Stochastic Geometric Models for Green Networking

Altman, Eitan; Hasan, Cengis; Hanawal, Manjesh K.; Shamai, Shlomo; Gorce, Jean-Marie; El-Azouzi, Rachid; Roullet, Laurent

doi:10.1109/access.2015.2503322

Cited by 14 publications

(8 citation statements)

References 18 publications

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“…Extensive research studies have been devoted to the EE of the system. EE maximization in the D2D communication underlaying cellular network in the cloud radio access network is studied by Zhou et al A closed‐form expression for EE is derived in the work of Altman et al by utilizing stochastic geometry tool. In this study, EE is maximized by optimizing the transmit power and density of the BS in cellular networks.…”

Section: Introductionmentioning

confidence: 99%

Energy‐efficient power allocation for device‐to‐device communications underlaid cellular networks using stochastic geometry

Zabetian

Mohammadi

Masoudi

2019

Trans Emerging Tel Tech

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In this paper, we study an energy efficiency maximization problem in uplink for device-to-device (D2D) communications underlaid with cellular networks on multiple bands. Utilizing stochastic geometry, we derive closed-form expressions for the average sum rate, successful transmission probability, and energy efficiency of cellular and D2D users. Then, we formulate an optimization problem to jointly maximize the energy efficiency of D2D and cellular users and obtain optimum transmission power of both D2D and cellular users. In the optimization problem, we guarantee the quality-of-service of users by taking into account the success transmission probability on each link. To solve the problem, first we convert the problem into canonical convex form. Afterwards, we solve the problem in two phases: energy efficiency maximization of devices and energy efficiency maximization of cellular users. In the first phase, we maximize the energy efficiency of D2D users and feed the solution to the second phase where we maximize the energy efficiency of cellular users. Simulation results reveal that significant energy efficiency can be attained, eg, 10% energy efficiency improvement compared to fix transmission power in a high-density scenario.Trans Emerging Tel Tech. 2019;30:e3768.wileyonlinelibrary.com/journal/ett of D2D communications while diminishing the performance degradation of the cellular system due to new sources of interference. In particular, power control is one of the promising approaches that mitigates interference. 5 In the work of Elsawy et al, 6 a power control scheme is proposed to manage the cross-tier interference between cellular and D2D users. Zhang et al 7 proposed an interference-aware algorithm for power control in D2D communications underlaying cellular networks. In the work of Memmi et al, 8 a single-cell D2D underlay cellular network is considered. Centralized and distributed algorithms are proposed to find the users' optimal power transmission. Dynamic power control for a D2D communication underlaying uplink multicell network is investigated in the work of Jiang et al, 9 considering interference mitigation. However, power control is a key technique to manage the interference; both the EE and quality-of-service (QoS) are still influenced by the interference in the network.EE is a metric quantifying the efficiency of resource utilization. In fact, EE not only brings considerable economic benefits into the network but also can be interpreted as concerning about the environment. 10 Extensive research studies have been devoted to the EE of the system. EE maximization in the D2D communication underlaying cellular network in the cloud radio access network is studied by Zhou et al. 1 A closed-form expression for EE is derived in the work of Altman et al 11 by utilizing stochastic geometry tool. In this study, EE is maximized by optimizing the transmit power and density of the BS in cellular networks. An energy-efficient power control algorithm is proposed in the work of Wu et al 12 to share resources among th...

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Section: Introductionmentioning

confidence: 99%

Energy‐efficient power allocation for device‐to‐device communications underlaid cellular networks using stochastic geometry

Zabetian

Mohammadi

Masoudi

2019

Trans Emerging Tel Tech

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“…As the easiest strategy for sleeping BSs, the RSS switches off each BS independently at a certain probability. 26 Liu et al designed an RSS for SBSs based on maximizing EE with the constraints for coverage probability. 27 Further, Zhang et al derived the maximum sleeping capacity when the outage probability and users' rate are guaranteed.…”

Section: Related Workmentioning

confidence: 99%

“…As the easiest strategy for sleeping BSs, the RSS switches off each BS independently at a certain probability . Liu et al designed an RSS for SBSs based on maximizing EE with the constraints for coverage probability .…”

Section: Related Workmentioning

confidence: 99%

A distance‐sensitive distributed repulsive sleeping approach for dependable coverage in heterogeneous cellular networks

Tang

Zhou

et al. 2019

Trans Emerging Tel Tech

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Base station (BS) sleeping technology has become one of the significant technologies in fifth‐generation green communications. However, enormous communication overhead and coverage holes produced by existing sleeping strategies will decline the robustness of the network. To tackle this problem, this paper proposes a distance‐sensitive distributed repulsive sleeping strategy (DSDRSS), based on hard‐core point process (HCPP). First, through information exchanges, presleeping BSs in the same region form a sleeping cluster (SC) whose size is limited by sleeping distance. Second, BSs in the SC perform BS sleeping with a mark method where BSs will be randomly assigned a mark, and BSs with the lowest mark will remain on to ensure the coverage. Third, to characterize the performance of DSDRSS, the analytical expressions of sleeping probability, coverage probability, and average achievable rate for user equipment (UE) under DSDRSS are derived. Finally, the coverage characteristics of UE under DSDRSS are analyzed and compared with those under different sleeping operations. DSDRSS realizes sleeping operations through the cooperation between BSs in an SC, not relying on the feedback links between a small BS and the control center. As a result, DSDRSS can not only enable flexible perception of traffic changes in sleeping area but also complete sleeping with less overhead. The simulation results show that DSDRSS supports more dependable coverage compared with random sleeping strategy and general repulsive sleeping strategy.

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“…There are also incentives to reduce energy consumption in wireless networks. EE is a performance metric in green communication [11]. Extensive researches have been done in EE maximization in D2D communication [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Energy efficiency optimization for device‐to‐device communication underlaying cellular networks in millimeter‐wave

Zabetian

Mohammadi

Kazemi

2019

Int J Communication

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Summary This paper studies energy efficiency maximization in device‐to‐device (D2D) communications underlaying cellular networks in millimeter‐wave (mm‐wave) band. A stochastic geometry framework has been used to extract the results. First, cellular and D2D users are modeled by independent homogeneous Poisson point process; then, exact expressions for successful transmission probability of D2D and cellular users have been derived. Furthermore, the average sum rate and energy efficiency for a typical D2D scenario have been presented. An optimization problem subject to transmission power and quality of service constraints for both cellular and D2D users has been defined, and energy efficiency of D2D communication is maximized. Simulation results reveal that by working in millimeter‐wave, significant energy efficiency improvement can be attained, e.g., 20% energy efficiency improvement compared with Rayleigh distribution in the practical scenarios by considering circuit power. Finally, to verify our analytical expressions, the simulation studies are carried out, and the excellent agreements have been achieved.

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Stochastic Geometric Models for Green Networking

Cited by 14 publications

References 18 publications

Energy‐efficient power allocation for device‐to‐device communications underlaid cellular networks using stochastic geometry

Energy‐efficient power allocation for device‐to‐device communications underlaid cellular networks using stochastic geometry

A distance‐sensitive distributed repulsive sleeping approach for dependable coverage in heterogeneous cellular networks

Energy efficiency optimization for device‐to‐device communication underlaying cellular networks in millimeter‐wave

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