Handover Decision Research Articles

Fuzzy Adaptive Hysteresis of RSS for Handover Decision in V2V VANET

A communication technology which enables the information exchanges between vehicles to support intelligent transportation system is the characteristic of vehicular ad hoc network (VANET). The vast applications of VANET including transportation safety, traffic management, and passenger amenities are also followed by some technical challenges. The rapid topology changes and high nodes mobility are the main source of the challenges. One of the prominent challenges is the handover decision, especially in vehicle to vehicle (V2V) communication. In this paper, handover decision method for V2V VANET is proposed using fuzzy system. The proposed method adjusts the value of received signal strength (RSS) hysteresis adaptively using fuzzy system so that the proposed handover decision method is called fuzzy adaptive hysteresis (FAH). To adjust the value of hysteresis, fuzzy system utilizes RSS value, speed difference, and connected time as the input. Based on the simulation results, the proposed method can reduce handover rate as well as maintaining the higher value of signal to noise ratio (SNR) average.

A Two-Step Handover Strategy for GEO/LEO Heterogeneous Satellite Networks Based on Multi-Attribute Decision Making

Low Earth Orbit (LEO) satellites can provide high-speed and low-delay services for terrestrial users; however, the rapid movement of LEO satellites and the insufficient size of the LEO constellations incurs the instability of the transmission links. The rapid movement of LEO satellites also leads to frequent handovers. Fortunately, GEO/LEO heterogeneous satellite systems can remedy this shortcoming. As the handover decision strategy which makes the selection among the available satellites will directly impact the performance of GEO/LEO heterogeneous satellite systems, we propose a two-step access and handover decision strategy for heterogeneous satellite networks in this paper. Firstly, a GEO/LEO network selection is carried out based on utility functions that reflect user’s QoS requirements. Then, the multi-attribute decision making (MADM) method is used to select the specific LEO satellite if users select LEO satellite network. We also propose an Importance-TOPSIS scheme to improve the weight setting for handover attributes. Simulation results show that the proposed method can reduce the number of handovers and the forced termination probability of the system, and the overall throughput of the system is also improved.

Proactive Cross-Layer Framework Based on Classification Techniques for Handover Decision on WLAN Environments

In recent years, modern technology has been increasing, and this has grown a derivate in big challenges related to the network and application infrastructures. New devices have been providing more high functionalities to users than ever before; however, these devices depend on a high functionality of network in order to ensure a correct functioning ability over applications. This is essential for mobile networking systems to evolve in order to meet the future requirements of capacity, coverage, and data rate. In addition, when a network problem happens, it could be converted into somethingmore disastrous and difficult to solve. A crucial point is the network physical change and the difficulties, such as loss continuity of services and the decision to select the future network to be connected. In this article, a new framework is proposed to forecast a future network to be connected through a mobile node in WLAN environments. The proposed framework considers a decision-making process based on five classifiers and the user’s position and acceleration data in order to anticipate the network change, reaching up to 96.75% accuracy in predicting the connection of this future network. In this way, an early change of network is obtained without packet and time loss during the network change.

Proactive Handover Decision for UAVs with Deep Reinforcement Learning.

The applications of Unmanned Aerial Vehicles (UAVs) are rapidly growing in domains such as surveillance, logistics, and entertainment and require continuous connectivity with cellular networks to ensure their seamless operations. However, handover policies in current cellular networks are primarily designed for ground users, and thus are not appropriate for UAVs due to frequent fluctuations of signal strength in the air. This paper presents a novel handover decision scheme deploying Deep Reinforcement Learning (DRL) to prevent unnecessary handovers while maintaining stable connectivity. The proposed DRL framework takes the UAV state as an input for a proximal policy optimization algorithm and develops a Received Signal Strength Indicator (RSSI) based on a reward function for the online learning of UAV handover decisions. The proposed scheme is evaluated in a 3D-emulated UAV mobility environment where it reduces up to 76 and 73% of unnecessary handovers compared to greedy and Q-learning-based UAV handover decision schemes, respectively. Furthermore, this scheme ensures reliable communication with the UAV by maintaining the RSSI above −75 dBm more than 80% of the time.

Abstract WP95: Acute Stroke Simulation Training For General Medical Trainees: Busting Clots, Myths And Fears

Background: Acute stroke assessment and management is a time critical process. The emergency “stroke call” should include a standardised NIHSS assessment, initial observations and investigations and a decision regarding thrombolysis and thrombectomy. We found this to be daunting for trainees, and it has been documented that physician experience impacts upon door-to-needle time. Aims: The aim of our simulation training was to provide a safe environment to improve the ability and confidence of trainees in leading stroke calls with a view to improving acute stroke care and patient access to revascularization therapies. Method: We designed a one-day simulation course aimed at internal medicine trainees who are expected to lead stroke calls currently or in the immediate future. The candidates are surveyed to establish baseline confidence and complete questionnaires to assess knowledge about managing acute strokes. Here we present data from the first 18 attendees. The course is led by stroke consultants, assisted by members of the multidisciplinary team, including medical trainees, stroke specialist nurses and a paramedic. Alongside structured seminars, simulated acute stroke scenarios lead each candidate through patient handover, assessment, investigation and management decisions followed by a structured debrief. Clinical results, equipment and live monitoring are used to increase the fidelity of the simulation. Results: The course improved candidates' confidence in leading a thrombolysis call from a mean of 2.83/10 to 7.11/10, found to be statistically significant on Paired T-Test(t=9.69, p=<0.0001). Furthermore, the candidates’ acute stroke management quiz scores rose from a mean of 5.67/10 to 8.17/10 (t=5.28, p=<0.0001). Qualitative feedback included: “Good broad interactive session on a subject we get limited teaching on” and “Fantastic, clinically relevant course. I feel much more confident to attend thrombolysis calls now”. Conclusions: Simulation is an established staple of acute medical education, and has a demonstrable benefit when applied to acute stroke management training. We propose that this opportunity should be widely available if not mandatory to internal medical training.

Performance Comparison between Handover Decision Algorithms for Horizontal Handover in Long Term Evolution Networks

Comparative Analyses of Handover Decision Algorithms (HODA) for horizontal handovers in Long Term Evolution Network (LTE-A) in terms of Key Performance Indicators (KPIs) were conducted using system level simulation in MATLAB. The KPIs analyzed were the users’ throughput, spectral efficiency, cell –edge Spectral efficiency, handover success rate , call drop Rate, Cumulative Distribution Function (CDF) of Received Signal Reference Power (RSRP), Average RSRP over Primary Component Carrier (PCC) and Secondary Carrier (SCC) and interruption time. . From the results obtained, the algorithm based on spectrum aggregation of carriers from wireless communication spectrum and television white space band referred to as the proposed-TVWS algorithm showed remarkable performance improvements in all the chosen KPIs when compared with MIF and CONV except in handover success rate at UEs speeds less than 70 km/hr of which MIF-HODA algorithm seemed to perform better. Therefore, the proposed-TVWS has yielded baseline certainty in the increase of wireless network quality of service in addition to the seamless network connectivity for the mobile users.

Vertical Handover Decision Making Algorithms for 5G Heterogeneous Networks: A Review

The wireless networks have been rapidly used in our daily lives, especially mobile wireless networks. An environment consisting of several networks such as WLAN, 4G-LTE, and 5G, is known as a heterogeneous network. Wireless networks are widely used to support various data services such as live video streaming, upload data, download data and cloud service depending upon their capacity and nature. When the mobile terminal is moving, the network availability will be changed. Therefore, the mobile terminal must perform handover from one network to another network in heterogeneous networks to maintain the call and quality of service (QoS). This process is known as Vertical Handover (VHO). A seamless handover is important to provide uninterrupted high quality service while performing the handover. To achieve this, various vertical handover decision making algorithms have been proposed and studied using advanced tools and proven concepts. This paper gives an overview of current research on handover decision making in fifth generation (5G) heterogeneous wireless networks. Keywords—Vertical Handover Decision Making, Heterogeneous Networks, Quality of Service, 5G, Seamless Handover

Heterogeneous network handover algorithm for unmanned aerial vehicles based on categorical fuzzy inference

In order to meet the requirements of 5G connected unmanned aerial vehicle (UAV) communication, wireless local area network (WLAN) and satellite network are integrated into UAV communication link, and form UAV heterogeneous communication network together with 5G. In order to achieve fast and reliable handover decisions and uninterrupted service, this paper presents a vertical handover (VHO) algorithm based on classification fuzzy reasoning. Here, this paper introduces six parameters of UAV terminal and divides them into two categories for decision-making. The received signal strength (RSS), remaining available bandwidth (RBW), and propagation delay were taken as the key variables, while the network cost, expected dwell time (EDT) and security were taken as the non-key variables. In the handover decision, the pre-screening based on the received signal strength threshold and the stabilization time is firstly carried out, and the classification fuzzy logic system is used to make the next decision on the candidate networks that meet the requirements. Finally, the output of each fuzzy system is weighted according to user requirements to obtain the final judgment index. Through simulations, it has been proved that the algorithm proposed in this paper can accurately make the handover decision according to the user’s requirements, avoid the ping-pong effect, and effectively improve the system performance.

Multi-Criteria Fuzzy-Based Decision Making Algorithm to Optimize the VHO Performance in Hetnets

Despite the seemingly exponential growth of mobile and wireless communication, this same technology aims to offer uninterrupted access to different wireless systems like Radio Communication, Bluetooth, and Wi-Fi to achieve better network connection which in turn gives the best quality of service (QoS). Many analysts have established many handover decision systems (HDS) to enable assured continuous mobility between various radio access technologies. Unbroken mobility is one of the most significant problems considered in wireless communication networks. Each application needs a distinct QoS, so the network choice may shift appropriately. To achieve this objective and to choose the finest networks, it is important to select a best decision making algorithm that chooses the most effective network for every application that the user requires, dependent on QoS measures. Therefore, the main goal of the proposed system is to provide an enhanced vertical handover (VHO) decision making program by using a Multi-Criteria Fuzzy-Based algorithm to choose the best network. Enhanced Multi-Criteria algorithms and a Fuzzy-Based algorithm is implemented successfully for optimal network selection and also to minimize the probability of false handover. Furthermore, a double packet buffer is utilized to decrease the packet loss by 1.5% and to reduce the number of handovers up to 50% compared to the existing systems. In addition, the network setup has an optimized mobility management system to supervise the movement of the mobile nodes.

An Artificial Neural Network-Based Handover Scheme for Hybrid LiFi Networks

Combining the ultra-high user throughput of the light fidelity (LiFi) and the ubiquitous coverage of wireless fidelity (WiFi), the hybrid LiFi and WiFi network (HLWNet) demonstrates unparalleled advantages in indoor wireless data transmission. Due to the line-of-sight propagation nature of the optical signal, the handover decision making problem in HLWNets, however, becomes more critical and challenging than that in previous heterogeneous networks. In this paper, the handover decision making problem in the HLWNet is regarded as a binary classification problem, and an artificial neural network (ANN)-based handover scheme is proposed. The complete handover scheme consists of two sets of ANNs that use the information about channel quality, user movement, and device orientation as input features to make handover decisions. After being trained with the labelled datasets that are generated with a novel approach, the ANN-based handover scheme is able to achieve over 95% handover accuracy. The proposed scheme is then compared with benchmarks under an indoor simulation scenario. The simulation results show that the proposed approach can significantly increase user throughput by 20.5-46.7% and reduce handover rate by around 59.5 - 78.2% as compared with the benchmarks; in the meanwhile, it maintains a great robustness performance against user mobility and channel variation.

User-Level Handover Decision Making Based on Machine Learning Approaches

User-Level Handover Decision Making Based on Machine Learning Approaches

A Handover Decision Model for Unmanned Vehicles for Ubiquitous Communication in Wireless Networks

Drone technology also referred to as Unmanned Aerial Vehicles (UAVs) provides many benefits to wireless communication systems. In the coming years, the linked drones will be commonly used in different fields such as delivery methods, monitoring services, and defense networks etc. However, security and logical characterization of handover probability in drone systems is still a big problem. Various fuzzy based methods were proposed by a number of researchers to resolve these issues, but the problem with conventional methods was that they considered only limited dependency factors. However, there are some other dependency factors as well that affect the performance of the drones. In this research, a fuzzy based decision model is proposed. The proposed fuzzy system uses five dependency factors such as coverage, speed limit, cost, connection time and security as inputs. To process these inputs 160 rules are defined in the given fuzzy system in order to produce an output in terms of handover probability. The performance analysis of the traditional and proposed model is analyzed in the MATLAB software when they were moving in random and straight directions, in terms of estimation level, PDR, throughput and delay. The simulation results showed that the proposed model is efficient and stable as compared to traditional fuzzy system.

An Adaptive Handover Scheme for Hybrid LiFi and WiFi Networks

The hybrid light fidelity (LiFi) and wireless fidelity (WiFi) network (HLWNet) is considered to be a potential component of the next generation indoor wireless networks. However, due to the susceptible line-of-sight propagation of optical signal and the ultra-dense deployment of LiFi access points, the handover problem in the HLWNet becomes challenging and it is hard to design a handover scheme that adapts to complex indoor working scenarios. In this paper, we propose a novel handover scheme, in which the handover events in HLWNet are classified into three categories and a particular strategy for each category is applied to calculate the optimal dwell time in the handover procedure. The proposed handover scheme is adaptive to different working situations, since the information about multiple attributes, such as channel quality, user velocity, and arrivals data rate, is gathered to make the handover decision and to calculate the dwell value. The simulation results show that, compared to the benchmarks, the proposed method is able to increase the user throughput by around 65%, decrease the handover rate by up to 80%, and reduce the packet delay by up to 57%. In addition, the proposed method significantly improves the robustness performance of user throughput and handover rate under different scenarios.

Performance Evaluation of Mobility Robustness Optimization (MRO) in 5G Network With Various Mobility Speed Scenarios

The massive deployment of small-sized cells for the Fifth Generation (5G) mobile network will increase the Handover Probability (HOP), potentially causing higher Handover Ping-Pong Probability (HPPP) and/or Radio Link Failure (RLF). Inappropriate usage of Handover Control Parameter (HCP) settings may further exasperate this issue. Therefore, Mobility Robustness Optimisation (MRO) has been introduced and further developed as a significant Self-Optimisation Network (SON) function in the 5G network and beyond. The main aim of MRO is to address Mobility Management (MM) issues during user mobility between cells to ensure a smooth connection. Although various algorithms were suggested in the literature, they mostly cater to 4G networks which may not be effective for the 5G network due to different network characterisations. This paper analyses the performance of various MRO algorithms with various system settings and scenarios for the 5G network. The investigated algorithms from the literature include the Distance (Dis), Cost Function (CF), Fuzzy Logic Controller (FLC) and Handover Performance Indicator (HPI). Validation has been accomplished for different mobility conditions in the 5G network. A simulation based on the MATLAB software has been conducted using various system tools. The evaluation analysis is in terms of Signal to-Interference-plus-Noise-Ratio (SINR), HPPP and RLF effects since these are major indicators in assessing system performance and selecting the handover decision during user mobility. The simulation outcomes show that the HPI algorithm performance is more reactive to mobile speed scenarios over time, significantly reducing the HPPP compared to the other algorithms which do not provide large reactions in the same conditions. Simultaneously, the HPI algorithm exhibits the highest RLF and SINR from among the other algorithms. The distance algorithm is the best in terms of RLF and SINR, achieving an acceptable level in terms of HPPP. These results point to that the MRO algorithm that operate based on distance is the most robust compared to the other investigated algorithms, confirming the potential of the Dis approach for the 5G network.

Predictive Algorithm for Handover Decisions between LTE and LTE-A Networks

A heterogeneous wireless network needs to maintain seamless mobility and service continuity; for this reason, we have proposed an approach based on the combination of particle swarm optimization (PSO) and an adaptive neuro-fuzzy inference system (ANFIS) to forecast a handover during a movement of a mobile terminal from a serving base station to target base station. Additionally, the handover decision is made by considering several parameters, such as peak data rate, latency, packet loss, and power consumption, to select the best network for handover from an LTE to an LTE-A network. The performance efficiency of the new hybrid approach is determined by computing different statistical parameters, such as root mean square error (RMSE), coefficient of determination (R2), mean square error (MSE), and error standard deviation (StD). The execution of the proposed approach has been performed using MATLAB software. The simulation results show that the hybrid PSO-ANFIS model has better performance than other approaches in terms of prediction accuracy and reduction of handover latency and the power consumption in the network.

Evaluation of handover effectiveness with progressive user behavior in self-organized LTE networks

In LTE networks, when a user moves from one sector to another sector within the same eNB, it’s essential to keep the ongoing call steady. This occurrence is referred to as a handover (HO). The ping-pong and call drop rate may be minimized with proper HO decision-making, resulting in a higher quality of service. For this reason, it’s quite difficult to set up both the handover failure and handover ping-pong simultaneously. This study evaluates HO performance by adjusting several HO control parameters, such as HO offset, time to trigger, and HO margin, to get the best possible results. The HO control settings may be adjusted using a hybrid method called tent chaotic map, adaptive inertia weight, opposition-based learning into whale optimization algorithm (TAOWOA). Apart from that, by automating MLB and TAOWOA, network performance is improved by using a self-organized network. Under progressive traffic loads and user mobility, the HO failure (HOF), call dropping ratio (CDR), HO ping-pong(HOPP), and packet delivery ratio are assessed. Performance parameters based on optimization-MLB have significantly increased performance when compared to conventional HO performance improvement methods, according to the simulation findings.

Neuro-fuzzy Architecture for Handover Decision Making in Emerging Heterogeneous Networks

In emerging heterogeneous networks, seamless vertical handover is a critical issue. There must be a trade-off between the handover decision delay and accuracy. This paper’s concern is to contribute to reliable vertical handover decision making that makes a trade-off between complexity and effectiveness. So, the paper proposes a neuro-fuzzy architecture that joints the capacity of learning of the artificial neural networks with the power of linguistic interpretation of the fuzzy logic. The architecture can learn from experience how executing a handover to a particular access network affects the quality of service. Simulation results reveal that this architecture is fast, enhances the overall performance and reliability better than the fuzzy logic-based approach.

Non-Orthogonal Multiple Access-Based Handover for Throughput Enhancement

As non-orthogonal multiple access (NOMA) technology is considered as a new multiple access scheme in cellular networks, a new handover decision algorithm and operational procedure need to be investigated. In this paper, we apply the NOMA technique to handover situations for throughput enhancement in cellular networks. We first derive the condition of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NOMA-based handover</i> (NOMA-HO) theoretically to increase the handover user rate while guaranteeing the rate of paired user in the target cell. From this condition, we define a new measurement parameter called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Interference to Background Interference-plus-Noise Ratio</i> (IBINR) and design a practical NOMA-HO procedure for the measurement of IBINRs and the handover decision. Results show that the proposed NOMA-HO scheme significantly improves cell-edge user throughput compared with the classical OMA-based handover schemes.

A Fuzzy Based Handover Decision Scheme for Mobile Devices Using Predictive Model

Handover usually deals with the mobility of the end users in a mobile network to assure about the ongoing session of a user. It is observed that frequent handover results in call dropping due to latency. In order to overcome this issue, a fuzzy based handover decision scheme for mobile devices using a predictive model is proposed. First, an MFNN (Multi-layer Feed Forward Network) is used to determine the next cell of the user along with best hand off time. To obtain the best access network, multiple-attribute Access Network Selection Function (ANSF) is used. The fuzzy rule is applied by considering the parameter data rate, reliability, signal strength, battery power and mobility as input and the output obtained is the optimal network. The proposed scheme selects the best access network and enhances the quality of services.

A survey on various handover technologies in 5G network using the modular handover modules

PurposeFifth-generation (5G) networks play a significant role in handover methods. 5G wireless network is open, flexible and highly heterogeneous along with the overlay coverage and small cell deployments. Handover management is one of the main problems in the heterogeneous network. Also, handover satisfies the needs of ultra-reliable communications along with very high reliability and availability in 5G networks. Handover management deals with every active connection of a user’s device, which moves the connection between the user’s device and the counterparty from one network point to another. Thus, the handover decision determines the best access network and also decides whether the handover is performed or not.Design/methodology/approachThe main intention of this survey is to review several existing handover technologies in 5G. Using the categories of analysis, the existing techniques are divided into different techniques such as authentication-based techniques, blockchain-based techniques, software-defined-based techniques and radio access-based techniques. The survey is made by considering the methods such as used software, categorization of methods and used in the research works. Furthermore, the handover rate is considered for performance evaluation for the handover techniques in 5G. The drawbacks present in the existing review papers are elaborated in research gaps and issues division.FindingsThrough the detailed analysis and discussion, it can be summarized that the widely concerned evaluation metric for the performance evaluation is the handover rate. It is exploited that the handover rate within the range of 91%–99% is achieved by three research papers.Originality/valueA survey on the various handover mechanisms in 5G networks is expected in this study. The research papers used in this survey are gathered from different sources such as Google Scholar and IEEE. Also, this survey suggests a further extension for the handover mechanism in 5G networks by considering various research gaps and issues.