A survey on energy efficient 5G green network with a planned multi-tier architecture

Sofi, Ishfaq Bashir; Gupta, Akhil

doi:10.1016/j.jnca.2018.06.002

Cited by 53 publications

(34 citation statements)

References 98 publications

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“…At the same time, building networks that support this growth of services have resulted in a corresponding rise in energy consumption. The new challenge will be to maintain the network's total energy consumption while introducing new features like increased data bandwidth and low latency [3]. Ericsson foresees that novel software features that can bring significant energy savings such as ultra-lean design and Massive Multiple-Input-Multiple-Output (MIMO) [4].…”

Section: Introductionmentioning

confidence: 99%

M-QAM Modulation Symbol-Level Precoding for Power Minimization: Closed-Form Solution

Krivochiza

Duncán

Chatzinotas

et al. 2019

2019 16th International Symposium on Wireless Communication Systems (ISWCS)

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In this paper, we derive a closed-form algorithm of the computationally efficient Symbol-Level Precoding (SLP) for power efficient communications when using M-QAM modulated waveforms. The channel state information (CSI) based and dataaided SLP technique optimizes power efficiency by solving a non-negative convex quadratic optimization problem per time frame of transmitted symbols. The optimization combines constructive inter-user interference to minimize the sum power of precoded symbols at the transmitter side under constraints for minimum SNR at the receiver side. The SLP implementation incurs extra computational complexity of the transmitter. We propose a convex quadratic optimization problem for M-QAM constellations and derive a closed-form algorithm with a fixed number of iterations to solve the problem.

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Section: Introductionmentioning

confidence: 99%

M-QAM Modulation Symbol-Level Precoding for Power Minimization: Closed-Form Solution

Krivochiza

Duncán

Chatzinotas

et al. 2019

2019 16th International Symposium on Wireless Communication Systems (ISWCS)

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“…Idea of fundamental multiple‐input multiple‐output (MIMO) network has been utilized as a part of numerous applications for a long time, yet its outline idea utilized as a part of remote systems is developing with cutting‐edge innovation. The concept of energy efficiency (EE) is introduced for reducing the cost and increasing the lifetime of the components used in the massive MIMO systems . With the arrival of the massive MIMO systems, the number of antennas will get increase, which results in increased transmission, processing, and reception circuitry .…”

Section: Introductionmentioning

confidence: 99%

“…The concept of energy efficiency (EE) is introduced for reducing the cost and increasing the lifetime of the components used in the massive MIMO systems. 1 With the arrival of the massive MIMO systems, the number of antennas will get increase, which results in increased transmission, processing, and reception circuitry. 2 The increase in the number of antennas and circuitry will give rise to the increase in the power consumption and complexity level.…”

Section: Introductionmentioning

confidence: 99%

Power and energy optimization with reduced complexity in different deployment scenarios of massive MIMO network

Sofi

Gupta

Jha

2019

Int J Communication

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Summary With the increase in the number of users, the role of massive MIMO has become more significant. But there is a significant increase in the power and energy consumption in the massive MIMO network for transmission, processing, and reception. Hence, the prior role is to reduce the power consumption and increase the energy efficiency of the network. In this paper, the work is done to reduce the power consumption, while maintaining the reduced complexity in the massive MIMO and small‐cell scenario, and to increase the energy efficiency, by optimizing the number of users and number of transmission antennas in the massive MIMO scenario. This paper has also found out the optimal values of the energy efficiency, number of transmission antennas, and number of users for a massive MIMO network in different deployment scenarios like indoor hotspot, ultradense, dense urban, urban, suburban, and rural areas in both single‐cell scenario and multicell scenario at the base station and user equipment side according to the ITU‐R M.2135 standard.

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“…Authors in [7] give an overview of EH techniques, in particular on EH power management policies optimizations, with specific stress on Wireless Sensor Networks (WSNs) and on ambient RF harvesting. In their survey [8], authors discuss 5G green network EE and promising technologies and architecture for its optimizations. Among these techniques, EH is discussed as well as other key enabling techniques, but specific EH integration is not detailed.…”

Section: Introductionmentioning

confidence: 99%

Overview on 5G Radio Frequency Energy Harvesting

Hassani¹,

Hassani²,

Boutammachte³

2019

Adv. sci. technol. eng. syst. j.

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The current industrial landscape is becoming increasingly aware of the need to optimize energy use and management for all domains, including telecommunications. Among others, RF Energy Harvesting is a promising technique for 5G systems as an alternative to traditional energy supply sources. This paper presents 5G landscape and ecosystem. It positions RF-Energy Harvesting in 5G context and reviews its techniques. It also considers constraints and research trends for different aspects needed to make RF energy harvesting ready for deployment along with 5G enabling technologies.

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A survey on energy efficient 5G green network with a planned multi-tier architecture

Cited by 53 publications

References 98 publications

M-QAM Modulation Symbol-Level Precoding for Power Minimization: Closed-Form Solution

M-QAM Modulation Symbol-Level Precoding for Power Minimization: Closed-Form Solution

Power and energy optimization with reduced complexity in different deployment scenarios of massive MIMO network

Overview on 5G Radio Frequency Energy Harvesting

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