HOF: A History-based Offloading Framework for LTE networks using mobile small cells and Wi-Fi

Cellular operators have concluded that small cell deployments are a very cost-effective and quick solution to meet the ever growing demands on capacity and coverage in cellular networks for indoor and outdoor environments. While traveling in public transit vehicles, cellular subscribers usually experience poor signal reception and low bandwidth. We hence consider deploying small cells onboard (i.e., mobile small cells) such vehicles. This should enhance subscribers' quality of experience (QoE). We consider a Small Base Station (SBS) mounted in a public transit bus (i.e., mobile SBS) to serve onboard users. The mobile SBS aggregates users' traffic to and from the macroBSs. To further extract the underlying rich multipath-Doppler diversities resulting from the fast mobility and the associated selective fading channel, a pre-coded transmission is deployed in the mobile SBS.We examine the achievable gain from enabling aggregation through mobile SBS in terms of the outage probability. We derive a tight-bound closed-form expression for the outage probability in the downlink (DL). Our results indicate that significant gain in outage probability and coverage are achievable.

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.

Wi-Fi, small cells and macrocell networks serve users with different advantages and drawbacks. In this paper, we propose a multi-leader multi-follower Stackelberg game between these three types of networks and mobile users. In the multi-leader game, each network first sets the optimal price according to the behaviors of the other networks and the prediction of each mobile user's optimal strategies. Subsequently, mobile users choose their optimal network, accordingly. In the proposed game, there exists a Stackelberg equilibrium between the leader level and the follower level, and two Nash Equilibria within leaders and within followers, respectively. This fact differentiates the proposed work from most existing literature. Simulation results show that the proposed approach yields high social welfare at the equilibrium.

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.

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.

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.

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.

Small cells are low power nodes, which have lower transmit power than the macro eNodeB. They are regarded as one of the important solutions in long term evolution advanced (LTE-A) to deal with the dramatically increased mobile data traffic. Small cells deployments in LTE-A release 12 with 4 small cells in a cluster suffer from stronger inter-cell interference than heterogeneous network (HetNet) in the previous release of LTE-A, where one small cell exists in a cluster. We investigate the joint least squares channel estimation (JLSCE) over demodulation reference signals (DMRS) spreading in time and frequency resources in LTE-A systems. Furthermore, different receiver structures using least square channel estimation (LSCE) and JLSCE are investigated in small cell deployments with the presence of strong inter-cell interference. Our results show the linear minimum mean square error (LMMSE)-interference rejection combining (IRC) with LSCE is the most efficient receiver structure compared to the enhanced (e)LMMSE-IRC with JLSCE, interference cancellation (IC) with eLMMSE-IRC and maximum ratio combining (MRC) receiver structures for signal detection in small cell deployments.

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.

With the exponential increase in mobile internet traffic driven by a new generation of wireless devices, future cellular networks face a great challenge to meet this overwhelming demand of network capacity. At the same time, the demand for higher data rates and the ever-increasing number of wireless users led to rapid increases in power consumption and operating cost of cellular networks. One potential solution to address these issues is to overlay small cell networks with macrocell networks as a means to provide higher network capacity and better coverage. However, the dense and random deployment of small cells and their uncoordinated operation raise important questions about the energy efficiency implications of such multi-tier networks. Another technique to improve energy efficiency in cellular networks is to introduce active/sleep (on/off) modes in macrocell base stations. In this paper, we investigate the design and the associated tradeoffs of energy efficient cellular networks through the deployment of sleeping strategies and small cells. Using a stochastic geometry based model, we derive the success probability and energy efficiency in homogeneous macrocell (single-tier) and heterogeneous K-tier wireless networks under different sleeping policies. In addition, we formulate the power consumption minimization and energy efficiency maximization problems, and determine the optimal operating regimes for macrocell base stations. Numerical results confirm the effectiveness of switching off base stations in homogeneous macrocell networks. Nevertheless, the gains in terms of energy efficiency depend on the type of sleeping strategy used. In addition, the deployment of small cells generally leads to higher energy efficiency but this gain saturates as the density of small cells increases. In a nutshell, our proposed framework provides an essential understanding on the deployment of future green heterogeneous networks.

Driven by the ever-increasing amount of mobile data, cellular networks evolve from small cell network to ultra-dense heterogeneous networks, to provide high system capacity and spectrum efficiency. By bringing base stations (BSs) to the approximate spatial scale and number magnitude, ultra-dense heterogeneous networks would definitely bring unprecedented paradigm changes to the network design. Firstly, along with densification of small cells, inter-cell interference becomes severe and may deteriorate performance of mobile users. Assigning network resources including bandwidth and time slots, while avoiding interference, desires serious consideration. Secondly, the coverage area of BSs becomes small and irregular, resulting in much frequent and complicated handovers when mobile users move around. How to ensure continuous communication and implement effective mobility management, and inter-cell resource allocation and cooperation, remains a challenging issue. Thirdly, such dynamic change in spatial dimension enables us to re-investigate available and ongoing communications and networking techniques, such as massive MIMO, CoMP, millimeter waves (mmWaves), carrier aggregation, full duplex radio, and D2D communications. To address the aforementioned challenging research issues, this book will investigate the service and QoE provisioning in ultra-dense heterogeneous networks. In particular, firstly we introduce ultra-dense heterogeneous networks by careful definition regarding spatial deployment, generic characteristics, and requirements of ultra-dense heterogeneous networks in order to ensure QoE of mobile users. Secondly, we depict the resource management among small cells in close proximity, mobility management for mobile users (address the super-frequent handovers), and interference management (dealing with the interference due to frequency-reuse in the vicinity). Thirdly, we study the enabling factors, and the integration of ultra-dense heterogeneous networks with enabling technologies, such as massive-MIMO, cloud-RAN, mmWaves, D2D, IoT. Finally, we conclude the book and indicate future directions and challenges.

As the mobile data demand keeps growing, an existing heterogeneous network (HetNet) composed of macrocells and small cells may still face the problem of not being able to provide sufficient capacity for unexpected but reoccurring hot spots. In this paper, we propose a mobile small-cell deployment strategy that avoids replanning the overall network while fulfilling the hot spot demand by optimizing the deployment of additional mobile small cells on top of the existing HetNet. We formulate the problem as a joint optimization over the number and locations of mobile small cells and the user associations of all cells in order to maximize the minimum user throughput. In order to solve it, we first propose a Fixed Number Deployment Algorithm (FNDA) to solve the problem with a fixed number of new small cells. Afterwards, we extend FNDA into a Deployment Over Existing Network Algorithm (DOENA) to solve the joint optimization problem. The simulation results show that DOENA offers a higher minimum user throughput while requiring less mobile small cells to be deployed than the deployment optimization based on maximizing sum user throughput.

With the evolution of various new types of application services for mobile devices, cellular operators have started providing multiple subscription plans to the mobile users. The plan subscribed determines the Quality of Service (QoS) to be provided to the user, and operators distinguish users as priority users and non-priority users, accordingly. To ensure better QoS to the priority users, necessary resources must be reserved at the base station. This demands analyzing the network traffic to predict future traffic pattern. This paper pioneers the use of machine learning methods to forecast mobile user traffic pattern for providing better QoS to the priority users. We analyze two different supervised learning methods, Naive Bayes Classifier and Logistic Regression, used for prediction of probable times when a priority user would be active. The prediction results are applied to the user scheduling strategies for efficient bandwidth management, thus improving system capacity and reduce blocking. Simulations on multiple real-life datasets validate the model and predict the mobile user pattern with very high accuracy, along with significant reduction in priority user service blocking ratio and improvement in their capacity.

The demand for high mobile data rate has been increasing due to introduction of various innovative mobile data services to improve our life. To accommodate the increasing data demand with satisfactory quality of service operators must continuously improve their network capacity. To that end, besides deploying fourth generation network, already operational third generation (3G) network capacity can be improved by applying some capacity improvement techniques including six-sectorization, densification, multiple input multiple output and using multiple carriers. Although these capacity improving techniques have been well investigated theoretically and practically, their performance is not studied well to address capacity challenges of most widely used 3G data network in Ethiopia. In this paper, we select six-sectorization and small cell based on relevance and device availability trends and investigate their performance in addressing capacity challenges of selected Addis Ababa 3G deployment scenario considering realistic capacity demand, propagation environment and legacy network topology. Propagation computation and network simulation are made using WinProp and dominant path model based on building and topography maps are used for propagation computation. Performance results show that deployment of six-sector or/and small cells provide significant performance gain compared to already existing full 3 sector cells. For instance, six-sector deployment provides a 284.23% average downlink throughput gain. Furthermore, deployment of small cells with 5% penetration presents a 114.67%, average throughput gains over the existing full 3-sector.

Performance analysis of handover management in 5G small cells