Handover Rate Research Articles

Inter-beam handover schemes for LEO satellites in 5G satellite–terrestrial integrated networks

Unlike terrestrial handover schemes, the handover schemes in 5G satellite–terrestrial integrated network (STIN) face several challenges, such as large propagation delay, complex channel environment, and fast satellite movement. Therefore, the handover schemes designed in the 5G terrestrial network is not suitable for the 5G STIN. In view of this, this paper considers the inter-beam handover problem for the 5G STIN with a low earth orbit satellite and a ground user. We establish the satellite channel model, which includes path loss, rain attenuation, and multi-beam antenna gain. To model the mobile feature of the user, we consider a two-dimensional random walk model. Then, we propose A5 event-based conditional handover (CHO) scheme, time factor-based CHO scheme, and load factor-based CHO scheme to achieve efficient inter-beam handover. Considering that there may be multiple beams that meet the triggering condition in each handover scheme, we also propose three kinds of beam selection schemes, namely random beam selection scheme, maximum power-based beam selection scheme, and minimum distance-based beam selection scheme. To evaluate the performance of the proposed handover schemes, key performance indicators, such as handover frequency, ping-pong handover rate, unnecessary handover rate, handover failure rate, and average transmission rate are analyzed. Simulation results verify the superiority of the proposed handover schemes by comparing them with the benchmark scheme.

Multiple Criteria-Based Intelligent Techniques for Efficient Handover in Next-Generation Networks

Background: Several wireless technologies are assumed to be operating in cooperation for next-generation networks. These networks offer various services to mobile users; however, efficient handover between different networks is the most challenging task in high mobility scenarios. The traditional signal strength-based handover algorithms are not able to cope with mobile users' high-quality requirements. Methods: In this paper, multiple criteria-based intelligent techniques are proposed to deal with inefficiencies related to handover. This technique makes use of artificial neural networks that take multiple parameters as inputs in order to predict the degradation of parameters. These predictions are further used to design rules for initiating handover procedures prior to service quality degradation. Results: Based on the prediction results obtained by deep neural networks, the handover decisions are recommended according to the type of application: conversational or streaming. Conclusion: The simulation results demonstrate the efficacy of the proposed method as there is an improvement of up to 40% and 25% in terms of handover rate and service disruption time, respectively, with an acceptable prediction error of 0.05.

A Weighted AIMD Congestion Control Algorithm for Device-to-Device Communication in 5G Network

ABSTRACT Device-to-Device (D2D) communication offers promising benefits in 5G networks in terms of improved throughput and reduced latency. However, efficient congestion control remains a critical challenge to ensure optimal performance and resource utilization. Existing congestion control mechanisms often struggle with adapting to dynamic network conditions, leading to inefficiencies, such as underutilization of network resources or excessive packet loss. This paper addresses these challenges by proposing a novel Weighted AIMD congestion control algorithm tailored specifically for D2D communication in 5G networks (WACC-D2DC-5GN). Here, the Weighted AIMD Congestion Control Algorithm is proposed for estimating available bandwidth and Hunger Games Search Optimization to optimize the weight parameter of WACC. The proposed algorithm integrates weighted factors that dynamically adjust congestion window sizes based on real-time network feedback. Simulations are run on the NS3-mmWave module, NS3 LTE-A module. The simulation outcomes show that the proposed WACC-D2DC-5GN method attains 27.26%, 20.41%, and 23.26% higher SNR and 16.20%, 26.97%, and 30.34% higher Throughput when analysed with existing techniques like NexGen D-TCP: next generation dynamic TCP congestion control approach (NGD-TCP-CCA), user-centric channel allocation system for 5G higher-speed users by using ML to decrease handover rate (UCC-HSU-MLA), and efficient with reliable hybrid deep learning-allowed method for congestion control in 5G/6G networks (HDL-EMC-CN) respectively.

Evaluation of Handover Probability and Handover Rate of UAVs Cellular Network

Evaluation of Handover Probability and Handover Rate of UAVs Cellular Network

Security Improvement of UAV Group IoT in Polygonal Area Based on Similarity Clustering Algorithm

Abstract Currently, unmanned aerial vehicle (UAV) group Internet of Things (IoT) has poor stability, poor endurance, and low security. In this regard, we report a polygon area UAV group IoT clustering algorithm for large-scale, high-speed mobile IoT environment. First, the UAV group IoT was divided into a polygon area and a few cluster heads were used to cover the polygon area uniformly. Then, the clustering index in the maximum speed similarity clustering algorithm was induced into the weighted clustering algorithm. Simultaneously, the link retention rate, relative node degree, and node residual energy ratio were improved to select the IoT node with the maximum weight as the cluster head. Finally, the IoT security was improved by reputation calculation. The results show that the clustering algorithm in this paper can reduce the number of clusters and lower the handover rate between clusters, improve the stability of clustering, prolong the survival time of the minimum node, improve the overall endurance of the IoT, and have high security.

Optimizing Wireless Connectivity: A Deep Neural Network-Based Handover Approach for Hybrid LiFi and WiFi Networks.

A Hybrid LiFi and WiFi network (HLWNet) integrates the rapid data transmission capabilities of Light Fidelity (LiFi) with the extensive connectivity provided by Wireless Fidelity (WiFi), resulting in significant benefits for wireless data transmissions in the designated area. However, the challenge of decision-making during the handover process in HLWNet is made more complex due to the specific characteristics of electromagnetic signals' line-of-sight transmission, resulting in a greater level of intricacy compared to previous heterogeneous networks. This research work addresses the problem of handover decisions in the Hybrid LiFi and WiFi networks and treats it as a binary classification problem. Consequently, it proposes a handover method based on a deep neural network (DNN). The comprehensive handover scheme incorporates two sets of neural networks (ANN and DNN) that utilize input factors such as channel quality and the mobility of users to enable informed decisions during handovers. Following training with labeled datasets, the neural-network-based handover approach achieves an accuracy rate exceeding 95%. A comparative analysis of the proposed scheme against the benchmark reveals that the proposed method considerably increases user throughput by approximately 18.58% to 38.5% while reducing the handover rate by approximately 55.21% to 67.15% compared to the benchmark artificial neural network (ANN); moreover, the proposed method demonstrates robustness in the face of variations in user mobility and channel conditions.

Reinforcement learning-based resource allocation for dynamic aggregated WiFi/VLC HetNet

High transmission rates and low power consumption make visible light communication (VLC) a highly promising supplementary technology for the next generation of mobile communication. Taking into account the limited coverage area, VLC can be combined with WiFi as a heterogeneous network (HetNet), thanks to their non-overlapping spectrum. In order to fully utilize the advantages of the WiFi and VLC technologies, a new aggregated WiFi/VLC HetNet consisting of a single WiFi AP and multiple VLC APs, is designed, where the user equipment (UE), for the first time, can access multiple VLC APs and one WiFi AP simultaneously, and be allowed to acquire multiple resource blocks (RB) in each AP at the same time. To optimize the performance of the above designed HetNet, a multi-objective optimization problem (MOOP) is formulated, which aims to maximize system throughput while reducing the handover rate. Since the above MOOP is non-convex and nonlinear, the traditional resource allocation (RA) algorithm has a complex calculation process and poor timeliness performance to deal with this problem. To solve the above MOOP, a reinforcement learning (RL)-based RA algorithm is proposed, and considering the RB handover overhead in the aggregated WiFi/VLC HetNet, a reward function related to the system throughput and the UE handover rate is carefully designed. System throughput, system handover rate, user satisfaction, as well as user fairness performance are analyzed under three typical indoor illumination layouts. Finally, compared with the greedy algorithm and the hypergraph-based carrier aggregation algorithm, numerical results show that the proposed RL-based RA algorithm could improve the system throughput over the former two algorithms by 30.26% and 19.71%, while reducing the system handover rate by 0.15% and 0.02%, respectively.

Initiative to improve handover notes in a tertiary psychiatric hospital

Clinical handover is an important process in hospital settings, but it is often carried out inadequately, posing potentially serious consequences for the patients. This project aimed to increase the effectiveness...

Association between prehospital physician clinical experience and discharge at scene - retrospective cohort study.

Clinical experience has been shown to affect many patient-related outcomes but its impact in the prehospital setting has been little studied. To determine whether rates of discharge at scene, handover to paramedics and supervision are associated with clinical experience. A retrospective study, performed on all prehospital interventions carried out by physicians working in a mobile medical unit ("service mobile d'urgence et de réanimationˮ[SMUR]) at Geneva University Hospitals between 1 January 2010 and 31 December 2019. The main exclusion criteria were phone consultations and major incidents with multiple casualties. The exposure was the clinical experience of the prehospital physician at the time of the intervention, in number of years since graduation. The main outcome was the rate of discharge at scene. Secondary outcomes were the rate of handover to paramedics and the need for senior supervision. Outcomes were tabulated and multilevel logistic regression was performed to take into account the cluster effect of physicians. In total, 48,368 adult patients were included in the analysis. The interventions were performed by 219 different physicians, most of whom were male (53.9%) and had graduated in Switzerland (82.7%). At the time of intervention, mean (standard deviation [SD]) level of experience was 5.2 (3.3) years and the median was 4.6 (interquartile range [IQR]: 3.4-6.0). The overall discharge at scene rate was 7.8% with no association between clinical experience and discharge at scene rate. Greater experience was associated with a higher rate of handover to paramedics (adjusted odds ratio [aOR]: 1.17, 95% confidence interval [CI]: 1.13-1.21) and less supervision (aOR: 0.85, 95% CI: 0.82-0.88). In this retrospective study, there was no association between level of experience and overall rate of discharge at scene. However, greater clinical experience was associated with higher rates of handover to paramedics and less supervision.

Fall risk management in interventional prenatal diagnosis perioperative pregnant women based on tracking methodology and failure mode and effect analysis application.

The aim was to explore the effectiveness of a tracing methodology combined with failure mode and effect analysis (FMEA) for managing falls in pregnant women during the perioperative period of interventional prenatal diagnosis. Using the tracing methodology, the process was evaluated and analyzed using FMEA after reviewing data, on-site interview, case tracking and on-site inspection, and improvement measures were proposed for the existing risk factors, and the fall-related quality indicators, satisfaction with fall-related health education, and risk priority number were compared before and after implementation. Effectiveness analysis for interventional prenatal diagnosis of perioperative maternal falls risk management resulted in a significant decrease in risk priority number (P < .01), a significant increase in the rate of correct fall risk identification and assessment, correct handover rate of pregnant women at risk of falls, correct intervention rate of pregnant women at high risk of falls, and effective coverage of falls-related health education (P < .01), a significant increase in satisfaction with falls-related health education (P < .001), and the incidence of falls among pregnant women decreased from 0.12% to 0%. The use of tracking methodology combined with FMEA can reduce the risk of perioperative maternal falls in interventional prenatal diagnosis and improve the safety of maternal visits.

Big Data Network Optimization for Mobile Cellular Networks in 5G

5G ensures the provision of intelligent network and application services by means of connectivity to remote sensors, massive amounts of Internet of Things data, and fast data transmissions. Through the utilization of distributed compute architectures and by supporting massive connectivity across diverse devices like sensors, gateways, and controllers, 5G brings about a transformative revolution in the conversion of both big data at rest and data in motion into real-time intelligence. Big Data Analytics play an important role in the evolution of 5G standards, enabling intelligence across networks, applications, and businesses. Administrators of mobile organizations have access to a plethora of opportunities to enhance service quality through big data. Network optimization serves as a crucial method to achieve this task, with network prediction forming the foundation for such optimization. Ensuring network stability and security is essential for 5G mobile communication, considering its significance as an important tool in national life. Therefore, this work focuses on presenting big data network optimization for mobile cellular networks within the context of 5G. In order to improve the Quality of Experience (QoE) for users, this work explores various methods for integrating network optimization and Big Data analytics. The performance of the presented model is evaluated in terms of QoE, Throughput, handover rate, mobility, reliability, and network slicing.

DQN-ALrM-Based Intelligent Handover Method for Satellite-Ground Integrated Network

The satellite-ground integrated network (SGIN), consisting of satellites and ground base stations, is regarded as the trend to solve the problem of global coverage. However, highly dynamic scenarios pose great challenges to users’ mobility management, especially handover decisions. With the rapid development of machine learning, reinforcement learning (RL), such as Q-learning and deep Q-network (DQN), has been applied to handover decision making owing to being good at solving dynamic multi-attribute optimization problems. However, Q-learning can be a waste of storage for large state spaces. Besides, the existing algorithms for training a neural network in a deep RL framework have problems of slow convergence or unstable convergence performance. Therefore, we propose a handover method exploiting the DQN framework of adaptive learning rate with momentum (DQN-ALrM), which can not only improve decision accuracy, but also improve learning efficiency. The customized DQN framework can solve problems with a large-size state space, and the proposed ALrM method can adjust the learning rate at any time according to the training error situation. Comprehensive simulations are conducted to validate the superiority of the proposed method in convergence speed, handover rate, call failure rate and multi-metric quality of service (QoS) over the alternative solutions in the literature.

Handover Network Selection for Vehicle in LTE-A Communication Networks

One of the challenges to achieving smooth communications in heterogeneous networks is to find the right balance between various handover parameters for network selection. The Receive Signal Strength (RSS) from a network's Point of Attachment (PoA) is the primary factor used in traditional approaches to network selection. Therefore, network selection is vital for a successful handover in the vehicle communications network. However, vehicles moving through such diverse networks faced difficulties like frequent unwanted handovers, selecting the wrong candidate networks for handovers, and taking too long to finish the drawn-out handover procedure, which results in failed handovers. All of these factors will impair the efficiency of the handover process, which will affect the smooth operation of the vehicles. Hence, the main idea of this research is to improve the handover network performance selection performance. The proposed work is for vehicles to self-evaluate a candidate list of Access Points (AP) that are located in the vehicle movement direction and select the best underlying candidate network. In this project, the network selection framework of the vehicle communications network with the distance between the target candidate and the trajectory of the vehicle movement was developed as well as the vehicle mobility information. The important parameters which are the relative direction index, proximity index and residence time index are chosen to identify the best candidate network. Once the target of the base station has been identified, then the optimum value of the distance is calculated using the Particle Swarm Optimization (PSO) technique to initiate the handover process. The performance of the proposed handover network selection is simulated and evaluated using MATLAB software. The results show that the proposed handover network selection method improved the successful handover rate as compared to the conventional method.

Enabling Proportionally-Fair Mobility Management With Reinforcement Learning in 5G Networks

Mobility management in 5G is challenging, and at higher frequencies, a larger number of cells is needed to provide similar coverage to that in 4G. Consequently, Base Stations (BSs) are placed much more densely and users experience frequent handovers, reducing network capacity. Advanced handover techniques are needed in 5G to perform smooth network operation. In this paper, we formulate an optimization problem, whose goal is to strive for fairness in data rates among users and to reduce handovers. To accomplish that, we consider jointly the decisions when to handover and to which BS a user is to be assigned. This is an integer nonlinear program, and by relaxing it, we obtain an upper bound. Further, due to its NP-hardness, we propose a centralized and a multi-agent Deep Q Network (DQN)-based algorithm, which both find near-optimal user-to-BS assignments. We evaluate our Reinforcement Learning-based solutions for networks of different sizes and users with different velocities. We compare our approaches with baselines and show that they outperform them considerably in terms of fairness and radio link failures while being within 95% of the optimum. Our DQN algorithms also reduce the handover rate by up to 93% and avoid ping-pong handovers almost completely.

Heterogeneous Ultradense Networks With Traffic Hotspots: A Unified Handover Analysis

With the ever-growing communication demands and the unceasing miniaturization of mobile devices, the Internet of Things is expanding the amount of mobile terminals to an enormous level. To deal with such numbers of communication data, plenty of base stations (BSs) need to be deployed. However, denser deployments of heterogeneous networks (HetNets) lead to more frequent handovers, which could increase network burden and degrade the users experience, especially in traffic hotspot areas. In this paper, we develop a unified framework to investigate the handover performance of wireless networks with traffic hotspots. Using the stochastic geometry, we derive the theoretical expressions of average distances and handover metrics in HetNets, where the correlations between users and BSs in hotspots are captured. Specifically, the distributions of macro cells are modeled as independent Poisson point processes (PPPs), and the two tiers of small cells outside and inside the hotspots are modeled as PPP and Poisson cluster process (PCP) separately. A modified random waypoint (MRWP) model is also proposed to eliminate the density wave phenomenon in traditional models and to increase the accuracy of handover decision. By combining the PCP and MRWP model, the distributions of distances from a typical terminal to the BSs in different tiers are derived. Afterwards, we derive the expressions of average distances from a typical terminal to different BSs, and reveal that the handover rate, handover failure rate, and ping-pong rate are deduced as the functions of BS density, scattering variance of clustered small cell, user velocity, and threshold of triggered time. Simulation results verify the accuracy of the proposed analytical model and closed-form theoretical expressions.

Dynamic handover cost modeling in hybrid VLC/RF networks

In this paper, a dynamic handover cost model is proposed to solve the cost evaluation problem in hybrid visible light communication (VLC)/ radio frequency (RF) heterogeneous networks. According to this model, the cost of handover scenarios with different network deployment and user mobility characteristics can be evaluated, providing a unified standard for the performance evaluation of handover schemes. By utilizing the received signal strength (RSS)-based association policy, the probability of user-to-access point (AP) association is derived. By utilizing stochastic geometry and modeling the locations of APs using a Poisson point process (PPP), the impact of user mobility and network parameters on handover performance is quantified and the expressions for the user’s cell dwell time and handover rate are derived. By analyzing the detailed flow of handover and combining it with the handover rate, a comprehensive handover cost expression is derived. Finally, by simulation and analysis, the applicability and effectiveness of the proposed handover cost model are demonstrated. Compared with the original handover cost model, the proposed model has better dynamic adaptability as well as reliability.

Handover Analysis in Ultra-Dense LEO Satellite Networks With Beamforming Methods

With the ever-growing demands of communication quality and rate, the combination of terrestrial networks and space networks is becoming the next recognized solution. The space-ground integrated network (SGIN) is a massive Internet of Things (IoT) network that accommodates large amounts of mobile terminals. The interactivity of information between those terminals will request higher communication rates and lead to more frequent handovers, which will increase the network burden and degrade the user's experience, especially in ultra-dense Low-Earth Orbit (LEO) satellite networks. In this article, a modified matching algorithm is proposed to get the optimal association matrix between users and base stations. Based on the results, the sum rate of users and the handover rate are calculated. A modified particle swarm optimization (MPSO) method is also proposed to generate the special-shaped beams on the satellites to deal with the complex optimization problems. By combining the MPSO-based beamforming algorithm and the modified matching algorithm, the non-convex sum-rate optimization problem is decomposed into two optimal subproblems, which are solved separately. The simulation results demonstrate that the SGIN significantly outperforms the non-integrated ones in terms of the sum data rate and backhaul capacity. The handover performance under the user mobility model is also discussed.

Seamless Handover in LEO Based Non-Terrestrial Networks: Service Continuity and Optimization

Developing non-terrestrial networks (NTN) in future wireless networks has been widely recognized to bring advanced communication services to remote and unserved areas. The Low-Earth-Orbit (LEO) constellation has emerged as a promising component for NTN to provide seamless and fast global connectivity. However, since natural dynamic features, the mobility management, in particular the handover (HO) between satellites, plays an important role in ensuring a stable and continuous data service for NTN. Motivated by this fact, this paper proposes a HO optimization strategy based on conditional handover (CHO) mechanism to enhance service continuity in LEO-based NTN. A reward function, related to link service time and service capability, is firstly designed to modify the monitoring conditions of target satellite candidates. The optimal target selection algorithm is proposed to obtain the maximum reward for each CHO. Then, a service continuity performance graph (SCG) model is constructed to predict different potential CHO combinations in service duration. On the basis of SCG, the HO sequence supporting a high-quality and stable data service is predictively calculated for each accessing user. Simulation results demonstrate that the proposed HO optimization scheme can obviously reduce handover rate under different NTN conditions and can better enhance NTN service continuity.

Evidence-based Communication Practice and Clinical Outcomes

Aim: This study aims to assess the effectiveness of evidence-based communication practice on clinical outcomes among nursing personnel at selected institution. Methods: The researcher adopted the quantitative evaluatory research approach with one group pre-test and post-test design. The sample size was 30 nursing personnel from selected institutions. The analysis of the data included both descriptive and inferential statistical methods. Results: Comparing pre-test and post-test scores to assess clinical outcomes, it was reported that 14.81% of adverse events occurred after the post-test, while 33.33% of medication errors occurred after the pre-test. The rate of bedside handover grew from 0% to 16.67%. Clinical documentation were 57.41% in post test. There was a rise in discrepancies from 23.53% to 40.74%. About 12.96% of patients were readmitted. Around 3.7% of subjects saw unexpected patient outcomes. The nurse-to-patient ratio rose from 0% to 3.70%. About 33.33% of the time allotted for handover was sufficient. The structured rating scale (Likert scale) is based on experience related to evidence-based communication practices in healthcare settings. The nursing personnel experience that the Introduction, Situation, Background, Assessment, and Recommendations (ISBAR) format enhances communication up to 100%. About 81.81% feel adequately trained. About 100% of the sample answered that this format improves patient safety. About 5.45% feel that the format was well-received by their colleagues. About 81.82% feel that it promotes a structured approach. About 81.82% feel a reduction in the information gap. About 72.73% of interdisciplinary collaboration improved. Patient outcomes improved by 81.82%. About 72.73% received training on the ISBAR format. About 81.82% recommended ISBAR to other healthcare professionals. About 72.73% agree to include the ISBAR format in nursing education. Conclusion: The ISBAR tool is useful for enhancing patient safety and treatment quality. It improves patient safety and quality, which is essential for an efficient flow of communication. The study’s conclusion highlights the necessity for increasing the utilization of evidencebased communication techniques.

Handover Optimization Algorithm Based on T2RFS-FNN.

As a key technology for highly reliable communication in the fifth generation mobile communication for railway (5G-R) high-speed railway wireless communication system, once the handover fails, it will pose a serious risk to the safe operation of high-speed railway. As the speed of high-speed trains continues to increase, the handover will become more frequent, and how to improve the success rate of the handover is a key problem that needs to be solved. In this paper, we proposed an optimization algorithm based on the interval type 2 feature selection recurrent fuzzy neural network (T2RFS-FNN), which is a recurrent fuzzy neural network with interval type 2 feature selection, to address the problem of fixed hysteresis threshold and single consideration for the handover algorithm between the control plane and the user plane of the high-speed railway under 5G-R. The algorithm integrates reference signal receiving power (RSRP). Reference signal receiving quality (RSRQ) and throughput to optimise the hysteresis threshold. First, a feedforward neural network structure is designed to implement fuzzy logic inference, and an interval type-two Gaussian subordination function is used to improve the nonlinear expressiveness of the model. Then, a feature selection layer is added to determine the output of the affiliation function, which completes the optimization of the hysteresis threshold and overcomes the drawback of the fixed hysteresis threshold of the handover algorithm. Finally, simulation analysis of the control-plane and user-plane handover algorithms is carried out separately. The results show that the proposed method can effectively improve the success rate and reduce the ping-pong handover rate compared to the comparison algorithms. The results provide a theoretical reference for the speedup of high-speed railway trains and the evolution of the global system for mobile communications for railway (GSM-R) to 5G-R.