Ultra-dense Networks Research Articles

Flexible Virtual Cell Design for Ultradense Networks: A Machine Learning Approach

With the ever-growing demand for even higher throughput, ultradense networks (UDNs) are being deployed for the fifth generation (5G) mobile communications. Although massively distributed radio access points (APs) result in a considerable increase in throughput, they also cause some critical problems. When employing a wireless backhaul, the backhaul capacity becomes a limiting factor, which may result in a high packet loss rate. Furthermore, dense deployment of APs leads to more frequent handoffs for mobile user equipments, which results in heavy measurement and signaling overhead. To address the problem of frequent handoffs, virtual cell (VC) has been considered as a promising solution. However, the limited wireless backhaul capacity encountered by inflexible VC design may still result in an intolerable packet loss rate. For a better trade-off between the packet loss rate and the handoff overhead, a machine learning approach for flexible VC design is proposed that leverages particle swarm optimization (PSO) to quickly find the optimal VC solution. To be responsive to the dynamic traffic demand and backhaul capacity of APs, a new parameter called “weighted distance” is employed in the modified K-means algorithm, which is nested in the PSO procedures for master AP selection and VC boundary determination. Compared with an exhaustive search, optimal VC solutions can be found efficiently through considerably fewer iterations. The proposed method is generic and applicable to disparate UDN application scenarios.

Deployment of Clustered‐Based Small Cells in Interference‐Limited Dense Scenarios: Analysis, Design, and Trade‐Offs

Network densification is one of the most promising solutions to address the high data rate demands in 5G and beyond (B5G) wireless networks while ensuring an overall adequate quality of service. In this scenario, most users experience significant interference levels from neighbouring mobile stations (MSs) and access points (APs) making the use of advanced interference management techniques mandatory. Clustered interference alignment (IA) has been widely proposed to manage the interference in densely deployed scenarios with a large number of users. Nonetheless, the setups considered in previous works are still far from the densification levels envisaged for 5G/B5G networks that are considered in this paper. Moreover, prior designs of clustered‐IA systems relied on oversimplified channel models and/or enforced single‐stream transmission. In this paper, we explore an ultradense deployment of small cells (SCs) to provide coverage in 5G/B5G wireless networks. A novel cluster design based on a size‐restricted k‐means algorithm to divide the SCs into different clusters is proposed taking into account path loss and shadowing effects, thus providing a more realistic solution than those available in the current literature. Unlike previous works, this clustering method can also cater for spatial multiplexing scenarios. Also, several design parameters such as the number of transmit antennas, multiplexed data streams, and deployed APs are analyzed in order to identify trade‐offs between performance and complexity. The relationship between density of network elements per area unit and performance is investigated, thus allowing to illustrate that there is an optimal coverage area value over which the network resources should be distributed. Moreover, it is shown that the spectral‐efficiency degradation due to the intercluster interference in ultradense networks (UDNs) points to the need of designing an interference management algorithm that accounts for both intracluster and intercluster interferences. Simulation results provide key insights for the deployment of small cells in interference‐limited dense scenarios.

Comparative Analysis of V-Band and E-Band mmWaves for Green Backhaul Solutions for 5G Ultra-Dense Networks

5G Ultra-Dense Networks (UDNs) will involve massive deployment of small cells which in turn form complex backhaul network. This backhaul network must be energy efficient for the 5G UDN network to be green. V-band and E-band mmWave technologies are among the wireless backhaul solutions tipped for 5G UDN. In this paper, we have compared the performance of the two backhaul solutions to determine which is more energy efficient for 5G UDN. We first formulated the problem to minimize power, then proposed an algorithm to solve the problem. This was then simulated using Network simulator 3.The first scenario made use of V-band mmWave while thesecond was E-band mmWave. The performance metricsused were power consumption and energy efficiency againstthe normalized hourly traffic profile. The performances ofthe two solutions were compared. The results revealed thatE-band mmWave outperformed V-band mmWave inbackhauling traffic in 5G UDN. It can be concluded that E-band green backhaul solution is recommended over V-bandmmWave for 5G UDN.

Nonlinear Forwarding Strategy for Firefly Ultra Dense Networks With mmWave Fronthaul Links

We consider the uplink of firefly ultra dense networks which combine the promising features of ultra dense deployment and centralized processing. In these networks, a large number of remote radio units which we denote as firefly nodes (FNs) are spatially distributed over an area. The mobile devices (MDs) in the coverage area are simultaneously connected via sub-6 GHz radio frequency links to all FNs. Unlike the cloud radio access network (C-RAN) architecture, in firefly ultra dense networks, the FNs forward the MDs’ data through multi-hop millimeter-wave (mmWave) links to one or multiple root nodes since the coverage radius of each mmWave link is limited. These root nodes then forward the data via optical fiber links further to a central unit (CU), where the MDs’ signals are decoded. The amount of data that is received at each FN is potentially huge, and hence, efficient signal processing is needed at each FN before the received signals can be forwarded to other FNs. Therefore, we propose a nonlinear processing strategy, which quantizes the received signals at each FN. In particular, we formulate an optimization problem for a local design strategy for the nonlinear forwarding at the FNs, and present an optimal solution by exploiting strong duality and using the Lagrangian method to convert the optimization problem into an unconstrained problem via its dual formulation. A closed-form solution for the primal variables and a bisection algorithm for finding the optimal dual variables are presented. Moreover, based on the cut-set bound, we develop an upper bound on the achievable sum rate of the considered firefly network. The proposed nonlinear forwarding strategy is shown to outperform a benchmark linear forwarding strategy and to approach the performance upper bound in relevant transmit power regimes at the expense of a higher computational complexity. Our results reveal that having more root nodes in the topology improves the performance of linear and nonlinear forwarding but requires additional optical fiber links to the CU.

Physical-Layer Security in 6G Networks

The sixth generation (6G) of mobile network will be composed by different nodes, from macro-devices (satellite) to nano-devices (sensors inside the human body), providing a full connectivity fabric all around us. These heterogeneous nodes constitute an ultra dense network managing tons of information, often very sensitive. To trust the services provided by such network, security is a mandatory feature by design. In this scenario, physical-layer security (PLS) can act as a first line of defense, providing security even to low-resourced nodes in different environments. This paper discusses challenges, solutions and visions of PLS in beyond-5G networks.

An Adaptive Cell Selection Scheme for 5G Heterogeneous Ultra-Dense Networks

Fifth-generation (5G) cellular networks are a promising technology to meet the rapid growth in wireless traffic. Small cells are critical in fulfilling the requirements of 5G networks. A heterogeneous ultra-dense network (HUDN) is an enabling technology consisting of several types of small cells to enhance the performance of 5G networks effectively. A critical issue of HUDN is the cell selection method because the traditional technique for cell selection is inapplicable in such a network. This study proposes a novel adaptive cell selection (ADA-CS) scheme. It adapts to various characteristics of HUDNs and vehicle movements. It performs six phases to select the best base station with which to be associated. Simulation results show that, with low- and medium-speed vehicles, the ADA-CS scheme outperforms the traditional protocol in terms of the average number of handovers by 42.39%. In addition, it is superior to some relevant recent schemes by up to 36.53%. The adaptation feature of the proposed protocol provides additional improvements regarding the average number of handovers with high-speed vehicles. Therefore, it achieves superiority in terms of the average number of handover failures and unnecessary handovers. In addition, the ADA-CS scheme enhances the average achievable downlink data rates and spectral efficiency per vehicle by 3.98% and 2.79% compared with the traditional and the relevant recent schemes.

Design of Miniature Planar Antennas for 5G Systems

The objective of this work is the design and simulation of an antenna based on metamaterials in order to miniaturize the dimensions of planar antennas. Metamaterials have been on the rise in recent years. The new properties make it possible to envisage the realization of new electronic components with new functions. Metamaterials are artificial materials designed for different telecommunications applications in order to improve the performance of antennas in terms of efficiency, compactness and miniaturization of structures. The use of these materials offers advantages such as reduction in weight and bulk, which is beneficial for their integration into 5G telecommunications and telephony systems. The fifth generation 5G mobile network is a set of emerging global telecommunications standards, typically using high frequency spectrum, to provide network connectivity with reduced latency and higher speed and capacity than the forerunners. It is argued that the recurring communication infrastructure is very inefficient in energy and that 5G should be designed to solve this problem, increasing energy efficiency by several orders of magnitude. To meet the demands of 5G, we need radically new network architectures and technologies, such as heterogeneous ultra-dense network, massive multi-output MIMO, and millimeter wave communications. Our goal is to achieve a planar antenna based on metamaterials which must operate at the resonance frequency of 5G which is f=3.5GHz by the CST Studio Suit electromagnetic design and simulation software and Matlab calculation.

Distributed Channel Assignment for Ultra-Dense Wireless Networks Using Belief Propagation

An efficient channel assignment plays an important role in mitigating co-channel interference in ultra-dense wireless networks. A simple solution is to separate interfering network nodes into orthogonal channels to reduce the interference among them. However, determining the optimal channel assignment is considered to be a non-linear problem, which may also be associated with practical implementation issues such as high computational complexity and control signaling issues. In an effort to cope with these challenging issues, we propose a distributed channel assignment algorithm that efficiently finds the optimal channel configuration by utilizing the concept of belief propagation. Based on a message-passing framework, the proposed distributed channel assignment algorithm maximizes the overall sum rate of the ultra-dense network with a low computational load for each network node. In addition, we design a network protocol and frame format to implement the proposed message-passing framework to real-world wireless networks. The main advantage of the proposed approach is that network nodes autonomously determine the optimal channel assignment and rapidly adapt to dynamic changes of the network. Simulation results confirm that the proposed distributed channel assignment algorithm outperforms conventional algorithms in terms of various network performance aspects, such as the sum rate, scalability, latency, and user mobility.

Research on MAC Protocols in Cluster‐Based Ad Hoc Networks

Mobile ad hoc networks can be widely used in many scenes, for example, military communication, emergency communication, and 5G wide area coverage as well as ultradense network scenes. A cluster‐based network can decrease network overhead effectively and then constitute a large‐scale network through extending network scale by clustering and cascading. In view of multiservice simultaneous transmission demand for small‐scale dense networking scene and large‐scale extended networking scene, a MAC protocol based on scheduling of cluster head is proposed, which can avoid collision and retransmission between nodes effectively, and then the network synchronizing in intercluster and intracluster as well as the time‐slot scheduling is solved fast and efficiently. A network synchronization algorithm based on clock spread of the sponsorship node is proposed to achieve network self‐synchronizing, and a modified schedule‐tree algorithm based on cluster head and exactly sponsorship node is proposed to decrease network overhead and to increase network fairness, then MAC protocol based on scheduling of cluster head is verified by simulated analysis. Finally, a dynamic gateway selection algorithm based on link stability is proposed in this paper to decrease intercluster communication control overhead and improve intercluster communication reliability.

A Fast Heuristic for Gateway Location in Wireless Backhaul of 5G Ultra-Dense Networks

In 5G ultra-dense networks, a distributed wireless backhaul is an attractive solution for forwarding traffic to the core. The macro-cell coverage area is divided into many small cells. A few of these cells are designated as gateways and are linked to the core by high-capacity fiber optic links. Each small cell is associated with one gateway and all small cells forward their traffic to their respective gateway through multi-hop mesh networks. We investigate the gateway location problem and show that finding near-optimal gateway locations improves the backhaul network capacity. An exact p-median integer linear program is formulated for comparison with our novel K-GA heuristic that combines a Genetic Algorithm (GA) with K-means clustering to find near-optimal gateway locations. We compare the performance of K-GA with six other approaches in terms of average number of hops and backhaul network capacity at different node densities through extensive Monte Carlo simulations. All approaches are tested in various user distribution scenarios, including uniform distribution, bivariate Gaussian distribution, and cluster distribution. In all cases, K-GA provides near-optimal results, achieving average number of hops and backhaul network capacity within 2% of optimal while saving an average of 95% of the execution time.

On the Use of Composite Indicators for Mobile Communications Network Management in Smart Sustainable Cities

Beyond 5G networks will be fundamental towards enabling sustainable mobile communication networks. One of the most challenging scenarios will be met in ultra-dense networks that are deployed in densely populated areas. In this particular case, mobile network operators should benefit from new assessment metrics and data science tools to ensure an effective management of their networks. In fact, incorporating architectures allowing a cognitive network management framework could simplify processes and enhance the network’s performance. In this paper, we propose the use of composite indicators based on key performance indicators both as a tool for a cognitive management of mobile communications networks, as well as a metric which could successfully integrate more advanced user-centric measurements. Composite indicators can successfully synthesize and integrate large amounts of data, incorporating in a single index different metrics selected as triggers for autonomous decisions. The paper motivates and describes the use of this methodology, which is applied successfully in other areas with the aim of ranking metrics to simplify complex realities. A use case that is based on a universal mobile telecommunications system network is analyzed, due to technology simplicity and scalability, as well as the availability of key performance indicators. The use case focuses on analyzing the fairness of a network over different coverage areas as a fundamental metric in the operation and management of the networks. To this end, several ranking and visualization strategies are presented, providing examples of how to extract insights from the proposed composite indicator.

Deep Reinforcement Learning-Assisted Energy Harvesting Wireless Networks.

Heterogeneous ultra-dense networking (HUDN) with energy harvesting technology is a promising approach to deal with the ever-growing traffic that can severely impact the power consumption of small-cell networks. Unfortunately, the amount of harvested energy, which depends on the transmission environment, is highly random and difficult to predict. Since there may be multiple sources of energy in the HUDN, e.g., macro base stations or TV towers, the challenging issue is when and where to harvest energy. Optimally controlling the HUDN can profoundly influence the performance of both data transmission and energy harvesting. However, the working pattern of individual small cell base stations needs to be determined in every time slot. To find an optimal solution in a highly random environment we propose reinforcement learning methods, such as deep deterministic policy gradient (DDPG) and wolpertinger DDPG (W-DDPG). Since the action space is large and discrete for the controlling tasks, a W-DDPG algorithm has been found to be the best approach. The simulation results verify that, compared with the original DDPG algorithm and deep Q-learning, the proposed W-DDPG method can achieve a superior performance in terms of both energy efficiency and throughput.

A Class of Evolutionary Algorithms for Determining Shortest Path Routing in 5G Ultra Dense Heterogeneous Networks

Ultra Dense Network (UDN), an important element of the upcoming 5G networks are characterised by extremely dynamic operations due to the presence and mobility of large number of users spread over small cells of varying sizes. It makes optimal path between the source/destination pairs for communication and data transmission be highly dynamic in nature and hence a challenging issue to deal with. Under such dynamic backdrops, routing procedures have to exhibit robustness, scalability and time efficiency in order to ensure seamless link reliability and Quality of Service (QOS) of the network. In the proposed work, the shortest optimal route of the source and destination pair is found using a combination of evolutionary optimization algorithms namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO) Algorithm and our novel hybrid PSOGA approach which searches for an optimized solution by determining cost functions of individual fitness state and comparing states generated between individual solutions. Application of all the three above mentioned algorithms to the Shortest Path Routing Problem in UDNs and the results obtained have shown that the hybrid PSO-GA comparatively provided enhanced optimized solution.

Optimal edge server deployment and allocation strategy in 5G ultra-dense networking environments

With the emergence of compute-intensive applications and rapid growth of mobile users and Internet of Things(IoT) devices, edge computing was proposed to provide cloud-like services in close proximity to meet the requirements of low-latency, security, and lower energy consumption. Ultra-dense network (UDN) is a promising technique to meet the requirements of explosive data traffic in 5G mobile communications to improve regional spectrum efficiency and network coverage. This paper proposed an optimal deployment and allocation strategy for deploying edge servers(ESs) in UDN to minimize the cost of service providers and to guarantee the completion time of services. Based on queuing theory and vector quantization(VQ) technique, the strategy can optimize both the number and locations of edge servers, and mobile users allocation for the given UDN environments. The experimental results show that the strategy is superior to existing algorithms in both qualitative and quantitative aspects, and can minimize the cost of deployment and guarantee the mobile user’s requirements for the completion time of the offloading task.

A pilot contamination avoidance based on pilot pattern design for ultra-dense network

With the ever-growing number of base stations (BSs) and user equipments (UEs) in ultra-dense networks (UDN), reusing the same pilot sequences among the cells is inevitable. With pilot reuse scheme, the channel estimation obtained at a BS contains not only the desired channel-state information (CSI) but also interference from neighboring cells, which can severely degrade CSI estimation performance and adversely affect communication performance. In this paper we consider a pilot contamination avoidance based on pilot pattern design for UDN where the pilot reuse employed and the interfering users from neighboring cells may be not at lower power levels at the BS compared to the in-cell users. We present a novel statistical interference model of sub-carriers to describe the non-deterministic interference from neighboring cells. Then, we provide a pilot pattern design model with non-uniform pilot distribution. Based on this, a pilot contamination avoidance based on pilot pattern design is proposed where pilot reuse scheme and the non-deterministic interference from neighboring cells are taken into consideration. Unlike existing interference mitigation approaches, the proposed method eliminates interference through the method of interference avoidance and can be applied to different kinds of channel estimation algorithms. Simulation results showed that the proposed approach can effectively avoid the interference and ensure the accuracy of channel estimation.

Performance evaluation of wireless data traffic in Mm wave massive MIMO communication

<span>Due to the evaluation of mobile devices and applications in the current decade, a new direction for wireless networks has emerged. The general consensus about the future 5G network is that the following should be taken into account; the purpose of thousand-fold system capacity, hundredfold energy efficiency, lower latency, and smooth connectivity. The massive multiple-input multiple-output (MIMO), as well as the Millimeter wave (mm Wave) have been considered in the ultra-dense cellular network (UDN), because they are viewed as the emergent solution for the next generations of communication. This article focuses on evaluating and discussing the performance of mm Wave massive MIMO for ultra-dense network, which is one of the major technologies for the 5G wireless network. More so, the energy efficiencies of two kinds of architectures for wireless backhaul networks were investigated and compared in this article. The results of the simulation revealed some points that should be considered during the deployment of small cells in the two architectures UDN with backhaul network capacity and backhaul energy efficiency, that the changing the frequency bands in Distribution approach gives the same energy efficiency reached to 600 Mb/s at 15 nodes while the Conventional approach results reached less than 100 Mb/s at the same number of nodes.</span>

The Impact of Heterogenous Ultra-dense Network Technologies on the Performance of 4G and 5GNetworks

The flood of applications that demand massive data has imposed a challenge for 5G cellular network in order to deliver high data rates, a better quality of service, and low energy consumption. Heterogenous ultra- dense networks are one of the major technologies to address such challenges. HUDNs play a big role in a cellular system. They deliver cost-effective coverage with low transmit power and high capacty to face the risen data and the high expectations of the user’s performance. In this paper, we introduce the impact of small cells on the cellular system and the technologies the small cells utilize to make the cellular system faces the subscriber’s demands. First, we discuss the fundamentals of used technologies in small cells. Next, we studied the small cell management. Then, self-organizing networks are studied. After that, we have reviewed the small cell’s power consumption, mobility, and handover. Finally, the real-world experience of mm-waves and MIMO in 5G small cells.

5G Integrated Spectrum Selection and Spectrum Access using AI-based Frame work for IoT based Sensor Networks

The convulsive advancement of multiple-input multiple-output devices and ultra-dense networks has been extensively considered as the key facilitators that ease the evolution and formation of 5G systems. The explosive growth of wireless devices necessitates the deployment of the Internet of Things (IoT), which is the potential of interconnecting diversified things using wireless communications. To enable wireless accesses of IoT devices, Artificial Intelligence (AI) plays a significant role in 5G network. While existing end-to-end learning and adaptive model require continuous monitoring and dynamic changes cannot achieve global optimization due to wireless signal classifiers and a higher amount of interference. In this work, an integrated spectrum selection and spectrum access using a greedy and AI-based framework to allow the forthcoming and subsequent demands on 5G and beyond is presented. Fractional Knapsack Greedy-based strategy is introduced, and Langrange Hyperplane-based approach is utilized to realize the AI-based strategies for spectrum selection and spectrum allocation for IoT-enabled sensor networks. This framework is called as Fractional Knapsack and Langrange Hyperplane Spectrum Access (FK-LHSA). First Fractional Knapsack Multi-band spectrum selection (FKMSS) model is designed along with an energy consumption model to optimize channel or spectrum throughput. Next, a Lagrange Hyperplane (LH) spectrum access model is designed to minimize spectrum access delay and improve spectrum access accuracy. The simulation results show that the proposed FKM model and LH model can effectively reduce the spectrum access delay along with the improvement of throughput and spectrum access accuracy.

Resource Allocation for NOMA-MEC Systems in Ultra-Dense Networks: A Learning Aided Mean-Field Game Approach

Attracted by the advantages of multi-access edge computing (MEC) and non-orthogonal multiple access (NOMA), this article studies the resource allocation problem of a NOMA-MEC system in an ultra-dense network (UDN), where each user may opt for offloading tasks to the MEC server when it is computationally intensive. Our optimization goal is to minimize the system computation cost, concerning the energy consumption and task delay of users. In order to tackle the non-convexity issue of the objective function, we decouple this problem into two sub-problems: user clustering as well as jointly power and computation resource allocation. Firstly, we propose a user clustering matching (UCM) algorithm exploiting the differences in channel gains of users. Then, relying on the mean-field game (MFG) framework, we solve the resource allocation problem for intensive user deployment, using the novel deep deterministic policy gradient (DDPG) method, which is termed by a mean-field-deep deterministic policy gradient (MF-DDPG) algorithm. Finally, a jointly iterative optimization algorithm (JIOA) of UCM and MF-DDPG is proposed to minimize the computation cost of users. The simulation results demonstrate that the proposed algorithm exhibits rapid convergence, and is capable of efficiently reducing both the energy consumption and task delay of users.

Reinforcement learning based computation-aware mobility management in ultra dense networks

Computation-aware delay optimal mobility management (MM) is an important problem in ultra-dense network (UDN) with mobile edge computing (MEC). Since the additional time delay caused by task computation is not taken into consideration, traditional radio access-oriented mobility management scheme cannot guarantee the overall delay performance of delay-sensitive user equipment (UE). In this paper, we propose a novel dynamic programming-based mobility management (DPMM) scheme to minimize the average delay under an energy consumption constraint. DPMM makes MM decisions using statistic information to handle the inaccurate state information. Cooperative data transmission is adopted to improve the delay performance. Simulation shows that the proposed DPMM scheme can achieve delay performance close to optimal and reduce the frequency of handover. However, the wireless link, computation resources and UE’s location in UDN environment is dynamic, which leads to information uncertainties. We further propose an MM scheme based on deep Q-network (DQN) to learn the system information from the environment. In this scheme, UE takes the current and past observed delay as experience, learning the optimal mobility management strategy through DQN training. Simulation shows that DQN-based MM can learn from experience and reduce the handover frequency to a certain degree.