fast approaching the information-theoretic capacity limits, dense cellular networks (DenseNets) can push data rates even further by shortening the transmitter-receiver distance and serving fewer users per cell [3]. The extremely populated topology of DenseNets raises several technical challenges, including managing the aggregate network interference and keeping the energy expenditure in check -the main topics of this paper.Understanding the interference behavior in DenseNets is challenging due to the rapid, irregular, and overlapping placement of nodes. In addition, in contrast to existing macro-cells where different parts of spectrum is allocated to neighboring cells, DenseNets employ an aggressive frequency reuse strategy where different nodes can access the same spectrum; thus highlighting the importance of interference management for facilitating efficient spectrum utilization [4]. On the other hand, legacy cellular networks and transmission technologies are designed and dimensioned to meet the coverage and capacity requirements in peak traffic conditions. This approach threatens the commercial viability of deploying many more network nodes which would substantially increase the total capital, operational, and environmental expenditure [5], [6]. An extensive design overhaul towards a flexible cel...

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Section: Optimal Deployment Solutionmentioning

confidence: 99%

Stochastic Geometric Analysis of Energy-Efficient Dense Cellular Networks

et al. 2017

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“…A larger fraction belongs to BSs. A study [43] is developed as part of the EARTH Project which aims to improve energy efficiency of LTE cellular networks while providing more wide coverage and to reduce the carbon footprint. Authors aim to enhance radio transmission techniques such as beamforming and MIMO antenna techniques to promote the objectives of the EARTH Project.…”

Section: Lte (Long-term Evolution)mentioning

confidence: 99%

A review on the recent energy-efficient approaches for the Internet protocol stack

Cengiz

Dağ

2015

J Wireless Com Network

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The reduction of energy consumption has become a key research area for the information and communication technology (ICT) industry, due to economical, environmental, and marketing reasons. While the environmental direction aims at minimization of greenhouse gas emissions by enforcing the usage of renewable energy in the ICT industry, economical and marketing directions lead researchers to design low-power components or develop and enhance energy-saving protocols without an impact on the level of the performance. With the steady increase in the cost of energy, the expanding number of energy-hungry components and widespread usage of ICT industry, most of the protocols that have become an integral part of our lives but are yet developed without any energy constraints in mind in the past will need to be restructured or developed again. For this reason, researchers are studying on all layers of the Internet protocol stack to develop energy-efficient protocols and algorithms. This paper reviews recent approaches for energy efficiency studies for each layer in the Internet protocol stack from the physical layer to the application layer. It is expected that with the deployment of current research output, the studies performed at each layer will result in significant energy savings for the ICT industry which in turn will have a positive impact on our lives for their economical and environmental results.

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“…According to the analysis in [16], the circuit power consumption of a massive MIMO BS can be modeled in the same way as a traditional BS, but the corresponding parameters will differ. Note that in (6) the feeder loss is removed considering that massive MIMO systems will employ active antennas where the RF module is integrated into antenna, which is different from a traditional BS with passive antennas [17].…”

Section: B Power Consumption Modelmentioning

confidence: 99%

“…By substituting S Z , I Z = I Z nP + I Z P and (20) into the objective function in (17), the approximate maximal EE of the system with ZFBF can be derived as…”

Section: Maximal Ee For Zfbfmentioning

confidence: 99%

Is Massive MIMO Energy Efficient?

Liu¹,

Han²,

Yang³

2015

Preprint

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Massive multi-input multi-output (MIMO) can support high spectral efficiency (SE) with simple linear transceivers, and is expected to provide high energy efficiency (EE). In this paper, we analyze the EE of downlink multi-cell massive MIMO systems under spatially correlated channel model, where both transmit and circuit power consumptions, training overhead, channel estimation and pilot contamination (PC) are taken into account. We obtain the maximal EE for the systems with maximum-ratio transmission (MRT) and zero-forcing beamforming (ZFBF) for given number of antennas and users by optimizing the transmit power. The closed-form expressions of approximated optimal transmit power and maximal EE, and their scaling laws with the number of antennas M are derived for the systems with MRT and ZFBF. Our analysis shows that the maximal EE decreases with M for both systems with and without PC, but with different descending speeds. For the system without PC, the optimal transmit power should be configured to increases with M , while for the system with PC, the optimal transmit power should be configured as a constant independent from M . The analytical results are validated by simulations under a more realistic three-dimensional channel model.

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Energy efficient transmission techniques for LTE

Cited by 10 publications

References 6 publications

Stochastic Geometric Analysis of Energy-Efficient Dense Cellular Networks

Stochastic Geometric Analysis of Energy-Efficient Dense Cellular Networks

A review on the recent energy-efficient approaches for the Internet protocol stack

Is Massive MIMO Energy Efficient?

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