Radio Link Failure Research Articles

Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

With the development of communication technology, train control operation system develops gradually, which significantly improves the reliability and efficiency of train operation. The current mobile Internet has gradually highlighted the many limitations of the mobile Internet in the high-speed mobile environment, which seriously deteriorate the service quality and user experience, and cause a waste of resources. In order to meet the real-time requirements of network communication resource scheduling in the mobile environment, aiming at the multidimensional dynamic adaptation framework constructed in a mobile environment, a service and network adaptation mechanism based on link failure state prediction is proposed in the paper. First, cross-layer theoretical analysis and actual data analysis are combined to construct a wireless link failure probability model. Then, reliable transmission requirements and transmission overhead are applied to optimize goals. Finally, simulation experiments are carried out according to the railway network data to evaluate the E-GCF adaptation algorithm. The experiment results show that compared with the current mainstream algorithms, the prediction accuracy of this adaptation algorithm is improved by 25%. The execution time of the algorithm is reduced by 9.6 seconds and the successful submission rate is as high as 99.99%. The advantages of the algorithm are significantly superior other algorithms. It proves that the research method of this paper can effectively improve the satisfaction rate and utility value of reliable transmission, as well as enhance the throughput performance. It solves the adaptation problems of frequent switching and low utilization of heterogeneous networks in a mobile environment, which contributes to the high-quality communication service of mobile network.

Towards 5G and beyond radio link diagnosis: Radio link failure prediction by using historical weather, link parameters

Weather-related phenomena such as clouds, rain, snow affect the performance of radio links. To reduce the adverse effects of radio link failures’ on the user experience, mobile operators require intelligent monitoring systems to predict link failures and take actions before they happen. In this study, we show how machine learning can be used for prediction using a real-world telecom operator dataset. We propose a novel architecture to process time-series data and non-times-series data together in the same neural model to have better performance in predictions. We compare our model with the traditional approaches such as logistic regression (LR), support vector machines (SVM), and Long Short-term Memory (LSTM). Through experimental evaluations, we show that the F1-score of our proposed model is 0.638, whereas for the pure LSTM model it is 0.601. SVM and LR methods perform significantly worse with F1 scores of 0.455 and 0.105, respectively.

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.

Fast Data Recovery for Improved Mobility Support in Multiradio Dual Connectivity

Data aggregation is one of the crucial features of the 3GPP Multi-Radio Dual Connectivity (MR-DC) technology. However, mobility events and radio link failures, which may occur during the data aggregation, may pose challenges in meeting the latency, reliability, and throughput key performance indicators (KPIs). Unlike single connectivity, the user equipment (UE) in MR-DC operation can experience such events in either of the two base stations (BSs) serving the UE with MR-DC. In typical MR-DC deployments, these events occur more frequently in the BS acting as the secondary node (SN) since the SN operates at a higher frequency band. In this paper, we show that handovers (HOs) and signal blockage events that occur at the SN can create out-of-order data reception or losses at the UE’s Packet Data Control Protocol (PDCP) layer, making the application stop receiving data for up to hundreds of milliseconds. Thus, challenging to meet the KPIs defined for such application. To mitigate this effect, we propose an intelligent and efficient mechanism that operates in the transmitting PDCP layer and significantly minimizes the data interruption periods suffered by the application when the UE aggregates data and HOs or failures of the SN occur. We use LTE/NR testbed experiments to show that the proposed mechanism achieves a high and stable aggregate throughput with near-zero interruption time and data reliability of at least five nines without transport layer retransmissions. The experiments are conducted for saturated TCP traffic and under link quality variations based on traces extracted from a Nokia-proprietary system-level simulator.

Coverage Hole Detection for mmWave Networks: An Unsupervised Learning Approach

The utilization of millimeter-wave (mmWave) bands in 5G networks poses new challenges to network planning. Vulnerability to blockages at mmWave bands can cause coverage holes (CHs) in the radio environment, leading to radio link failure when a user enters these CHs. Detection of the CHs carries critical importance so that necessary remedies can be introduced to improve coverage. In this letter, we propose a novel approach to identify the CHs in an unsupervised fashion using a state-of-the-art manifold learning technique: uniform manifold approximation and projection. The key idea is to preserve the local-connectedness structure inherent in the collected unlabelled channel samples, such that the CHs from the service area are detectable. Our results on the DeepMIMO dataset scenario demonstrate that the proposed method can learn the structure within the data samples and provide visual holes in the low-dimensional embedding while preserving the CH boundaries. Once the CH boundary is determined in the low-dimensional embedding, channel-based localization techniques can be applied to these samples to obtain the geographical boundaries of the CHs.

A State Based Resource Allocation Game for Distributed Optimization in 5G Small-Cell Networks

The upcoming 5G advocates efficient utilization of the existing spectrum through cell densification using small cells. However, dense packing of small-cells over a given area experiences severe inter-cell interference and fierce competition for shared resources. In this paper, we address the topic of resource allocation in interference-prone 5G small-cell networks (SCNs). We formulate a state based potential game for resource sharing problems to fulfill and maximize users’ quality of experience (QoE). The designed game uses knowledge exchange among players through a communication graph to solve the optimization problem. We employ the time-invariant and time-varying structure of the communication graph in the proposed game. In addition, we propose a decentralized scheme for the joint optimization of spectrum allocation, power selection, and user scheduling. Through extensive simulations, we show that our algorithm surpasses the existing schemes in terms of QoE, computational complexity, and convergence rate for stable resource allocations. Furthermore, it is robust to wireless link failure and state transition in the communication graph. Besides, under imperfect channel state information, it suffers relatively less performance degradation compared to the existing schemes.

Multi-criteria handover mobility management in 5G cellular network

To fulfill the future demand and expansion of the coverage of the network, ultra-dense deployment of small cell (SC) is an optimal solution for future 5G networks, which will ensure the UEs (User Equipment) continuous connectivity. However, these small cells (SCs) lead to the issue of interference, additional unnecessary handover (HO), signaling overhead, and which in turn decreases the overall quality of service (QoS) of the users. In this paper, an intelligent mobility management system based on Enhanced Multi-Objective Optimization Method by Ratio Analysis (E-MOORA) and Q-learning approach is introduced for handover optimization. E-MOORA method is the combination of modified entropy weighting technique and Multi-Objective Optimization Method by Ratio Analysis (MOORA) which introduces vector normalization. The proposed E-MOORA method judicially exploits the performance parameters and thus reduces ranking abnormality when it selects a HO target cell. Q-learning approach is applied to select the optimal triggering points to minimize the effect of frequent unnecessary handovers for satisfying user QoS requirements. The performance analysis results depict significant performance improvement in terms of minimizing the unnecessary HO, radio link failure, and user throughput compared to other existing Multi-Criteria Decision Making (MCDM) methods.

Soft Mobility: Transparent Handover with Zero Handover Failure in User‐Centric Networks

User‐centric network (UCN) is regarded as a promising candidate to approach the challenges of more radio link failures (RLFs) due to the ultradense deployment of small base stations (SBSs) and meet the requirements of ultrahigh throughput, ultrahigh reliability, and ultralow latency for the 6G system. In this paper, soft mobility is proposed for UCN with the split of control and user plane (C/U‐plane) and shared physical cell identifier (PCI) to achieve the goal of zero handover failure (HOF) probability, where transparent handover (HO) within a cell is realized with user configuration duplication and measurement enhancement. Specifically, the cell is composed of several SBSs around the user, where one anchor SBS is selected for controlling, and others act as slave SBSs for transmission with duplicated UE configuration from the anchor SBS. Based on the proposed architecture, the user measures downlink channel quality for cells and SBSs distinguishingly, via SS/PBCH Block (SSB) and channel‐state information‐reference signal (CSI‐RS), respectively, and then makes the HO decision. Results show that soft mobility can reduce the number of HOF by about 50% over the current system, and the HOF probability is lower than 1% for TTT = 40 ms and offset = −1 dB.

A Robust Self-Optimization Algorithm Based on Idiosyncratic Adaptation of Handover Parameters for Mobility Management in LTE-A Heterogeneous Networks

Mobility robustness optimization (MRO) is a fundamental issue regarding self-optimization network (SON). In long-term evolution-advanced (LTE-A), handover (HO) optimization in heterogeneous networks (HetNets) is an urgent need to improve system performance. This improvement is in terms of immunity against unnecessary HO (UHO) such as ping-pong HO (PPHO) and against failed HOs in the sense of radio link failure (RLF) such as too-early HO (TEHO) and too-late HO (TLHO), that is, RLF HO. The occurrence of these undesired HOs increases the consumption of network resources and decreases the quality of service (QoS). In this study, we propose a robust algorithm to reduce the number of PPHO, TLHO, and TEHO events to a minimum by an innovative mechanism that adaptively sets the HO control parameters (HCPs). This reduction is obtained without the need for unjustified techniques that assume certain thresholds for the ratio of PPHOs or the ratio of RLF HOs relative to the total number of HOs, as proposed in the most recent literature. We also present the importance of thinking about the existence of a direct relationship between the hysteresis margin (HM) and time-to-trigger (TTT). We invest this relationship in determining adaptive HCPs. Simulation results show that RLF HOs and PPHOs are minimal, almost zero, compared to the literature and the classical method. This study opens a new avenue for research on mobility management. It reorients research axes toward thinking about the possibility of a correlation between HM and TTT. This is what the research community has not yet realized.

Faster Recovery From Radio Link Failure During Handover

In LTE and NR, the user equipment initiates a recovery procedure when a radio link failure (RLF) is declared. A shorter RLF timer T312 is introduced to speed up the recovery from an RLF and lower the outage time during a handover. However, T312 cannot remove the outage time completely and guarantee a successful recovery. In this letter, an event-based faster RLF trigger scheme is proposed to remedy the shortcomings of T312. The simulation results show that the proposed scheme can almost fully remove the outage time and reduce the interruption time caused by the recovery procedure, outperforming T312.

Multi-criteria Handover Decision for Heterogeneous Networks: Carrier Aggregation Deployment Scenario

Carrier aggregation and integration of heterogeneous networks allow the mobile user equipment to benefit from wider bandwidth and radio coverage of different access technologies. However, these technologies have increased handoff scenario probability through user equipment’s mobility, leading to high outage probability and low throughput. Handover is an important aspect of mobility management which allows users to migrate from one cell to another without losing connections. But no lone access technology meets the requirements of providing seamless communication without loss and delay. Therefore, high-quality service continuity and reliable user equipment access to network anywhere and at any time require the design of an appropriate handover decision algorithm. In this paper, a multi-criteria based handover decision-making algorithm is proposed to evade loss of communication and provide better performance to the system. It adaptively makes handover decisions based on different decision criteria (load, availability of resources, and the handover scenario type) in addition to signal interference to noise ratio. The multicriteria handover decision making algorithm-based method chooses among the cells that satisfy the requirements for the handover. As compared to some existing handover decision algorithms, simulation result shows that this algorithm improves system performance in terms of handover failure by 93%, 72%, and 58%; radio link failure reduction by 77%, 43%, and 22%; and handover ping–pong by 81%, 59%, and 36% over the conventional received signal strength, received wireless transmission line, and multiinfluence factor hand over decisions respectively.

Mobility Management Based on Beam-Level Measurement Report in 5G Massive MIMO Cellular Networks

Massive multiple-input-multiple-output (MMIMO) in the mmWave band is an essential technique to achieve the desired performance for 5G new radio (NR) systems. To employ mmWave MMIMO technology, an important challenge is maintaining seamless mobility to users because we need to consider beam-switching within a cell besides the handover between cells. For mobility management in 5G NR systems, 3GPP specified a beam-level-mobility scheme that includes beam pairing and maintenance between a transmitter (Tx) and receiver (Rx) pair. We propose a unific-measurement report based mobility management scheme for improved radio-link-failure (RLF) rate and the accuracy of the Tx-Rx-beam-pair (TRP) selection with low overhead in 5G mmWave MMIMO networks where both handover and beam-switching are required. Furthermore, we modeled a finite-state-machine (FSM) for a user terminal to evaluate performance gain based on a system-level-simulation (SLS). We use the FSM-based Monte-Carlo SLS for the experiment and compare the performance of the proposed scheme with that of existing schemes in the scenario where both beam and cell-level-mobility are necessary. We show that the proposed scheme achieves an improvement in terms of the 3-dB loss probabilities representing the accuracy of the TRP selection, signal-to-interference-and-noise-ratio (SINR), and RLF rates with a lower signaling overhead compared to existing methods.

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

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.

Geometry-Based Analysis of Optimal Handover Parameters for Self-Organizing Networks

Handover failure and ping-pongs are the common thorny issues in modern mobile networks. While handover failures caused by radio link failure (RLF) significantly reduces the reliability of network operation, ping-pongs drastically waste signaling resources. In the upcoming fifth-generation (5G) networks especially, a complex deployment of small cells can exacerbate the two problems, even though the network can be integrated with a self-organizing network (SON), which is an automation-based solution. Due to the coupling of RLFs and ping-pongs as explained in the literature, it is difficult to analyze handovers and minimize both RLFs and ping-pongs simultaneously. In this paper, we model a handover procedure with geometric elements (Apollonian circles and the straight line), and analyze handover performance. The analysis provides an optimal handover setting for minimizing both RLFs and ping-pongs together, whereas previous works only considered trade-offs between them. We show that our analysis accurately estimates the optimal setting by comparing it with an NS-3 simulation. From the analysis, different environments can require different optimal values: fading (as well as interference) limit the optimal values; user speed has a scaling impact; and time-to-trigger has a shifting effect.

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

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.

Enhancement of System Information Acquisition for User Based CSG Femtocells in Heterogeneous Network

In LTE/LTE-Advanced networks, system information (SI) acquisition is one of the important parts for accomplishment of handover (HO) to closed subscriber group femtocells. Long measurement gaps and transfer of information during system information (SI) acquisition leads to a degradation of the service quality of the user or radio link failure. In this paper, we run simulations on SI acquisition schemes for user based CSG femtocell inbound using system acquisition from the nearest co-Base station users to avoid interruption ongoing services via intra-frequency measurement scenario and compare with the existing of SI acquisition schemes such as scheduled, semi scheduled and autonomous scheme. Additionally, we propose an improved SI acquisition scheme to reduce measurement gap and minimize the latency of the SI acquisition during the inbound HO evaluation phase with a reduction of time during consideration and valuation of the HO phase. Simulation results show that the performance of the proposed scheme is better than that of the existing SI acquisition schemes by approximately 85%.

Self-Optimization of Coverage and System Throughput in 5G Heterogeneous Ultra-Dense Networks

We propose a new approach for maximizing system throughput while simultaneously obtaining optimal-coverage in terms of minimal radio link failure (RLF) rates in 5G heterogeneous ultra-dense networks (HUDN). To overcome the trade-off issue between the system throughput maximization and the coverage optimization where we can hardly achieve both optimality together, we employ a joint method of “iteration and convex optimization based power control (PC),” and “advanced range expansion (RE) technique.” This algorithm works via a self organizing networks (SON) module, and is compatible with the radio protocol layer defined in specifications. In experimental simulations, we could achieve compositive optimality with considerable performance gain by the proposed approach over existing methods.

Self-optimization of Handover Control Parameters for Mobility Management in 4G/5G Heterogeneous Networks

A large number of small cells in the next-generation mobile networks is expected to be deployed to satisfy 5G requirements. Mobility management is one of the important issues that require considerable attention in heterogeneous networks, where 5G ultra-dense small cells coexist with the current 4G networks. An efficient handover (HO) mechanism is introduced to address this issue and improve mobility management by adjusting HO control parameters (HCPs), namely, time-to-trigger and HO margin. Dynamic HCPs (D-HCPs), which explores user experiences to adjust HCPs and make an HO decision in a self-optimizing manner, is proposed in this paper. D-HCPs classify HO failure (HOF) into three categories, namely, too late, too early and wrong cell HO, and simultaneously adjust HCPs according to the dominant HOF. The algorithm is evaluated using different performance metrics, such as HO ping-pong, radio link failure and interruption time, with different mobile speed scenarios. Simulation results show that the proposed D-HCPs algorithm adaptively optimizes the HCPs and outperforms other algorithms from the literature.

Energy efficient multi-connectivity algorithms for ultra-dense 5G networks

Two radio air interfaces, Evolved-LTE and New Radio, coexist in new 5G systems. New Radio operates in the millimeter band and provides a better bandwidth, but the higher frequencies also imply worse radio conditions. Multi-connectivity, a feature of 5G that allows users to connect to more than one base station simultaneously, can offer the advantages of both interfaces. In this paper, we investigate how multi-connectivity can improve user reliability and the system’s energy efficiency. Five algorithms for secondary cell association are presented and evaluated. We show a decrease in the radio link failure rate of up to 50% at high speeds and improvements of the energy efficiency of up to 20% at low speeds.

Energy Efficient Mobility Enhancement in LTE Pico\u2013Macro HetNet Systems

Mobility management plays an important role in heterogeneous networks (HetNets), because it helps to reduce radio link failures, handover delays, and power consumption, and enhances offloading benefits. In the present study, two cases are identified regarding mobility in HetNets. The first one is related to signaling and the second one is related to data transmission; i.e., handover and link rate adaptation.It has been shown that both processes are affected by the user mobility level and that performance deteriorates near cell edges. The aim is to optimize the user offloading to picocells depending on their estimated speeds so that overall system performance is maximized. The network-centric solution and cooperation between cells that achieve seamless handover is presented, and the impact of the performed measurements for link adaptation during motion is investigated.