Ultra-dense Heterogeneous Networks Research Articles

Performance Analysis of Backhaul‐Aware User Association in 5G Ultradense Heterogeneous Networks

In 5G ultradense heterogeneous networks, wireless backhaul, as one of the important base station (BS) resources that affect user services, has attracted more and more attention. However, a user would access to the BS which is the nearest for the user based on the conventional user association scheme, which constrains the network performance improvement due to the limited backhaul capacity. In this paper, using backhaul‐aware user association scheme, semiclosed expressions of network performance metrics are derived in ultradense heterogeneous networks, including coverage probability, rate coverage, and network delay. Specifically, all possible access and backhaul links within the user connectable range of BSs and anchor base stations (A‐BSs) are considered to minimize the analytical results of outage probability. The outage for the user occurs only when the access link or backhaul link which forms the link combination with the optimal performance is failure. Furthermore, the theoretical analysis and numerical results evaluate the impact of the fraction of A‐BSs and the BS‐to‐user density ratio on network performance metric to seek for a more reasonable deployment of BSs in the practical scenario. The simulation results show that the coverage probability of backhaul‐aware user association scheme is improved significantly by about 2× compared to that of the conventional user association scheme when backhaul is constrained.

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.

Interference Mitigation in HetNets to Improve the QoS Using Q-Learning

Small cells (SCs) based ultra-dense heterogeneous networks (HetNets) are one of the promising solutions for increased coverage and capacity in 5G cellular networks. However, in multi-tiered architecture, co-tier and cross-tier interferences are both performance-limiting factors. Efficient resource allocation techniques can handle interferences effectively, however, their complexity linearly increases with the density of the HetNets resulting from dynamic and unplanned deployment of SCs. Therefore, HetNets can be implemented only through an algorithm that is self-organizing and adaptive to the dynamic conditions. In this research article, a Q-Learning (QL) based adaptive resource allocation scheme is proposed and evaluated for SC-based ultra-dense HetNets. This QL scheme allocates optimal power to the small cell base station (SBS) to meet the minimum required capacity of macrocell user equipment (MUEs) and the small cell user equipment (SUEs) to provide quality of service (QoS). The proposed QL scheme not only maintains the minimum required capacities of the MUEs and SUEs but has also shown a significant improvement in the capacities of MUEs and SUEs in high interference scenarios as compared to the prior works. In the high co-tier and cross-tier interference scenario, where the state of the art schemes fail to maintain the minimum required capacity of the MUE, the proposed scheme provides a minimum MUE capacity of 2 b/s/Hz, which is twice the minimum required QoS threshold. In a similar way, the proposed solution guarantees QoS up to 16 SCs which are 37.5% more SC than the previously reported works in high interference scenario while maintaining a minimum SUE capacity of 1.5 b/s/Hz, which is 33% higher than the minimum required QoS threshold. By simultaneously mitigating co-tier and cross-tier interferences in ultra-dense HetNets, the proposed solution not only improved the minimum capacities of MUEs and SUEs but also sets a new benchmark for minimum QoS threshold.

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.

Handoff Algorithm Based on Location Prediction in Ultra-dense Heterogeneous Wireless Network

In the ultra-dense heterogeneous wireless network composed of heterogeneous cellular networks and wireless local area networks, vehicle terminals with variable speeds will face more frequent handovers, resulting in the deterioration of user’s Quality of Service (QoS). For the above problems, firstly, the Gauss Markov mobility model is used to predict the position of the vehicle terminal at the next moment, and the candidate network set that meets the terminal service quality is selected to make the intersection with the current candidate network set. Secondly, if the current access network is not in the intersection, the variable-step firefly algorithm is used to find the best network. Thirdly, the terminal that fails to switch due to the prediction error is migrated to the macro cellular to ensure the continuity of communication. Simulation results show that the proposed algorithm can reduce the frequent handoff phenomenon, such as ping pong handoff in the ultra-dense heterogeneous wireless network. Meanwhile, it can improve the user service quality and network throughput.

A Fully Distributed and Clustered Learning of Power Control in User-Centric Ultra-Dense HetNets

In this paper, we investigate a power control of uplink connection in the user-centric ultra-dense heterogeneous networks (HetNets), which are studied as different types of access points (APs). The main objective of this investigation is to engage users in a cooperative game to confront with the problem of per-user power control and coordinate multi-user interferences. Thus, we formulate the optimization problem as a sum of the users’ cooperative energy efficiency (EE) functions. Firstly, a realistic and new model of the user device's power consumption is proposed. This model includes the power used for operating modes and signal processing of mobile devices during the uplink data transmission. Secondly, the EE optimization problem is formulated by maximizing the sum of the users’ cooperative EE function subject to each user's quality-of-service (QoS) and a power constraint. Then, we propose a fully distributed and clustered learning scheme for solving the optimization problem, where neighboring users are clustered to engage in the cooperative game of power control in order to coordinate the multi-user interferences. It is theoretically proved that the size of the clusters has an impact on the sum of the users’ cooperative EE. Additionally, our scheme can achieve convergence with imperfect channel feedback and the local knowledge of the user's constraints. Finally, simulation results confirm the theoretical analysis and demonstrate the robust performance of the proposed scheme compared with two benchmark methods.

Game Theory-Based Multi-Objective Optimization Interference Alignment Algorithm for HSR 5G Heterogeneous Ultra-Dense Network

The high-speed railway wireless communication network architecture is gradually transforming from a traditional GSM-Railway (GSM-R) cellular network to a 5G heterogeneous ultra-dense communication network. How to effectively deal with the interference while obtaining the capacity gain has become an inevitable problem. This article proposes a power allocation interference alignment algorithm based on game equilibrium with the goal of joint optimization about throughput and energy efficiency for imperfect channels in high-speed railway communication. Firstly, the imperfect Channel State Information including delay and fading is calculated. Then, in order to improve system throughput and energy efficiency, we establish a game model and prove the existence of Nash equilibrium in the model. At the same time, a power allocation iterative algorithm based on Recurrent Neural Network is proposed. Finally, combined with the interference alignment algorithm based on a maximal signal-to-noise ratio, the optimal power matrix is calculated by iteration to achieve interference management optimization. The simulation results prove that our algorithm has superior performance in improving the system throughput, energy efficiency and transmission reliability in high-speed railway wireless communication with imperfect channels.

Victim Aware AP-PF CoMP Clustering for Resource Allocation in Ultra-Dense Heterogeneous Small-Cell Networks

Heterogeneous networks with dense deployment of femto cells has provided the promising solution to enhance the system throughputs for the next generation wireless communication. When the large number of heterogeneous networks are overlapped, then traditional intercell interface technique failed to mitigate the interference in between the cells. So, to mitigate the interference, it requires advanced approach for improving the cell edge throughputs and spectral efficiency. For this, the paper presents a frame work to allocate the efficient resource among the users in dense networks. We proposed affinity propagation unsupervised learning to form the cluster with center and then regularized the cluster for effectively allocated the resource. Users on the cluster edge has suffering the inter cluster interface, a victim aware and coordination multipoint mechanism is further proposed to allocated the required resources for these victimized users. We analyzed the performance of our proposed framework with proportional fair based criteria. The total throughputs, edge throughput and spectral efficiency of the system are significantly enhanced in our simulation results through this proposed framework.

Movement Aware CoMP Handover in Heterogeneous Ultra-Dense Networks

The densification of base station (BS) deployments is driving the evolution of network structures towards heterogeneous ultra-dense networks (UDN), making coordinated multipoint (CoMP) a viable and promising transmission solution. However, the BS cooperation regions formed by applying CoMP in the UDN are small and irregular, which causes frequent handover for mobile users. Different from most existing work that focus on the trigger time of handover, we explore how to choose the appropriate BS cooperation set to reduce handover rate. In this paper, we consider movement aware CoMP handover (MACH). By estimating cell dwell time, a user would be intelligently assigned to macro cell or small cell according to its movement trend. To enhance reliability, we further proposed improved MACH (iMACH) to achieve a trade-off between BSs with long dwell time and the current best performed BS for multipoint cooperation while user moving. Using stochastic geometry method, expressions of coverage probability, handover probability and throughput that characterize performance of the proposed schemes are derived. The numerical results indicate that the theoretical analyses fit the simulation results well and the proposed schemes surpass the existing schemes in terms of the aforementioned metrics, and more intelligent and suitable for ultra-dense scenarios.

Adaptive Relay Selection Scheme by Using Compound Channel

As a cellular network of the fifth generation (5G) is commercialized, mobile devices and data throughput have rapidly increased. According to the spatial density for communication increases, the nodes of cell are overloaded. Therefore, the heterogeneous ultra dense network (UDN) is suggested. Furthermore, the techniques for selecting a relay have been proposed in the Heterogeneous Net (HetNet). The relays are needed to improve communication performance and mitigate overload of nodes. In this paper, an adaptive relay selection scheme is proposed to obtain the diversity gain from multiple relays. To enhance the reliability of communication, the proposed scheme suggests a new algorithm considering outage probability and diversity gain of compound channel. Furthermore, the selected relays use an antenna selection algorithm to improve the channel capacity. Simulation results show that the proposed scheme improves the bit error rate (BER) and the data throughput.

Performance analysis of ultra-dense heterogeneous network switching technology based on region awareness Bayesian decision

The network switching is one of the most important techniques, which keeps continuous communication between two users. A variety of approaches and strategies (such as fuzzy logic control, neural network, smart algorithm etc.) have been proposed to confront this problem. These approaches and strategies play an important role in reducing delays, decreasing drop call rates, and improving QoS during switching. However, the existing techniques and strategies often apply to some special scenarios, such as between WLAN and WiFi (or WiMAX, or 3G, or UTMS and LTE). Facing the ultra-dense heterogeneous network in the 5G communication system, this brings great difficulties to the switching, especially how to properly select a service network. Whether the existing methods and strategies are feasible remains to be studied. For solving the switching in a complication networks environment, a novel switching way is proposed in this paper. We adopt the technology of regional awareness and combine with Bayes’ decision strategy to explore the switching of ultra-dense heterogeneous network. This way effectively solves the difficult problem of selecting a service network in the convention. Finally, we analyze the err probability of the proposed way. The experimental results show that our scheme can properly select the switched network in the 5G system, and the probability of the handover error is the lowest, which ensures the rationality and effectiveness of the network handover. Therefore, the proposed way in this paper is feasible.

Envisioning Device-to-Device Communications in 6G

To fulfill the requirements of various emerging applications, the future sixth generation (6G) mobile network is expected to be an innately intelligent, highly dynamic, ultradense heterogeneous network that interconnects all things with extremely low-latency and high speed data transmission. It is believed that artificial intelligence (AI) will be the most innovative technique that can achieve intelligent automated network operations, management and maintenance in future complex 6G networks. Driven by AI techniques, device-to-device (D2D) communication will be one of the pieces of the 6G jigsaw puzzle. To construct an efficient implementation of intelligent D2D in future 6G, we outline a number of potential D2D solutions associating with 6G in terms of mobile edge computing, network slicing, and non-orthogonal multiple access (NOMA) cognitive networking.

Interference Management, Backhaul Routing & Switching off in 5G Hetnets with Mmwave Backhaul Links: A Relative Perspective

Today most of the next generation wireless communication systems(5G) faces the challenge of supporting the demands for higher data rates growing exponentially and ensuring to provide a stable quality of service (QoS) throughout the network. Over the next 2 years meeting up these requirements need to maximize the capacity of the network by a factor which will multiply it by thousands. Moreover, the most important societal as well as economical concerns include the power usage of data and communication technology industry and pollution mostly energy-related. On 5G two contrary requirements of providing capacity to support higher cellular networks along with consuming less energy needs to be focused upon. Due to the scarcity of spectral sources, a wide consensus can only be achieved by increasing significantly the number of antennas operating per unit area. Here a heterogeneous network named HetNet is analysed, comprising a macro base station (BS) along with several antennas and an overlaid dense tier possessing SCAs(small cell access points)through a wireless backhaul to get data traffic. The SCAs mostly associate with static as well as low mobility user equipment whereas macro BS serve the medium-to-high mobility. This work analyses the methods of the ultra-dense wireless 5G heterogeneous networks considering the interference management along the mm-Wave backhaul links to utilize the spectrum and network densification to operate mm-Wave 5G HetNet. The study reviews several literature works, their drawbacks and developing a joint model by combining base station switch-off technique with interface mitigation. This study further throws light on the scope of managing the backhaul-aware interference which in turn enhances potential capacity of the system and depending on the suitable backhaul the capacity is improved. Millimetre wave(mm-Wave) has proved to be a remarkable candidate to overcome the issue of ensuring a broad bandwidth having secure link transmission. The ultra-dense heterogeneous networks are discussed along with discussing the role interference management algorithms to minimize energy consumption. The importance of interference management is discussed along with discussing issues related. The research problem is formulated following a comparative analysis.

Skipping-based handover algorithm for video distribution over ultra-dense VANET

Next-generation networks will pave the way for video distribution over vehicular Networks (VANETs), which will be composed of ultra-dense heterogeneous radio networks by considering existing communication infrastructures to achieve higher spectral efficiency and spectrum reuse rates. However, the increased number of cells makes mobility management schemes a challenging task for 5G VANET, since vehicles frequently switch among different networks, leading to unnecessary handovers, higher overhead, and ping-pong effect. In this sense, an inefficient handover algorithm delivers videos with poor Quality of Experience (QoE), caused by frequent and ping-pong handover that leads to high packets/video frames losses. In this article, we introduce a multi-criteria skipping-based handover algorithm for video distribution over ultra-dense 5G VANET, called Skip-HoVe. It considers a skipping mechanism coupled with mobility prediction, Quality of Service (QoS)- and QoE-aware decision, meaning the handovers are made more reliable and less frequently. Simulation results show the efficiency of Skip-HoVe to deliver videos with Mean Opinion Score (MOS) 30% better compared to state-of-the-art algorithms while maintaining a ping-pong rate around 2%.

Mobility Prediction Based Proactive Dynamic Network Orchestration for Load Balancing With QoS Constraint (OPERA)

Load imbalance among small and macro cells is a major challenge that undermines the gains of emerging ultradense heterogeneous networks (HetNets). Existing load balancing (LB) schemes have one common caveat which is operating in reactive mode i.e., cell parameters are tweaked reactively in accordance with the dynamics of cell loads. The inherent reactiveness of these LB schemes hinder in achieving promising quality of experience (QoE) gains from 5G and beyond. To cope with this issue, in this paper we propose a novel proactive load balancing framework “OPERA” empowered by mobility prediction paradigm for future ultra dense networks (UDNs). The pro-activeness of OPERA stems from its novel capability that instead of passively waiting for congestion indicators to be observed and then reacting to them, OPERA predicts future cell loads and then proactively optimizes key antenna parameters and cell individual offsets (CIOs) to preempt congestion before it happens. OPERA also incorporates capacity and coverage constraints and load aware association strategy for ensuring conflict free operation of LB and coverage and capacity optimization (CCO) self-organizing network (SON) functions. Simulation results show that compared to real network deployments settings and published state-of-the-art reactive schemes, OPERA can yield significant gain in terms of fairness in load distribution and percentage of satisfied users. Superior performance of OPERA on several fronts compared to current schemes stems from its following features: 1) It preempts congestion instead of reacting to it; 2) it actuates more parameters than any current LB schemes thereby increasing system level capacity instead of just shifting it among cells; 3) while performing LB OPERA simultaneously maximizes residual capacity while incorporating throughput and coverage constraints; 4) it incorporates a load aware association strategy for ensuring conflict free operation of LB and CCO SON functions; 5) the ahead of time estimation of cell loads allows ample time for heuristics search algorithms to find LB solutions with high gain.

The analysis of coverage probability, ASE and EE in heterogeneous ultra-dense networks with power control

The ultra-dense network is a promising technology to increase the network capacity in the forthcoming fifth-generation (5G) mobile communication networks by deploying lots of low power Small Base Stations (SBSs) which overlap with Macro Base Stations (MBSs). The interference and energy consumption increase rapidly with the number of SBSs although each SBS transmits with small power. In this paper, we model a downlink heterogeneous ultra-dense network where a lot of SBSs are randomly deployed with MBSs based on the Poisson point process. We derive the coverage probability and its variance, and analyze the area spectral efficiency and energy efficiency of the network considering three Fractional Power Control (FPC) strategies. The numerical results and Monte Carlo simulation results show that power control can mitigate the interference and balance the performances of inner-user and edge-user equipments. Especially, a great improvement of energy efficiency is archived with a little loss of area spectral efficiency when FPC is adopted. Finally, we analyze the effect of base stations’ (BSs’) sleeping on the performance of the network when it is partially loaded.

Joint C-OMA and C-NOMA Wireless Backhaul Scheduling in Heterogeneous Ultra Dense Networks

Heterogeneous ultra dense network (UDN) emerges as one of the potential techniques to tackle the $1000\times $ data challenge in 5G network. However, by cell densification, backhaul becomes one of the key issues in 5G heterogeneous UDN. In this paper, the cooperative wireless backhaul scheduling is concerned. First, a novel two-layers hierarchical model is proposed for the cooperative wireless backhaul, based on which energy efficiencies (EEs) of the cooperative orthogonal multiple access (C-OMA) and the cooperative non-orthogonal multiple access (C-NOMA) schemes are derived and analyzed. Moreover, based on the proposed model, a cooperative wireless backhaul optimization which maximizes EE is formulated. To solve this mixed integer non-linear optimization problem, the greedy algorithm-based C-OMA, C-NOMA and joint C-OMA and C-NOMA (JCC) schemes for cooperative wireless backhaul are proposed. Due to the benefits of both the C-OMA and C-NOMA, the proposed JCC scheme shows its superiority over C-OMA, C-NOMA and the traditional OMA schemes, which is validated by the simulation results.

Analysis of SIR and Rate Meta Distributions for 3D Heterogenous Ultra-Dense Networks With Joint Offloading and Resource Partitioning

In heterogeneous ultra-dense networks (HUDNs), load balancing is always considered by offloading users from the macro base station (MBS) tier to the small base station (SBS) tier, to alleviate the data traffic of the MBS tier, while it causes a serious problem that the offloaded users will suffer strong interference from the MBS tier, and hence the signal-to-interference ratio (SIR) is in low region for the offloaded users. In order to improve the performance of these offloaded users, an interference mitigation scheme based on resource partitioning is employed in the literature. Specifically, a fraction of resource blocks (RBs) is reserved for the offloaded users, and then the SIRs of these users will be enhanced because there is only interference from the SBS tier. In summary, the combination of offloading and resource partitioning is called joint offloading and resource partitioning scheme, which has been investigated in the HUDNs built on the 2D space, while it has rarely been deliberated in 3D HUDNs that are used to model the scenario of high-rise buildings. Moreover, a two-tier 3D HUDN is proposed in this paper, and the MBS and SBS tiers are modeled as two independent Poisson point processes. Then, the SIR and rate meta distributions, which are used to characterize the fraction of users that achieve a desired link reliability given SIR or rate thresholds, are formulated for the proposed 3D HUDN. Lastly, numerical results show that with the usage of the joint offloading and resource partitioning scheme, the performance of the proposed 3D HUND can be significantly improved.

Green Deployment Method of Micro Base Station for Ultra-Dense Heterogeneous Cellular Networks Based on Constrained Dolphin Swarm Algorithm

This paper proposes a green deployment method for micro base stations for ultra-dense heterogeneous cellular networks to balance network energy efficiency and electromagnetic radiation and meet certain user service quality. Firstly, a constrained multi-objective mathematical model for the green deployment of the micro base station is established for the two-dimensional communication scenario, with the user rate as the constraint, aiming at maximizing the network energy efficiency and minimizing the average electromagnetic radiation. Then, a multi-objective dolphin swarm algorithm which considering the evolutionary advantages of excellent infeasible solutions and feasible solutions, improving the individual search mechanism in the dolphin group algorithm, combined with the improved two-population strategy is proposed and tested on the CTP test set. It shows that compared with the other three methods, the method has certain advantages in convergence and distribution. Finally, a green deployment method for micro base stations based on constrained multi-objective dolphin swarm algorithm is established. Experiments on nine communication scenarios show that the proposed method can balance network energy efficiency and electromagnetic radiation.