Breathe to Save Energy: Assigning Downlink Transmit Power and Resource Blocks to LTE Enabled IoT Networks

Kotagi, Vijeth J.; Thakur, Rahul; Mishra, Sudeepta; Murthy, C. Siva Ram

doi:10.1109/lcomm.2016.2570224

Cited by 33 publications

(12 citation statements)

References 3 publications

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“…Further, Kotagi et al [112] proposed a breathing technique to reduce the BS energy consumption by assigning the T x (Transmission) power efficiently. Firstly the SINR of the user is compared with pre-specified SINR threshold value and if found greater Tx Power has modified accordingly.…”

Section: B Iot Based Resource Allocation Techniquesmentioning

confidence: 99%

A Survey on Energy Efficient Narrowband Internet of Things (NBIoT): Architecture, Application and Challenges

2019

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The advancement of technologies over years has poised Internet of Things (IoT) to scoop out untapped information and communication technology opportunities. It is anticipated that IoT will handle the gigantic network of billions of devices to deliver plenty of smart services to the users. Undoubtedly, this will make our life more resourceful but at the cost of high energy consumption and carbon footprint. Consequently, there is a high demand for green communication to reduce energy consumption, which requires optimal resource availability and controlled power levels. In contrast to this, IoT devices are constrained in terms of resources-memory, power, and computation. Low power wide area (LPWA) technology is a response to the need for efficient utilization of power resource, as it evinces characteristics such as the capability to proffer low power connectivity to a huge number of devices spread over wide geographical areas at low cost. Various LPWA technologies, such as LoRa and SigFox, exist in the market, offering a proficient solution to the users. However, in order to abstain the need of new infrastructure (like base station) that is required for proprietary technologies, a new cellular-based licensed technology, narrowband IoT (NBIoT), is introduced by 3GPP in Rel-13. This technology presents a good candidature to handle LPWA market because of its characteristics like enhanced indoor coverage, low power consumption, latency insensitivity, and massive connection support towards NBIoT. This survey presents a profound view of IoT and NBIoT, subsuming their technical features, resource allocation, and energy-efficiency techniques and applications. The challenges that hinder the NBIoT path to success are also identified and discussed. In this paper, two novel energy-efficient techniques ''zonal thermal pattern analysis'' and energy-efficient adaptive health monitoring system have been proposed towards green IoT. INDEX TERMS Internet of Things (IoT), narrowband Internet of Things (NBIoT), low power wide area network (LPWAN), green communication, smart agriculture, smart health.

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Section: B Iot Based Resource Allocation Techniquesmentioning

confidence: 99%

A Survey on Energy Efficient Narrowband Internet of Things (NBIoT): Architecture, Application and Challenges

2019

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“…First and the easiest method is to get a confirmation about the location of the BS from the cloud ⃝ whether the location is already in a 3D network coverage plan or suitable for that. This is done with the help of an already available cellular coverage map in 7 ⃝.…”

Section: A Overall Architecture and Overview On Path Loss And Radio Data Managementmentioning

confidence: 99%

“…Thus, to serve the needs in such a complex environment, a 3D model of network [6] is appropriate as opposed to existing 2-Dimensional (2D) network model while utilizing the third spatial dimension for cellular coverage planning and radio resource management. Since there are no fixed boundaries or coverage areas for the cells in real life, it is beneficial to model cells in 3D SC networks to be amorphous or semi-rigid [7] while paving the way to change their sizes in overcoming problems like elimination of coverage holes, managing the number of devices served, strengthening cell or cluster boundary coverage and avoidance of undue coverage overlaps. In this study, 3D SC network or 3D network is a cellular wireless network consists of 3D SCs and a cluster is a set of collocated devices or a group of devices in the close vicinity of each other.…”

Section: Introductionmentioning

confidence: 99%

Crowdsensing-Assisted Path Loss Estimation and Management of Dynamic Coverage in 3D Wireless Networks With Dense Small Cells

2021

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Emerging vertical applications enabled by connected devices and smart infrastructures have created an ever-increasing demand for high data rates over 5th-Generation (5G) and beyond wireless networks. Deployment of dense small cells (SCs) and millimeter wave (mmWave) communication systems have become inevitable in future wireless networks. Consequently, it is more accurate to model such networks in the 3-Dimensional (3D) space due to the spatially distributed nature of the SCs, locations of the devices, radio resources and propagation environment. Accurate estimation of location-specific path loss parameters is then essential for efficient utilization of radio resources and management of dynamic coverage in 3D SC networks. In the paper, a framework for location-specific path loss estimation is developed for efficient radio resource management, based on the principle of crowdsensing together with Linear Algebra (LA) and machine learning (ML) techniques considering 2.5 GHz and 28 GHz bands. The corresponding procedure for capturing dynamic coverage of a SC base station (BS) serving to an arbitrary cluster is proposed and examined based on its 3D propagation characteristics. Results show that the accuracy of 3D channel parameter estimation using gradient descent ML techniques is superior compared to LA technique and can achieve over 98% estimation accuracy. It is shown that using the proposed process, parameters can be extrapolated for the slightly extended 3D communication distances from the cluster boundary for the worst-case locations of devices based on already estimated propagation parameters with accuracy over 74% for certain distances. Although numerical results are presented for a single amorphous 3D cell of a wireless network, the framework given in the paper can be extended to any arbitrary 3D wireless cellular network.INDEX TERMS 3D cells, clusters, crowdsensing, cell coverage, machine learning.

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“…The novelty of the model is that it can be used to describe the process of resource blocks allocation in LTE downlink concerning user equipment request priority. Kotagi et al (2016) studied the downlink energy efficiency aspect by proposed a power control and RB allocation scheme called Breathing for LTE-A in IoT networks. Wu et al (2015) suggested a low complexity joint user grouping and resource allocation algorithm due to solve the problem of resource allocation for multi-user dual layer beamforming.…”

Section: Introductionmentioning

confidence: 99%

Optimization in Quality of Service for Lte Network Using Bandwidth Expansion

Sirait

Dani

Yuliza

et al. 2019

Sinergi

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QoS (Quality of Service) of LTE networks can bring the providers to provide broadband services with high performance to end user. Furthermore, the expected data rate transfer is up to 300 Mbit/s per user while the range of bandwidth varies from 1.4 MHz to 20 MHz. The network worked in 1800 MHz bands, 64 QAM modulation technique and used 10 MHz and 15 MHz channel bandwidth. There is a congestion problem for LTE network with 10 MHz channel bandwidth due to high utilization. The paper tries to analyze the QoS parameters, named Key Performance Indicators (KPI) for LTE Networks to solve the problem using bandwidth expansion. The KPIs parameter that is measured by drive test is accessibility, retainability, PRB downlink utilization, and user number. Based on the KPIs measurements results, it is showed that the proposed method to expand the bandwidth from 10 MHz to 15 MHz can avoid congestion problem and impact on improving the performance of LTE network.

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Breathe to Save Energy: Assigning Downlink Transmit Power and Resource Blocks to LTE Enabled IoT Networks

Cited by 33 publications

References 3 publications

A Survey on Energy Efficient Narrowband Internet of Things (NBIoT): Architecture, Application and Challenges

A Survey on Energy Efficient Narrowband Internet of Things (NBIoT): Architecture, Application and Challenges

Crowdsensing-Assisted Path Loss Estimation and Management of Dynamic Coverage in 3D Wireless Networks With Dense Small Cells

Optimization in Quality of Service for Lte Network Using Bandwidth Expansion

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