Optimizing Handover Performance in 5G Networks Using Dual Connectivity

Prevention of Desynchronization Attack in 4G LTE Networks Using Double Authentication Scheme

The dual connectivity is emerging as a promising solution to boost capacity in heterogeneous networks. However, it is challenging to obtain an optimal user association in heterogeneous networks with dual connectivity, due to its non-convex and combinatorial nature. In this paper, we propose a user association scheme based on deep reinforcement learning to maximize the overall network utility, which takes both throughput and user fairness into account, in the downlink of a heterogeneous network. Particularly, each user associates with the macro base station (BS) and a micro BS. We apply a deep Q-network (DQN) to obtain the nearly optimal policy to associate the users and micro BSs. Simulation results demonstrate that DQN-based user association performs better compared to the conventional user association schemes in heterogeneous networks with dual connectivity, and it behaves good scalability when the environment changes.

Nowadays, numerous primary technologies, like ultra-dense networks (UDNs) and Base Stations (BSs) sleeping state, are developed in fifth-generation (5G) networks. Due to the UDNs, the number of BSs in 5G networks is proliferating, along with the energy consumption. Therefore, it is necessary to cut down the energy attrition in 5G networks under the assurance of delay. Till now, some researchers have proved that the association of users and the sleeping states of BSs have a significant effect on energy consumption and latency in 5G networks. However, the traditional solutions associate users and select states nonadaptively without the dual consideration of energy-saving and delay. In view of this, we propose a dual-adaptive delay-aware and energy-saving system (DADEs) in 5G networks. To further optimize the energy and delay of 5G BSs, the model is split into two tandem problems: user association and BS state selection. Meanwhile, a tandem deep reinforcement learning (T-DRL) algorithm is presented to make decisions in these problems for optimizing and balancing performance between delay and energy adaptively. Additionally, the real datasets of 5G users and BSs are used and trained in this paper. Finally, simulation results show that the DADEs saves more than 50% of energy with an adaptive and satisfying latency.

The fifth generation (5G) of mobile networks utilizing millimeter Wave (mmWave) bands can be considered the leading player in meeting the continuously increasing hunger of the end user demands in the near future. However, 5G networks are characterized by high power consumption, which poses a significant challenge to the efficient management of base stations (BSs) and user association. Implementing new power consumption and user association strategies is imperative to address this issue. For that in this work, we focus on the Dual Connectivity-User and Power Allocation (DC-UPA) problem utilizing BS switching on/off along with user dual connectivity. The problem is mathematically formulated as an Integer Linear Program (ILP), and its NP-hardness is proven by showing its equivalence to a variant of the set covering problem. Moreover, we developed two heuristic algorithms: Simulated Annealing (SA) and Distance-Aware (DA) greedy, to mitigate the impact of the problem complexity and resolve the ILP scalability issue. We conducted extensive simulations to validate the effectiveness of the proposed heuristics in a two-dimensional area containing multiple BSs and users with uniform and nonuniform geographical distributions. The performance of the SA and DA algorithms was compared against the ILP approach. We evaluated the performance of the proposed solutions considering different aspects such as the number of users, the BS radius, and the traffic load changes. The numerical results show that SA outperforms the DA in both uniform and nonuniform geographical distributions of users. The SA provides a sub-optimal solution with an optimality gap of about 3.2%, while the optimality gap of the DA is 8.62% in the case of the uniform distribution. Moreover, the optimality gap in the case of nonuniform distribution is equal to about 1% and 5.2% of SA and DA, respectively. Additionally, by utilizing our solutions, the reduction of the level of power consumption up to 16.1% and 20% in the case of uniform and nonuniform distributions can be achieved. The obtained results highlight the efficiency of the proposed algorithms in addressing the DC-UPA problem, providing practical solutions for managing power consumption and maintaining continuous user connectivity in 5G mmWave networks.

Handover (HO) is one of the key aspects of next-generation (NG) cellular communication networks that need to be properly managed since it poses multiple threats to quality-of-service (QoS) such as the reduction in the average throughput as well as service interruptions. With the introduction of new enablers for fifth-generation (5G) networks, such as millimetre wave (mm-wave) communications, network densification, Internet of things (IoT), etc., HO management is provisioned to be more challenging as the number of base stations (BSs) per unit area, and the number of connections has been dramatically rising. Considering the stringent requirements that have been newly released in the standards of 5G networks, the level of the challenge is multiplied. To this end, intelligent HO management schemes have been proposed and tested in the literature, paving the way for tackling these challenges more efficiently and effectively. In this survey, we aim at revealing the current status of cellular networks and discussing mobility and HO management in 5G alongside the general characteristics of 5G networks. We provide an extensive tutorial on HO management in 5G networks accompanied by a discussion on machine learning (ML) applications to HO management. A novel taxonomy in terms of the source of data to be utilized in training ML algorithms is produced, where two broad categories are considered; namely, visual data and network data. The state-of-the-art on ML-aided HO management in cellular networks under each category is extensively reviewed with the most recent studies, and the challenges, as well as future research directions, are detailed.

NR5G dual connectivity (DC) with control/user (C/U)-plan split is the best capacity enhancing approach for high-speed users. In this work, we present DC-architecture with non-standalone (NSA) model of 5G network in which we consider LTE evolved Node Base station (eNBs) as macro base station and NR5G next-generation node Base station (gNBs) as micro base station. These macro eNB and micro gNBs are responsible for C/U-plan traffics respectively, so called EUTRAN(LTE)-NR5G dual connectivity (ENDC). We analyze the transition of C-plane or D-plane from the source network to the target network in the proposed HetNet i.e., Control-Plane Handoffs (CPHO), User-Plane Handoffs (UPHO). we evaluate the performance of ENDC C/U plan split in the shadowing environment for various range of user equipment (UE) velocities.KeywordsNR5G HetNetDual connectivity (DC)C/U-Plane splitControl Plane Handoffs (CPHO)User Plane Handoffs (UPHO)User Equipment (UE)eNB and gNB

Background. The introduction of fifth-generation (5G) networks creates new opportunities for fast and continuous data exchange, but there are still some problems with the quality of voice services in such networks. With the rapid development of technology and the further spread of 5G, there is a need to understand the impact of key aspects of 5G on voice quality. This requires research that can systematically analyse the features of 5G networks that affect the quality of voice services. Objective. Identification of ways to improve the quality of voice services in 5G networks. Assessment of key indicators of voice service quality in 5G networks. Determination of the best option for the gradual transition to the Standalone mode and the use of VoNR technology in the fifth generation networks. Methods. Analysis of factors affecting the quality of voice services in fifth-generation networks. Analysis of well-known publications on the implementation of 5G networks. Comparison of the implementation of Non-Standalone and Standalone modes in the 5G network. Testing of the modern EVS codec, which provides an opportunity to improve the customer experience. Results. Confirmation that 5G networks can significantly improve the quality of voice services compared to previous mobile communication technologies such as 4G and 3G. Certain factors that may affect the quality of voice services and require additional attention when planning and deploying 5G networks are identified. The optimal steps for the transition to the Standalone mode and the use of VoNR technology in fifth-generation networks are determined. The main differences in the QoS architecture between LTE and 5G are identified, and the purpose of DRB flows for separating traffic types and services is established. Conclusions. It has been confirmed that 5G networks can significantly improve the quality of voice services compared to previous technologies such as 4G and 3G. This is possible due to the broadband capabilities of 5G networks, improved data transmission, low latency, the use of an advanced EVS codec and reduced response time. However, certain factors, such as network coverage, optimisation level and traffic characteristics, can affect the quality of voice services and require additional attention when planning and deploying 5G networks. The QoS management architecture consists of QoS flows, which allow separating packet assignment to flows (managed by the CN) from the assignment of DRB flows (managed by the RAN). As 5G networks are being rolled out gradually, it is important to properly integrate the 5G domain into the existing telecoms provider's network. The transition from Non-Standalone to Dual connectivity is a necessary step for the implementation of VoNR technology in Standalone mode. Using the modern EVS codec allows not only improving the customer experience, but also introducing new voice services.

Prediction of future traffic load is a crucial task to support the automatic Operations, Administration, and Management (OAM) of communication networks. Existing Machine Learning (ML) models require big data to accomplish this task. However, large data sets are not always available, due to the limited storage capacity and the high storage cost at Base Stations (BSs). To solve the problem, we leverage the spatial-temporal correlation among different BSs, which allows other BSs’ data to be used for the prediction of the target BS. One major challenge in realizing this idea is the imbalance of data amounts between neighbor BSs and a prediction target BS. If one simply aggregates the data from both neighbors and target, the target’s traffic features would be overwhelmed by the neighbors’ data. To address this challenge, we propose a Spatial-Temporal Transfer (STT) framework, which trains a base model with an aggregated data set from multiple BSs, and then carefully refines the base model to serve a target BS. To strike a perfect balance between general tendency and individual features, STT adopts an advanced transfer learning technique that exploits regularization on model parameters. Experiments show the efficiency of the proposed STT framework.

5G network supports end-to-end logically isolated networks in the form of network slices, catering to the needs of users of various primary network services, namely enhanced Mobile Broadband (eMBB), ultra Reliable Low Latency Communications (uRLLC), and massive Machine Type Communication (mMTC). Mobile Virtual Network Operators (MVNO)s often face challenges in achieving strong slice isolation and High Availability per slice during overload and scaling situations as the 5G network uses a shared environment for slices with multiple domains, especially considering a variety of services and devices. In this paper, we propose a novel Self Optimizing Network Slicing framework (SONS) leveraging Self Organizing Network by building it as an autonomous slice system in 5G network slicing management for efficient slice sharing and isolation. Precisely, we formulate a system model with Probabilistic Graphical Model (PGM) based Markov Network, building it as an Artificial Intelligence based learning framework. We propose Slice Belief Propagation based algorithms and Deep Learning based Long Short Term Memory (LSTM) methods to aid in serving user requests and reconfiguration of self optimizing slice. Our experiments on the proposed SONS framework shows improvement in serving higher number of users with uninterrupted connectivity by 80% in eMBB, 35% in uRLLC, and 52% in mMTC when compared to standard slice deployments, while handling the worst case of peak traffic in the control plane of 5G Core network.

This paper presents and evaluates a simple but effective approach for substantially reducing inter-frequency handover (HO) failure rate. We build a machine learning model to forecast inter-frequency HO failures. For improved accuracy compared to the state-of-the-art models, we use domain knowledge to identify and leverage the model input features. These features include reference signal received power (RSRP) of the source and target base stations as well as the RSRP of the interferers for both the source and the target layers. Six machine learning classifiers are tested with the highest accuracy of 93% observed for the XGBoost classifier. The novel idea to include the RSRP of the interferes improved the accuracy of XGBoost by 10%.

In this paper, we explore the effect of dual connectivity (DC) on delay-aware resource control with statistical Quality of Service (QoS) provisioning in heterogeneous aeronautical networks. Firstly, the Software Defined Networking (SDN) controller is introduced to support the DC for aircraft by centralized management of different base stations. In light of the delay analysis of user traffic, the delay-aware resource control of SDN controller is established as a multi-objective stochastic optimization problem to guarantee the statistical QoS provisioning. Secondly, the multi-objective stochastic optimization problem is transformed into a weighted single-objective optimization and relaxed by the Lagrangian relaxation. Then, the Lyapunov optimization theory is utilized to solve the relaxed stochastic optimization for optimal resource control. Finally, the performance of resource control under DC mode is verified in two aeronautical scenarios.

Integrated Sensing and Communication (ISAC) has recently emerged as an additional communication service within the Internet of Things (IoT) and Cyber-Physical Systems (CPS) era, through which distributed nodes are able to communicate their sensing information to a Base Station (BS) using integrated signals. In this paper, we study the coexistence of ISAC with other communication types of the nodes, by introducing a software-defined framework to control the nodes' uplink transmission powers related to each service. Each node is simultaneously engaged in two types of communications with different BSs for ISAC and generic data transmission to the cloud via dual connectivity. The uplink power splitting/control problem between the BSs is formulated as a non-cooperative game in satisfaction form, through which each node autonomously concludes to a Satisfaction Equilibrium (SE) point that meets its minimum ISAC and pure communication-oriented requirements. Different achievable SE points are analyzed, while an Reinforcement Learning (RL) and a searching-based algorithm are introduced to conclude to the SE and Minimum Efficient SE (MESE) of the studied problem. Simulation results demonstrate the operation of the algorithms and the overall proposed framework in achieving an efficient share of the resources to the different services.

Newly developed fifth- and sixth-generation (5G and 6G) networks are very secure, reliable, less latent, and versatile. These networks provide various services, like Massive Machine Type Communication (mMTC), Ultra-Reliable Low Latency Communication (URLLC), and Enhance Mobile Broadband (eMBB). Network slicing (NS) is important in both 5G and 6G networks as it enables the provision of the above services in a single physical network. This paper proposes an Optimal Hybrid Quantum-Classical Convolutional Neural Network for efficient 5G Network Slicing (HQCNN-5GNS). Initially, the input data are gathered from Unicauca IP flow version 2 and 5G network slicing dataset. The collected data are fed to the pre-processing phase using a Pseudo-linear Maximum Correntropy Kalman Filter (PMCKF) to remove duplicate data and replace the missing values from the dataset. The pre-processing information is provided to the Hybrid Quantum-Classical Convolutional Neural Network (HQCNN) to classify the network slicing into mMTC, eMBB, and URLLC. In general, HQCNN does not adopt any optimization methods to determine the optimal parameters to guarantee accurate categorization. Therefore, the Quantum Beetle Antennae Search Algorithm (QBASOA) is employed to optimize the weight parameter of HQCNN. The experimental outcomes show that the proposed HQCNN-5GNS attains 17.25%, 15.65%, and 19.55% higher accuracy than the existing approaches: Harris Hawks optimization depends on hybrid deep learning for effectual network slicing on 5G network (HHO-HDL-5GNS), Highly accurate with dependable wireless network slicing in the 5G network: a hybrid deep learning model (WNS-HDL-5GNS), Deep reinforcement learning-basis network slicing for beyond 5G (DRL-5GNS), respectively.

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.

Fifth-generation (5G) networks are evolving from special purpose-built networks to software-defined, intelligent, multipurpose networks, enabling service innovation and autonomous network operation. In this article, we present an intelligent mobile edge network based on standardized 5G network function entities and New Radio (NR) radio access networks (RANs). We propose a multitier, self-organizing network (SON) with a middle-tier SON (mSON), centralized SON (cSON), and distributed SON (dSON) to support increasing network dynamicity for different verticals and to take advantage of the latest high-layer F1 interface. This is essential to support low-latency applications and ultrahigh-reliability services. It also enables more dynamically tight interworking of 5G NR and LTE with dual connectivity, expanding initial 5G NR coverage. We analyze a lower-layer RAN split to optimize fronthaul and so further improve radio efficiency by utilizing the multitier SON among various radio nodes as well as distributed units (DUs) and centralized units (CUs). This will facilitate massive multiple-input/multiple-output (MIMO) antenna systems and accelerate centralized and virtualized RAN deployments.