Robust Handover Optimization Technique with Fuzzy Logic Controller for Beyond 5G Mobile Networks †

Beyond 5G (B5G) networks require advanced handover algorithms to guarantee seamless connectivity and optimum quality of service. Traditional handover methods are not sufficient to meet the stringent latency and reliability requirements of next-generation networks. To meet these challenges, the integration of fuzzy logic into handover algorithms offers a viable solution. The proposed approach utilizes parameters such as reference signal received power (RSRP), reference signal received quality (RSRQ), signal-to-interference plus noise ratio (SINR), and user equipment (UE) speed as inputs, while dynamically adjusting the time-to-trigger (TTT) and handover margin (HOM) as outputs. To assess the effectiveness of this algorithm, handover latency (HOL) and handover interruption time (HIT) are evaluated and compared with existing algorithms in the literature. The results show better and more efficient performance in both terms of latency and interruption time.

The design and maintenance of the radio access network (RAN) in mobile telecommunications requires meticulous monitoring of network performance to ensure optimal quality and coverage.Drive testing is a prevalent methodology for collecting data on network status across delineated geographical areas.This process involves systematically traversing various routes, including streets and pathways, to assess network performance metrics such as Reference Signal Received Power (RSRP).Although drive testing provides essential insights for identifying regions with inadequate signal quality, it is inherently resource-intensive, involving considerable time and financial expenditures.This paper proposes an innovative optimization methodology aimed at enhancing the efficiency of data collection during drive tests.The proposed approach is organized into four fundamental steps: (1) partitioning the map into smaller sections, (2) selecting critical points within each section, (3) employing map-matching techniques to accurately align these points with actual streets and pathways, and (4) determining the optimal routefor traversing the critical points.A rectangular area of interest is selected and divided into K smaller sub-regions, within which M critical points are identified according to a predefined criterion.These points, which may not initially correspond with the existing street network, are corrected through mapmatching techniques to ensure feasible traversal paths.Lastly, an optimization algorithm is utilized to compute the shortest route that encompasses all identified critical points.The efficacy of the proposed method is assessed through experimental studies that manipulate key parameters K (denoting the number of sub-regions) and M (indicating the maximum critical points per zone).The evaluation emphasizes two critical metrics: the total distance traveled and the success rate in detecting areas with RSRP values below -100 dBm.Results indicate that the proposed approach significantly decreases the distance required for drive testing while achieving substantial coverage of areas with weak signals.For example, in an experiment where the optimized route covered only 18.54 kilometers-equivalent to 34% of the distance of an entire drive test-it successfully identified 70% of regions with poor RSRP.Furthermore, this paper introduces a criterion for assessing the effectiveness of drive test routes, highlighting the balance between route length and coverage of low-signal areas.The findings substantiate that adequate data for network performance analysis can be secured with considerable savings in both cost and time compared to conventional exhaustive drive testing.While this study concentrates on RSRP measurements within 4G networks, the proposed methodology is adaptable for other metrics, including Reference Signal Received Quality (RSRQ).Future research may also examine the integration of alternative data sources, such as satellite imagery, to further refine map partitioning and critical point selection, thereby enhancing the overall efficacy of the proposed method.

HetNet networks are one of the key features of LTE and the ones beyond. Despite the development created in HetNet, recent studies have shown that HOFs and PPs can cause serious problems given the small size of the cell. On these networks, given the difference in base station's coverage, the handover performance of the user equipment (UE) may significantly reduce; this is especially obvious in situations where UE passes through the small cells at a high-speed. In this paper, a context-aware mobility management (MM) approach is presented for small-cellular networks, where reinforcement learning. BSs collectively learn their long-term traffic loads and optimal extension of their cell range and schedule their UE according to the speed and rate of history data, along with the consideration of QOS, which is exchanged over rows. The proposed method not only provides better performance than MM in terms of power, but also provides better fairness conditions that reduce the likelihood of a Handover and Ping Pong failure. In the survey, macro and picocells learn how to optimize their longterm traffic, whereas in the short term, the UE communication process is based on history and timing based on speed and QOS. Thus, a long-term load balancing approach aims to improve overall system capacity while reducing the probability of Hand Over Failure (HoF) and Ping Pong (PP).

In this paper, a generalized model is proposed to analyze the impact of propagation environment and velocity on the handover performance of user equipment (UE) for long term evolution (LTE) systems. An adaptive time window is defined within the generalized model, to provide additional flexibility to perform handover procedure over conventional reference signal received power (RSRP) and reference signal received quality (RSRQ) based procedures. The proposed algorithm reduces the call dropping rate during handover at the cell boundary of urban cells, due to its enhanced degree of freedom in decision making process. In providing so, the process requires additional statistics, which are readily available at the receiver. Therefore, it does not require any additional computational complexity at UE. Finally, to demonstrate the validity of the proposed algorithm, simulation results along with the observations are presented.

To receive the highest possible data rate or/and the most reliable connection, the User Equipment (UE) may want to choose between different networks. However, current LTE and LTE-Advanced mobile networks do not supply the UE with an explicit indicator about the currently achievable data rate. For this reason, the mobile device will only see what it obtains from the network once it actively sends data. A passive estimation in advance is therefore not doable without further effort. Although the device can identify its current radio conditions based on the received signal strength and quality, it has no information about the cell's traffic load caused by other users. To close this gap we present an Enhanced Client-based Control-Channel Analysis for Connectivity Estimation (EC3ACE), which uncovers the cell load broken down by each single user. Based on this information and in conjunction with existing indicators like Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ), a neural network is trained to perform a data rate prediction for the current LTE link. Compared to an earlier work, our approach reduces the average prediction error below one third. Applied in public networks, the predicted data rate differs by less than 1.5 Mbit/s in 93% of cases.

Based on the Long Term Evolution (LTE) coverage and capacity limitation for indoor and interference area, femtocells with low power nodes have been proposed and deployed for add more coverage and capacity in mobile data network. For inter working between femtocells and LTE network handover and mobility management are the important key to achieve the best performance. Cell range of femtocells is smaller than macro cells and main propose for applying and using femtocells are increasing up link coverage. Because of femtocells have low coverage area, so probability of frequently active and no -active for these cells has high value. According to this property, number of handover attempt between LTE to femtocells and femtocells to femtocells has been increased, thus percentage successful handover may be affected, and we face degradation in this index. One important and practical solution is to use mobility robustness feature and load balancing for reduce unnecessarily and handover failure. In this paper we consider on a gradient method and cost function based on Mobility Robustness optimization (MRO) and load balancing scheme to improve the handover and mobility management performance. Self-organizing network(SON) is used to enhance the efficiency and reliability. The simulation results show that the proposed scheme with load balancing has better performance than Non-balancing.

Wireless cellular networks have many parameters that are normally tuned upon deployment and re-tuned as the network changes. Many operational parameters affect reference signal received power (RSRP), reference signal received quality (RSRQ), signal-to-interference-plus-noise-ratio (SINR), and, ultimately, throughput. In this paper, we develop and compare two approaches for maximizing coverage and minimizing interference by jointly optimizing the transmit power and downtilt (elevation tilt) settings across sectors. To evaluate different parameter configurations offline, we construct a realistic simulation model that captures geographic correlations. Using this model, we evaluate two optimization methods: deep deterministic policy gradient (DDPG), a reinforcement learning (RL) algorithm, and multi-objective Bayesian optimization (BO). Our simulations show that both approaches significantly outperform random search and converge to comparable Pareto frontiers, but that BO converges with two orders of magnitude fewer evaluations than DDPG. Our results suggest that data-driven techniques can effectively self-optimize coverage and capacity in cellular 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.

The rapidly-growing number of mobile subscribers has led to the creation of a large number of signalling messages. This makes it difficult to efficiently handle the mobility of subscribers in mobile cellular networks. The long-term evolution (LTE) architecture provides software-defined networking (SDN) to meet the requirements of 5G networks and to forward massive mobile data traffic. The SDN solution proposes separation of the control and data planes of a network. Centralized mobility management (CMM) is widely used in current mobile network technologies, such as 4G networks. One of the problems related to CMM is a single point of failure. To solve the problems of CMM and in order to provide for efficient mobility management, IETF has developed a solution called distributed mobility management (DMM), in which mobility is handled via the nearest mobility anchor. In this paper, we propose a DMM solution with handover operations for SDN-enabled mobile networks. The advantage of the proposed solution is that intra and inter handover procedures are defined with the data buffering and forwarding processes between base stations and mobility anchors. We adopt a simulation model to evaluate and compare the proposed solution with the existing solution in terms of handover latency, packet loss and handover failures.

With the explosive increase in traffic volume in fifth-generation (5G) mobile wireless networks, an ultra-dense network (UDN) architecture, composed of highly concentrated millimeter-wave base stations within the fourth-generation (4G) system, has been developed. User equipment (UE) may encounter more frequent handover opportunities when moving in a UDN. Conventional handover schemes are too simple to adapt to the diverse handover scenarios encountered in 5G UDNs because they consider only UE signal strength. Unnecessary handovers aggravate the ping-pong effect and degrade the quality of service of cellular networks. Fuzzy logic (FL) is considered the best technique to unravel the handover problem in a high-density scenario of small cells for 4G/5G networks. In this paper, we propose an FL-based handover scheme to dynamically adjust the values of two handover parameters, namely handover margin (HOM) and time to trigger (TTT), with respect to each UE. The proposed scheme, abbreviated as FLDHDT, has dynamic adjustment of TTT in addition to HOM by using the signal to interference plus noise ratio and horizontal moving speed of the UE as inputs to the FL controller. To demonstrate the effectiveness and superiority of FLDHDT, we perform simulations using the well-known ns-3 simulator. The performance measures include the number of handovers, overall system throughput, and ping-pong ratio. The simulation results demonstrate that FLDHDT improves the handover performance of 5G UDNs in terms of the number of handovers, ping-pong ratio, and overall system throughput compared to a conventional handover scheme, namely Event A3, and an FL-based handover scheme with dynamic adjustment of only HOM.

With the purpose of providing higher data rate and ultra-reliable and low-latency communications for the users, employing the small cells in the upcoming Fifth-Generation-New Radio (5G-NR) cellular networks and beyond is leading to the serious challenges in mobility management due to dynamicity of the user equipments (UEs). Among different issues related to the mobility of the UEs, the handover management is one of the key procedures to let the UEs experience a high quality of service (QoS)/quality of experience (QoE). So far, many protocols and algorithms have been proposed to enhance the mobility management in 5G-NR from various aspects, but still a thorough survey has not been incorporated to summarize the recent advances and future directions of the mentioned issue. Thus, the handover management and the corresponding challenges stand in the center of concentrations in this paper, with a glance on 4G to the advancements of 5G-NR. The challenges include QoS/QoE, throughput, delay, traffic load, the attacks during authentication process and resource allocation. To overcome the mentioned challenges, the handover procedure is evaluated by some key performance indicators such as handover ratio, handover failure, radio link failure and such like which depend on the received signal quality. The lack of new advancements and novel aspects of handover plus the separation of security and handover issues in previous works are perceived as research gaps and motivations for the current paper. In this regard, this paper aims to specify and analyze the technical issues, to provide an overview on the proposed methods and recent advances and to bring the future directions into the limelight. We categorize the concurrent standards and methods about the handover, and then, we survey the proposed algorithms including theoretical, algorithm-based and pattern-capturing approaches following the authentication process along with the vulnerabilities and the algorithms to counteract the attacks during handover. Also, we study various issues corresponding to network performance during the handover procedure. Finally, we discuss the open problems and future research directions.

This paper evaluates the hard handover performance based on the different hard handover algorithms for UTRAN LTE system. The focus is on the impact that two kinds of hard handover algorithms compare to A3 event based on reference signal received power (RSRP) and reference signal received Quality (RSRQ) measurements in different velocity environment. These have been designed and implemented in a dynamic system level simulator and have been studied for different parameter sets in a high-speed railway (HSR) simulation scenario. RSRP and RSRQ parameter have been simulated in a dynamic link level simulation and have been investigated the influence factors via the simulation. The results suggest that optimized hard handover algorithms have higher handover success rate than the classical A3 event in different velocity environment.

The use of mobile data is rapidly increasing along with the popularity of smartphones and tablets. The use of indoor data, especially, is expected to be further increased. Indoor cell evolved Node Bs (eNBs) are being installed and they are expected to increase the areas overlaid by indoor and outdoor cells to offload indoor mobile data. We confirmed increases in the handover (HO) failure rates from our analysis of field measurements due to rapid drops in communication quality when user equipment (UE) moved through the doors of a building. If HO failure occurred, the quality of experience (QoE) for real-time applications, e.g., voice over Internet protocol (VoIP) and streaming, degraded due to service interruptions and rebuffering. Thus, it was important to reduce the HO failure rates and disconnection times due to HO failure. We propose a scheme of dual HO triggers that improves the performance of HO from/to an indoor cell while maintaining that of HO between outdoor cells. The proposed scheme introduces events to UE measurement reports to detect rapid drops in communication quality in addition to conventional events that are optimized to the HO between outdoor cells. LTE simulations confirmed that the new HO trigger prevented UE from going out-of-synchronization before the completion of the HO process. The simulation results indicated that the proposed scheme improved indoor-cell-related QoE. More specifically, the number of dropped calls reduced by 87%, the number of service interruptions for VoIP reduced by 92%, and the HO failure rate reduced by 23 points. Moreover, the proposed scheme also improved outdoor-cell-related QoE. In short, it significantly improved QoE by reducing HO failure rates in indoor and outdoor cell environments.

The promising emergence of heterogeneous networks (HetNets) composed of Long-Term Evolution-Advanced (LTE-A) macro cells and fifth-generation (5G) small cells is envisioned as a paradigm shift in modern wireless communications due to their capability of operating in millimeter-wave (mmWave) bands. However, there are significant variabilities between these two different cell types in terms of the transmission power levels, system capacity, coverage areas and deployment densities. Also, the random distribution of users in different directions of movement causes an uneven distribution of traffic load between cells. In this study, we suggest a fuzzy logic-based algorithm for adjusting the handover (HO) margin (HOM) in HetNets by leveraging cell load and radio channel quality. In addition, a two-step model of target cell determination is presented that takes into account the load level of the cells and the reference signal received power (RSRP). The performance of the suggested algorithm is compared with selected existing load balancing algorithms. The simulation findings depict that the suggested algorithm improves load balancing, increases throughput and reduces call dropping ratio (CDR).

Channel estimation plays a crucial role in intelligent reflecting surface (IRS)-aided communication systems, in which the channel state information must be acquired to configure the optimal IRS reflection coefficient. However, using large numbers of IRS elements induce large channel estimation overheads, which cause crucial problems in systems utilizing IRSs. In this study, we investigate the optimal pilot interval design for IRS-aided communication systems to reduce the channel estimation overheads. The channel coherent time depends on user equipment (UE) velocity. Therefore, the proposed method aims to find a pilot interval that balances accuracy of the IRS reflection coefficient with system throughput by considering UE velocity. In the proposed method, we identify the relationship between the mobility of the UE and complex spatial correlation of the channel matrix, and the transmission capacity in a time slot is formulated to maximize the achievable sum rate. Numerical results from simulations show that the proposed method adaptively adjusts the pilot interval under various communication environments.