Dense Deployment Research Articles

A novel power consumption optimization framework in 5G heterogeneous networks

The fifth-generation (5G) mobile networks have the capacity to handle the dynamic traffic demands of the user equipment (UE). One approach is the dense deployment of small base stations (s-BSs), which can control the dynamic traffic demands. We consider s-BSs which are interconnected through the mmWave backhaul (BH) link to transfer traffic from the s-BS to the core network through the macro base station (MBS). In this setting, the network power consumption is affected by the way UEs are connected to the base stations and the traffic routes through the BH links. The main objective of this paper is to minimize the power consumption of the heterogeneous network (HetNet) with the intelligent backhauling and s-BS/BH link sleeping (IBSBS) framework. The proposed framework provides the minimized power consumption in HetNets through UE association, backhauling, and sleep s-BS/BH links. The UE association and backhauling uses a heuristic function based intelligent backhauling algorithm to assess the minimum power consumption from the s-BS to the MBS while considering the power and capacity constraints of the network. The load sharing based s-BS sleeping algorithm dynamically changes the states (active/sleep) of the s-BSs according to their loads without compromising UE demands. Different distributions of UEs and different data rates over the HetNets architecture are considered for performance evaluation. The evaluation results of the proposed framework outperform the state-of-the-art algorithms in terms of network energy efficiency, power consumption, and the number of active s-BSs/BH links.

RSSI Fingerprint Height Based Empirical Model Prediction for Smart Indoor Localization.

Smart indoor living advances in the recent decade, such as home indoor localization and positioning, has seen a significant need for low-cost localization systems based on freely available resources such as Received Signal Strength Indicator by the dense deployment of Wireless Local Area Networks (WLAN). The off-the-shelf user equipment (UE's) available at an affordable price across the globe are well equipped with the functionality to scan the radio access network for hearable single strength; in complex indoor environments, multiple signals can be received at a particular reference point with no consideration of the height of the transmitter and possible broadcasting coverage. Most effective fingerprinting algorithm solutions require specialized labor, are time-consuming to carry out site surveys, training of the data, big data analysis, and in most cases, additional hardware requirements relatively increase energy consumption and cost, not forgetting that in case of changes in the indoor environment will highly affect the fingerprint due to interferences. This paper experimentally evaluates and proposes a novel technique for Received Signal Indicator (RSSI) distance prediction, leveraging transceiver height, and Fresnel ranging in a complex indoor environment to better suit the path loss of RSSI at a particular Reference Point (RP) and time, which further contributes greatly to indoor localization. The experimentation in different complex indoor environments of the corridor and office lab during work hours to ascertain real-life and time feasibility shows that the technique's accuracy is greatly improved in the office room and the corridor, achieving lower average prediction errors at low-cost than the comparison prediction algorithms. Compared with the conventional prediction techniques, for example, with Access Point 1 (AP1), the proposed Height Dependence Path-Loss (HEM) model at 0 dBm error attains a confidence probability of 10.98%, higher than the 2.65% for the distance dependence of Path-Loss New Empirical Model (NEM), 4.2% for the Multi-Wall dependence on Path-Loss (MWM) model, and 0% for the Conventional one-slope Path-Loss (OSM) model, respectively. Online localization, amongst the hearable APs, it is seen the proposed HEM fingerprint localization based on the proposed HEM prediction model attains a confidence probability of 31% at 3 m, 55% at 6 m, 78% at 9 m, outperforming the NEM with 26%, 43%, 62%, 62%, the MWM with 23%, 43%, 66%, respectively. The robustness of the HEM fingerprint using diverse predicted test samples by the NEM and MWM models indicates better localization of 13% than comparison fingerprints.

Vehicle assisted bridge damage assessment using probabilistic deep learning

Vehicle assisted monitoring has shown promising potential for the condition assessment of existing bridges in a road network, by removing practical complications faced in traditional Structural health monitoring (SHM) methods such as traffic interruption and dense deployment of sensors. However, the combination of different measurement sources during vehicle assisted monitoring has not yet been fully explored. This paper aims to evaluate the potential benefit of considering multiple measured responses from various sources, including fixed sensors on the bridge and on-board vehicle sensors. To this end, this paper proposes a Probabilistic Deep Neural Network, a stochastic data-driven framework for damage assessment. This framework enables the combination of vehicle and bridge responses to extract damage sensitive features for the classification of different damage states. In addition, the proposed method estimates the uncertainty of its predictions, providing an indication of the reliability of the result. The proposed method is validated using two numerical based case studies while considering realistic operational conditions, which include temperature oscillations, additional traffic, and measurement noise. The results from this study indicate that combining multiple sensor information results in lower uncertainties in damage detection and localisation. The results also suggest that the proposed method is robust in handling measurement noise and varying environmental conditions.

Binary PSO with Classification Trees Algorithm for Enhancing Power Efficiency in 5G Networks.

The dense deployment of small cells (SCs) in the 5G heterogeneous networks (HetNets) fulfills the demand for vast connectivity and larger data rates. Unfortunately, the power efficiency (PE) of the network is reduced because of the elevated power consumption of the densely deployed SCs and the interference that arise between them. An approach to ameliorate the PE is proposed by switching off the redundant SCs using machine learning (ML) techniques while sustaining the quality of service (QoS) for each user. In this paper, a linearly increasing inertia weight-binary particle swarm optimization (IW-BPSO) algorithm for SC on/off switching is proposed to minimize the power consumption of the network. Moreover, a soft frequency reuse (SFR) algorithm is proposed using classification trees (CTs) to alleviate the interference and elevate the system throughput. The results show that the proposed algorithms outperform the other conventional algorithms, as they reduce the power consumption of the network and the interference among the SCs, ameliorating the total throughput and the PE of the system.

Joint Resource Allocation to Minimize Execution Time of Federated Learning in Cell-Free Massive MIMO

Due to its communication efficiency and privacy-preserving capability, federated learning (FL) has emerged as a promising framework for machine learning in 5G-and-beyond wireless networks. Of great interest is the design and optimization of new wireless network structures that support the stable and fast operation of FL. Cell-free massive multiple-input–multiple-output (CFmMIMO) turns out to be a suitable candidate, which allows each communication round in the iterative FL process to be stably executed within a large-scale coherence time. Aiming to reduce the total execution time of the FL process in CFmMIMO, this article proposes choosing only a subset of available users to participate in FL. An optimal selection of users with favorable link conditions would minimize the execution time of each communication round while limiting the total number of communication rounds required. Toward this end, we formulate a joint optimization problem of user selection, transmit power, and processing frequency, subject to a predefined minimum number of participating users to guarantee the quality of learning. We then develop a new algorithm that is proven to converge to the neighborhood of the stationary points of the formulated problem. Numerical results confirm that our proposed approach significantly reduces the FL total execution time over baseline schemes. The time reduction is more pronounced when the density of access point deployments is moderately low.

On the Deployment of Small Cells in 3D HetNets With Multi-Antenna Base Stations

With the dense deployment of small cells, the impact of height difference between base stations (BSs) and user equipment (UE) on the performance of heterogeneous networks (HetNets) becomes significant. The traditional two-dimensional models are no longer sufficient to capture the three-dimensional (3D) features of dense HetNets. On the other hand, deploying multiple antennas on BSs is a promising approach to improve network capacity. In this paper, we propose a 3D model for a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -tier HetNet with multi-antenna BSs, where different tiers share the same frequency band but may differ in BS height, BS density, number of antennas per BS, BS transmit power, association bias, and path loss exponent. We analytically derive the per-tier association probability under both the strongest received signal and the closest BS cell-association strategies. Based on that, we derive the expressions for the downlink ergodic rate, area spectral efficiency (ASE) and energy efficiency. The numerical results reveal that in the presence of macrocell BSs, for low to medium small-cell BS (SBS) densities, the closest BS cell-association strategy leads to low ergodic rate, ASE and energy efficiency regardless of the SBS height; while at very high SBS densities, under both cell-association strategies, SBSs should be deployed at the same height as UE to achieve high ergodic rate, ASE and energy efficiency. Moreover, we find that for a given SBS height, there exists an optimal combination of SBS density and number of antennas per SBS that maximizes the system energy efficiency.

An Enhanced Spatial Correlation Framework for Heterogenous Wireless Sensor Networks

Background: Event detection and monitoring applications involve highly populated sensor nodes in Wireless Sensor Networks (WSNs). Dense deployment of nodes leads to correlated sensor observations in the spatial and temporal domain. Most of the previous works focused on constant sensing radii for spatially correlated sensor observations. However, in real time scenario, the sensor nodes may have variable sensing coverage areas, which comprise a Heterogeneous WSN. background: Spatial correlation model discussed in prior literature focused on Homogeneous sensing of sensor nodes. But, real time scenario the condition changes due to interferences obstructing sensing areas. Also, different manufacturers may provide different specifications for sensing areas, thus resulting into Heterogeneous sensing. Objective: To address this issue, we present an Enhanced Weighted Spatial Correlation Model for Heterogeneous sensor nodes in WSNs. Method: The mathematical framework considers the spatial coordinates of sensor nodes, the distances between the sensor nodes, and their sensing coverage. Furthermore, the correlation coefficient is calculated in terms of overlapping areas for randomly deployed nodes. Performance of the correlation model is evaluated and analyzed in terms of event distortion function. In addition to this, a macro and micro-zone concept is introduced, wherein sensor information is weighted for better event estimation at the sink node. Moreover, dynamic weighing of nodes like Inverse, Shepard’s and Gaussian distance weighing algorithms are simulated and analyzed for minimal event distortion. Over and above, the system performance is evaluated for different approaches considering reporting nodes with and without clustering of sensor nodes for macro and micro-zone concept. Simulation results for the Enhanced Weighted Spatial Correlation Model developed are obtained using MATLAB software. method: In order to evaluate the performance of the enhanced correlation model considering Macro and Micro-zone concept, simulations are carried out inMATLAB. Simulations are performed for ‚ trials and averaging of the values are finally used for analysis of results. Results: The comparative study shows an improved system performance in terms of minimal distortion obtained for non-clustered nodes; thereby reducing the computational complexity of cluster formation. Furthermore, the dynamic weighing algorithms outperform the existing fixed weighing algorithms for the correlation model with the lowest distortion function. Conclusion: Moreover, in the above algorithms, the event distortion gradually decreases and later becomes constant with the increase in the number of representative nodes. Hence, it illustrates that minimal distortion can be achieved by activating lesser number of representative nodes, thereby preserving the energy of other sensor nodes and increasing the lifetime of WSNs.

An Energy-Efficient Channel Access With Target Wake Time Scheduling for Overlapping 802.11ax Basic Service Sets

Higher requirements in wireless Internet access have led to a dense deployment of Wi-Fi networks, which results in severe collisions and deteriorated throughput. In this article, we propose a grouping-based medium access control protocol with target wake time (TWT) scheduling for IEEE 802.11ax overlapping basic service sets (OBSSs), i.e., GTSO. It aims to improve the reutilization of the resource units (RUs) by increasing the number of parallel transmissions with sleep/wake-up period scheduling which is adaptive to the traffic loads. First, we divide the stations into different groups by mapping the OBSS architecture into an undirected graph. Based on the graph, we use the Welch Powell algorithm to find the minimum number of colors in the graph coloring problem to make as many parallel transmissions as possible. Finally, an optimization problem is proposed to realize a contention-free channel access with simultaneously active stations no more than the number of RUs under the constraint of packet delay through TWT scheduling. Simulation results show that the GTSO performs better than existing schemes in terms of throughput and energy efficiency in dense scenarios.

Routing in Solar-Powered UAV Delivery System

As interest grows in unmanned aerial vehicle (UAV) systems, UAVs have been proposed to take on increasingly more tasks that were previously assigned to humans. One such task is the delivery of goods within urban cities using UAVs, which would otherwise be delivered by terrestrial means. However, the limited endurance of UAVs due to limited onboard energy storage makes it challenging to practically employ UAV technology for deliveries across long routes. Furthermore, the relatively high cost of building UAV charging stations prevents the dense deployment of charging facilities. Solar-powered UAVs can ease this problem, as they do not require charging stations and can harvest solar power in the daytime. This paper introduces a solar-powered UAV goods delivery system to plan delivery missions with solar-powered UAVs (SPUs). In this study, when the SPUs run out of power, they charge themselves on landing places provided by customers instead of charging stations. Some advanced path planning algorithms are proposed to minimize the overall mission time in the statically charging efficiency environment. We further consider routing in the dynamically charging efficiency environment and propose some mission arrangement protocols to manage different missions in the system. The simulation results demonstrate that the algorithms proposed in our work perform significantly better than existing UAV path planning algorithms in solar-powered UAV systems.

Benchmarking 4G and 5G-Based Cellular-V2X for Vehicle-to-Infrastructure Communication and Urban Scenarios in Cooperative Intelligent Transportation Systems

Vehicle-to-Infrastructure (V2I) communication is expected to bring tremendous benefits in terms of increased road safety, improved traffic efficiency and decreased environmental impact. In 2017, The 3rd Generation Partnership Project (3GPP) released 3GPP Release 14, which introduced Cellular Vehicle-to-Everything communication (C-V2X), bringing Vehicle-to-Everything (V2X) communication capabilities to cellular networks, hence creating an alternative to Dedicated Short-Range Communications (DSRC) technology. Since then, every new 3GPP Release including Release 15, a first full set of 5G standards, offered V2X capabilities. In this paper, we present a complex simulation study, which benchmarks the performance of LTE-based and 5G-based C-V2X technologies deployed for V2I communication in an urban setting. The study compares LTE and 5G deployed both in the Device-to-Device in mode 3 and in infrastructural mode. Target performance indicators used for comparison are average end-to-end (E2E) latency and Packet Delivery Ratio (PDR). The performance of those technologies is studied under varying communication conditions realized by a variation of vehicle traffic intensity, communication perimeter and message generation frequency. Furthermore, the effects of infrastructure deployment density on the performance of selected C-V2X communication technologies are explored by comparing the performance of the investigated technologies for three infrastructure density scenarios, i.e., involving two, four and eight base stations (BSs). The performance results are put into a context of the connectivity requirements of the most popular V2I communication services. The results indicate that both C-V2X technologies can support all the considered V2I services without any limitations in terms of the communication perimeter, traffic intensity and message generation frequency. When it comes to the infrastructure density deployment, the results show that increasing the density of the infrastructure deployment from two BSs to four BSs offers a remarkable performance improvement for all the considered V2I services as well as investigated technologies and their modes. Further infrastructure density increase (from four BSs to eight BSs) does not yield any practical benefits in the investigated urban scenario.

Self-backhauling failure mitigation using 5G new radio

5G network operators will consider the dense deployment of small cells to increase network coverage and capacity. However, in order to install a large number of gNBs, operators will face the challenge of backhauling their traffic to the core network in a cost-effective manner. Although Integrated Access and Backhaul (IAB) using 5G new radio is a promising solution to solve the dilemma of small cells’ backhauling, densifying the network will increase the probability of failure of these links. To cope with this problem, a self-healing scheme is used to mitigate or at least alleviate the effect of backhaul failure. We propose to use IAB with the collaboration of neighboring gNBs to mitigate the failed backhaul link(s). Hence, our goal is to design a pre-planned network capable of providing the minimum rate requirements to its users in the presence of backhaul failure. We formulate a joint resource allocation/backhaul outage compensation optimization problem as a non-convex mixed integer non-linear program. Due to the difficulty of this problem, we divide this problem into two sub-problems, in addition to the use of different approximation and relaxation techniques to find an approximated sub-optimal solution. Simulation results show that the proposed scheme is capable of providing the network users an acceptable continuous, albeit slightly degraded, service during backhaul failure. The degradation percentage is inversely proportional to the network densification for single and multiple failures. Meanwhile, the Degree of Recovery (DoR), i.e., subtracting the degradation percentage from 100, of single failure scenarios range from 95% (for the best case) to 54% (for the worst case). For the multiple failures scenario, the DoR is much lower and can reach zero in case of three concurrent failures

A Review of Energy Efficiency and Power Control Schemes in Ultra-Dense Cell-Free Massive MIMO Systems for Sustainable 6G Wireless Communication

The traditional multiple input multiple output (MIMO) systems cannot provide very high Spectral Efficiency (SE), Energy Efficiency (EE), and link reliability, which are critical to guaranteeing the desired Quality of Experience (QoE) in 5G and beyond 5G wireless networks. To bridge this gap, ultra-dense cell-free massive MIMO (UD CF-mMIMO) systems are exploited to boost cell-edge performance and provide ultra-low latency in emerging wireless communication systems. This paper attempts to provide critical insights on high EE operation and power control schemes for maximizing the performance of UD CF-mMIMO systems. First, the recent advances in UD CF-mMIMO systems and the associated models are elaborated. The power consumption model, power consumption parts, and energy maximization techniques are discussed extensively. Further, the various power control optimization techniques are discussed comprehensively. Key findings from this study indicate an unprecedented growth in high-rate demands, leading to a significant increase in energy consumption. Additionally, substantial gains in EE require efficient utilization of optimal energy maximization techniques, green design, and dense deployment of massive antenna arrays. Overall, this review provides an elaborate discussion of the research gaps and proposes several research directions, critical challenges, and useful recommendations for future works in wireless communication systems.

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

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

Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces

The emerging technology of reconfigurable intelligent surfaces (RISs) is provisioned as an enabler of smart wireless environments, offering a highly scalable, low-cost, hardware-efficient, and almost energy-neutral solution for dynamic control of the propagation of electromagnetic signals over the wireless medium, ultimately providing increased environmental intelligence for diverse operation objectives. One of the major challenges with the envisioned dense deployment of RISs in such reconfigurable radio environments is the efficient configuration of multiple metasurfaces with limited, or even the absence of, computing hardware. In this article, we consider multiuser and multi-RIS-empowered wireless systems and present a thorough survey of the online machine learning approaches for the orchestration of their various tunable components. Focusing on the sum-rate maximization as a representative design objective, we present a comprehensive problem formulation based on deep reinforcement learning (DRL). We detail the correspondences among the parameters of the wireless system and the DRL terminology, and devise generic algorithmic steps for the artificial neural network training and deployment while discussing their implementation details. Further practical considerations for multi-RIS-empowered wireless communications in the sixth-generation (6G) era are presented along with some key open research challenges. Different from the DRL-based status quo, we leverage the independence between the configuration of the system design parameters and the future states of the wireless environment, and present efficient multiarmed bandits approaches, whose resulting sum-rate performances are numerically shown to outperform random configurations, while being sufficiently close to the conventional deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> network (DQN) algorithm, but with lower implementation complexity.

Nanosensors Based on a Single ZnO:Eu Nanowire for Hydrogen Gas Sensing.

Fast detection of hydrogen gas leakage or its release in different environments, especially in large electric vehicle batteries, is a major challenge for sensing applications. In this study, the morphological, structural, chemical, optical, and electronic characterizations of ZnO:Eu nanowire arrays are reported and discussed in detail. In particular, the influence of different Eu concentrations during electrochemical deposition was investigated together with the sensing properties and mechanism. Surprisingly, by using only 10 μM Eu ions during deposition, the value of the gas response increased by a factor of nearly 130 compared to an undoped ZnO nanowire and we found an H2 gas response of ∼7860 for a single ZnO:Eu nanowire device. Further, the synthesized nanowire sensors were tested with ultraviolet (UV) light and a range of test gases, showing a UV responsiveness of ∼12.8 and a good selectivity to 100 ppm H2 gas. A dual-mode nanosensor is shown to detect UV/H2 gas simultaneously for selective detection of H2 during UV irradiation and its effect on the sensing mechanism. The nanowire sensing approach here demonstrates the feasibility of using such small devices to detect hydrogen leaks in harsh, small-scale environments, for example, stacked battery packs in mobile applications. In addition, the results obtained are supported through density functional theory-based simulations, which highlight the importance of rare earth nanoparticles on the oxide surface for improved sensitivity and selectivity of gas sensors, even at room temperature, thereby allowing, for instance, lower power consumption and denser deployment.

Historical wind deployment and implications for energy system models

A critical parameter in modeling studies of future decarbonized energy systems is the potential future capacity for onshore wind power. Wind power potential in energy system models is subject to assumptions regarding: (i) constraints on land availability for wind deployment; (ii) how densely wind turbines may be placed over larger areas, and (iii) allocation of capacity with respect to wind speed. By analyzing comprehensive databases of wind turbine locations and other GIS data in eleven countries and seventeen states in Australia, Canada, and the US; all with high penetration levels of wind power, we find that: i) large wind turbines are installed on most land types, even protected areas and land areas with high population density; ii) it is not uncommon with a deployment density up to 0.5 MW/km2 on municipality or county level, with rare outlier municipalities reaching up to 1.5 MW/km2 installed capacity; and iii) wind power has historically been allocated to relatively windy sites with average wind speed above 6 m/s. In many cases, allocation methods used in energy system models do not consistently reflect actual installations. For instance, we find no evidence of concentration of installations at the windiest sites, as is frequently assumed in energy system models. We conclude that assumptions made in models regarding wind power potentials are poorly reflective of historical installation patterns, and we provide new data to enable assumptions that have a more robust empirical foundation.

Research on Agricultural Enterprise Accounting Information Resource Sharing Model Based On Big Data Technology

The conventional enterprise accounting information resource sharing model has the problem of fuzzy accounting information characteristics, resulting in low schedulability. An agricultural enterprise accounting information resource sharing model based on big data technology is designed. Obtain the goal of agricultural enterprise business process reengineering, establish the standard process of mutual integration of enterprise accounting information, combined with the principle of data mining, extract the characteristics of accounting information by using big data technology, divide the spectrum module according to the deployment density of small cells, design the heterogeneous wireless network resource management model, optimize the resource distribution level, and construct the resource sharing model by using hierarchical scheduling algorithm. Experimental results: the mean schedulability rates of the enterprise accounting information resource sharing model and the other two models are 60.073%, 49.801% and 49.319%, which proves that the enterprise accounting information resource sharing model integrating big data technology has more advantages.

Data-Driven Capacity Planning for Vehicular Fog Computing

The strict latency constraints of emerging vehicular applications make it unfeasible to forward sensing data from vehicles to the cloud for processing. To shorten network latency, vehicular fog computing (VFC) moves computation to the edge of the Internet, with the extension to support the mobility of distributed computing entities [a.k.a fog nodes (FNs)]. In other words, VFC proposes to complement stationary FNs co-located with cellular base stations with mobile ones carried by moving vehicles (e.g., buses). Previous works on VFC mainly focus on optimizing the assignments of computing tasks among available FNs. However, capacity planning, which decides where and how much computing resources to deploy, remains an open and challenging issue. The complexity of this problem results from the spatiotemporal dynamics of vehicular traffic, varying computing resource demand generated by vehicular applications, and the mobility of FNs. To solve the above challenges, we propose a data-driven capacity planning framework that optimizes the deployment of stationary and mobile FNs to minimize the installation and operational costs under the quality-of-service constraints, taking into account the spatiotemporal variation in both demand and supply. Using real-world traffic data and application profiles, we analyze the cost efficiency potential of VFC in the long term. We also evaluate the impacts of traffic patterns on the capacity plans and the potential cost savings. We find that high traffic density and significant hourly variation would lead to dense deployment of mobile FNs and create more savings in operational costs in the long term.

Self-Adapting Handover Parameters Optimization for SDN-Enabled UDN

Increasing the deployment density of small base stations (SBS) is a key method designed to satisfy high data traffic in 5th generation mobile network (5G). However, a large number of SBSs in such ultra-dense network (UDN) may cause ping-pong handovers (HOs), accompanied by increased delay and HO failure. In addition, because of the separation of control and data signaling in 5G, the HO procedure must be performed in both layers. In this paper, we introduce an SDN-based intelligent dynamic HO parameter optimization strategy to minimize both HO failures and ping-pong HOs together. The goal of the proposed strategy is to reduce the HO failure rate and redundant HO (i.e. ping-pong HO) while enabling user equipment (UE) to make full use of the benefits of dense deployment of BSs. Simulation results present that the method proposed in this paper effectively suppresses the ping-pong effect and keeps it at a low level in all of the investigated scenes. In addition, compared with the other algorithms, the HO failure rate is significantly reduced and the throughput of UE is greatly increased, especially in the case of high BS density. Therefore, the benefits of intensive BS deployment are retained.

Decentralized Vehicular Mobility Management Study for 5G Identifier/Locator Split Networks

The identifier/locator split (ILS) architectures are highly promising to reduce the signaling latency of frequent handovers in fifth generation (5G) networks, while decentralized vehicular mobility management holds greater potential than the traditional centralized management to enhance the critical performance of highly dynamic and dense cell networks. By carefully exploiting ILS, dual connectivity, and multiaccess edge computing (MEC) concepts, this paper proposes a decentralized vehicular mobility management mechanism in the network with dense 5G Non-Standalone deployment. Under such a mechanism, we design an ILS-based local anchor handover management architecture to reduce signaling costs and handover latency. Specifically, we propose a quality of service- (QoS-) based handover decision algorithm using a long short-term memory- (LSTM-) based trajectory prediction method to obtain the cell sojourn time of connected vehicles (CVs) in predefined QoS coverage areas. Combining a built-in dynamic handover trigger condition, this algorithm can ensure a flexible load balance as well as low handover times. Extensive simulation results are presented to verify the effectiveness of the proposed mechanism in improving network performance.