Ultra-dense Cellular Networks Research Articles

5G ұялы желілердегі ұяшықтардың орналасу тығыздығын талдау

The increase in high data rate demand has caused an increase in the density of small cells to meet the maximum needs of subscribers. The emergence of an ultra-dense cellular network is due to a powerful MIMO antenna and communication technologies in millimeter waves. One of the most anticipated ways to overcome the uncertainty of the millimeter wave network (mmWave) is to deploy ultra-dense small cells in the future fifth generation (5G). An ultra-dense cellular network is one of the most important tasks for 5G cellular networks. The purpose of this article is to provide an overview of cell location density in cellular networks. The article demonstrated the effect of the number of different small cellular BSS on the bandwidth of the Feedback Network and the energy efficiency of the feedback of ultra-dense cellular networks. The result shows that ultra-dense cellular networks have a small cell density limit.

Correction to: Performance Modeling of Call Admission Control Policy and Handover Management in 5G Ultra-Dense Cellular Network

Correction to: Performance Modeling of Call Admission Control Policy and Handover Management in 5G Ultra-Dense Cellular Network

Performance Modeling of Call Admission Control Policy and Handover Management in 5G Ultra-dense Cellular Network

Performance Modeling of Call Admission Control Policy and Handover Management in 5G Ultra-dense Cellular Network

Blockchain-Based Lightweight Multifactor Authentication for Cell-Free in Ultra-Dense 6G-Based (6-CMAS) Cellular Network

Cell-Free mMIMO is a part of technology that will be integrated with future 6G ultra-dense cellular networks to ensure unlimited wireless connectivity and ubiquitous latency-sensitive services. Cell-Free gained researchers’ interest as it offers ubiquitous communication with large bandwidth, high throughput, high data transmission, and greater signal gain. Cell-Free eliminates the idea of cell boundary in cellular communication that reduces frequent handover and inter-cell interference issues. However, the effectiveness of the current authentication protocol could become a serious issue due to the dynamic nature of Cell-Free in densely distributed, high number of users, high mobility, and frequent data exchange. Secondly, secure communication may be achieved in such a dynamic environment at the expense of high authentication overhead, high communication and computational costs. To address the above security challenges, we proposed a lightweight multifactor mutual authentication protocol for Cell-Free communication using ECC-based Deffie Hellman (ECDH). This scheme utilizes timestamping, one-way hash function, Blind-Fold Challenge scheme with public key infrastructure. The proposed cryptosystem integrates with blockchain technology using proof of staked (POS) as a consensus mechanism to ensure integrity, non-repudiation and traceability. The proposed scheme can enforce the mitigation of several major security attacks on communication links such as spoofing attacks, eavesdropping, user location privacy issues, replay attacks, denial of service attacks, and man-in-the-middle (MITM) attacks, which is one of the significant features of the scheme. Furthermore, this scheme contributes to reducing authentication, communication, and computational overheads with an average of 32.8%, 52.4% and 53.2% better performance respectively as compared baseline authentication protocols.

An Analysis of Distributed Joint Spectrum Resource Allocation With Power Control in Ultra-Dense Cellular Networks

In the view of spatial statistical average, when users are associated with their nearest small base stations (SBSs), they are mainly interfered by their second closest SBSs, especially for edge users. To reduce inter-cell interference (ICI) caused by the second closest SBSs, a distributed joint spectrum resource allocation strategy with power control in the downlink of one-tier ultra-dense networks (UDNs) is proposed in this paper. According to the distance from users to their nearest SBSs, users are divided into inner users and edge users. We then try to make the subchannel randomly allocated so they be different from that of edge users of their second closest SBS. Meanwhile the transmission power of SBSs on different subchannels is adjusted. Using stochastic geometry we derive the analytical expressions of the meta distribution of signal-to-interference ratio (SIR) to obtain more fine-grained information such as the mean and variance of conditional success probability, the local delay. Furthermore, we revise and redefine the area spectrum efficiency (ASE) and energy efficiency (EE) to accurately evaluate the benefits. Compared with the traditional random resource allocation strategy, which does not exploit resource allocation and interference coordination scheme, the proposed strategy can balance the performance of users and improve the whole network EE. Our analytical results are validated through simulation.

Dynamic Coverage Optimization for 5G Ultra-dense Cellular Networks Based on Their User Densities

This paper has proposed a user-density-based coverage optimization technique for ultra-dense cellular networks. Antenna tilting is a promising coverage optimization technique to be used in 5G networks, that significantly improve the signal to interference plus noise ratio (SINR) by choosing the appropriate angle of tilt. In this paper, the cellular coverage has been optimized for scattered user densities/user hotspots using an adaptive antenna tilting mechanism that steers the beams towards the temporal hot spot in the coverage area. The proposed method has the competence to improve the desired SINR level and coverage area for a group of users rather than a single user. In this work, a reinforcement learning (RL) algorithm has been implemented to optimize the tilt angle. The performance of the proposed technique has been evaluated in the simulation platform considering a three-sectored multicellular mobile network where the groups of user clusters are distributed randomly. The result confirms the improvement in RSS and SINR values in the group of users having high density with maximum user satisfaction.

Coverage and Meta Distribution Analysis in Ultra-Dense Cellular Networks With Directional Antennas

This article proposes a one-tier ultra-dense cellular network framework based on the characteristics of directional beamforming to explore the downlink signal-to-interference ratio (SIR) performance. The features of the proposed cellular network include: small base stations (SBSs) are equipped with directional antennas; the beam boresight directions of antennas are adaptively adjusted with the locations of users on the two-dimensional horizontal plane while the boresight directions are perfectly aligned with their associated users. In addition, an angle fading model, which follows from the exponential distribution, is developed to simulate the signals attenuation of interference links. The angle fading of the interference signals depends on the angle offset between the locations of users and the beam boresight directions of their interfering SBSs. With the assistance of stochastic geometry, the analytical expressions of coverage probability and average ergodic rate are derived by combining with the probability density function of angle offset variable. Then, the meta distribution of SIR is obtained to capture the performance changes of individual links for users. The numerical results indicate that the performance advantages of the proposed directional network obviously exceed that of the conventional isotropic antennas case. Monte-Carlo simulations validate the correctness of numerical results.

G/M/1-Based DDoS Attack Mitigation in 5G Ultradense Cellular Networks

With the 5G millimeter wave (mmWave) application, ultradense cellular networks are gradually becoming one of the core characteristics of 5G cellular networks. In the edge computing environment, considering load balancing among edge nodes is beneficial to slow down the process of distributed denial of service (DDoS) attack. However, most existing studies have given less consideration to congestion in the multiuser and multiedge server models. Someone who uses the M/M/1 model also seems to ignore the effect of scheduling algorithms on the Markov property of the task arrival process. In this manuscript, based on ensuring the quality of experience (QoE) for users, the G/M/1 model is introduced to the task scheduling of edge servers for the first time to improve load balancing between edge servers. For the multi armed bandit (MAB) algorithm framework, specific metrics are established to quantify the degree of its equilibrium. The number of users assigned to the edge nodes and each edge node’s processing of specific tasks is taken into account. We experimentally evaluated its performance against two baseline approaches and three state-of-the-art approaches on a real-world dataset. And the experimental results validate the effectiveness of this method.

Secrecy‐oriented user association in ultra dense heterogeneous networks against strategically colluding adversaries

Network densification is recognized as the key technology to meet the ever growing demand of data traffic in next generation wireless networks. However the proliferation of small cell base stations (SCBSs) introduces vulnerabilities to information security, as they are prone to eavesdropping attacks. In this paper, it is studied how user association strategies can be specifically tailored to meet this security challenge in a ultra dense heterogeneous cellular network(UDHCN) with a group of colluding eavesdroppers. In particular, the situation is considered where eavesdroppers may have limited capabilities in intercepting and decoding data traffic. In this setting each eavesdropper has to make its own decision on eavesdropping targets, and they are able to take concerted actions to sabotage information security. To address this challenge, a zero sum game framework to reflect the conflicting interests of users and eavesdroppers is proposed and a user association scheme is devised that aims to maximize system sum secrecy rate against these adversaries whose actions intend to minimize sum secrecy rate. The case that all adversaries have limited eavesdropping capabilities is first considered, and it is shown that the corresponding zero sum game is indeed a bilinear game, and its Nash equilibrium solution can be readily approximated using a saddle point Frank Wolfe(SP-FW)algorithm. Then the framework is extended to the case where adversaries with a diverse configuration of eavesdropping capabilities exist. In this case, it is shown that the underlying minimax optimization problem is indeed a nonsmooth convex-nonconcave one. A two stage method is proposed by first smoothing the objective function with a Hermite cubic polynomial approximation, and then obtaining a nearly stationary solution via a recently proposed proximal point gradient descent method. Simulation results show that the proposed framework leads to significant increases in sum secrecy rate and secrecy probability against both the capability-limited adversaries and a hybrid type of adversaries.

Intelligent Recommendation-Based User Plane Handover With Enhanced TCP Throughput in Ultra-Dense Cellular Networks

In ultra-dense cellular networks (UDNs) with user/control plane(U/C) splitting, frequent handovers in user planes are unavoidable, which seriously degrades the transmission control protocol (TCP) throughput of mobile station (MS). To enhance the TCP throughput in UDNs, this paper proposes an intelligent recommendation-based user plane handover scheme. Firstly, based on intelligent recommendation algorithms, a mobility prediction algorithm called content-based collaborative hybrid filters (CCHF) is proposed to predict the target small base station (SBS). When the MS moves into the cell-edge of the source SBS, it can pre-access the predicted target SBS and set up connections to the predicted target SBS and the source SBS simultaneously. This is the proposed CCHF-dual-handover scheme. With an accurate prediction and a simultaneous connection, CCHF-dual-handover can present enhanced signal to interference and noise ratio (SINR) at cell-edge, reduced handover interruption ratio (HIR), and improved MS’s TCP throughput. Moreover, TCP throughput of CCHF-dual-handover is analyzed to show the impact of various key parameters (such as MS’s velocity and pre-access threshold). Finally, simulations are carried out to evaluate the performance of the proposed CCHF-dual-handover. Given random trajectory, the prediction accuracy using CCHF is increased by more than 100% compared with existing prediction algorithms. Given accurate prediction, the CCHF-dual-handover can improve the TCP throughput by up to 150% compared with that of existing handover schemes.

A survey on sleep mode techniques for ultra-dense networks in 5G and beyond

The proliferation of mobile users with an attendant rise in energy consumption mainly at the base station has requested new ways of achieving energy efficiency in cellular networks. Many approaches have been proposed to reduce the power consumption at the base stations in response to the contribution of energy cost to the increase of OPEX of the mobile operators and the rise of the carbon footprint on global climate. As a springboard to the application of sleep mode methods in ultra-dense cellular networks, this paper provides a comprehensive survey of the base station sleep mode strategies in heterogeneous mobile networks from perspectives of modeling and algorithm design. Specifically, the sleep mode enabling strategies and sleep wake-up schemes are reviewed. The base station sleep-mode techniques in ultra-dense networks are further discussed as well as the challenges and possible solutions.

Fast Channel Allocation for Ultra-dense D2D-enabled Cellular Network with Interference Constraint in Underlaying Mode

Fast Channel Allocation for Ultra-dense D2D-enabled Cellular Network with Interference Constraint in Underlaying Mode

Adaptive offloading in mobile-edge computing for ultra-dense cellular networks based on genetic algorithm

With the combination of Mobile Edge Computing (MEC) and the next generation cellular networks, computation requests from end devices can be offloaded promptly and accurately by edge servers equipped on Base Stations (BSs). However, due to the densified heterogeneous deployment of BSs, the end device may be covered by more than one BS, which brings new challenges for offloading decision, that is whether and where to offload computing tasks for low latency and energy cost. This paper formulates a multi-user-to-multi-servers (MUMS) edge computing problem in ultra-dense cellular networks. The MUMS problem is divided and conquered by two phases, which are server selection and offloading decision. For the server selection phases, mobile users are grouped to one BS considering both physical distance and workload. After the grouping, the original problem is divided into parallel multi-user-to-one-server offloading decision subproblems. To get fast and near-optimal solutions for these subproblems, a distributed offloading strategy based on a binary-coded genetic algorithm is designed to get an adaptive offloading decision. Convergence analysis of the genetic algorithm is given and extensive simulations show that the proposed strategy significantly reduces the average latency and energy consumption of mobile devices. Compared with the state-of-the-art offloading researches, our strategy reduces the average delay by 56% and total energy consumption by 14% in the ultra-dense cellular networks.

Intelligent fast cell association scheme based on deep Q-learning in ultra-dense cellular networks

To support dramatically increased traffic loads, communication networks become ultra-dense. Traditional cell association (CA) schemes are time-consuming, forcing researchers to seek fast schemes. This paper proposes a deep Q-learning based scheme, whose main idea is to train a deep neural network (DNN) to calculate the Q values of all the state-action pairs and the cell holding the maximum Q value is associated. In the training stage, the intelligent agent continuously generates samples through the trial-and-error method to train the DNN until convergence. In the application stage, state vectors of all the users are inputted to the trained DNN to quickly obtain a satisfied CA result of a scenario with the same BS locations and user distribution. Simulations demonstrate that the proposed scheme provides satisfied CA results in a computational time several orders of magnitudes shorter than traditional schemes. Meanwhile, performance metrics, such as capacity and fairness, can be guaranteed.

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>

Optimization of unmanned aerial vehicle augmented ultra-dense networks

In this paper, we study the integration of unmanned aerial vehicle small cells (UAV-SCs) for the purpose of augmenting or temporarily restoring service to an ultra-dense cellular network. The aim is to minimize the overall power consumption of the network by jointly optimizing the number of UAV-SCs, their placement, associations, and the power allocation, subject to user QoS (quality of service), transmit power, and fronthaul capacity constraints. As the resulting optimization problem is non-convex and computationally inefficient to solve, we investigate lower complexity alternatives. By reformulating the original problem, a linear structure can be obtained that is efficiently solved by off-the-shelf solvers. Furthermore, we also propose a meta-heuristic method that is based on particle swarm optimization. The performance of the proposed methods is evaluated via simulation studies and compared to state-of-the-art techniques. The results illustrate that the proposed methods consistently outperform conventional techniques by deploying fewer UAV-SCs and also lowering the transmit powers. Furthermore, considerable power savings were observed particularly for low QoS demands and dense scenarios.

A Multidepth Load-Balance Scheme for Clusters of Congested Cells in Ultradense Cellular Networks

Densely deploying small cells will be a solution to provide explosive data requirements in fifth-generation networks. Because users often cluster together in popular locations of an urban area, congested small cells in the popular sites are also gathered. Traditional load balancing schemes generally only consider neighboring cells when offloading users are not suitable for clusters of overloaded cells. This paper considers groups of overloaded cells in the load balancing problem. The objective is to maximize the quality-of-service satisfaction ratio. To solve the problem, we propose a multidepth offloading algorithm with the consideration of the radio resource allocation. The proposed multidepth offloading algorithm can be applied no matter that congested small cells are gathered or not. Compared with a previous offloading algorithm and a baseline, the simulation results show that our proposed algorithm can increase 16% QoS satisfaction ratio in a real user distribution and 13% QoS satisfaction ratio in a clustered user placement.

Energy-Efficient SWIPT-Empowered D2D Mode Selection

While mode selection has been envisioned as the most cost-effective way to address the interference issue in Device-to-Device (D2D) communications, existing works have been largely conducted without consideration of the energy depletion of devices. In this paper we investigate simultaneous wireless information and power transfer (SWIPT) empowered mode selection based on stochastic geometry. As a mean of solving it, system energy efficiency is formulated by determining the closed-form ergodic energy-harvested and ergodic capacity of D2D and cellular users in reuse, dedicated, and cellular communication modes with the time switching and power splitting architectures of SWIPT. We then leverage the derived results, along with the energy efficiency to design an energy-efficient mode selection mechanism. Our simulation results show that the developed mechanism is able to select the best mode for D2D communication with better energy efficiency, especially in an ultra-dense cellular network as compared with a state-of-the-art mode selection approach.

Topology Optimization for 6G Networks: A Network Information-Theoretic Approach

The classical approach of avoiding or ignoring interference in wireless networks cannot accommodate the ambitious quality-of-service (QoS) demands of ultradense cellular networks (CNs). However, recent ground-breaking information-theoretic advances have changed our perception of interference from that of a foe to a friend. This article aims to shed light on harnessing the benefits of integrating modern interference management (IM) schemes into future CNs. To this end, we envision a hybrid multiple-access (HMA) scheme that decomposes the network into subtopologies of potential IM schemes for more efficient utilization of network resources. Preliminary results show that an HMA scheme can multiply nonorthogonal multiple-access (NOMA) performance, especially under dense user deployment.

Multidimensional Cooperative Caching in CoMP-Integrated Ultra-Dense Cellular Networks

Small base stations (BSs) equipped with caching units are potential to provide improved quality of service (QoS) for multimedia service in ultra-dense cellular networks (UDCNs). In addition, the Coordinated MultiPoint (CoMP) transmission method, allowing multiple BSs to jointly serve users, is proposed to increase the throughput of cell-edge mobile terminals (MTs). Yet, the combination of content caching and CoMP in UDCNs is still not well explored for future networks. In this paper, we focus on the application of caching in CoMP-integrated UDCNs, where the cache-enabled BSs can collaboratively serve each MT using either joint transmission or single transmission. We propose a multidimensional cooperative caching (MDCC) scheme, supporting storage-dimension and transmission-dimension cooperations for the content placement. In particular, we analyze the delivery delay based on request patterns, transmission method, and the proposed cooperation strategy. Then the content placement problem is formulated as a problem of minimizing the overall expected delay. The problem is a mixed binary integer linear programming (BILP) problem, which is NP-hard. Therefore, we address the problem with approximation and substitution, and design a genetic algorithm (GA) based method to solve it. Simulation results demonstrate that the proposed MDCC scheme contributes performance gain in terms of content delivery delay in both cell-core and cell-edge areas.