Deployment Of Small Cell Networks Research Articles

Energy-Efficient Hierarchical Resource Allocation in Uplink–Downlink Decoupled NOMA HetNets

The dense deployment of small cell networks is a key feature of next-generation mobile networks aimed at providing the necessary capacity increase. It is noteworthy that small cell networks employ high-capacity backhaul links on millimeter-wave bands to develop multi-hop topologies in order to mitigate data transmission costs. The current static backhaul infrastructures cannot control severe fluctuating network traffic. To resolve this problem, this paper proposed a novel adaptive backhaul topology with the ability to adapt to different traffic patterns. Based on the graph theory, the adaptive system dynamically allows changes to the hybrid millimeter-wave backhaul architecture, and it also provides the possibility of effective channel allocation to each backhaul link to meet capacity and QoS demands. Also, regarding the importance of green networking in integrated-access-and-backhaul networks we proposed a dynamic optimization model which minimizes the overall energy consumption of UL/DL Decoupled NOMA heterogeneous networks in addition to providing the essential coverage and capacity. The proposed model optimizes user association/power utilization and presents an effective modular and scalable framework for analytical technology-oriented modeling of integrated multi-hop backhauls. The numerical results proved that the joint power optimization and hybrid backhaul architecture can increase the total network throughput by 18 percent compared to the current optimized static architectures. It can also reduce the energy consumption level by 30 percent, and enhance users’ quality satisfaction by 24.5 percent with respect to user distribution patterns.

Heterogeneous Computational Resource Allocation for NOMA: Toward Green Mobile Edge-Computing Systems

Mobile Edge Computing (MEC) is a viable solution in response to the growing demand for broadband services in the new-generation heterogeneous systems. The dense deployment of small cell networks is a key feature of next-generation radio access networks aimed at providing the necessary capacity increase. Nonetheless, the problem of green networking and service computing will be of great importance in the downlink, because the uncontrolled installation of too many small cells may increase operational costs and emit more carbon dioxide. In addition, given the resource and computational limitation of the user layer, energy efficiency (EE) and fairness assurance are critical issues in MEC-based cellular systems. Considering the user fairness criteria, this paper proposes a dynamic optimization model which maximizes the total UL/DL EE along with satisfying the necessary QoS constraints. Based on the non-convex characteristics of the EE maximization problem, the mathematical model can be divided into two separate subproblems, i.e., computational carrier scheduling and resource allocation. So that, a subgradient method is applied for the computational resource allocation and also successive convex approximation (SCA) and dual decomposition methods are adopted to solve the max-min fairness problem. The simulation results exhibit considerable EE improvement for various traffic models in addition to guaranteeing the fairness requirements. It also proved that the proposed computational partitioning scheme managed to significantly improve the total throughput for mobile computing services.

Energy-Aware Hierarchical Resource Management and Backhaul Traffic Optimization in Heterogeneous Cellular Networks

The dense deployment of small-cell networks is a key feature of the next-generation mobile networks employed to provide the necessary capacity increase. The small cells are installed in the areas covered by macro base stations (eNBs) to supply the required local capacity based on the known concept of the hierarchical HetNets. Moreover, small-cell networks use high-capacity backhaul links on millimeter-wave bands to develop multihop topologies to mitigate the costs of data transmission. Nonetheless, green networking gains great importance for the uncontrolled installation of too many small cells may escalate operational costs and emit more carbon dioxide. This article proposes a dynamic optimization model to minimize the overall energy consumption of fifth-generation (5G) heterogeneous networks and provide the essential coverage and capacity. Optimizing carrier allocation and power utilization, the proposed model determines when to turn <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> or <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> small cells to meet the quality of service constraints of users with the highest level of energy efficiency. We also proposed a multihop backhauling strategy to effectively use the existing infrastructure of small-cell networks for simultaneous dual-hop transmissions. The numerical results indicated considerable rates of power saving in different traffic models while guaranteeing the throughput requirements for uniform and hotspot user equipment distribution patterns. Also, according to the simulation results, energy efficiency and system data rates can significantly be improved.

A novel approach to energy-aware resource management: Toward green NOMA heterogeneous networks

The dense deployment of small cell networks is a key feature of next generation mobile networks aimed at providing the necessary capacity increase. In order to reach an acceptable performance in such ultra-dense networks, real-time resource management is of great importance. Therefore, self-optimization networking is proposed as the only viable solution to increase the networks’ utility. This paper proposed a self-optimizing model to enhance network performance and guarantee the users’ QoS requirements by considering limited resources and using effective user association, carrier scheduling and handover optimization algorithms. In order to maximize the network performance, we applied the smart backhauling technique in order to analyze the signaling to increase the validity of the decision making process. Based on the semantic information extracted from the access layer, the network decision-making center is able to adjust the network parameters and resource allocation effectively. The goal function is defined as maximizing the total energy efficiency by considering the transmission power, energy harvesting capability and the user QoS constraints so that the idle small cells are considered turned off temporarily to boost the power efficiency. Although the optimization problem is non-convex, a quadratic mixed-integer function is solved to obtain a global optimal solution. Since the actual implementation of the real-time algorithm has high computational complexity, two algorithms with different complexity levels are proposed. These algorithms use the carrier matching feature and optimal transmission power for problem-solving. The simulation results prove that, despite the increased computational complexity, effective resource allocation and optimal HO relations made the proposed approach capable to increase performance indices such as network throughput by up to 30%.

A Novel Approach to Adaptive Resource Allocation for Energy Saving in Reconfigurable Heterogeneous Networks

Abstract The dense deployment of small cell networks is a key feature of next generation mobile networks aimed at providing the necessary capacity increase. Wireless heterogeneous networks are created by combining several radio access technologies, each with its own potentials, capabilities and limitations. In these networks, providing real-time services with quality assurance is essential. For effective use of radio resources, the Radio Resource Management method was introduced which its performance and efficiency is better than the control of independent radio resources in any radio access technology. In this paper, we introduced a novel approach to select the most effective radio access technologies by taking into account some performance parameters like the type of service, users’ distribution pattern and the cost of the services. It also optimizes the handover relations between macrolayer and small cells. The proposed approach is a self-optimizing model can be employed to control resources and improve performance indices associated with mobile networks without human interference by only relying on network intelligence. In order to maximize the network performance, we applied the dynamic backhauling technique to analyze the uplink signaling data which increased the validity level of the decision-making process. Based on the extracted semantic information, the network decision-making engine is able to adjust the network parameters and efficiently allocate the resources. The numerical results exhibit considerable power saving for different traffic models in addition to reduce the rate of vertical handovers. The results also show increase the network throughput by up to 30%.

Learning-Based Predictive Transmitter-Receiver Beam Alignment in Millimeter Wave Fixed Wireless Access Links

Millimeter wave (mmwave) fixed wireless access is a key enabler of 5G and beyond small cell network deployment, exploiting the abundant mmwave spectrum to provide Gbps backhaul and access links. Large antenna arrays and extremely directional beamforming are necessary to combat the mmwave path loss. However, narrow beams increase sensitivity to physical perturbations caused by environmental factors. To address this issue, in this paper we propose a predictive transmit-receive beam alignment process. We construct an explicit mapping between transmit (or receive) beams and physical coordinates via a Gaussian process, which can incorporate environmental uncertainties. To make full use of underlying correlations between transmitter and receiver and accumulated experiences, we further construct a hierarchical Bayesian learning model and design an efficient beam predictive algorithm. To reduce dependency on physical position measurements, a reverse mapping that predicts physical coordinates from beam experiences is further constructed. The designed algorithms enjoy two folds of advantages. Firstly, thanks to Bayesian learning, a good performance can be achieved even for a small sample setting as low as 10 samples in our scenarios, which drastically reduces training time and is therefore very appealing for wireless communications. Secondly, in contrast to most existing algorithms that only predict one beam in each time-slot, the designed algorithms generate the most promising beam subset, which improves robustness to environment uncertainties. Simulation results demonstrate the effectiveness and superiority of the designed algorithms against the state of the art.

Radio Network Planning towards 5G mmWave Standalone Small-Cell Architectures

The 5G radio networks have introduced major changes in terms of service requirements and bandwidth allocation compared to cellular networks to date and hence, they have made the fundamental radio planning problem even more complex. In this work, the focus is on providing a generic analysis for this problem with the help of a proper multi-objective optimization algorithm that considers the main constraints of coverage, capacity and cost for high-capacity scenarios that range from dense to ultra-dense mmWave 5G standalone small-cell network deployments. The results produced based on the above analysis demonstrate that the denser the small-cell deployment, the higher the area throughput, and that a sectored microcell configuration can double the throughput for ultra-dense networks compared to dense networks. Furthermore, dense 5G networks can actually have cell radii below 400 m and down to 120 m for the ultra-dense sectored network that also reached spectral efficiency 9.5 bps/Hz/Km2 with no MIMO or beamforming.

Small Cells Integration with the Macro-Cell Under LTE Cellular Networks and Potential Extension for 5G

Heterogeneous Networks (HetNet) requires the deployment of small cell networks that can co-exist with the existing macro-cell. To provide a high Quality of Service (QoS), a massive multiple-input-multiple-output (MIMO) must be equipped with a HetNet of a macro-cell base station to the multiple users. This paper highlights three important aspects of the emerging small cell wireless networks. First, the architectures of small cell networks in LTE wireless network is reviewed, with specific references to the current wireless network standards. Second, the performance evaluation of overlay small cells integrated with the underlay macro-cell is investigated. The third part of this study focuses on the future trends of small cell deployment in 5G and the critical technical preparation needs to be done to allow integration with the existing 4G network. The results show that the proposed integration of 5G small cells into existing macro 4G networks has improved both of the data rate (91.46%) and energy efficiency (98.66%).

Resource allocation in two-tier small-cell networks with energy consumption constraints

Small-cell networks (SCNs) technology is being considered as a promising solution to improve the coverage and capacity for small-cell wireless equipment (SWE). However, the deployment of SCNs is challenging due to wireless channel interference, stochastic tasks arrival, and more prominently, the long-term energy consumption constraint of SWEs. In this paper, we provide a novel distributed dynamic resource management approach for energy-aware applications in two-tier SCNs. The joint admission control (AC) at the transport layer and resource allocation (RA) at the physical layer in SWEs is proposed to solve these challenges. Specifically, the AC and RA problem under two-tier SCNs is formulated as a stochastic optimization model which aims at maximizing the long-term average throughput of SWEs in SCN subject to time-average energy consumption limitation of each SWE and network stability constraint. By adopting Lyapunov optimization theory and Lagrangian dual decomposition technique, we propose a distributed online energy-constraint throughput optimal algorithm (ETOA) to obtain optimal AC and RA decisions. Furthermore, we derive the analytical bounds for the time-average system throughput and the time-average queue backlog achieved by our proposed approach under the constraints of long-term average energy consumption and network stability. The evaluation confirms theoretical analysis on the performance of ETOA and also shows that our approach outperforms other resource allocation methods in satisfying the time-average energy consumption requirement of SWEs.

Performance of Small Cell Networks Under Multislope Bounded Pathloss Model: From Sparse to Ultradense Deployment

Network densification is proved to be the most efficient method to expand network capacity over the past few decades. Nevertheless, can network densification always boost network performance? To shed light on this, we explore the pros and cons of network densification in $k$ -dimension downlink small cell networks by analyzing transmission success probability (SP) and network spatial throughput (ST). Remarkably, different from long-distance propagation in sparse networks, short-distance propagation of distinctive features dominates as well under dense deployment of small cells. On this account, we apply a multislope bounded pathloss model (MBPM) to characterize signal attenuation within long and short propagation distance. Under MBPM, it is surprisingly found that network densification would enhance SP and ST only when small cell base station density $\lambda \left(\mathrm{SBS}/\mathrm{m}^{k}\right)$ is smaller than some critical densities. Beyond the critical densities, both SP and ST are proved to exponentially diminish with $\lambda$ and even approach zero when $\lambda$ is sufficiently large. In other words, ultradense deployment of small cells indeed deteriorates network performance. Besides, the numerically derived critical densities are helpful for the practical deployment of small cell networks.

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

An enormously increasing number of mobile communications devices and IoT sensors have driven rapid advance in wireless and cellular network technologies. Owing to limited energy resources, 5G technology has been expected to be designed as a ‘green’ network system. To achieve the requirement of future ‘green’ 5G networks to serve a huge number of mobile devices, this work investigates the problem of deployment and sleep control of a ‘green’ heterogeneous cellular network along a highway composed of base stations (BSs), legacy relay stations (RSs), and small cells (SCs), with two objectives: minimizing the energy consumption to decrease the impact of limited energy; as well as minimizing the electromagnet pollution from radiation of the three device types to avoid the potential harm to creatures. For decision variables, the deployment and sleep control of legacy RSs and SCs affect the total power consumption, and their coverage affects the total electromagnet pollution. First, this work creates a mathematical model for the optimization problem, and then proposes a hybrid algorithm of genetic algorithm (GA) and differential evolution (DE) with three local search operators to solve the problem, in which GA and DE can effectively handle discrete and continues decision variables, respectively. Simulation of the concerned green cellular networks verifies performance of the proposed algorithm.

Exploiting Polarization for System Capacity Maximization in Ultra-Dense Small Cell Networks

Ultra-dense deployment of small cell networks is widely regarded as a key role to meet the increasing demand for huge capacity of wireless communication systems. Meanwhile, network densification will result in severe inter-cell interference and thus impair system performance. Traditional radio resource management schemes pay attention to interference mitigation through resource allocation in time, frequency, space, and power domains, or a mix of them. In this paper, polarization, an important and underutilized property of electromagnetic waves, is exploited as a novel means to enhance system capacity. We propose a multicell joint polarization, power and subcarrier allocation (MC-JPPSA) scheme to maximize system capacity through joint optimization of transmitting polarization states, power, and subcarriers with an iterative approach. Though the iterative solution is suboptimal, simulation results demonstrate that MC-JPPSA scheme can strike a balance between performance and complexity compared with the optimal exhaustive search. Furthermore, the proposed scheme outperforms traditional joint power and subcarrier allocation schemes by means of exploiting polarization in ultra-dense small cell networks.

Green Full-Duplex Self-Backhaul and Energy Harvesting Small Cell Networks With Massive MIMO

With the dense deployment of small cell networks, the powering and backhaul problem of small cell base stations (SBSs) has attracted great attention, and energy harvesting technology and self-backhaul technology have been proposed as promising solutions. Although some excellent works have been done on energy harvesting and self-backhaul in small cell networks, most existing works do not consider them jointly. In this paper, we aim at green small cell networks by jointly achieving self-backhaul and energy harvesting. In addition, full-duplex and massive multiple-input and multiple-output technologies are also exploited to enhance the system performance. In order to improve the energy efficiency (EE) further, a novel precoding scheme is designed to eliminate both the inter-tier and multi-user interference. Based on the proposed precoding scheme, we formulate the cell association and power allocation problem as an optimization problem to optimize the system EE performance, with the energy arrival rate and remaining battery energy in SBSs involved. The formulated optimization problem implies a sleep mechanism to control the ON/OFF of SBSs, which will further reduce the energy consumption of small cell networks. In addition, to reduce the computation complexity to solve this non-convex problem, we propose to transform the original problem into a difference of convex program, which can be efficiently solved via a constrained concave convex procedure-based algorithm. Extensive simulation results are presented to justify the effectiveness of the proposed scheme with different system configurations.

Power allocation in small cell networks with full-duplex self-backhauls and massive MIMO

With the dense deployment of small cell networks, low-cost backhaul schemes for small cell base stations (SBSs) have attracted great attentions. Self-backhaul using cellular communication technology is considered as a promising solution. Although some excellent works have been done on self-backhaul in small cell networks, most of them do not consider the recent advances of full-duplex (FD) and massive multiple-input and multiple-output (MIMO) technologies. In this paper, we propose a self-backhaul scheme for small cell networks by combining FD and massive MIMO technologies. In our proposed scheme, the macro base station (MBS) is equipped with massive MIMO antennas, and the SBSs have the FD communication ability. By treating the SBSs as special macro users, we can achieve the simultaneous transmissions of the access link of users and the backhaul link of SBSs in the same frequency. Furthermore, considering the existence of inter-tier and intra-tier interference, we formulate the power allocation problem of the MBS and SBSs as an optimization problem. Because the formulated power allocation problem is a non-convex problem, we transform the original problem into a difference of convex program by successive convex approximation method and variable transformation, and then solve it using a constrained concave convex procedure based iterative algorithm. Finally, extensive simulations are conducted with different system configurations to verify the effectiveness of the proposed scheme.

Machine learning-based dynamic frequency and bandwidth allocation in self-organized LTE dense small cell deployments

Self-organizing networks (SONs) are expected to minimize operational and capital expenditure of the operators while improving the end users’ quality of experience. To achieve these goals, the SON solutions are expected to learn from the environment and to be able to dynamically adapt to it. In this work, we propose a learning-based approach for self-optimization in SON deployments. In the proposed approach, the learning capability has the central role to perform the estimation of key performance indicators (KPIs) which are then exploited for the selection of the optimal network configuration. We apply this approach to the use case of dynamic frequency and bandwidth assignments (DFBA) in long-term evolution (LTE) residential small cell network deployments. For the implementation of the learning capability and the estimation of KPIs, we select and investigate various machine learning and statistical regression techniques. We provide a comprehensive analysis and comparison of these techniques evaluating the different factors that can influence the accuracy of the KPI predictions and consequently the performance of the network. Finally, we evaluate the performance of learning-based DFBA solution and compare it with the legacy approach and against an optimal exhaustive search for best configuration. The results show that the learning-based DFBA achieves on average a performance improvement of 33 % over approaches that are based on analytical models, reaching 95 % of the optimal network performance while leveraging just a small number of network measurements.

Heterogeneous Networks in Shared Spectrum Access Communications

We investigate an advanced two-phase shared spectrum access communication scheme as an efficient approach to enhance the spectral utilization of a network. In the first phase, we devise a spectrum-sharing policy based on demands, fairness, and so on, which utilizes a priority scheme in fulfilling operators’ demands, and envision a secure operator-specific information sharing policy where no critical information is exchanged between the operators. In the second phase, a macro cell network (MCN) benefits through offloading services offered by small cell network (SCN). This allows the MCN to satisfy its users’ capacity demands, improve its quality of services and coverage under Nakagami fading channel. As a repayment, the SCN is rewarded with licenses to share and operate on the spectrum originally owned by the MCN. We devise a density division-based shared spectrum access model, where the density of the licensee’s SCN deployment is exploited as network resources. A fair division of the densities of the licensee operator’s small cell base stations into fractions of licensed small cell base stations serving its own users and offloading small cells is presented. Unlike most of the previous research works that considered Poisson point process (PPP) to model the distribution of the network entities even when PPP modeling is not accurate for the networks, where the number of MCN/SCN base stations is definite and the number of MCN/SCN base stations in disjoint areas is not independent, we employ a more realistic network model known as binomial point process to perform an analytical analysis of the cumulative interference and performance of the system. Furthermore, we analyze the rate coverage and outage performances considering a wide range of values for path-loss exponent and fading severity parameter of Nakagami fading.

Energy harvesting small cell networks: feasibility, deployment, and operation

Small cell networks (SCNs) have attracted great attention in recent years due to their potential to meet the exponential growth of mobile data traffic and the increasing demand for better quality of service and user experience in mobile applications. Nevertheless, a wide deployment of SCNs has not happened yet because of the complexity in the network planning and optimization, as well as the high expenditure involved in deployment and operation. In particular, it is difficult to provide grid power supply to all the small cell base stations (SCBSs) in a cost effective way. Moreover, a dense deployment of SCBSs, which is needed to meet the capacity and coverage of the next generation wireless networks, will increase operators' electricity bills and lead to significant carbon emission. Thus, it is crucial to exploit off-grid and green energy sources to power SCNs, for which energy harvesting (EH) technology is a viable solution. In this article, we will conduct a comprehensive study of EH-SCNs, and investigate important aspects, including the feasibility analysis, network deployment, and network operation issues. The advantages, as well as unique challenges, of EH-SCNs will be highlighted, together with potential solutions and effective design methodologies.

Network synchronization for dense small cell networks

Although the development of the next generation (i.e., 5G) of wireless networks is in its initial phase, it is widely agreed that some key technologies will play a vital role in the realization of 5G, such as millimeter waves, massive MIMO, small cells, and new radio air interfaces. Among these, densely deployed small cell networks are believed to be one of the most practical solutions to the 5G promise of truly ubiquitous mobile broadband. However, synchronization between densely deployed small cells is the key to the materialization of the benefits a small cell network promises, and is one of the most challenging issues of small cell networks. In this article, we overview the existing network synchronization techniques and address their limitations in small cell network deployment environments. There are two primary synchronization techniques in today's communication networks. GNSS provides the most accurate synchronization accuracy. However, due to environmental and cost limitations, GNSS may not be available in some small cell deployment scenarios (e.g., indoors). Another synchronization technique is IEEE 1588, the accuracy of which depends highly on backhaul conditions. We therefore study the feasibility of a radio-interface- based solution as a practical alternative to the synchronization problem of a dense small cell network with an application to LTE.

Living on the edge: The role of proactive caching in 5G wireless networks

This article explores one of the key enablers of beyond 4G wireless networks leveraging small cell network deployments, proactive caching. Endowed with predictive capabilities and harnessing recent developments in storage, context awareness, and social networks, peak traffic demands can be substantially reduced by proactively serving predictable user demands via caching at base stations and users' devices. In order to show the effectiveness of proactive caching, we examine two case studies that exploit the spatial and social structure of the network, where proactive caching plays a crucial role. First, in order to alleviate backhaul congestion, we propose a mechanism whereby files are proactively cached during off-peak periods based on file popularity and correlations among user and file patterns. Second, leveraging social networks and D2D communications, we propose a procedure that exploits the social structure of the network by predicting the set of influential users to (proactively) cache strategic contents and disseminate them to their social ties via D2D communications. Exploiting this proactive caching paradigm, numerical results show that important gains can be obtained for each case study, with backhaul savings and a higher ratio of satisfied users of up to 22 and 26 percent, respectively. Higher gains can be further obtained by increasing the storage capability at the network edge.

Cell Selection inWireless Two-Tier Networks: A Context-Aware Matching Game

The deployment of small cell networks is seen as a major feature of the next generation of wireless networks. In this paper, a novel approach for cell association in small cell networks is proposed. The proposed approach exploits new types of information extracted from the users’ devices and enviro