Showing papers on "Handover published in 2020"

Mobility in the Sky: Performance and Mobility Analysis for Cellular-Connected UAVs

Abstract: Providing connectivity to unmanned aerial vehicle-user equipment (UAV-UE), such as drones or flying taxis, is a major challenge for tomorrow’s cellular systems. In this paper, the use of coordinated multi-point (CoMP) transmission for providing seamless connectivity to UAV-UEs is investigated. In particular, a network of clustered ground base stations (BSs) that cooperatively serve a number of UAV-UEs is considered. Two scenarios are studied: scenarios with static, hovering UAV-UEs and scenarios with mobile UAV-UEs. Under a maximum ratio transmission, a novel framework is developed and leveraged to derive upper and lower bounds on the UAV-UE coverage probability for both scenarios. Using the derived results, the effects of various system parameters such as collaboration distance, UAV-UE altitude, and UAV-UE speed on the achievable performance are studied. Results reveal that, for both static and mobile UAV-UEs, when the BS antennas are tilted downwards, the coverage probability of a high-altitude UAV-UE is upper bounded by that of ground user equipments (UEs) regardless of the transmission scheme. Moreover, for low signal-to-interference-ratio thresholds, it is shown that CoMP transmission can improve the coverage probability of UAV-UEs, e.g., from 28% under the nearest association scheme to 60% for an average of 2.5 collaborating BSs. Meanwhile, key results on mobile UAV-UEs unveil that not only the spatial displacements of UAV-UEs but also their vertical motions affect their handover rate and coverage probability. In particular, UAV-UEs that have frequent vertical movements and high direction switch rates are expected to have low handover probability and handover rate. Finally, the effect of the UAV-UE vertical movements on its coverage probability is marginal if the UAV-UE retains the same mean altitude.

Key Challenges, Drivers and Solutions for Mobility Management in 5G Networks: A Survey

Abstract: Ensuring a seamless connection during the mobility of various User Equipments (UEs) will be one of the major challenges facing the practical implementation of the Fifth Generation (5G) networks and beyond. Several key determinants will significantly contribute to numerous mobility challenges. One of the most important determinants is the use of millimeter waves (mm-waves) as it is characterized by high path loss. The inclusion of various types of small coverage Base Stations (BSs), such as Picocell, Femtocell and drone-based BSs is another challenge. Other issues include the use of Dual Connectivity (DC), Carrier Aggregation (CA), the massive growth of mobiles connections, network diversity, the emergence of connected drones (as BS or UE), ultra-dense network, inefficient optimization processes, central optimization operations, partial optimization, complex relation in optimization operations, and the use of inefficient handover decision algorithms. The relationship between these processes and diverse wireless technologies can cause growing concerns in relation to handover associated with mobility. The risk becomes critical with high mobility speed scenarios. Therefore, mobility issues and their determinants must be efficiently addressed. This paper aims to provide an overview of mobility management in 5G networks. The work examines key factors that will significantly contribute to the increase of mobility issues. Furthermore, the innovative, advanced, efficient, and smart handover techniques that have been introduced in 5G networks are discussed. The study also highlights the main challenges facing UEs’ mobility as well as future research directions on mobility management in 5G networks and beyond.

Joint Optimization of Handover Control and Power Allocation Based on Multi-Agent Deep Reinforcement Learning

Abstract: In this paper, we study the handover (HO), and power allocation problem in a two-tier heterogeneous network (HetNet), which consists of a macro base station, and some millimeter-wave (mmWave) small base stations. We establish an HO management, and power allocation scheme to maximize the overall throughput while reducing the HO frequency. In particular, considering the interrelationship among decisions made by different user equipments (UEs), we first model the HO, and power allocation problem as a fully cooperative multi-agent task, in which all agents, i.e., UEs, have the same target. Then, to solve the multi-agent task, and get decentralized policies for each UE, we develop a multi-agent reinforcement learning (MARL) algorithm based on the proximal policy optimization (PPO) method, by introducing the centralized training with decentralized execution framework. That is, we use global information to train policies for each UE, and after the training is finished, each UE obtains a decentralized policy, which can be implemented only based on each UE's local observation. Specially, we introduce the counterfactual baseline to address the credit assignment problem in centralized learning. Due to the centralized training, the decentralized polices learned by multi-agent PPO (MAPPO) can work more cooperatively. Finally, the simulation results demonstrate that our method can achieve better performance comparing with other existing works.

Device Association for RAN Slicing Based on Hybrid Federated Deep Reinforcement Learning

Abstract: Network slicing (NS) has been widely identified as a key architectural technology for 5G-and-beyond systems by supporting divergent requirements in a sustainable way. In radio access network (RAN) slicing, due to the device-base station (BS)-NS three layer association relationship, device association (including access control and handoff management) becomes an essential yet challenging issue. With the increasing concerns on stringent data security and device privacy, exploiting local resources to solve device association problem while enforcing data security and device privacy becomes attractive. Fortunately, recently emerging federated learning (FL), a distributed learning paradigm with data protection, provides an effective tool to address this type of issues in mobile networks. In this paper, we propose an efficient device association scheme for RAN slicing by exploiting a hybrid FL reinforcement learning (HDRL) framework, with the aim to improve network throughput while reducing handoff cost. In our proposed framework, individual smart devices train a local machine learning model based on local data and then send the model features to the serving BS/encrypted party for aggregation, so as to efficiently reduce bandwidth consumption for learning while enforcing data privacy. Specifically, we use deep reinforcement learning to train the local model on smart devices under a hybrid FL framework, where horizontal FL is employed for parameter aggregation on BS, while vertical FL is employed for NS/BS pair selection aggregation on the encrypted party. Numerical results show that the proposed HDRL scheme can achieve significant performance gain in terms of network throughput and communication efficiency in comparison with some state-of-the-art solutions.

A Comprehensive Survey on Mobility Management in 5G Heterogeneous Networks: Architectures, Challenges and Solutions

Abstract: With the rapid increase in the number of mobile users, wireless access technologies are evolving to provide mobile users with high data rates and support new applications that include both human and machine-type communications. Heterogeneous networks (HetNets), created by the joint installation of macro cells and a large number of densely deployed small cells, are considered an important solution to deal with the increasing network capacity demands and provide high coverage to wireless users in future fifth generation (5G) wireless networks. Due to the increasing complexity of network topology in 5G HetNets with the integration of many different base station types, in 5G architecture mobility management has many challenges. Intense deployment of small cells, along with many advantages it provides, brings important mobility management problems such as frequent handover (HO), HO failure, HO delays, ping-pong HO and high energy consumption which will result in lower user experience and heavy signal loads. In this paper, we provide a comprehensive study on the mobility management in 5G HetNet in terms of radio resource control, the initial access and registration procedure of the user equipment (UE) to the network, the paging procedure that provides the location of the UE within the network, connected mode mobility management schemes, beam level mobility and beam management. Besides, this paper addresses the challenges and suggest possible solutions for the 5G mobility management.

5G Handover using Reinforcement Learning

Abstract: In typical wireless cellular systems, the handover mechanism involves reassigning an ongoing session handled by one cell into another. In order to support increased capacity requirement and to enable newer use cases, the next generation wireless systems will have a very dense deployment with advanced beam-forming capability. In such systems, providing a better mobility along with enhanced throughput performance requires an improved handover strategy. In this paper, we will detail a novel method for handover optimization in a 5G cellular network using reinforcement learning (RL). In contrast to the conventional method, we propose to control the handovers between base-stations (BSs) using a centralized RL agent. This agent handles the radio measurement reports from the UEs and choose appropriate handover actions in accordance with the RL framework to maximize a long-term utility. We show that the handover mechanism can be posed as a contextual multi-armed bandit problem and solve it using Q-learning method. We analyze the performance of the methods using different propagation and deployment environment and compare the results with the state-of-the-art algorithms. Results indicate a link-beam performance gain of about 0.3 to 0.7 dB for practical propagation environments.

Handover Management for mmWave Networks With Proactive Performance Prediction Using Camera Images and Deep Reinforcement Learning

Abstract: For millimeter-wave networks, this paper presents a paradigm shift for leveraging time-consecutive camera images in handover decision problems. While making handover decisions, it is important to predict future long-term performance—e.g., the cumulative sum of time-varying data rates—proactively to avoid making myopic decisions. However, this study experimentally notices that a time-variation in the received powers is not necessarily informative for proactively predicting the rapid degradation of data rates caused by moving obstacles. To overcome this challenge, this study proposes a proactive framework wherein handover timings are optimized while obstacle-caused data rate degradations are predicted before the degradations occur. The key idea is to expand a state space to involve time-consecutive camera images, which comprises informative features for predicting such data rate degradations. To overcome the difficulty in handling the large dimensionality of the expanded state space, we use a deep reinforcement learning for deciding the handover timings. The evaluations performed based on the experimentally obtained camera images and received powers demonstrate that the expanded state space facilitates (i) the prediction of obstacle-caused data rate degradations from 500 ms before the degradations occur and (ii) superior performance to a handover framework without the state space expansion.

A Lightweight and Secure Group Key Based Handover Authentication Protocol for the Software-Defined Space Information Network

Abstract: With rapid advances in satellite technology, space information network (SIN) has been proposed to meet the increasing demands of ubiquitous mobile communication due to its advantages in providing extensive access services. However, due to satellites’ resource constraint and SIN’s highly dynamic topology, it poses a challenge on management and resource utilization in the development of SIN. There have been some works integrating the software defined network (SDN) into SIN, defined as software defined space information network (SD-SIN), so as to simplify the management and improve resource utilization in SIN. However, these works ignore the security issue in SD-SIN. Meanwhile, the existing security mechanisms in SDN are still unable to cope with the uniqueness of satellite network, and some other critical security issues still haven’t yet been well addressed. In this paper, based on $(t,n)$ secret sharing, an SIN-specific lightweight group key agreement protocol is proposed for SD-SIN to ensure both the security and applicability. Moreover, considering the highly dynamic network topology, we also design a group key-based secure handover authentication scheme to reduce the overhead of handover authentication. Security analysis shows that the handover authentication protocol can resist to various known attacks. In addition, further performance evaluation shows its efficiency in terms of computation and communication overheads. Finally, the simulation results of computing overhead to the network entities demonstrate that our protocol is feasible in practical implementation.

A Comprehensive Study on Simulation Techniques for 5G Networks: State of the Art Results, Analysis, and Future Challenges

Abstract: Ιn this review article, a comprehensive study is provided regarding the latest achievements in simulation techniques and platforms for fifth generation (5G) wireless cellular networks. In this context, the calculation of a set of diverse performance metrics, such as achievable throughput in uplink and downlink, the mean Bit Error Rate, the number of active users, outage probability, the handover rate, delay, latency, etc., can be a computationally demanding task due to the various parameters that should be incorporated in system and link level simulations. For example, potential solutions for 5G interfaces include, among others, millimeter Wave (mmWave) transmission, massive multiple input multiple output (MIMO) architectures and non-orthogonal multiple access (NOMA). Therefore, a more accurate and realistic representation of channel coefficients and overall interference is required compared to other cellular interfaces. In addition, the increased number of highly directional beams will unavoidably lead to increased signaling burden and handovers. Moreover, until a full transition to 5G networks takes place, coexistence with currently deployed fourth generation (4G) networks will be a challenging issue for radio network planning. Finally, the potential exploitation of 5G infrastructures in future electrical smart grids in order to support high bandwidth and zero latency applications (e.g., semi or full autonomous driving) dictates the need for the development of simulation environments able to incorporate the various and diverse aspects of 5G wireless cellular networks.

Efficient Handover Mechanism for Radio Access Network Slicing by Exploiting Distributed Learning

Abstract: Network slicing is identified as a fundamental architectural technology for future mobile networks since it can logically separate networks into multiple slices and provide tailored quality of service (QoS). However, the introduction of network slicing into radio access networks (RAN) can greatly increase user handover complexity in cellular networks. Specifically, both physical resource constraints on base stations (BSs) and logical connection constraints on network slices (NSs) should be considered when making a handover decision. Moreover, various service types call for an intelligent handover scheme to guarantee the diversified QoS requirements. As such, in this article, a multi-agent reinforcement LEarning based Smart handover Scheme, named LESS, is proposed, with the purpose of minimizing handover cost while maintaining user QoS. Due to the large action space introduced by multiple users and the data sparsity caused by user mobility, conventional reinforcement learning algorithms cannot be applied directly. To solve these difficulties, LESS exploits the unique characteristics of slicing in designing two algorithms: 1) LESS-DL, a distributed ${Q}$ -learning algorithm to make handover decisions with reduced action space but without compromising handover performance; 2) LESS-QVU, a modified ${Q}$ -value update algorithm which exploits slice traffic similarity to improve the accuracy of ${Q}$ -value evaluation with limited data. Thus, LESS uses LESS-DL to choose the target BS and NS when a handover occurs, while ${Q}$ -values are updated by using LESS-QVU. The convergence of LESS is theoretically proved in this article. Simulation results show that LESS can significantly improve network performance. In more detail, the number of handovers, handover cost and outage probability are reduced by around 50%, 65%, and 45%, respectively, when compared with traditional methods.

Performance Analysis of Mobile Cellular-Connected Drones under Practical Antenna Configurations

Abstract: Providing seamless connectivity to unmanned aerial vehicle user equipment (UAV-UE) is very challenging due to the encountered line-of-sight interference and reduced gains of down-tilted base station (BS) antennas. For instance, as the altitude of UAV-UEs increases, their cell association and handover procedure become driven by the side-lobes of the BS antennas. In this paper, the performance of cellular-connected UAV-UEs is studied under 3D practical antenna configurations. Two scenarios are studied: scenarios with static, hovering UAV-UEs and scenarios with mobile UAV-UEs. For both scenarios, the UAV-UE coverage probability is characterized as a function of the system parameters. The effects of the number of antenna elements on the UAV-UE coverage probability and handover rate of mobile UAV-UEs are then investigated. Results reveal that the UAV-UE coverage probability under a practical antenna pattern is worse than that under a simple antenna model. Moreover, vertically-mobile UAV-UEs are susceptible to attitude handover due to consecutive crossings of the nulls and peaks of the antenna side-lobes.

FTGPHA: Fixed-Trajectory Group Pre-Handover Authentication Mechanism for Mobile Relays in 5G High-Speed Rail Networks

Abstract: For high-speed rail networks, data transmission suffers from severe penetration loss and when the train moves from one base station to another, a large number of User Equipments (UEs) on board carry out the handover authentication procedure simultaneously, which incurs a lot of handover overheads. The introduction of Mobile Relay Node (MRN) can improve the link quality and decrease the handover overheads. However, MRNs still suffer from several protocol attacks and frequent handovers and thus, the introduction of MRNs deteriorates the handover success rate and handover performance. At the same time, considering the diversity of future 5G high-speed rail networks, in this paper, we propose two fixed-trajectory group pre-handover authentication schemes for MRN: the first proposed scheme FTGPHA1 which establishes most of the important security properties and costs low handover overheads, and the second proposed scheme FTGPHA2 which furnishes better security properties than the first one. In these two schemes, since all of the MRNs in the same train and the next base station can accomplish the handover authentication with the help of the donor software defined networking controller before the MRN arrives, the handover delay can be ignored and thus, uninterrupted services can be provided for UEs on board. The security and performance evaluations demonstrate that the two proposed schemes outperform other related schemes.

Delegating Authentication to Edge: A Decentralized Authentication Architecture for Vehicular Networks

Abstract: Secure and efficient access authentication is one of the most important security requirements for vehicular networks, but it is difficult to fulfill due to potential security attacks and long authentication delay caused by high vehicle mobility, etc. Most of the existing authentication protocols, either do not consider attacks like single point of failure or do not focus on reducing authentication delay. To address these issues, we introduce an edge-assisted decentralized authentication (EADA) architecture, which provides secure and more communication-efficient authentication by enabling an authentication server to delegate its authentication capability to distributed edge nodes (ENs) such as roadside units (RSUs) and base stations (BSs). Under the architecture, we propose a threshold mutual authentication protocol that supports fast handover, which involves two scenarios, Auth-I and Auth-II. Auth-I only happens once when a vehicle tries to access the network for the first time, while Auth-II happens when a vehicle seamlessly roams between two ENs, i.e., handover. Specifically, for Auth-I, each vehicle can be cooperatively authenticated by t out of n ENs with identity-based signature techniques to obtain an authentication token and the involved ENs can be efficiently authenticated in a batch by the vehicle. For Auth-II, the vehicle can utilize the token as its private credential to achieve fast handover based on identity-based signature without interacting with multiple ENs, which further reduces the authentication delay significantly. In addition, we design a flexible method to support dynamic joining and leaving of ENs without the assistance of a trusted center. We demonstrate that the proposed protocol is secure and efficient through security analysis and performance evaluation.

Velocity-Aware Handover Self-Optimization Management for Next Generation Networks

Abstract: The fifth generation (5G) network is an upcoming standard for wireless communications that coexists with the current 4G network to increase the throughput. The deployment of ultra-dense small cells (UDSC) over a macro-cell layer yields multi-tier networks, which are known as heterogeneous networks (HetNets). HetNets play a key role in the cellular network to provide services to numerous users. However, the number of handovers (HOs) and radio link failure (RLF) greatly increase due to the increase in the UDSC in the network. Therefore, mobility management becomes a very important function in a self-organizing network to improve the system performance. In this paper, we propose a velocity-based self-optimization algorithm to adjust the HO control parameters in 4G/5G networks. The proposed algorithm utilizes the user’s received power and speed to adjust the HO margin and the time to trigger during the user’s mobility in the network. Simulation results demonstrate that the proposed algorithm achieves a remarkable reduction in the rate of ping-pong HOs and RLF compared with other existing algorithms, thereby outperforming such algorithms by an average of more than 70% for all HO performance metrics.

Individualistic Dynamic Handover Parameter Self-Optimization Algorithm for 5G Networks Based on Automatic Weight Function

Abstract: Ensuring a reliable and stable communication throughout the mobility of User Equipment (UE) is one of the key challenges facing the practical implementation of the Fifth Generation (5G) networks and beyond. One of the main issues is the use of suboptimal Handover Control Parameters (HCPs) settings, which are configured manually or generated automatically by certain self-optimization functions. This issue becomes more critical with the massive deployment of small base stations and connected mobile users. This will essentially require an individual handover self-optimization technique for each user individually instead of a unified and centrally configured setting for all users in the cell. In this paper, an Individualistic Dynamic Handover Parameter Optimization algorithm based on an Automatic Weight Function (IDHPO-AWF) is proposed for 5G networks. This algorithm dynamically estimates the HCPs settings for each individual UE based on UE’s experiences. The algorithm mainly depends on three bounded functions and their Automatic Weights levels. First, the bounded functions are evaluated, independently, as a function of the UE’s Signal-to-Interference-plus-Noise-Ratio (SINR), cells’ load and UE’s speed. Next, the outputs of the three bounded functions are used as inputs in a new proposed Automatic Weight Function (AWF) to estimate the weight of each output bounded function. After that, the final output is used as an indicator for optimizing HCPs settings automatically for a specific user. The algorithm is validated throughout various mobility conditions in the 5G network. The performance of the analytical HCPs estimation method is investigated and compared with other handover algorithms from the literature. The evaluation comparisons are performed in terms of Reference Signal Received Power (RSRP), Handover Probability (HOP), Handover Ping-Pong Probability (HPPP), and Radio Link Failure (RLF). The simulation results show that the proposed algorithm provides noticeable enhancements for various mobile speed scenarios as compared to the existing Handover Parameter Self-Optimization (HPSO) algorithms.

CoMP-Based Dynamic Handover for Vehicular VLC Networks

Abstract: Visible light communication (VLC) has emerged as a potential wireless connectivity solution for infrastructure-to-vehicle networks where street lights can be configured to serve as access points. In this letter, we propose dynamic soft handover algorithm based on coordinated multipoint (CoMP) transmission. The proposed algorithm takes the rate of change in the received power as an input and accordingly revises the handover margin and time-to-trigger value without explicit information of the vehicle velocity. Our simulation results demonstrate that the proposed algorithm outperforms conventional CoMP and hard handover and maintains a stable signal quality regardless of vehicle velocity.

Forecast Based Handover in an Extensible Multi-Layer LEO Mobile Satellite System

Abstract: Low earth orbit mobile satellite system (LEO-MSS) is the major system to provide communication support for the regions beyond the coverage of terrestrial network systems. However, passive handover happens frequently caused by the quick movement of LEO satellites in LEO-MSS. It not only causes the waste of radio resource, but also makes it hard to guarantee the quality of service (QoS), especially for user groups in hot-spot regions. To tackle this problem, we propose an extensible multi-layer network architecture to reduce the handover rate, especially group handover rate by introducing high-altitude platforms (HAPs) and terrestrial relays (TRs) to this system. We then propose a multi-layer handover management framework and also design different handover procedures based on handover forecast for different kinds of handovers according to the proposed architecture and framework to reduce handover delay and signalling cost. Furthermore, we propose a dynamic handover optimization to reduce the dropping probability and guarantee the QoS of mobile terminals. Numerical results show that the proposed architecture reduces group handovers significantly. The proposed handover procedures also provide better performance on delay and signalling cost compared with traditional handover protocols. With the proposed dynamic handover optimization, the proposed handover procedures provide better performance on dropping probability and throughput. The proposed dynamic handover optimization has an excellent performance on dropping probability while guaranteeing the QoS of mobile terminals.

A survey on femtocell handover management in dense heterogeneous 5G networks

Abstract: The Heterogeneous Network is widely used in the Fifth Generation wireless network to solve the problem of increasing demand on wireless communication. Femtocell or called Home-evolved NodeB (HeNB) is one of the small cells nominated to be used in this generation. HeNB is a low-power, low-cost, and short coverage area base station randomly assigned by the user. Therefore, HeNB is used in Long Term Evolution (LTE/LTE-Advanced) to support Quality of Service next to conventional cell. Based on the short range and dense use of HeNB, the seamless handover procedure is one of the challenges HeNet system is facing. Though there are numerous present literature work for handover decision issues, this paper attempts to present handover management challenges and the outline of modern handover decision algorithms in between the evolved NodeB (eNB) and HeNB. Furthermore, comprehensive details of the handover procedure in LTE-A Heterogeneous Networks are presented. This survey categorizes the recent studies in decision algorithms for the two-tier network (eNB and HeNB) regarding the main decision technique. Finally, a comprehensive summary of input parameters, techniques, and performance evaluation for each handover decision scheme are discussed by providing the advantage and disadvantage of each category.

Enabling roaming across heterogeneous IoT wireless networks: LoRaWAN meets 5G

Abstract: Despite the latest research efforts to foster mobility and roaming in heterogeneous Low Power Wide Area Networks (LP-WANs) networks, handover roaming of Internet of Things (IoT) devices is not a success mainly due to fragmentation and difficulties to establish trust across different network domains as well as the lack of interoperability of different LP-WANs wireless protocols. To cope with this issue, this paper proposes a novel handover roaming mechanism for Low Range Wide Area Network (LoRaWAN) protocol that relies on the trusted 5G network to perform IoT device's authentication and key management, thereby extending the mobility and roaming capabilities of LoRaWAN to global scale. The proposal enables interoperability between 5G network and LoRaWAN, whereby multi Radio Access Technologies IoT (multi-RAT IoT) devices can exploit both technologies interchangeably, thereby fostering novel IoT mobility and roaming use cases for LP-WANs not experimented so far. Two integration approaches for LoRaWAN and 5G have been proposed, either assuming 5G spectrum connectivity with standard 5G authentication or performing 5G authentication over the LoRaWAN network. The solution has been deployed, implemented and validated in a real and integrated 5G-LoRaWAN testbed, showing its feasibility and security viability.

Design and Evaluation of a Multihoming-Based Mobility Management Scheme to Support Inter Technology Handoff in PNEMO

Abstract: Handoff management is an indispensable component in supporting network mobility. The handoff situation raises while the Mobile Router (MR) or Mobile Node (MN) crosses the different wireless communication access technologies. At the time of inter technology handoff the multiple interface based MR can accomplish multihoming features such as enhanced availability, traffic load balancing with seamless flow distribution. These multihoming topographies greatly responsible reducing network delays during inter technology handoff. This article proposes a multihoming based Mobility management in Proxy NEMO (MM-PNEMO) scheme that considers benefits of using multiple interfaces. To support the proposed scheme design a numerical framework is developed that will be used to assess the performance of the proposed MM-PNEMO scheme. The performance is evaluated in the state-of-art numerical simulation approach focusing the key success metrics of signalling cost and packet delivery cost, that eventually scaling the total handoff cost. The numerical simulation result shows that the proposed MM-PENMO delightedly reduces the average handoff cost to 60% compared to existing NEMO Basic support protocol (NEMO-BSP) and PNEMO.

A User-Centric Handover Scheme for Ultra-Dense LEO Satellite Networks

Abstract: In recent years, low earth orbit (LEO) satellite networks (LSNs) have attracted increasing attention due to economic prospect and advantages in high bandwidth and low latency. In order to provide higher quality of service (QoS) and address the frequent handover problem among LEO satellites, we propose a user-centric handover scheme for ultra-dense LSNs in this letter. Our basic idea is to exploit satellite’s storage capability to improve user’s communication quality. By buffering user’s downlink data in multiple satellites simultaneously, the terrestrial user can realize seamless handover and always access the satellite with the best link quality. Simulation results show that our user-centric handover scheme outperforms the traditional handover scheme in terms of throughput, handover delay and end-to-end latency.

QoE-Driven Intelligent Handover for User-Centric Mobile Satellite Networks

Abstract: Recently, many satellites are being launched for providing global internet broadband service to individual consumers. Since satellites and users could move separately, providing seamless connectivity has become one of the most important tasks for mobile satellite networks. Current handover methods are based on either signal strength or service time, however, due to the randomness of user terminal (UT) arrivals and the unbalanced traffic distribution for high-mobility satellite networks, the success rate can hardly be guaranteed. To this end, we propose a QoE-driven intelligent handover mechanism for user-centric mobile satellite networks, through which the access satellites can be selected by predicted service time and communication channel resources. Accordingly, to ensure the service duration, a spatial relationship coupling model is proposed to predict relative motion pattern between UT and satellites; To improve the effectiveness of handover, an available channel estimation model is then developed based on the mobility pattern of adjacent satellites. Finally, reinforcement learning is adopted to maximize the UT $^\prime$ s Quality of Experience (QoE) through predicted handover factors. Simulation results show that the proposed handover mechanism offers good performance in terms of handover times, handover success rate and end-to-end delay.

A Low Latency and Consistent Cellular Control Plane

Abstract: 5G networks aim to provide ultra-low latency and higher reliability to support emerging and near real-time applications such as augmented and virtual reality, remote surgery, self-driving cars, and multi-player online gaming. This imposes new requirements on the design of cellular core networks. A key component of the cellular core is the control plane. Time to complete control plane operations (e.g. mobility handoff, service establishment) directly impacts the delay experienced by end-user applications. In this paper, we design Neutrino, a cellular control plane that provides users an abstraction of reliable access to cellular services while ensuring lower latency. Our testbed evaluations based on real cellular control traffic traces show that Neutrino provides an improvement in control procedure completion times by up to 3.1x without failures, and up to 5.6x under control plane failures, over existing cellular core proposals. We also show how these improvements translate into improving end-user application performance: for AR/VR applications and self-driving cars, Neutrino performs up to 2.5x and up to 2.8x better, respectively, as compared to existing EPC.

QoS-Driven Optimized Design-Based Integrated Visible Light Communication and Positioning for Indoor IoT Networks

Abstract: With the rapid development of the Internet of Things (IoT) in the smart city, smart grid, and smart industry, indoor communication and positioning are important for IoT. However, radio-frequency (RF)-based wireless networks may fail to guarantee different quality-of-service (QoS) requirements of devices, due to the limited bandwidth, severe interference, and multipath reflections. Hence, this article presents a new integrated visible light communication (VLC) and VLC positioning (VLCP) network for IoT to provide both high-speed communication and high-accuracy positioning services. As the network consists of multiple VLC access points (APs), we propose jointly optimizing the AP selection, bandwidth allocation, adaptive modulation, and power allocation approach to satisfy different QoS requirements of indoor devices while maximizing the network data rate. A low-complexity iterative algorithm is presented to solve the resource management (RM) optimization problem by decomposing it into two subproblems. Finally, a robust handover mechanism and a pedestrian dead reckoning (PDR)-assisted VLCP scheme are presented to maintain good performance under line-of-sight (LOS) blockages. The simulation results verify that the proposed solutions outperform other existing solutions in terms of effectively enhancing the data rate, improving the positioning accuracy, and guaranteeing devices’ QoS requirements. In detail, the mean position error is reduced from 20 to 4.3 cm by using our presented integrated VLCP model. The proposed RM approach achieves a satisfied QoS level improvement of up to 20.3% compared with the non-QoS-driven RM approach, and it achieves the high data rate up to 1.31 Gb/s.

Improving the Congestion Control Performance for Mobile Networks in High-Speed Railway via Deep Reinforcement Learning

Abstract: Due to the poor Transmission Control Protocol (TCP) performance in high-speed mobile scenarios, passengers have bad network experiences on High-Speed Railway (HSR). As a result, improving network performance for HSR scenarios has become urgent and widespread concerns. Some previous works quantitatively analyzed the TCP performance on HSR and proposed relevant solutions. Other works focused on the handover problem (the leading cause of poor network performance on HSR), and proposed a series of handover algorithms for HSR scenarios. However, the existing works are either limited to only measurement studies without algorithm implementation or lack of integration with real-world scenarios. In this paper, with a large amount of field measurement data in real HSR networks, we study the main reasons why traditional TCP performs poorly in HSR scenarios. To improve the TCP performance, we propose Hd-TCP, a customized Congestion Control (CC) algorithm designed to deal with frequent handover on HSR from the transport layer perspective. For the transmission characteristic on HSR, Hd-TCP can accurately evaluate the link condition and apply a Deep Reinforcement Learning (DRL) method to make a fine control. The simulation results show that Hd-TCP outperforms traditional CC algorithms in both throughput and latency by fully utilizing the transmission gap between handovers.

Cellular Network Traffic Prediction Incorporating Handover: A Graph Convolutional Approach

Abstract: Cellular traffic prediction enables operators to adapt to traffic demand in real-time for improving network resource utilization and user experience. To predict cellular traffic, previous studies either applied Recurrent Neural Networks (RNN) at individual base stations or adapted Convolutional Neural Networks (CNN) to work at grid-cells in a geographically defined grid. These solutions do not consider explicitly the effect of handover on the spatial characteristics of the traffic, which may lead to lower prediction accuracy. Furthermore, RNN solutions are slow to train, and CNN-grid solutions do not work for cells and are difficult to apply to base stations. This paper proposes a new prediction model, STGCN-HO, that uses the transition probability matrix of the handover graph to improve traffic prediction. STGCN-HO builds a stacked residual neural network structure incorporating graph convolutions and gated linear units to capture both spatial and temporal aspects of the traffic. Unlike RNN, STGCN-HO is fast to train and simultaneously predicts traffic demand for all base stations based on the information gathered from the whole graph. Unlike CNN-grid, STGCN-HO can make predictions not only for base stations, but also for cells within base stations. Experiments using data from a large cellular network operator demonstrate that our model outperforms existing solutions in terms of prediction accuracy.

SDN-based Handover Scheme in Cellular/IEEE 802.11p Hybrid Vehicular Networks

Abstract: With the arrival of 5G, the wireless network will be provided with abundant spectrum resources, massive data transmissions and low latency communications, which makes Vehicle-to-Everything applications possible. However, VANETs always accompany with frequent network topology changes due to the highly mobile feature of vehicles. As a result, the network performance will be affected by the frequent handover. In this paper, a seamless handover schemeis proposed where the Software-Defined Networking (SDN) and Mobile Edge Computing (MEC) technologies are employed to adapt to the dynamic topology change in VANETs. The introductionof SDN provides a global view of network topology and centralized control, which enables a stable transmission layer connection when a handover takes place, so that the upper layer performance isnot influenced by the network changes. By employing MEC server, the data are cached in advance before a handover happens, so that the vehicle can restore normal communication faster. In order toconfirm the superiority of our proposal, computer simulations are conducted from different aspects. The results show that our proposal can significantly improve the network performance when ahandover happens.

Vertical handover algorithm based on multi-attribute and neural network in heterogeneous integrated network

Abstract: A novel vertical handover algorithm based on multi-attribute and neural network for heterogeneous integrated network is proposed in this paper. The whole frame of the algorithm is constructed by setting the network environment in which we use the network resources by switching between UMTS, GPRS, WLAN, 4G, and 5G. Each network build their own three-layer BP (Back Propagation, BP) neural network model and then the maximum transmission rate, minimum delay, SINR (signal to interference and noise ratio, SINR), bit error rate, user moving speed, and packet loss rate which can affect the overall performance of the wireless network are employed as reference objects to participate in the setting of BP neural network input layer neurons and the training and learning process of subsequent neural network data. Finally, the network download rate is adopted as prediction target to evaluate performance on the five wireless networks and then the vertical handover algorithm will select the right wireless network to perform vertical handover decision. The simulation results on MATLAB platform show that the vertical handover algorithm designed in this paper has a handover success rate up to 90% and realizes efficient handover and seamless connectivity between multi-heterogeneous networks.