LTE UE Power Consumption Model: For System Level Energy and Performance Optimization

Jensen, Anders R.; Lauridsen, Mads; Mogensen, Preben; Sørensen, Troels B.; Jensen, Per

doi:10.1109/vtcfall.2012.6399281

Cited by 109 publications

(109 citation statements)

References 9 publications

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“…Even though these energy consumption models are fairly simple, by adjusting properly the constants k tx,1 , k tx,2 , k rx,1 , and k rx,2 , they can provide estimations for the spent power that are close to practical measurements, as those presented in [26]. Moreover, because of their simplicity, the models in (1) and (2) make easier to capture the essential trade-offs of the problem considered, as compared to other more complex models available in the literature (e.g., [26]). …”

Section: B Energy Consumption Modelsmentioning

confidence: 95%

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Energy Efficiency in Latency-Constrained Application Offloading From Mobile Clients to Multiple Virtual Machines

Lagén

Pascual‐Iserte

Muñoz

et al. 2018

IEEE Trans. Signal Process.

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Abstract-This paper addresses the energy-latency trade-off in distributed application offloading, in which an energy-limited handset offloads totally or partially an application to one or several virtual machines (VMs) located in remote locations or access points (APs) close to the mobile terminal (MT). One of the APs (the serving AP) provides radio access to the MT and is connected to the VMs through non-ideal backhaul (BH) links. In this setting, we optimize the offloading strategy (including the joint optimization of radio and computational resources) to minimize the energy consumption at the MT subject to a maximum latency constraint. In addition, we propose robust designs to cope with imperfect acquisition of the channel state information (CSI) and the BH parameters. Our findings show that, as far as the energy-latency trade-off is concerned, the optimal order of activation of the VMs does not depend on their processing capabilities but the delays of the BH links. However, once a VM is selected to participate in the processing, the optimal amount of processing allocated to such VM depends on its computational capabilities as well as on the features (capacity and delay) of the BH link. Additionally, offloading decisions become more conservative as the uncertainty in CSI and BH parameters increases.

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Section: B Energy Consumption Modelsmentioning

confidence: 95%

“…In the DL, the power consumption at the MT, p DL , increases with the decoding rate. Hence, the energy spent by the MT to receive s DL information bits during t DL seconds in the DL transmission can be modeled by [20], [26] …”

Section: B Energy Consumption Modelsmentioning

confidence: 99%

Energy Efficiency in Latency-Constrained Application Offloading From Mobile Clients to Multiple Virtual Machines

Lagén

Pascual‐Iserte

Muñoz

et al. 2018

IEEE Trans. Signal Process.

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“…2. The measurements were made using the Downlink Fixed MAC Padding for 0 and 25 DL PRBs, see [8] for further information. Based on those measurements a linear function of channel bandwidth is implemented in the R8 model and the receiver's power consumption is:…”

Section: Downlink Ca Ue Power Model Designmentioning

confidence: 99%

“…announced a CA chip set [7], but because it is not yet commercially available an empirical model cannot be established. Therefore a CA model is derived from the existing LTE release 8 (R8) power model in [8]. The block diagram of the R8 model is shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

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LTE UE Energy Saving by Applying Carrier Aggregation in a HetNet Scenario

Lauridsen

Wang

Mogensen

2013

2013 IEEE 77th Vehicular Technology Conference (VTC Spring)

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Abstract-In this work it is examined if downlink CarrierAggregation (CA) can be used to save UE energy. A dual-receiver LTE release 10 UE is compared with a single-receiver LTE release 8 UE. The models are based on scaling of an existing LTE release 8 UE power model. The energy consumption of the UEs is examined in a Heterogeneous Network scenario consisting of macro and small cells. The unexpected conclusion is that CA UEs can save energy, compared to LTE release 8 UEs, if they, depending on cell load, experience a throughput gain of 20 %. However if the UE throughput is unaltered the energy consumption can increase up to 20 %.

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Performance analysis of wireless power harvesting over signal to interference plus noise channel for full‐duplex services in LTE MIMO system

Abuhaija

2021

Int J Communication

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The evolution of wireless communication systems and its strive to provide 5G services accompanied with the expanded use of communication devices with greedy applications have strained the device's battery power. In its quest to provide a solution to this problem, wireless communication systems proposed wireless energy harvesting. Communication channel time shall be split between sending and receiving data as well as energy harvesting. Maximizing spectral efficiency and minimizing power consumption are aims of 5G technology. In the literature, the time split was analyzed without considering LTE resource blocks or interference from adjacent devices and objects. In this second paper, we first pay special attention to interference and its impact on the system, as well as the mathematical equations derived for time split protocol while considering two main types of internet traffic services: VoIP and FTP.Second, the attention will be directed towards reasonable and logical analysis of LTE MIMO systems considering different internet traffic profiles.

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LTE UE Power Consumption Model: For System Level Energy and Performance Optimization

Cited by 109 publications

References 9 publications

Energy Efficiency in Latency-Constrained Application Offloading From Mobile Clients to Multiple Virtual Machines

Energy Efficiency in Latency-Constrained Application Offloading From Mobile Clients to Multiple Virtual Machines

LTE UE Energy Saving by Applying Carrier Aggregation in a HetNet Scenario

Performance analysis of wireless power harvesting over signal to interference plus noise channel for full‐duplex services in LTE MIMO system

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