Small Cell Networks Research Articles

Energy Efficient Resource Allocation for Uplink MC-NOMA Based Heterogeneous Small Cell Networks With Wireless Backhaul

In this paper, we investigate energy efficient resource allocation for uplink multi-carrier non orthogonal multiple access (MC-NOMA) based two-tier heterogeneous networks (HetNets) with wireless backhaul. We have exploited channel diversity by assigning multiple subchannels to various MC-NOMA users (NUs), while allowing multiple NUs to use the same subchannel. We formulate a joint NU clustering, and power and subchannel allocation problem for system's energy efficiency (EE) maximization under the constraints of minimum quality of service (QoS) requirement, maximum allowable transmit power for each NU, and successive interference cancelation. The formulated problem is non-convex with fractional objective function, and therefore, to convert it into a tractable from, it is divided into two subproblems, i.e., subchannel allocation and NU clustering subproblem, and power allocation subproblem. The subchannel allocation and NU clustering subproblem is solved through a suboptimal algorithm, which exploits the channel diversity by maintaining maximum possible difference in transmit power levels of clustered NUs. Whereas for the power allocation subproblem, the EE optimization problem is convexified through Dinkelbach and successive convex approximation techniques and a closed-form suboptimal solution is obtained. The simulation results show that the proposed energy efficient algorithm outperforms the existing NOMA and orthogonal multiple access (OMA) algorithms. The proposed approach also guarantees user fairness by giving priority to the NUs with unfulfilled throughput requirements.

Spectrum-efficient user association in caching enabled dense small cell network

In this paper, we reveal the fundamental limitation of network densification on the performance of caching enabled small cell network (CSCN) under two typical user association rules, namely, content- and distance-based rules. It indicates that immoderately caching content would significantly change the interference distribution in CSCN, which may degrade the network area spectral efficiency (ASE). Meanwhile, it is shown that content-based rule outperforms the distance-based rule in terms of network ASE only when small cell base stations (BSs) are sparsely deployed with low decoding thresholds. Moreover, it is proved that network ASE under distance-based user association serves as the upper bound of that under content-based rule in dense BS regime. To enable more spectrum-efficient user association in dense CSCN, we further optimize network ASE by designing a probabilistic content retrieving strategy based on distance-based rule. With the optimized retrieving probability, network ASE could be substantially enhanced and even increase with the growing BS density in dense BS regime.

Performance analysis of UAV multiple antenna-assisted small cell network with clustered users

Performance analysis of UAV multiple antenna-assisted small cell network with clustered users

3D Uplink Channel Modeling of UAV-Based mmWave Fronthaul Links for Future Small Cell Networks

In this study, we consider an unmanned aerial vehicle (UAV)-assisted heterogeneous network that is offered as a cost effective and easy to deploy solution to solve the problem related to transferring traffic of distributed small cells to the core network. For any given distribution of small cell base stations (SBSs), we first characterize an accurate millimeter wave (mmWave) channel model for SBS to networked flying platform (NFP) by taking into consideration real parameters such as UAV's vibrations, distribution of SBSs, position of UAVs in the sky, real three-dimensional (3D) antenna pattern model provided by 3GPP along with interference caused by antenna side lobes and frequency reuse. Then, for the characterized channel, we derive an analytical closed-form expression for the end-to-end signal-to-noise plus interference ratio (SINR). Based on that, we derive the closed-form expressions for the outage probability and channel capacity of the considered UAV-based mmWave uplinks. The accuracy of the derived analytical expressions is verified by Monte Carlo simulations. Finally, we investigate the effects of different channel parameters such as antenna pattern gain, strength of UAV's vibrations, UAVs' positions in the sky, distribution of SBSs, and frequency reuse on the performance of the considered UAV-based uplink channel in terms of average capacity and outage probability.

Distributed Noncoherent Joint Transmission Based on Multi-Agent Reinforcement Learning for Dense Small Cell Networks

In dense small cell networks, the coordinated multi-point noncoherent joint transmission (JT) is a key technique to mitigate inter-cell interference and enhance network capacity. However, the capacity-maximizing power control and beamforming problem subject to a total transmit power constraint at each individual small cell base station (BS) is inherently nonconvex and NP-hard. To solve this problem, most existing algorithms require global channel state information (CSI) and a lot of computations, which are infeasible and impractical in dynamic wireless networks with limited computing power and link capacity. In this paper, we characterize a low-dimensional solution structure for the power control of the sum-rate maximization problem in time division duplex (TDD) dense small cell networks. Taking advantage of this low-dimensional structure, a distributed noncoherent JT scheme based on multi-agent reinforcement learning (MARL) is proposed to maximize the sum-rate of the dense small cell networks with reduced information overhead. In the proposed scheme, each BS acts as an agent and makes decisions locally. It is proved that the optimal sum-rate can be achieved for single-transmit-antenna BSs by using the proposed scheme. Compared to the best method presently known, the proposed scheme achieves a similar sum-rate with considerably lower computational complexity and information overhead, which makes it more appealing for practical implementations.

Distributed content placement in cache-enabled small cell networks in the presence of malicious users

Distributed content placement in cache-enabled small cell networks in the presence of malicious users

Analysis of Mean Delay in Small Cell Networks with Dynamic Traffic and FIFO scheduling

Analysis of Mean Delay in Small Cell Networks with Dynamic Traffic and FIFO scheduling

Privacy-Aware Secure Region-Based Handover for Small Cell Networks in 5G-Enabled Mobile Communication

The 5G mobile communication network provides seamless communication between users and service providers and promises to achieve several stringent requirements, such as seamless mobility and massive connectivity. Although 5G can offer numerous benefits, security and privacy issues still need to be addressed. For example, the inclusion of small cell networks (SCN) into 5G brings the network closer to the connected users, providing a better quality of services (QoS), resulting in a significant increase in the number of Handover procedures (HO), which will affect the security, latency and efficiency of the network. It is then crucial to design a scheme that supports seamless handovers through a secure authentication process. With this aim, in this article, we propose a secure region-based handover scheme that supports seamless connectivity for SCNs in 5G. Our proposed scheme is based on asymmetric-key-based authenticated key exchange and handover protocols that preserve user privacy and network security while providing a seamless region-based handover mechanism and effective membership revocation management. In this context, we introduce three privacy-preserving protocols, i.e., an initial authentication protocol, an intra-region handover protocol and an inter-region handover protocol, for dealing with three communication scenarios. To the best of our knowledge, this is the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first</i> paper to consider the privacy and security in both the intra-region and inter-region handover scenarios in 5G communication with effective membership revocation management support. Detailed security and performance analysis of our proposed scheme is presented to show that it is resilient against many security threats, is cost-effective and provides an efficient solution for 5G-enabled mobile communication.

QoE-Maximized Beam Assignment and Rate Control for Dynamic mm-Wave-Based Full-Duplex Small Cell Networks

This study investigates the resource management problem for millimeter-wave-based switched beam (SWB) full-duplex small cell networks with the consideration of user equipment’s (UE’s) quality of experience (QoE) requirement and time-varying wireless channels. An optimization problem is formulated to maximize the long-term QoE by implementing beam assignment (BA) and rate control (RC) under short-term beam and long-term energy efficiency constraints. By leveraging the Lyapunov optimization technique, the original problem is converted into a series of BA and RC problems in each time slot. To solve the converted problem with affordable complexity, novel closed-form solutions for BA and RC are first derived by considering beam constraints in SWB systems. A decomposition-based BA and RC (DBR) algorithm with only polynomial computational complexity is then proposed based on the derived closed-form solutions. The simulation results demonstrate that the proposed DBR method can effectively[‘effectively’ appears to be a more suitable word in this case.] balance the performance and complexity because the DBR scheme outperforms the benchmark scheme and achieves nearly optimal performance in terms of system delay and QoE.

Joint user association and fair scheduling for dual connectivity heterogeneous networks

Heterogeneous small cell network is one of the most effective solutions to overcome spectrum scarcity for the next generation of mobile networks. Dual connectivity (DC) can improve the throughput for each individual user by allowing concurrent access to two heterogeneous radio networks. In this paper, we propose a joint user association and fair scheduling algorithm (JUAFS) to deal with the resource allocation and load balancing issues for DC heterogeneous small cell networks. Considering different coverage sizes, numbers of users, and quality of experience characteristics of heterogeneous cells, we present a proportional fair scheduling for user association among cells and utilize interference graph to minimize the transmission conflict probability. Simulation results show the performance improvement of the proposed algorithm in spectrum efficiency and fairness comparing to the existing schemes.

Low-Complexity Q-Learning for Energy-Aware Small-Cell Networks With Integrated Access and Backhaul

Low-Complexity Q-Learning for Energy-Aware Small-Cell Networks With Integrated Access and Backhaul

Performance comparison of successive interference cancellation decoding order for base station association and power control in small cell networks incorporating handoff optimization

The co-channel interference and frequent handoff are two important issues that have a great impact on the spectrum and energy efficiency in the non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) in the small cell networks (SCN). This paper investigates the performance of SIC decoding order based on the users’ channel state information (CSI) and random SIC decoding order under the perfect and imperfect conditions for the handoff optimization in SCNs with NOMA. The effect of SIC decoding order in the joint base station association and power control problem is studied by considering the motion of mobile users (MUs). Two optimization problems are formulated using the number of associated MUs, the number of handoffs, and the transmit power of all MUs, under the maximum allowable transmit power and minimum average-rate constraints. Two objectives are combined into a single-stage optimization problem through the weighted sum method. The formulated problem is solved using a game theory-based algorithm and primal decomposition theory. The simulation results show that changing the SIC decoding order can lead to performance improvement in the uplink NOMA- based SCNs. Also, it is shown that the proposed algorithm can significantly reduce the frequent handoffs in small cell networks.

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.

Cost-Optimal Deployment of Millimeter-Wave Base Stations Under Outage Requirement

Today’s growth in the volume of wireless devices coupled with the demand for data-intensive use cases has motivated the deployment of millimeter-wave (mmWave) small-cell networks. Although it is true that mmWave networks can carry a large volume of traffic, highly intermittent connectivity and the challenges related to installing many small-cell base stations (BSs) in urban geometry have impeded its progression into practical networks. To cope with these challenges, we present, in this paper, an approach to the mmWave BS deployment (site planning) problem, based on the minimum-deployment-cost criterion that is subject to user equipment (UE) outage constraints. Unlike the prior works, the proposed model captures the randomness of link blockage and signal-to-interference-plus-noise-ratio (SINR) statistics in mmWave networks. We formulate the minimum-cost deployment problem as large-scale integer nonlinear programming (INP). To deal with the coupled and combinatorial of the problem, the large-scale INP has approached to devise a suboptimal but efficient algorithm by decomposing it into two subproblems: (i) cell coverage optimization and (ii) minimum subset selection. We provide the solutions to each subproblem as well as theoretical justifications of them. Simulation results that illustrate UE outage guarantees of the proposed BS deployment method are presented. The results reveal that the proposed method uniquely distributes the macro-diversity orders that are distinct from other benchmarks.

Service-Oriented Resource Allocation for Blockchain-Empowered Mobile Edge Computing

Integrating dense small cell (DSC) networks with mobile edge computing is employed by 5G to tackle the contradiction between the computation limitations of user equipment (UE) and the stringent latency requirement of services. This paper investigates the service-oriented edge resource allocation problem in DSC networks, determining where to deploy the service entity, how many service entities should be deployed at each edge cloud, and how to assign the UEs to service entities. The problem is challenging for the following three aspects: 1) Service entity deployment and UE assignment are highly coupled. 2) Due to the overlap of coverage regions of densely deployed small cells, the allocation mechanism of different base stations has mutual effects on the overall service performance. 3) Considering the limited resources of edge clouds, it is a thorny problem to encourage edge clouds to cache and share service startup images. We devote the following efforts to tackle the problem under these challenges. First, we explore blockchain’s decentralized, traceable, and secure characteristics, and propose a scheme to encourage image sharing in mobile edge computing. Second, we formulate the service-oriented edge resource allocation as mixed integer non-linear programming. Third, towards the target of reducing the computational complexity, we decouple UE assignment from service entity deployment and solve it through Gibbs sampling. Moreover, the power of Lyapunov optimization and convex optimization is incorporated to reduce the long-term power consumption and budget. Experiment results demonstrate the superiority of our approach over current notable solutions.

Unlicensed band sharing based on bargaining game for heterogeneous networks

Currently, limited licensed frequency bands cannot meet the increasing demands for various wireless communication applications any more. It is necessary to extend wireless communication networks to unlicensed spectrum. In this paper, we propose a new bargaining framework for unlicensed band access to achieve high spectrum efficiency, where one radio access technology (RAT) (such as macro cellular network) "competes" the unlicensed bands with multiple other RATs (such as small cellular networks or Wi-Fi) virtually. Considering that macro cell can share unlicensed frequencies with multiple small cells which are in the same coverage area for more freedom, we use bargaining game theory to fairly and effectively share the unlicensed spectrum between macro and multiple heterogeneous small cell networks, where bargaining loss and time dissipation loss for virtual "price" of unlicensed bands are mainly considered. In the one-to-many bargaining process, we also develop a multiple RAT alliance game strategy to reduce transmission loss in a joint manner. Simulation results show that the proposed unlicensed band sharing algorithm significantly improves the spectrum efficiency performance compared with the other practical schemes for heterogeneous networks.

Caching in Narrow-Band Burst-Error Channels via Meta Self-Supervision Learning

We consider a coordinated small-cell network in narrow-band deep fading channels, where the transmissions from small base-stations (SBSs) to users usually encounter burst errors. Each SBS has limited cache-capacity via which partial contents can be cached to avoid being fetched from remote cloud and hence reduce the transmission delay. However, due to deep fading, the content transmission may occur numerous burst errors. To solve this problem, in this letter, a meta-based self-supervision learning method is introduced to find the optimal cache policy for minimizing the burst errors. In contrast to the traditional methods, the proposed one has better performance and learning efficiency. More particularly, the proposed can determine much proper cache policy by just training with a few samples of average fading duration of each channel. We demonstrate the effectiveness of the proposed method by numerical simulations which indicate that the proposed meta-learning method is superior to common existing methods in combat of the burst-error problem regarding convergence time and accuracy.

Joint Optimization of Cache Placement, User Association, and Channel Selection in Cache-Enabled Multicasting Small-Cell Networks

Caching and multicasting are two promising methods to alleviate redundant data transmissions and improve transmission capability in wireless networks. In this article, we focus on the joint resource optimization of cache placement, user association, and channel selection in cache-enabled multicasting small-cell networks. First, we present two weighted hypergraphs to analyze the complex transmission and interference relationships among mobile users (MUs) and small-cell base stations (SBSs) with different files multicasting in different channels. Then, we formulate the combinatorial problem as a nonlinear optimization problem. Considering the hierarchical actions of SBSs and MUs, we further divide the complicated combinatorial optimization problem into following two subproblems, i.e., i) the joint cache placement and channel selection, and ii) the user association. Simultaneously, we propose two potential games <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$G1$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$G2$</tex-math></inline-formula> to analyze the two subproblems, respectively. Thanks to the better response property of potential games, we propose a hierarchical resource allocation game <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$G_w$</tex-math></inline-formula> by combining <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$G1$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$G2$</tex-math></inline-formula> to optimize wireless resources iteratively. Lastly, we propose two hierarchical better response algorithms, which can converge to the hierarchical Nash equilibria. Simulation results show that our proposed distributed game method achieves good expectations of global accumulated downloading files.

Traffic-Aware Mean-Field Power Allocation for Ultradense NB-IoT Networks

The narrowband Internet of Things (NB-IoT) is a cellular technology introduced by the third-generation partnership project (3GPP) to provide connectivity to a large number of low-cost Internet of Things (IoT) devices with strict energy consumption limitations. However, in an ultradense small cell network employing NB-IoT technology, intercell interference can be a problem, raising serious concerns regarding the performance of NB-IoT, particularly in uplink transmission. Thus, a power allocation method must be established to analyze uplink performance, control and predict intercell interference, and avoid excessive energy waste during transmission. Unfortunately, standard power allocation techniques become inappropriate as their computational complexity grows in an ultradense environment. Furthermore, the performance of NB-IoT is strongly dependent on the traffic generated by IoT devices. In order to tackle these challenges, we provide a consistent and distributed uplink power allocation solution under spatiotemporal fluctuation incorporating NB-IoT features, such as the number of repetitions and the data rate, as well as the IoT device’s energy budget, packet size, and traffic intensity, by leveraging stochastic geometry analysis and mean-field game (MFG) theory. The effectiveness of our approach is illustrated via extensive numerical analysis, and many insightful discussions are presented.

Retraction Note to: Modeling for small cell networks in 5G communication environment

Retraction Note to: Modeling for small cell networks in 5G communication environment