The Inter-Cell Interference Dilemma in Dense Outdoor Small Cell Deployment

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.

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.

Due to their low cost and easy deployment, small cells provide a viable and cost-effective way of improving the cellular coverage and capacity both for homes and enterprises, both in metropolitan and rural areas. Stimulated by their attractive features and advantages, the ongoing development and deployment of small cells by manufacturers and mobile network operators have seen a surge in recent years. Together with macro-cells, they form, what are called Heterogeneous Networks or HetNets. However, the successful rollout and operation of small cells are still facing significan issues. In this paper the need for, challenges and solutions of small cell deployments are analyzed. This analysis is conducted with respect to self-organizing features, interference coordination, energy efficiency and spectrum efficiency. The analysis is complemented with numerical results based on system simulations in Macro-only and HetNet scenarios and also on real measurements performed on an mobile operator network. Results show the clear improvement that a HetNet brings in term of user throughput and also the amunt of spectrum waste that is present in nowadays' operator networks.

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.

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%.

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.

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.

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.

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.

The rapid evolution of wireless communication systems has led to an unprecedented demand for high-capacity, cost-effective network infrastructure in urban environments. This research presents a novel wideband ribbon antenna design optimized for 5G small cell base stations operating in the 3.5 GHz frequency band. The proposed antenna leverages a modified tapered ribbon structure implemented on low-cost FR4 substrate to achieve a significantly expanded bandwidth of 1.2 GHz, representing a 30% improvement over comparable designs in literature. Through electromagnetic simulation and prototype testing, the antenna demonstrates omnidirectional radiation patterns with gain ≥5 dBi and efficiency ≥85%, while maintaining compact dimensions (10 cm × 10 cm) and single-layer construction. Network simulations reveal that enhanced bandwidth enables a 25-30% increase in small cell capacity, supporting 40-50 simultaneous users compared to 32-35 for baseline designs. Economic analysis indicates a 40-50% manufacturing cost reduction compared to Rogers-based designs, with potential to reduce small cell count by 20-25% in urban networks. The significance of this work lies in its potential to address a critical bottleneck in 5G network densification by enabling higher capacity through expanded bandwidth, while simultaneously reducing implementation costs through material selection and manufacturing simplicity. This pragmatic solution offers network operators tangible economic benefits through reduced infrastructure requirements, ultimately accelerating the deployment of 5G small cells in urban environments.

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.

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.

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.

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.