Motion Sensor-Based Small Cell Sleep Scheduling for 5G Networks

Dense deployment of small cells is seen as one of the major approaches for addressing the traffic demands in next-generation wireless networks. However, dense deployment of large number of small cells necessitates effective techniques for placing under-loaded small cells into sleep mode, so as to save energy. Such techniques should be low complexity and should not also compromise quality of service of users such as short access delay, while they can also result in significant energy savings for delay-tolerant network traffic. In this study, we introduce energy efficient, low-complexity techniques for load-based sleep mode optimization in densely deployed 5G small cell networks. We define a new analytic model to characterize the distribution of the traffic load of a small cell using a Gamma distribution, find its distribution parameters, and verify the validity of the model using computer simulations. We also compare the throughput of various sleep mode techniques as a function of different delay tolerance levels, where our simulation results show that the proposed technique achieves the highest throughput.

The proliferation of network devices and novel bandwidth hungry applications over the existing network imposes novel challenges in terms of fulfilling the users’ requirements. Dense deployment of small cells is thought to be a promising solution to fulfill these requirements. However, the user association in such dense networks becomes challenging and can greatly affect the network performance, as a user in such dense deployments can be connected to any of the available base stations. Traditionally, the user association has been performed based on the signal strength, however, such an approach does not apply when taking into account novel bandwidth hungry applications. Moreover, in recent years, a successful paradigm has been proposed to handle such bandwidth hungry applications, i.e., caching at small cell base stations. In this paper, we aim to solve this joint problem of user association and content caching in a dense small cell setting. To solve this problem, we present a novel iterative scheme that uses matching theory and a learning approach to find a suboptimal solution of the joint NP hard problem. Note that the user association and cache placement are strongly coupled, i.e., the association of users at a base station will determine the cache placement at base stations and the availability of cache at base stations will force the users to change their associations. Simulation results show that the proposed scheme (i.e., cache aware user association) significantly outperforms the cache unaware scheme and achieves a performance gain of up to 31% in terms of normalized utility and saves up to twice the backhaul bandwidth. Moreover, the proposed scheme also achieves up to 82% of the utility obtained by the optimal solution.

Traffic offloading through small cells has been considered as a promising approach to accommodate tremendous traffic growth in future heterogeneous cellular networks (HCNs). The dense deployment of small cells, however, has led to a growing concern about the excessive carbon- based on-grid energy consumption in HCNs. In this article, we first overview the green-oriented traffic offloading in future HCNs that exploits the recent advanced energy technologies including energy harvesting (EH), local energy sharing (ES) enabled by smart grid, and wireless power transfer (WPT). We then discuss the challenges in resource management when exploiting EH, ES, and WPT to support traffic offloading, and provide possible solutions, especially by using the emerging dual connectivity (DC) in recent 3GPP specifications. Furthermore, we present a case study on the optimal DC-enabled traffic offloading through small cells that are powered by EH, with the objective of minimizing the total on-grid power consumption of all small cells and macrocells. The case study validates the benefit of exploiting the DC feature for traffic scheduling and the harvested energy to reduce the total on-grid power consumption. We finally share our view of some research directions in the green-oriented traffic offloading in HCNs.

Heterogeneous cellular architectures are a promising technology direction for upcoming generations of wireless communication networks. Increasing performance requirements are fulfilled by utilizing a dense deployment of low-power small cells in addition to existing macro cells. In such dense cellular networks it is critical to prevent performance losses from increasing interferences and uneconomic operating costs caused by high power consumptions. These changes in the network architecture create the need for effective control mechanisms specifically designed for heterogeneous networks. Range expansion for small cells has been proposed and extensively researched to achieve load balancing between macro cells and small cells. In this work, we propose a decentralized approach for cell range expansion in small cell networks that in operation only requires very limited local interaction between neighboring cells. We use multiclass support vector machines as a classifier to select suitable parameters for each small cell. Experimental results show that the proposed decentralized approach achieves close to optimal load balancing performance.

For the upcoming fifth generation of wireless mobile communication networks, heterogeneous cellular architectures with a dense deployment of low-power small cells have been proposed to satisfy increasing data demands. Energy harvesting small cells, which operate with renewable energy sources such as solar energy and the potential for power storage, can be utilized to decrease the power consumption of the network if their activities are carefully scheduled based on the forecasted demand. In this work, we propose a novel approach for small cell energy management and activity scheduling, where not only the small cell activity but also the duration of time-slots in which the schedule is applied, are optimized. Cell load balancing is performed by solving separate mixed integer optimization problems, one each for activity optimization and for timescale optimization. Simulation results show that the proposed scheme is successful in finding an optimized small cell activity schedule for given energy levels and demand forecasts, and that there is a significant benefit achievable through the proposed timescale optimization approach.

The effect of LH on the intracellular free Ca2+ concentration ([Ca2+]i) was investigated in highly purified small and large bovine luteal cell populations. Luteal cells were obtained from midcycle corpora lutea dispersed with collagenase and separated by flow cytometry into large and small cells. Resting levels of Ca2+ were higher (P less than 0.05) in the large than small cells [314 +/- 25 nM (n = 5) vs. 186 +/- 13 nM (mean +/- SE; n = 13) for large and small cells, respectively]. LH rapidly increased [Ca2+]i in both small and large cells loaded with the fluorescent Ca2+ probe fura-2. In the small cells, [Ca2+]i was immediately increased 2- to 6-fold (from 176 +/- 8 to 468 +/- 8 nM; n = 5) after adding LH. The LH induced [Ca2+]i rise occurred in two phases: an initial peak due to intracellular Ca2+ mobilization and a secondary rise due to Ca2+ influx from extracellular sources. Preincubation of the small cells with EGTA reduced the initial phase and abolished the secondary rise in [Ca2+]. Both forskolin and 8-bromo-cAMP increased [Ca2+]i in the small cells. In contrast, only a single phase of [Ca2+]i rise was observed in LH-treated large cells, and the response was 1.5- to 2-fold greater than the resting Ca2+ levels [314 +/- 25 vs. 435 +/- 60 nM (n = 4), for resting vs. LH-treated values, respectively]. The addition of both LH and prostaglandin F2 alpha (PGF2 alpha) to the large cells resulted in increases in [Ca2+]i that were greater than those induced by each hormone separately (2.0-fold for LH and 2.7-fold for PGF2 alpha vs. 7- to 9-fold in the presence of both hormones). These findings demonstrate that LH induces rapid increases in intracellular [Ca2+]i that differ in magnitude and profile between the small and large bovine luteal cells. Furthermore, LH and PGF2 alpha interacted to promote increases in [Ca2+]i in the large cells, that were higher than the sum of [Ca2+]i induced by each hormone separately.

As one of the main technologies in 5G networks, Ultra-dense networks (UDNs) can be used to improve the network coverage. The dense deployment of small cells in UDN hotspots generates an uneven traffic distribution. In this paper, we propose a novel mechanism in order to transfer the extra users from the small cells to the macrocells based on several load balancing approaches implemented within the small cells, which are formed based on the Radio-over-Fiber (RoF) system. To select the best overlapping zone and then the best candidate user to be handed-over between the access points of the small cells, a common zone approach, a worst zone approach and a mixed approach are proposed. With the objective of transferring the extra users to the macrocells, we suggest a transfer after approach, a transfer before approach and an active approach. The simulation results indicate that the proposed approaches succeed to balance the load among the access points and to migrate the required load from the overloaded small cells to the macrocells in selective way. In some cases, the balance improvement ratio can reach 97.94%. Moreover, the overall balance efficiency is increased by 51.32% compared to the case without transferring users to the macrocells.

Big things come in small packages; a particularly apt description of small cell deployment in cellular networks. Small cells have a big role to play in orchestrating a cellular network that can overcome the explosive mobile traffic upsurge at little cost to the network operator. However, if left unchecked, a large-scale small cell deployment can substantially increase the network energy consumption with strong ecological and economic implications. In this article, we introduce energy-efficient SLEEP mode algorithms for small cell base stations in a bid to reduce cellular networks' power consumption. The designed algorithms allow the hardware components in the BS to be astutely switched off in idle conditions, such that the energy consumption is modulated over the variations in traffic load. Three different strategies for algorithm control are discussed, relying on small cell driven, core network driven, and user equipment driven approaches. Based on a mixed voice and data traffic model, the algorithms present energy saving opportunities of approximately 10-60 percent in the network with respect to no SLEEP mode activation in small cells, coupled with additional capacity incentives.

Stochastic geometry analysis of coverage probability in energy efficient dense heterogeneous network with sleep control mechanism

Over the past decade, the telecommunication industry has witnessed excessive growth in the number of mobile users. Market forecasts envision that there will be nearly 8.6 billion mobile devices worldwide by 2017. This tremendous increase in the number of cellular users demands an expansion in the wireless Base Stations (BSs) for improved coverage and capacity. However, this hike in the deployment of base stations will lead to immense energy consumption, because in mobile networks 70–80 % of the power is consumed by BSs. This upsurge in the energy consumption of telecommunication networks implies an increase in CO\(_{2}\) emissions in the environment. In addition, energy bills also represent a major chunk of wireless network operators’ expenditures. These ecological and economical challenges have provoked the curiosity of telecommunication standardization bodies and researchers in an emerging research area termed ‘energy-aware Heterogeneous Networks (HetNets)’. HetNets are a mix of various cell shapes and sizes, including high power macro cells and low power nodes such as micro cells, pico cells and relays. The large macro cells are responsible for the basic coverage of the cell users, and the small cells are effective in providing higher data rates to their nearby users in dense areas with reduced power consumption. The combination of various BSs with different cell sizes and a wide range of power levels can lead to substantial gains in network energy consumption by creating hotspots and enabling dense spatial reuse. It is envisioned that a dense deployment of low power BSs will take place in the near future. HetNets in particular are considered as a promising solution for Fifth Generation (5G) in order to meet the exponentially growing demand for multimedia traffic. The main focus of this chapter is to investigate optimal energy efficient deployment strategies for low power nodes such as relays and small cells in 5G HetNets. In this chapter, a comprehensive overview of remarkable small cell deployment schemes is presented in order to facilitate the debate on technical challenges in deploying HetNets. It goes on to discuss some useful techniques to mitigate the severe interference in 5G dense HetNets. Finally, a novel Long Term Evolution (LTE)-Advanced relay deployment scheme is introduced using graph theory, not only to address some of the identified deficiencies of existing solutions, but also to optimize the energy efficiency of 5G cellular networks.

Small round cell tumors (SRCTs) are a heterogeneous group of neoplasms composed of small, primitive, and undifferentiated cells sharing similar histology under light microscopy. SRCTs include Ewing sarcoma/peripheral neuroectodermal tumor family tumors, neuroblastoma, desmoplastic SRCT, rhabdomyosarcoma, poorly differentiated round cell synovial sarcoma, mesenchymal chondrosarcoma, small cell osteosarcoma, small cell malignant peripheral nerve sheath tumor, and small cell schwannoma. Non-Hodgkin’s malignant lymphoma, myeloid sarcoma, malignant melanoma, and gastrointestinal stromal tumor may also present as SRCT. The current shift towards immunohistochemistry and cytogenetic molecular techniques for SRCT may be inappropriate because of antigenic overlapping or inconclusive molecular results due to the lack of differentiation of primitive cells and unavailable genetic service or limited moleculocytogenetic experience. Although usage has declined, electron microscopy (EM) remains very useful and shows salient features for the diagnosis of SRCTs. Although EM is not always required, it provides reliability and validity in the diagnosis of SRCT. Here, the ultrastructural characteristics of SRCTs are reviewed and we suggest that EM would be utilized as one of the reliable modalities for the diagnosis of undifferentiated and poorly differentiated SRCTs.

In dense and heterogeneous networks, the decoupled uplink#x002F;downlink (UL#x002F;DL) access (DUDA) design has drawn great attentions for improving system performance. Energy efficiency (EE) becomes a major concern for densely deployed heterogeneous cellular networks (HetNets). In this paper, we theoretically analyze the energy efficient sleep strategy for DUDA HetNets. Through using stochastic geometry theory, we first examine the applicability of conventional sleep strategy to DUDA networks and design a new DUDA sleep strategy. We then formulate the energy consumption minimization problem and EE optimization problem, and derive the optimal BS sleep probability. Numerical results reveal that conventional sleep strategy may provide inaccurate guidance for sleep design in DUDA networks, which may lead to excessive sleeps and decrease system EE. Meanwhile our DUDA sleep strategy can effectively reduce network energy consumption. We also find that the dense deployment of small cells may generally increase network EE, but this improvement saturates as the BS density further increases.

Heterogeneous small cell networks (HSCN), as a promising paradigm to increase end-user data rates and improve the overall capacity, is expected to be a key cellular architecture in 5G wireless networks. However, energy consumed in HSCN is considerable due to the massive use of small cells. In this paper, we investigate the energy consumption issue which stems from the enormous number of running small cell base stations (SBSs) deploying in the HSCN. We first propose power consumption models so as to characterize the active state and the idle state of SBSs, respectively. Then two sleep modes for SBSs tier, i.e. random sleep mode and load-awareness dynamic sleep mode, are proposed. The random sleep is designed based on a binomial distribution of the SBS operation probability. Through the analysis on activeness of SBSs, we define the operation probability for the SBS applying the proposed dynamic sleep mode is associated to its traffic load level. The closed-form expressions of success probability for coverage, which is used to decide whether an active user can connect to a SBS successfully, are derived for the proposed sleep modes. Energy consumption minimizations are presented for the two proposed sleep modes under the success probability constraint. Simulation results prove the effectiveness of the proposed two sleep modes. Different energy saving gains can be achieved via using of the energy saving strategy. The superior of the dynamic sleep mode by comparing the random sleep is also verified in terms of energy consumption, success probability and power efficiency.

1. Semi-isolated preparations of the nine-celled cardiac ganglion of the crab, Portunus sanguinolentus, were studied electrophysiologically, using simultaneous recording from extracellular and two or three intracellular electrodes. Nine penetrations of small cells were achieved. 2. Three large (80 x 120 micron) cells lie near the anterior end of the 5-mm main trunk; two large and four small (less than 50 micron) cells at the posterior end. Large-cell axons pass along the main trunk and then exit to innervate cardiac muscle; small-cell axons do not leave the ganglion. 3. The semi-isolated ganglion produces spontaneous electrical activity organized into regularly patterned, rhythmic bursts of large- and small-cell impulses recurring at rates of 0.3-0.6/s and lasting 500-800 ms. Small impulse activity commences and ends each burst. Small cells fire trains during the burst, but impulses are not synchronized among them. Large-cell trains are synchronous, are at about one-half the frequency, and have fewer impulses than small-cell trains. 4. Intracellular recordings from small cells show a slow, pacemaker depolarization from a maximum membrane potential of -54 mV leading with only a slight inflection at ca. -50 mV to a depolarized plateau at ca. -40 mV; nonovershooting impulses are superimposed on this but cease before it repolarizes. Impulses, therefore, arise at a site distant from the soma and do not invade it. Deflections suggesting synaptic potentials are not seen. 5. Intracellular recordings from large cells show complex depolarizations corresponding to extracellularly recorded bursts. These represent excitatory postsynaptic potentials (EPSPs) corresponding with individual small-cell impulses, attenuated, non-overshooting spikes, and an underlying slow depolarization; usually no pacemaker depolarization is apparent between bursts. Chemically mediated transmission is probable for the EPSPs because they show delay, increase in amplitude with hyperpolarization, sometimes show facilitation, and are reduced in saline having one-third Ca, 3 x Mg. 6. EPSPs, impulses, and the slow depolarization occur synchronously among the large cells. Potentials recorded from posterior cells are attenuated and slower than those of the anterior cells. This is interpreted to reflect sites of occurrence more distant from the soma in the posterior than in the anterior cells. Impulses do not invade the somata. 7. Intracellular recordings from large-cell axons 4 mm from the soma show overshooting action potentials arising sharply from a base line. EPSPs are absent or highly attenuated and there is little underlying depolarization (less than 2 mV). 8. Current passing with electrodes intracellular to two cells has established directly that all large cells are electrotonically coupled and that an anterior cell and a small cell are coupled. Changes of burst rate during current passing into any large cell indicate that all large cells and small cells are electrotonically coupled. 9...

Storaci-Mobile phone communication has become heart of the Global economic development over past decade. As the number of users (human as well as devices) grow exponentially with each year, there is an immense need to increase capacity and throughput with minimum possible upgradation in vast existing network. The 4G-LTE cellular network capacity can be increased by massive deployment of small cells and leveraging very high frequency bands in conjunction with a Macro cell. The presence and usage of small (Phantom) cells are dynamically controlled by a Macro cell. The connection between user equipment (UE) and small cell nodes is achieved by using control and user (C/U) planes splitted at radio link. It can achieve high capacity enhancement by massive deployment of small cells. NS-3 simulations were carried out and results shows capacity enhancement and throughput.