A Flexible and Future-Proof Power Model for Cellular Base Stations

Debaillie, Björn; Desset, Claude; Louagie, Filip

doi:10.1109/vtcspring.2015.7145603

Cited by 159 publications

(157 citation statements)

References 14 publications

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“…Table II (in Appendix A) lists, in the first column, all the necessary digital subcomponents, which contribute to the overall delay [14]. In the second column, the associated GOPS per each communication sub-component function is listed.…”

Section: Delay Model Based On Function Splitmentioning

confidence: 99%

Delay-aware green hybrid CRAN

Alabbasi

Çavdar

2017

2017 15th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

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Abstract-As a potential candidate architecture for 5G systems, cloud radio access network (CRAN) enhances the system's capacity by centralizing the processing and coordination at the central cloud. However, this centralization imposes stringent bandwidth and delay requirements on the fronthaul segment of the network that connects the centralized baseband processing units (BBUs) to the radio units (RUs). Hence, hybrid CRAN is proposed to alleviate the fronthaul bandwidth requirement. The concept of hybrid CRAN supports the proposal of splitting/virtualizing the BBU functions processing between the central cloud (central office that has large processing capacity and efficiency) and the edge cloud (an aggregation node which is closer to the user, but usually has less efficiency in processing). In our previous work, we have studied the impact of different split points on the system's energy and fronthaul bandwidth consumption. In this study, we analyze the delay performance of the end user's request. We propose an end-to-end (from the central cloud to the end user) delay model (per user's request) for different function split points. In this model, different delay requirements enforce different function splits, hence affect the system's energy consumption. Therefore, we propose several research directions to incorporate the proposed delay model in the problem of minimizing energy and bandwidth consumption in the network. We found that the required function split decision, to achieve minimum delay, is significantly affected by the processing power efficiency ratio between processing units of edge cloud and central cloud. High processing efficiency ratio (≈ 1) leads to significant delay improvement when processing more base band functions at the edge cloud.Index Terms-5G, network architecture, cloud RAN, end-toend delay, network function split, virtualized cloud RAN.

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Section: Delay Model Based On Function Splitmentioning

confidence: 99%

Delay-aware green hybrid CRAN

Alabbasi

Çavdar

2017

2017 15th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

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“…Power consumption models such as the EARTH and GreenTouch models [6], [9] have shown that significant amount of power is consumed when the BS is not loaded with data, i.e., idle. In the idle mode, power consumption reduction is minimal since the BS hardware component remains completely active.…”

Section: Base Station Sleep Modesmentioning

confidence: 99%

“…The latter work shows that the relationship between the transmit power and the BS power consumption is nearly linear. A more advanced flexible power consumption model, which is based on a combination of the BS's components and subcomponents as well as power scaling rules as functions of system parameters such as bandwidth, spectral efficiency, number of antennas, system load, number of spatial streams and quantization, has been recently proposed in [7]- [9]. The relaxed constraints for BS on/off operation in the control and data separation architecture [10], which is a candidate architecture for 5G networks, mean that accurate modeling of the sleep mode power consumption is also very essential for evaluating the energy saving gain of this architecture.…”

Section: Introductionmentioning

confidence: 99%

“…Traffic can be offloaded to neighboring BS(s) in the case of homogeneous deployment and overlapping cells in heterogeneous deployment. In [9], the authors identified four different sleep depth with different transition times and power savings based on the hardware capability and network flexibility. By using queuing theory, the authors of [12], [13] presented the average power consumption of a BS in sleeping mode.…”

Section: Introductionmentioning

confidence: 99%

“…The latter is made up of the BS's subcomponent/component deactivation and activation latencies, and the actual sleep period. This proposed model allows for flexibility in the sleep level as against the four discrete sleep levels in [9], [14]. Using the proposed analytical model, we derive the average power consumption and analyze the optimal sleep depth of the BS, taking into consideration the activation and deactivation cost.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analytical approach to base station sleep mode power consumption and sleep depth

Onireti

Mohamed

Pervaiz

et al. 2017

2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

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Abstract-In this paper, we present an analytical framework to model the sleep mode power consumption of a base station (BS) as a function of its sleep depth. The sleep depth is made up of the BS deactivation latency, actual sleep period and activation latency. Numerical results demonstrate a close match between our proposed approach and the actual sleep mode power consumption for selected BS types. As an application of our proposed approach, we analyze the optimal sleep depth of a BS, taking into consideration the increased power consumption during BS activation, which exceeds its no-load power consumption. We also consider the power consumed during BS deactivation, which also exceeds the power consumed when the actual sleep level is attained. From the results, we can observe that the average total power consumption of a BS monotonically decreases with the sleep depth as long as the ratio between the actual sleep period and the transition latency (deactivation plus reactivation latency) exceeds a certain threshold.

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