Abstract: Mobile edge computing (MEC) provides a promising approach to significantly reduce network operational cost and improve quality of service (QoS) of mobile users by pushing computation resources to the network edges, and enables a scalable Internet of Things (IoT) architecture for time-sensitive applications (e-healthcare, real-time monitoring, and so on.). However, the mobility of mobile users and the limited coverage of edge servers can result in significant network performance degradation, dramatic drop in QoS, and even interruption of ongoing edge services; therefore, it is difficult to ensure service continuity. Service migration has great potential to address the issues, which decides when or where these services are migrated following user mobility and the changes of demand. In this paper, two conceptions similar to service migration, i.e., live migration for data centers and handover in cellular networks, are first discussed. Next, the cutting-edge research efforts on service migration in MEC are reviewed, and a devisal of taxonomy based on various research directions for efficient service migration is presented. Subsequently, a summary of three technologies for hosting services on edge servers, i.e., virtual machine, container, and agent, is provided. At last, open research challenges in service migration are identified and discussed.

Abstract: Fog computing is a technology that brings computing and storage resources near to the end user. Being in its infancy, fog computing lacks standardization in terms of architectures and simulation platforms. There are a number of fog simulators available today, among which a few are open-source, whereas rest are commercially available. The existing fog simulators mainly focus on a number of devices that can be simulated. Generally, the existing simulators are more inclined toward sensors’ configurations, where sensors generate raw data and fog nodes are used to intelligently process the data before sending to back-end cloud or other nodes. Therefore, these simulators lack network properties and assume reliable and error-free delivery on every service request. Moreover, no simulator allows researchers to incorporate their own fog nodes management algorithms, such as scheduling. In existing work, device handover is also not supported. In this paper, we propose a new fog simulator called FogNetSim++ 1 that provides users with detailed configuration options to simulate a large fog network. It enables researchers to incorporate customized mobility models and fog node scheduling algorithms, and manage handover mechanisms. In our evaluation setup, a traffic management system is evaluated to demonstrate the scalability and effectiveness of proposed simulator in terms of CPU and memory usage. We have also benchmarked the network parameters, such as execution delay, packet error rate, handovers, and latency. 1 available at https://fognetsimpp.com

Abstract: In this paper, we propose a two-layer framework to learn the optimal handover (HO) controllers in possibly large-scale wireless systems supporting mobile Internet-of-Things users or traditional cellular users, where the user mobility patterns could be heterogeneous. In particular, our proposed framework first partitions the user equipments (UEs) with different mobility patterns into clusters, where the mobility patterns are similar in the same cluster. Then, within each cluster, an asynchronous multiuser deep reinforcement learning (RL) scheme is developed to control the HO processes across the UEs in each cluster, in the goal of lowering the HO rate while ensuring certain system throughput. In this scheme, we use a deep neural network (DNN) as an HO controller learned by each UE via RL in a collaborative fashion. Moreover, we use supervised learning in initializing the DNN controller before the execution of RL to exploit what we already know with traditional HO schemes and to mitigate the negative effects of random exploration at the initial stage. Furthermore, we show that the adopted global-parameter-based asynchronous framework enables us to train faster with more UEs, which could nicely address the scalability issue to support large systems. Finally, simulation results demonstrate that the proposed framework can achieve better performance than the state-of-art online schemes, in terms of HO rates.

Abstract: The sensitivity of millimeter wave (mmWave) signals to blockages is a fundamental challenge for mobile mmWave communication systems. The sudden blockage of the line-of-sight (LOS) link between the base station and the mobile user normally leads to disconnecting the communication session, which highly impacts the system reliability. Further, reconnecting the user to another LOS base station incurs high beam training overhead and critical latency problem. In this paper, we leverage machine learning tools and propose a novel solution for these reliability and latency challenges in mmWave MIMO systems. In the developed solution, the base stations learn how to predict that a certain link will experience blockage in the next few time frames using their observations of adopted beamforming vectors. This allows the serving base station to proactively hand-over the user to another base station with highly probable LOS link. Simulation results show that the developed deep learning based strategy successfully predicts blockage/hand-off in close to 95% of the times. This reduces the probability of communication session disconnection, which ensures high reliability and low latency in mobile mmWave systems.

Abstract: Modern fog network architectures, empowered by IoT applications and 5G communications technologies, are characterized by the presence of a huge number of mobile nodes, which undergo frequent handovers, introducing a significant load on the involved network entities. Considering the distributed and flat nature of these architectures, DMM can be the only viable option for efficiently managing handovers in these scenarios. The existing DMM solutions are capable of providing smooth handovers, but lack robustness from the security point of view. Indeed, DMM depends on external mechanisms for handover security and uses a centralized device, which has obvious security and performance implications in flat architectures where hierarchical dependencies can introduce problems. We propose a new DMM schema based on the blockchain, capable of resolving hierarchical security issues without affecting the network layout, and also satisfying fully distributed security requirements with less consumption of energy.

Abstract: Vehicular communications have developed rapidly contributing to the success of intelligent transportation systems. In VANET, continuous connectivity is a huge challenge caused by the extremely dynamic network topology and the highly variable number of mobile nodes. Moreover, message dissemination efficiency is a serious issue in traffic effectiveness and road safety. The heterogeneous vehicular network, which integrates cellular networks with DSRC, has been suggested and attracted significant attention recently. VANET-cellular integration offers many potential benefits, for instance, high data rates, low latency, and extended communication range. Due to the heterogeneous wireless access, a seamless handover decision is required to guarantee QoS of communications and to maintain continuous connectivity between the vehicles. On the other hand, VANET heterogeneous wireless networks integration will significantly help autonomous cars to be functional in reality. This paper surveys and reviews some related studies in the literature that deals with VANET heterogeneous wireless networks communications in term of vertical handover, data dissemination and collection, gateway selection and other issues. The comparison between different works is based on parameters like bandwidth, delay, throughput, and packet loss. Finally, we outline open issues that help to identify the future research directions of VANET in the heterogeneous environment.

Abstract: Small cells were introduced to support high data-rate services and for dense deployment. Owing to user equipment (UE) mobility and small-cell coverage, the load across a small-cell network recurrently becomes unbalanced. Such unbalanced loads result in performance degradation in throughput and handover success and can even cause radio link failure. In this paper, we propose a mobility load balancing algorithm for small-cell networks by adapting network load status and considering load estimation. To that end, the proposed algorithm adjusts handover parameters depending on the overloaded cells and adjacent cells. Resource usage depends on signal qualities and traffic demands of connected UEs in long-term evolution. Hence, we define a resource block-utilization ratio as a measurement of cell load and employ an adaptive threshold to determine overloaded cells, according to the network load situation. Moreover, to avoid performance oscillation, the impact of moving loads on the network is considered. Through system-level simulations, the performance of the proposed algorithm is evaluated in various environments. Simulation results show that the proposed algorithm provides a more balanced load across networks (i.e., smaller standard deviation across the cells) and higher network throughput than previous algorithms.

Abstract: Ultra-dense network deployment is a key technology for potentially achieving the capacity target of next-generation wireless communication systems. However, such a deployment results in cell proliferation and cell size decrement, leading to an increased number of handovers and limited sojourn time within a cell, which severely degrade the user’s quality of service (QoS). In this paper, we propose two intelligent handover skipping techniques to overcome the high handover rates. The first technique considers a user associated with a single base station (BS) and the decision to skip a handover is based on the upcoming cell’s topology; we consider three criteria: 1) the area of the cell; 2) the trajectory distance within the cell; and 3) the distance of the BS from the cell edge. The second technique exploits BS cooperation and enables a dynamic handover skipping scheme, where the skipping decision is taken based on the BSs of three consecutive cells in the user’s trajectory. This technique achieves a balance between BS cooperation and single BS transmission and manages to maintain a good QoS during the skipping phase. We show that the proposed techniques reduce both the handover rate and handover cost and outperform the conventional techniques for moderate to high-velocity values.

Abstract: The femtocell networks that use home base station and existing xDSL or other cable line as backhaul connectivity can fulfill the upcoming demand of high data rate for wireless communication system as well as can extend the coverage area. Hence the modified handover procedure for existing networks is needed to support the macrocell/femtocell integrated network. Some modifications of existing network and protocol architecture for the integration of femtocell networks with the existing UMTS based macrocell networks are essential. These modifications change the signal flow for handover procedures due to different 2-tier cell (macrocell and femtocell) environment. The measurement of signal-to-interference ratio parameter should be considered for handover between macrocell and femtocell. A frequent and unnecessary handover is another problem for hierarchical network environment that must be optimized to improve the performance of macrocell/femtocell integrated network. In this paper, firstly we propose the concentrator based and without concentrator based femtocell network architecture. Then we present the signal flow with appropriate parameters for the handover between 3GPP UMTS based macrocell and femtocell networks. A scheme for unnecessary handoff minimization is also presented in this paper. We simulate the proposed handover optimization scheme to validate the performance.

Abstract: The emerging real-time and computation-intensive services driven by the Internet of Things, augmented reality, automatic driving, and so on, have tight quality of service and quality of experience requirements, which can hardly be supported by conventional cloud computing. Fog computing, which migrates the features of cloud computing to the network edge, guarantees low latency for location-aware services. However, due to the locality feature of fog computing, maintaining service continuity when mobile users travel across different access networks has become a challenging issue. In this article, we propose a novel software-defined-networking-based fog computing architecture by decoupling mobility control and data forwarding. Under the proposed architecture, we design efficient signaling operations to provide seamless and transparent mobility support to mobile users, and present an efficient route optimization algorithm by considering the performance gain in data communications and system overhead in mobile fog computing. Numerical results from extensive simulations have demonstrated that the proposed scheme can not only guarantee service continuity, but also greatly improve handover performance and achieve high data communication efficiency in mobile fog computing.

Abstract: To satisfy requirements on future mobile network, a large number of small cells should be deployed. In such scenario, mobility management becomes a critical issue in order to ensure seamless connectivity with a reasonable overhead. In this paper, we propose a fuzzy logic-based scheme exploiting a user velocity and a radio channel quality to adapt a hysteresis margin for handover decision in a self-optimizing manner. The objective of the proposed algorithm is to reduce a number of redundant handovers and a handover failure ratio while allowing the users to exploit benefits of the dense small cell deployment. Simulation results show that our proposed algorithm efficiently suppresses ping pong effect and keeps it at a negligible level (below 1%) in all investigated scenarios. Moreover, the handover failure ratio and the total number of handovers are notably reduced with respect to existing algorithms, especially in scenario with high number of small cells. In addition, the proposed scheme keeps the time spent by the users connected to the small cells at a similar level as the competitive algorithms. Thus, the benefits of the dense small cell deployment for the users are preserved.

Abstract: Emerging 5G URLLC wireless systems are characterized by minimal over-the-air latency and stringent decoding error requirements. The low latency requirements can cause conflicts with 5G EE design targets. Therefore, this work provides a perspective on various trade-offs between energy efficiency and user plane delay for upcoming URLLC systems. For network infrastructure EE, we propose solutions that optimize base station on-off switching and distributed access network architectures. For URLLC devices, we advocate solutions that optimize EE of discontinuous reception (DRX), mobility measurements, and the handover process, respectively, without compromising on delay.

Abstract: Object handover is a basic, but essential capability for robots interacting with humans in many applications, e.g., caring for the elderly and assisting workers in manufacturing workshops. It appears deceptively simple, as humans perform object handover almost flawlessly. The success of humans, however, belies the complexity of object handover as collaborative physical interaction between two agents with limited communication. This paper presents a learning algorithm for dynamic object handover, for example, when a robot hands over water bottles to marathon runners passing by the water station. We formulate the problem as contextual policy search, in which the robot learns object handover by interacting with the human. A key challenge here is to learn the latent reward of the handover task under noisy human feedback. Preliminary experiments show that the robot learns to hand over a water bottle naturally and that it adapts to the dynamics of human motion. One challenge for the future is to combine the model-free learning algorithm with a model-based planning approach and enable the robot to adapt over human preferences and object characteristics, such as shape, weight, and surface texture.

Abstract: The sensitivity of millimeter wave (mmWave) signals to blockages is a fundamental challenge for mobile mmWave communication systems The sudden blockage of the line-of-sight (LOS) link between the base station and the mobile user normally leads to disconnecting the communication session, which highly impacts the system reliability Further, reconnecting the user to another LOS base station incurs high beam training overhead and critical latency problems In this paper, we leverage machine learning tools and propose a novel solution for these reliability and latency challenges in mmWave MIMO systems In the developed solution, the base stations learn how to predict that a certain link will experience blockage in the next few time frames using their past observations of adopted beamforming vectors This allows the serving base station to proactively hand-over the user to another base station with a highly probable LOS link Simulation results show that the developed deep learning based strategy successfully predicts blockage/hand-off in close to 95% of the times This reduces the probability of communication session disconnection, which ensures high reliability and low latency in mobile mmWave systems

Abstract: The heterogeneous network architecture based on control/user (C/U) plane split is a research hot spot in the fifth generation (5G) communication system. This new architecture for the high-speed railway (HSR) communication system can provide high quality of service (QoS) for the passengers, such as higher system transmission capacity, better transmission reliability, and lower co-channel interference. The relatively critical C plane is expanded and maintained in a reliable low-frequency band to guarantee transmission reliability, and the U plane is supported by the available high-frequency band to meet the increasing system capacity demands. However, there are still many problems to be solved in the C/U plane split network to ensure reliable transmission. In the HSR communication system, the C plane and the U plane are supported by the macro evolved NodeBs (eNBs) and the small eNBs, respectively. The handover between the different macro eNBs involves two types of handovers, which directly reduces its applicability and reliability in HSR. Therefore, a dual-link soft handover scheme for C/U plane split network in HSR is proposed in this paper. By deploying a train relay station (TRS) and two antennas in the train, the handover outage probability will be reduced. Moreover, the bi-casting is adopted to decrease the communication interruption time and the signaling flows of the intra-macro eNB handover and inter-macro eNB handover are designed in detail. Simulation results show that the proposed handover scheme can significantly reduce the outage probability and improve the handover success probability in the inter-macro eNB handover.

Abstract: Satellite communications (SatCom) plays a vital role in ensuring seamless access to telecommunications services anytime, and is a viable option for delivering telecommunication services in a wide range of sectors such as aeronautical, military, maritime, rescue and disaster relief It should be an important component of 5G-and-beyond wireless architectures as it can complement terrestrial telecommunication solutions in various scenarios to provide highly reliable and secure connectivity over a wide geographical area This book explores promising scenarios for 5G SatCom, novel paradigms for hybrid/integrated satellite-terrestrial integration, and emerging technologies for the next generation of SatCom systems Topics covered include: Role of SatCom in the 5G Era; 5G satellite use cases and scenarios; SDN-enabled networks, NFV-based scenarios and on-board processing for satellite-terrestrial integration; EHF broadband aeronautical SatCom systems; Next-generation NGSO SatCom systems; Diversity combining and handover techniques for MEO satellites; Non-linear countermeasures for multicarrier satellites; SDN demonstrator for multi-beam satellite precoding; Beam-hopping SatCom systems; Optical on-off keying data links for LEO downlink applications; Ultra-high speed data relay systems; On-board interference detection and localization; Advanced random access schemes for SatCom systems; Interference avoidance, mitigation and dynamic spectrum sharing for hybrid satellite-terrestrial systems; and Two-way satellite relaying

Abstract: Ultra-dense small cell deployment in future 5G networks is a promising solution to the ever increasing demand of capacity and coverage. However, this deployment can lead to severe interference and high number of handovers, which in turn cause increased signaling overhead. In order to ensure service continuity for mobile users, minimize the number of unnecessary handovers and reduce the signaling overhead in heterogeneous networks, it is important to model adequately the handover decision problem. In this paper, we model the handover decision based on the multiple attribute decision making method, namely Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The base stations are considered as alternatives, and the handover metrics are considered as attributes to selecting the proper base station for handover. In this paper, we propose two modified TOPSIS methods for the purpose of handover management in the heterogeneous network. The first method incorporates the entropy weighting technique for handover metrics weighting. The second proposed method uses a standard deviation weighting technique to score the importance of each handover metric. Simulation results reveal that the proposed methods outperformed the existing methods by reducing the number of frequent handovers and radio link failures, in addition to enhancing the achieved mean user throughput.

Abstract: The millimeter-wave (mm-wave) frequencies offer the potential of orders of magnitude that increases in capacity for next-generation cellular systems. However, links in mm-wave networks are susceptible to blockage and may suffer from rapid variations in quality. Connectivity to multiple cells at mm-wave and/or traditional frequencies is considered essential for robust communication. One of the challenges in supporting multi-connectivity in mm-waves is the requirement for the network to track the direction of each link in addition to its power and timing. To address this challenge, we implement a novel uplink measurement system that, with the joint help of a local coordinator operating in the legacy band, guarantees continuous monitoring of the channel propagation conditions and allows for the design of efficient control plane applications, including handover, beam tracking, and initial access. We show that an uplink-based multi-connectivity approach enables less consuming, better performing, faster and more stable cell selection, and scheduling decisions with respect to a traditional downlink-based standalone scheme. Moreover, we argue that the presented framework guarantees: 1) efficient tracking of the user in the presence of the channel dynamics expected at mm-waves and 2) fast reaction to situations in which the primary propagation path is blocked or not available.

Abstract: In this paper, we propose a distributed mobility robustness optimization algorithm to minimize handover failures due to radio link failures by adjusting time-to-trigger and offset parameters. According to the reason for failure, the algorithm classifies handover failure into three categories (too late, too early, and wrong cell), and simultaneously optimizes three handover parameters according to the dominant failure. Moreover, the algorithm considers handover failures to each neighboring cell and adjusts handover parameters individually. Via simulation, we show how the proposed algorithm adaptively optimizes the parameters and outperforms previous algorithms in various mobile environments.

Abstract: As the indispensable supplement of terrestrial communications, Low Earth Orbit (LEO) satellite network is the crucial part in future space-terrestrial integrated networks because of its unique advantages. However, the effective and reliable routing for LEO satellite network is an intractable task due to time-varying topology, frequent link handover, and imbalanced communication load. An Extreme Learning Machine (ELM) based distributed routing (ELMDR) strategy was put forward in this paper. Considering the traffic distribution density on the surface of the earth, ELMDR strategy makes routing decision based on traffic prediction. For traffic prediction, ELM, which is a fast and efficient machine learning algorithm, is adopted to forecast the traffic at satellite node. For the routing decision, mobile agents (MAs) are introduced to simultaneously and independently search for LEO satellite network and determine routing information. Simulation results demonstrate that, in comparison to the conventional Ant Colony Optimization (ACO) algorithm, ELMDR not only sufficiently uses underutilized link, but also reduces delay.

Abstract: According to an aspect of the present invention, there is provided an interworking method between networks of a user equipment (UE) in a wireless communication system, including: performing a first interworking procedure for changing a network of the UE from a 5-generation core network (5GC) network to an evolved packet core (EPC) network, wherein, when an interface between the 5GC and the EPC networks does not exist, the performing of the first interworking procedure includes: receiving a first indication from an access and mobility management function (AMF) of the 5GC network; and performing a handover attach procedure in the EPC network based on the first indication.

Abstract: The optimization of handover (HO) parameters for in-building systems is investigated in this paper We proposed a novel methodology that provides in-building base stations with the flexibility to customize HO parameters to specific radio frequency conditions at the cell-edge for different loading scenarios We propose the use of machine learning and data mining techniques to allow the base stations to autonomously learn and identify characteristic patterns in the received signal strength values (reported by users during the HO process), and apply optimal HO parameters for each case Our optimization strategy jointly considers the radio frequency conditions at the cell-edge and the load levels of the base stations, to determine optimal HO parameters that maximize the quality of service and guarantee the continuity of service at the cell-edge We evaluated our methodology with experimental data collected from two fully operational LTE in-building systems deployed in a university campus Our results show that with our methodology the spectral efficiency at the cell-edge can be greatly improved Downlink data rate gains at the cell-edge reached a value close to 150% for a certain loading scenario compared to the traditional approach of selecting a unique set of HO parameters for the entire in-building system

Abstract: So far, research on Smart Cities and self-organizing networking techniques for fifth-generation (5G) cellular systems has been one-sided: a Smart City relies on 5G to support massive machine-to-machine (M2M) communications, but the actual network is unaware of the information flowing through it. However, a greater synergy between the two would make the relationship mutual, since the insights provided by the massive amount of data gathered by sensors can be exploited to improve the communication performance. In this paper, we concentrate on self-organization techniques to improve handover efficiency using vehicular traffic data gathered in London. Our algorithms exploit mobility patterns between cell coverage areas and road traffic congestion levels to optimize the handover bias in heterogeneous networks and dynamically manage mobility management entity (MME) loads to reduce handover completion times.

Abstract: As a measurement, quality of service (QoS) has been commonly taken into account in the traditional vertical handoff schemes for the heterogeneous wireless access networks However, the QoS is not sufficient to correlate well with the user satisfaction In this paper, quality of experience (QoE) is introduced into the decision mechanism of the vertical handoff and a random neural network -based QoE estimation is proposed to determine the correlation between the QoE and the QoS in the heterogeneous networks In addition, a Q-learning-based vertical handoff algorithm, designated as a QoE-Q algorithm, is presented in order to maximize the QoE utility for users It can be observed from the simulation results that the proposed method not only outperforms the existing schemes with enhanced call blocking probability and handoff dropping probability property but also obtains better QoE performance in the service charges and the terminal power consumption than other schemes

Abstract: To meet the drastic growth of the mobile traffic, 5G network is designed to optimize the transmission efficiency and provide higher quality of service (QoS). Small cell is considered as a promising and feasible approach to meet the increasing traffic demand. For this purpose, in this paper, we study a dynamic Femtocell gNB (F-gNB) ON/OFF strategies in 5G heterogeneous cellular networks (HCNs), which aims at maximizing the network energy efficiency (NEE) by optimizing jointly the traffic load prediction, the cell association, and the dynamic F-gNB ON/OFF strategies with respected to the time-varying traffic load, while taking into account the load balancing and the outage probability of the network. However, the optimization problem is a nonconvex problem. In order to relax the computation complexity, the original optimization problem is divided into two steps: cell association with load balancing (CALB) scheme and energy efficiency-based dynamic F-gNBs ON/OFF (DFOO) strategies. Specifically, the proposed CALB scheme could guarantee the load balancing as well as the minimum signal-to-interference-plus-noise ratio requirement of user equipments (UEs). In addition, the proposed DFOO strategies consider the operation of base stations (BSs) according to the predicted time-varying traffic load from Markov procedure. Furthermore, dual connectivity-based seamless handover procedure is introduced to guarantee the transmission QoS of UEs. Simulation results illustrate that the proposed DFOO algorithm provide considerable improvement of NEE while ensuring the load balancing of the HCN.

Abstract: A wireless device receives from a first base station, a radio resource control message indicating a command of a conditional handover towards a target cell of a second base station. The radio resource control message comprises: a cell identifier of the target cell; and at least one handover execution condition. The wireless device determines that at least one criteria of the at least one handover execution condition is met by the target cell of the second base station. The wireless device transmits to the first base station and in response to the determining that the at least one criteria is met, a first signal indicating a handover execution notification associated with the command of the conditional handover. The wireless device transmits to the second base station, a random access preamble via the target cell of the second base station.

Abstract: For a base station that supports cellular communications in sub-6 GHz LTE and millimeter (mmWave) bands, we propose a supervised machine learning algorithm to improve the success rate in the handover between the two radio frequencies using sub-6 GHz and mmWave prior channel measurements within a temporal window. The main contributions of our paper are: 1) introduce partially blind handovers, 2) employ machine learning to perform handover success predictions from sub-6 GHz to mmWave frequencies, and 3) show that this machine learning based algorithm combined with partially blind handovers can improve the handover success rate in a realistic network setup of colocated cells. Simulation results show improvement in handover success rates for our proposed algorithm compared to standard handover algorithms.