Reducing Signaling Overhead for Femtocell/Macrocell Networks

Fu, Huai-Lei; Lin, Pengzhi; Lin, Yi‐Bing

doi:10.1109/tmc.2012.132

Cited by 41 publications

(30 citation statements)

References 16 publications

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“…Figure 5 shows that the energy consumed by the wireless access network represents 59% to 82% of the total end-to-end energy for various video applications. For these applications one of the most effective ways to reduce the energy consumption of the 4G access network is to offload the heavy data services from macro to small cells, in spite of the fact that femto-cell offloading might cause network signaling overhead due to heterogeneous user mobility [38,39].…”

Section: Offloading Heavy Mobile Services From Macro Cells To Smallermentioning

confidence: 99%

Modeling the Total Energy Consumption of Mobile Network Services and Applications

et al. 2019

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Reducing the energy consumption of Internet services requires knowledge about the specific traffic and energy consumption characteristics, as well as the associated end-to-end topology and the energy consumption of each network segment. Here, we propose a shift from segment-specific to service-specific end-to-end energy-efficiency modeling to align engineering with activity-based accounting principles. We use the model to assess a range of the most popular instant messaging and video play applications to emerging augmented reality and virtual reality applications. We demonstrate how measurements can be conducted and used in service-specific end-to-end energy consumption assessments. Since the energy consumption is dependent on user behavior, we then conduct a sensitivity analysis on different usage patterns and identify the root causes of service-specific energy consumption. Our main findings show that smartphones are the main energy consumers for web browsing and instant messaging applications, whereas the LTE wireless network is the main consumer for heavy data applications such as video play, video chat and virtual reality applications. By using small cell offloading and mobile edge caching, our results show that the energy consumption of popular and emerging applications could potentially be reduced by over 80%.Energies 2019, 12, 184 2 of 18 increase since 2014 [2]. Furthermore, the data centers of Akamai consumed 233,090 MWh electricity in 2016, which is an increase of around 50% since 2012 [3].The increasing number of smartphone users together with the emergence of data-heavy mobile applications such as high-definition video play, virtual reality (VR) and augmented reality (AR) are driving the growth in mobile data traffic. These applications typically require very high network bandwidth and smartphone computing resources [4][5][6]. Moreover, the instant messaging (IM) applications, such as WeChat, Twitter, etc., attract a huge number of users because they offer a variety of mobile services including text/picture/voice messages, audio/video chat, moments, etc., and also consume a lot of network resources. Existing research to reduce the overall energy consumption of the Internet, including communication networks, cloud data centers and mobile devices, has focused on segment-specific energy consumption modeling and assessment of the end-to-end delivery of mobile applications, such as power models for smartphones, BSs, and edge and core networks. For example, various power models have been developed for estimating the energy consumption of different smartphone components such as 3G/4G, WiFi, central processing unit (CPU), liquid crystal display (LCD) and global positioning system (GPS). The researches in [7][8][9][10] show that the energy consumption of smartphones is influenced by different traffic characteristics and signaling patterns of mobile applications. Various power models of a long-term evolution (LTE) BS proposed in [11][12][13][14][15] try to assess energy consumption of mobile applications by sep...

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Section: Offloading Heavy Mobile Services From Macro Cells To Smallermentioning

confidence: 99%

Modeling the Total Energy Consumption of Mobile Network Services and Applications

et al. 2019

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“…We assume that the timer for the deadline of the task of IoT device i follows an exponential distribution with mean 1/κ i [33,34]. Then, when the task is not completed (i.e., T M i = 0 or T O i = 0), the probability that the timer expires during a decision epoch is κ i τ.…”

Section: Transition Probabilitymentioning

confidence: 99%

Energy Efficient Cooperative Computation Algorithm in Energy Harvesting Internet of Things

Lee

Jang

et al. 2019

Energies

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The limited battery capacity of Internet of Things (IoT) devices is a major deployment barrier for IoT-based computing systems. In this paper, we propose an energy efficient cooperative computation algorithm (EE-CCA). In an EE-CCA, a pair of IoT devices decide whether to offload some parts of the task to the opponent by considering their energy levels and the task deadline. To minimize the energy outage probability while completing most of tasks before their deadlines, we formulate a constraint Markov decision process (CMDP) problem and the optimal offloading strategy is obtained by linear programming (LP). Meanwhile, an optimization problem of finding pairs of IoT devices (i.e., IoT device pairing problem) is formulated under the optimal offloading strategy. Evaluation results demonstrate that the EE-CCA can reduce the energy outage probability up to 78% compared with the random offloading scheme while completing tasks before their deadlines with high probability.

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“…In [7], the authors investigated the network operator's profit gain from offering dual services through both February 27, 2018 DRAFT macrocells and femtocells. The authors of [8] considered the tradeoff between reducing the paging cost in mobility management and registration signaling overhead, and proposed a delay registration algorithm that postpones the registration and reduces signaling overhead while sustaining the traffic offloading capability of the femtocell. In [9], optimal sleep/wake up schemes were studied for the base stations of network-operated femtocells to offload part of its traffic to minimize the energy consumption of the overall heterogeneous network while preserving quality of service (QoS).…”

Section: Related Workmentioning

confidence: 99%

Pricing Mobile Data Offloading: A Distributed Market Framework

Wang

Lau

Chen

et al. 2016

IEEE Trans. Wireless Commun.

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Mobile data offloading is an emerging technology to avoid congestion in cellular networks and improve the level of user satisfaction. In this paper, we develop a distributed market framework to price the offloading service, and conduct a detailed analysis of the incentives for offloading service providers and conflicts arising from the interactions of different participators. Specifically, we formulate a multi-leader multi-follower Stackelberg game (MLMF-SG) to model the interactions between the offloading service providers and the offloading service consumers in the considered market framework, and investigate the cases where the offloading capacity of APs is unlimited and limited, respectively. For the case without capacity limit, we decompose the followers' game of the MLMF-SG (FG-MLMF-SG) into a number of simple follower games (FGs), and prove the existence and uniqueness of the equilibrium of the FGs from which the existence and uniqueness of the FG-MLMF-SG also follows. For the leaders' game of the MLMF-SG, we also prove the existence and uniqueness of the equilibrium. For the case with capacity limit, by considering a symmetric strategy profile, we establish the existence and uniqueness of the equilibrium of the corresponding MLMF-SG, and present a distributed algorithm that allows the leaders to achieve the equilibrium. Finally, extensive numerical experiments demonstrate that the Stackelberg equilibrium is very close to the corresponding social optimum for both considered cases.

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Reducing Signaling Overhead for Femtocell/Macrocell Networks

Cited by 41 publications

References 16 publications

Modeling the Total Energy Consumption of Mobile Network Services and Applications

Modeling the Total Energy Consumption of Mobile Network Services and Applications

Energy Efficient Cooperative Computation Algorithm in Energy Harvesting Internet of Things

Pricing Mobile Data Offloading: A Distributed Market Framework

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