Green-Oriented Traffic Offloading through Dual Connectivity in Future Heterogeneous Small Cell Networks

In future heterogeneous cellular networks (HCNs), the device-to-device (D2D) communication is considered as an efficient technique emerging mobile social network (MSN) services. Due to spectrum reuse among devices, the D2D however also brings in more interference, which will cause degradation in spectral efficiency (SE) when the density or centralization of devices is relatively high. With the help of control and data planes separation architecture (SARC) in the HCN, a smaller granularity of cooperation at device level is expected to be available for interference mitigation. Therefore, this letter introduces the coordinated multipoint transmission (CoMP) into D2D- enabled MSN to enhance SE performance. A CoMP scheme based on the average received signal power is designed for the D2D-enabled MSN (namely MSN-D2D- CoMP), on which basis the SE performance is analyzed using stochastic geometry for a hierarchical HCN with underlaying D2D devices. It is shown that MSN- D2D-CoMP effectively helps mitigating interference and outperforms the conventional paired D2D in SE (38.4% on average according to simulation results). And the gain of MSN-D2D-CoMP in SE increases with closer social relations for devices.

As cells are getting smaller, more random and chaotic in future heterogeneous cellular networks (HCNs), mitigating interference to enhance coverage performance has become one of the key challenges. To elucidate the coverage and throughput performance of downlink HCNs, a tractable framework with frequency division (FD) scenario is provided. Coverage probability and average cell throughput expressions are carried out to further understand how FD effects system performance with the stochastic geometry treatment. Furthermore, with some plausible assumptions, we derive the specific closed-form expressions in some special cases. Simulation results show that the coverage performance is effectively improved by using FD comparing with conventional frequency sharing (FS) scenario, which are firmly consistent with our theoretical derivation. The analytical results of the present work also demonstrate that although some throughput will be lost compared to FS, it is still acceptable since UEs could choose any tier which offers greatest performance under FD scenario, hence this work further provides mobile operators a guideline for resource allocation schemes in future chaotic multi-tier networks.

This paper focuses on the multi‐pair amplify‐and‐forward massive multiple‐input multiple‐output (MIMO) relay enabled two‐tier heterogeneous cellular networks in terms of aggregate spectral efficiency (SE), where the macro cells are overlaid with dense small cells. Specially, the maximum ratio combining/maximal ratio transmission and zero‐forcing processing schemes are employed at massive MIMO relay, respectively. The macro base stations located in the center of the cells are equipped with massive MIMO antenna array, while the randomly distributed macro users (MUs), small cell base stations, and small cell users are equipped with single antenna. The positions of MUs, small cell users and small cell base stations are modelled as independent Poisson point processes. For such two‐tier heterogeneous cellular networks, to obtain the aggregate SE in target cell, we first formulate the end‐to‐end signal‐to‐interference noise ratios for a communication pair as well as the expression of power amplification factor at target macro base stations so that the total spectrum efficiency per cell is formulated. Then, by employing stochastic geometry, the closed‐form expressions of the power amplification factor and the expectation of the reciprocal of the equivalent end‐to‐end signal‐to‐interference noise ratios are achieved. With these results, we derive the lower bounds of SEs for maximum ratio combining/maximal ratio transmission and zero‐forcing schemes. The derivations display a clear perspective about the impact of network parameters on the system‐level performance, which includes macro cell radius, small cell radius, macro cell guard radius, the mean number of MUs per cell and so on. To achieve optimal (or suboptimal) system performance, we find that the radius of small cell should be less than the guard radius. For the guard radius, there exists an upper bound, only under which the increase of the guard radius would contribute to the improvement of SE. At the same time, our results show that with given network parameters, especially the guard radius and the small cell radius, there exists a unique greatest lower bound in terms of macro cell radius. When the macro cell radius is less than the greatest lower bound, the aggregate SE is increasing with the macro cell radius. On the contrary, the achievable aggregate SE saturates to the corresponding constant, approximately. In addition, the greatest lower bound of the macro cell radius is the function of the guard radius and the small cell radius. Copyright © 2016 John Wiley & Sons, Ltd.

Massive multiple input multiple output (MIMO) and full duplex (FD) communication are being considered as potential candidates for the spectrum efficient 5G wireless networks. In this paper, we develop a tractable model for downlink (DL) and uplink (UL) transmission in K-tier heterogeneous cellular networks (HCNs) with massive MIMO macrocells and full duplex (FD) small cells for spectrum efficiency. In the considered HCNs, the performance of the mobile user (MU) is limited by several sources of interference, specifically due to FD nature of small cell base stations (SBSs). A stochastic geometry based model of the proposed HCNs is provided which allows to derive the DL and UL rate coverage probabilities of such a system. Monte Carlo simulations confirm the accuracy of the analytical results, while numerical results reveal that equipping large number of MIMO antennas at macro base stations (MBSs) enhances the DL rate coverage probability of a random MU in HCNs. The results show that to achieve the maximum joint DL and UL performance gain in HCNs with FD small cells, both SBSs' density and SBSs' transmit power should be optimized. Moreover, the UL performance can be improved by decreasing the SBSs receivers sensitivity and increasing the UL power control factor.

How many small cell (SC) access points (APs) are required to guarantee a chosen quality of service in a heterogeneous network? In this chapter, we answer this question considering two different network models. The first is the downlink of a finite-area SC network where the locations of APs within the chosen area are uniformly distributed. A key step in obtaining the closed-form expressions is to generalize the well-accepted moment matching approximation for the linear combination of lognormal random variables. For the second model, we focus on a two-layer downlink heterogeneous network with frequency reuse-1 hexagonal macro cells (MCs), and SC APs that are placed at locations that do not meet a chosen quality of service from macro base stations (BSs). An important property of this model is that the SC AP locations are coupled with the MC coverage. Here, simple bounds for the average total interference within an MC makes the formulation possible for the percentage of MC area in outage, as well as the required average number of SCs (per MC) to overcome outage, assuming isolated SCs. Introduction Heterogeneous cellular networks (HCNs) are being considered as an efficient way to improve system capacity as well as effectively enhance network coverage [1, 2]. Comprising multiple layers of access points (APs), HCNs encompass a conventional macro cellular network (first layer) overlaid with a diverse set of small cells (SCs) (higher layers). Cell deployment is an important problem in heterogeneous networks, both in terms of the number and positioning of the SCs. Traditional network models are either impractically simple (such as the Wyner model [3]) or excessively complex (e.g., the general case of random user locations in a hexagonal lattice network [4]) to accurately model SC networks. A useful mathematical model that accounts for the randomness in SC locations and irregularity in the cells uses spatial point processes, such as Poisson point process (PPP), to model the location of SCs in the network [5–10]. The independent placement of SCs from the MC layer, has the advantage of analytical tractability and leads to many useful SINR and/or rate expressions. However, even assuming that wireless providers would deploy SCs to support mobile broadband services, the dominant assumption remains that SCs are deployed randomly and independent of the MC layer [11].

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.

The fifth generation of mobile systems will rely on a dense deployment of small cells in order to meet its ambitious Key Performance Indicator targets. However, this dense deployment will result in a significant increase in the energy consumption of 5G networks, and thus, OPEX costs. Several small cell sleeping algorithms have been proposed in the literature but they require complex algorithms to decide when to switch on/off the small cells. On the basis of a distributed self-organizing approach, in this paper, we propose a low-cost, low-complexity small cell sleep scheduling algorithm to minimize the energy consumption of small cells in 5G and beyond networks. Our control utilizes a motion detection circuit to toggle the small cell between sleep and active modes based on the presence of a user. We evaluate the performance the algorithm on an experimental testbed and the results show that our algorithm achieves up to 20% reduction in energy consumption when compared to ‘always on’ approach.

To enhance the utilization of base stations (BSs) and face the challenge of the upcoming mobile data tsunami, energy saving and traffic offloading are two important issues to address in green cellular networks. In this paper, we design an optimal green policy, called Genie, to strike a balance between energy saving and traffic offloading in the heterogeneous cellular network, so that the system can either activate hotspot cells for traffic offloading or to deactivate the hotspot cells for energy saving. We prove that the optimal green policy is monotone hysteretic, and our system can thus be realized by simple switch-up and switch-down thresholds, while avoiding the ping-pong effect suffered by existing works. We show via simulations that our optimal green policy can significantly reduce total energy cost and perform energy saving and traffic offloading intelligently under all traffic conditions.

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.

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.

SummaryWe investigate the problem of joint downlink wireless backhaul bandwidth (WBB) and power allocation in heterogeneous cellular networks (HCNs). A WBB partitioning scheme is considered, which allocates the whole bandwidth between the macrocell and small cells for data transmission and backhauling. We formulate an optimization problem to maximize the weighted sum logarithmic utility function by jointly optimizing WBB portion and fronthaul power allocation of each base station with consideration of the backhaul capacity limitation on each small cell. In order to solve this joint optimization problem, we propose a hierarchical two‐level approach and decompose the original problem into two independent subproblems: the WBB allocation at the macrocell base station (MBS) and the power allocation at both the MBS and small cell base stations (SBSs). Accordingly, the optimal WBB portion and power allocation solutions are obtained, respectively. Furthermore, we develop a distributed algorithm to implement the joint WBB and power allocation. Numerical results verify the effectiveness of the proposed approach and analyze the impact of the weighted coefficient and backhaul capacity limitation on the network performance. In addition, significant performance gains can be achieved by the proposed approach over the benchmark.

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.

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.

Minimizing electromagnetic pollution and power consumption in green heterogeneous small cell network deployment

With small cell base stations (SBSs) densely deployed in addition to\nconventional macro base stations (MBSs), the heterogeneous cellular network\n(HCN) architecture can effectively boost network capacity. To support the huge\npower demand of HCNs, renewable energy harvesting technologies can be\nleveraged. In this paper, we aim to make efficient use of the harvested energy\nfor on-grid power saving while satisfying the quality of service (QoS)\nrequirement. To this end, energy-aware traffic offloading schemes are proposed,\nwhereby user associations, ON-OFF states of SBSs, and power control are jointly\noptimized according to the statistical information of energy arrival and\ntraffic load. Specifically, for the single SBS case, the power saving gain\nachieved by activating the SBS is derived in closed form, based on which the\nSBS activation condition and optimal traffic offloading amount are obtained.\nFurthermore, a two-stage energy-aware traffic offloading (TEATO) scheme is\nproposed for the multiple-SBS case, considering various operating\ncharacteristics of SBSs with different power sources. Simulation results\ndemonstrate that the proposed scheme can achieve more than 50% power saving\ngain for typical daily traffic and solar energy profiles, compared with the\nconventional traffic offloading schemes.\n