Optimized Small Cell Range Expansion in Mobile Communication Networks using Multi-Class Support Vector Machines

LTE-Advanced (LTE-A) macro-cell deployments are being enhanced with small cells, i.e., low-power base stations, to increase the network coverage and capacity. However, simultaneous co-channel transmissions from macro and small cells cause increased inter-cell interference and under-utilize the spectrum resources at the small cells. The following LTE-A design techniques are used to improve system performance in such deployments: (i) Carrier Aggregation (CA) to increase capacity by using additional carrier bandwidth; (ii) enhanced Inter-Cell Interference Coordination (elCIC), that includes (a) Cell Selection Biasing (CSB) to increase small cell spectrum utilization via cell range expansion; and (b) blanking data transmission on the macro cells for a certain duration of time to increase cell-edge user throughput Our objective is to maximize the CSB of the small cell, subject to user QoS constraints and blanking support from the macro cell. Towards this end, we develop an analytical model that captures the inter-dependency between elCIC techniques. We observe that, not accounting for the complex inter-dependencies between these techniques leads to a degraded network performance. We propose a framework that jointly optimizes elCIC and the assignment of multiple component carriers in an LTE-A deployment for increasing spectrum utilization at the small cells with appropriate blanking support from the macro cells. Our simulation results show that our approach increases the small cell spectrum utilization and aggregate cell-edge throughput by as much as 200%.

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].

Today's cellular networks are built with``macro cell'' basestations connected to the Internet via a rigid, complicated backhaul. Even with state-of-art technologies like LTE, users get limited throughput and high latency, with high variance. Performance enhancing IP boxes are deployed in the cellular operator's datacenters, far from the user. As a result, the most compelling cloudlet applications are difficult to realize on such networks and cloudlet researchers have thus far focused on Wi-Fi networks only.

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 low power radio access nodes, also known as small cells, are always applied to compensate the coverage of macro cells so as to increase the network capacity. The radio spectrum condition is greatly affected by the buildings and there will be many communication dead zones in the metropolitan area environment if only macro cells are deployed. Generally, the communication dead zone is always small and in irregular shape, therefore, the deployment of small cells is one of the most convincible ways to deal with it. However, the interference, necessary overlapping for handoff, coverage improvement, and cost are correlated to one another. And it is hard to determine the number and locations of small cells to be deployed. In this paper, we propose the Particle Swarm Optimization (PSO) based small cell placement algorithm to effectively deploy the small cells in the communication dead zone of metropolitan area. The fitness value in the PSO process of the proposed scheme is tuned by the adaption function to compromise the coverage maximization and necessary overlapping of small cells. A practical area of Taipei City was adopted as an experimental example to investigate the performance of the proposed scheme. The experimental results illustrate that the proposed scheme places small cells in proper locations so that the coverage can be effectively improved.

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.

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.

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.

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.

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.

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.

Mobile communication is facing new challenges to the soaring traffic demand of numerous user devices; thus, the notion of the small cell has been proposed and realized in recent years. However, licensed spectrum has been occupied by various underlying access technologies, so the deployment of small cells needs a sophisticated planning algorithm. In this article, we provide an overview of reconfigurable radio and small cell technologies, then introduce the tentative network architecture for 5G. Two planning approaches (i.e., genetic-based and graphbased) are proposed that accommodate cognitive radio technology to improve user throughput by eliminating communication interference. Since cognitive radio networking provides frequency allocation with cognition cycle for better spectral efficiency, we tackle the deployment of ultradense small cells and consider the coordination of unlicensed spectrum at the same time. Results show that the proposed algorithms with spectrum cognition improve network performance in terms of throughput and signal-to-interference- plus-noise ratio. Specifically, the genetic- based algorithm increases 232 percent in throughput and 150 percent in signal-to-interference- plus-noise ratio compared to the graphbased algorithm. Finally, we conclude this article by discussing potential challenges and opportunities.

In next generation networks, ultra-dense small cell networks are emerging to deal with exponential data traffic increasing. With the deployment of small cells (SCs) becoming increasingly dense, these cells may be under-utilized during quite a few period of time. Depending on this circumstance, we aim to maximize the network energy efficiency by deactivating such under-utilized cells, subjecting to the rate fairness constraints among users. To overcome this issue, we propose an energy efficient SCs on/off approach that allows SCs to activate or deactivate based on estimated traffic load and UEs' preference requirements. Small cells are coupled into clustered based on distance firstly, and then UE-preference aware network selection scheme is performed, after which the SCs on/off pattern is derived iteratively in each formed cluster. For the network selection scheme, the optimal user association is obtained according to the SINR, estimated traffic load, traffic type and delay. Due to the complexity of finding the optimal SCs on/off pattern, we also give a practical suboptimal greedy SCs on/off algorithm with polynomial computational complexity. Simulation results demonstrate that energy expenditure can be reduced by 72% at most. Comparing to optimal search and traditional algorithms, the greedy algorithm achieves a comparable performance with much lower complexity.

We develop a framework for downlink heteroge- neous cellular networks with line-of-sight (LoS) and non-line- of- sight (NLoS) transmissions. Using stochastic geometry, we derive a tight approximation of the achievable downlink rate that enables us to compare the performance between densifying small cells and expanding base station (BS) antenna arrays. Interestingly, we find that adding small cells into a sparse network improves the achievable rate much faster than expanding antenna arrays at the macro BS. However, when the small cell density exceeds a critical threshold, the spacial densification will lose its benefits and further impair the network capacity. To this end, we present the optimal small cell density that maximizes the rate as practical deployment guidance. In contrast, expanding macro BS antenna array can always increase the capacity until reaching an upper bound caused by pilot contamination, and this bound also surpasses the peak rate obtained from deployment of small cells. Therefore, small cells are preferred for low rate requirements due to the rapid rate gain, and the massive MIMO is preferred for higher rate requirements due to better achievable rate.

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.