Search Results (27)

The deployment of Augmented Reality (AR) is a necessity as an enabling technology for intelligent transportation systems (ITSs), with the potential to boost the implementation of Vehicle-to-Everything (V2X) networks while improving driver experience and increasing driving safety to fulfill AR functionality requirements. In this regard, V2X networks must maintain a high quality of service AR functionality, which is more challenging because of the nature of 5G V2X networks. Moreover, the execution of diverse traffic requirements with varying degrees of service quality is essential for seamless connectivity, which is accomplished by introducing efficient handover (HO) techniques. However, existing methods are still limited to basic services, including conversional, video streaming, and general traffic services. In this study, a Multiple Criteria Decision-Making (MCDM) technique is envisioned to address the handover issues posed by high-speed vehicles connected to ultra-high-density (UDN) heterogeneous networks. Compared with existing methods, the proposed HO mechanism handles high mobility in dense 5G V2X environments by performing a holistic evaluation of network conditions and addressing connection context requirements while using cutting-edge applications such as AR. The simulation results show a reduction in handover delays, failures, and ping-pong, with 84% prevention of unnecessary handovers. Full article

Mobile edge computing (MEC) is a modern technique that has led to substantial progress in wireless networks. To address the challenge of efficient task implementation in resource-limited environments, this work strengthens system performance through resource allocation based on fairness and energy efficiency. Integration of energy-harvesting (EH) technology with MEC improves sustainability by optimizing the power consumption of mobile devices, which is crucial to the efficiency of task execution. The combination of MEC and an ultra-dense network (UDN) is essential in fifth-generation networks to fulfill the computing requirements of ultra-low-latency applications. In this study, issues related to computation offloading and resource allocation are addressed using the Lyapunov mixed-integer linear programming (MILP)-based optimal cost (LYMOC) technique. The optimization problem is solved using the Lyapunov drift-plus-penalty method. Subsequently, the MILP approach is employed to select the optimal offloading option while ensuring fairness-oriented resource allocation among users to improve overall system performance and user satisfaction. Unlike conventional approaches, which often overlook fairness in dense networks, the proposed method prioritizes fairness-oriented resource allocation, preventing service degradation and enhancing network efficiency. Overall, the results of simulation studies demonstrate that the LYMOC algorithm may considerably decrease the overall cost of system execution when compared with the Lyapunov–MILP-based short-distance complete local execution algorithm and the full offloading-computation method. Full article

Mobile communications have experienced exponential growth both in connectivity and multimedia traffic in recent years. To support this tremendous growth, device-to-device (D2D) communications play a significant role in 5G and beyond 5G networks. However, enabling D2D communications in an underlay, heterogeneous cellular network poses two major challenges. First, interference management between D2D and cellular users directly affects a system’s performance. Second, achieving an acceptable level of link quality for both D2D and cellular networks is necessary. An optimum resource allocation is required to mitigate the interference and improve a system’s performance. In this paper, we provide a solution to interference management with an acceptable quality of services (QoS). To this end, we propose a machine learning-based resource allocation method to maximize throughput and achieve minimum QoS requirements for all active D2D pairs and cellular users. We first solve a resource optimization problem by allocating spectrum resources and controlling power transmission on demand. As resource optimization is an integer nonlinear programming problem, we address this problem by proposing a deep Q-network-based reinforcement learning algorithm (DRL) to optimize the resource allocation issue. The proposed DRL algorithm is trained with a decision-making policy to obtain the best solution in terms of spectrum efficiency, computational time, and throughput. The system performance is validated by simulation. The results show that the proposed method outperforms the existing ones. Full article

With the rapid development of 5G, artificial intelligence, the internet of things (IoT) and other technologies, the number of intelligent terminal devices is growing explosively, bringing huge challenges to the existing communication network and cloud computing service mode. The dense edge computing system (DECS), which combines mobile edge computing (MEC) with an ultra-dense network (UDN), has the potential to significantly improve low latency of communications and enhance the quality of experience (QoE) of user equipments (UEs). In this paper, to achieve energy-efficient MEC, computation efficiency (CE) is maximized by jointly optimizing computation offloading, subchannel allocation and power allocation, which yields a challenging non-convex problem. Specially, due to the heterogeneity of UE battery capacities and residual energy, the residual energy of UEs should be taken into consideration in order to achieve better QoE. Therefore, we develop a residual energy-based computation efficiency (RECE) optimization scheme to maximize CE, where the optimization problem is divided into three subproblems. Firstly, the computation offloading subproblem is addressed by a many-to-one matching strategy. Secondly, the subchannel allocation subproblem is dealt with by adopting the graph coloring algorithm. Finally, the power allocation subproblem is solved by the concave–convex procedure (CCCP) method. The numerical results illustrate that UEs’ CE can be optimized based on their residual energy in the proposed RECE scheme. Additionally, compared to a scheme without considering UE residual energy, the system CE can be much enhanced, and the UE energy consumption can be significantly reduced in the RECE scheme. Full article

User equipment (UE) location estimation in emerging 5G/B5G/6G Ultra-Dense Networks (UDNs) is a breakthrough technology in future wireless info-communication ecosystems. Apart from communication aspects, network infrastructure densification promises significant improvement in UE positioning accuracy. Unlike networks of previous generations, an increased number of gNodeBs (gNBs) per unit area and/or volume in UDNs allows to perform measurements for UE positioning only with those base stations whose topologies are most suitable from the geometric point of view. Quantitative measurements of gNB topology suitability include horizontal (HDOP), vertical (VDOP), and position (PDOP) dilution of the precision (DOP) criteria on the plane, in height, and in space, respectively. In the current work, we formalize a set of methods for gNB topology search using time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA), and combined TOA–AOA and TDOA-AOA measurements. The background of the topology search using DOP criteria is a significantly increased number of gNBs per unit volume in UDNs. Based on a simulation, we propose a novel approach for a topology search in a positioning service area, resulting in a PDOP less than one for the Gazprom Arena with only five gNBs. The contribution of the current research includes algorithm and software for an iterative search of all possible gNB and UE locations in space, minimizing UE geometric DOP. The practical application of the algorithm is the gNB topology substantiation for the given positioning scenarios in 5G/B5G/6G UDNs. Full article

Millimeter-wave (mmWave) communication is considered a promising technology for fifth-generation (5G) wireless communications systems since it can greatly improve system throughput. Unfortunately, because of extremely high frequency, mmWave transmission suffers from the signal blocking problem, which leads to the deterioration of transmission performance. In this paper, we solve this problem by the combination of ultra-dense network (UDN) and user-centric virtual cell architecture. The deployment of dense small base stations (SBSs) in UDN can reduce transmission distance of signals. The user-centric virtual cell architecture mitigates and exploits interference to improve throughput by using coordinated multipoint (CoMP) transmission technology. Nonetheless, the backhaul burden is heavy and interbeam interference still severe. Therefore, we propose a novel iterative backhaul capacity-limited joint user association and power allocation (JUAPA) scheme in ultra-dense mmWave networks under user-centric virtual cell architecture. To mitigate interference and satisfy quality of service (QoS) requirements of users, a nonconvex system throughput optimization problem is formulated. To solve this intractable optimization problem, we divide it into two alternating optimization subproblems, i.e., user association and power allocation. During each iteration, a many-to-many matching algorithm is designed to solve user association. Subsequently, we perform power allocation optimization using a successive convex approximation (SCA) algorithm. The results confirm that the performance of the proposed scheme is close to that of the exhaustive searching scheme, which greatly reduces complexity, and clearly superior to that of traditional schemes in improving system throughput and satisfying QoS requirements. Full article

Location-Aware Beamforming (LAB) in Ultra-Dense Networks (UDN) is a breakthrough technology for 5G New Radio (NR) and Beyond 5G (B5G) millimeter wave (mmWave) communication. Directional links with narrow antenna half-power beamwidth (HPBW) and massive multiple-input multiple-output (mMIMO) processing systems allows to increase transmitter and receiver gains and thus facilitates to overcome high path loss in mmWave. Well known problem of pencil beamforming (BF) is in construction of precoding vectors at the transmitter and combining vectors at the receiver during directional link establishing and its maintaining. It is complicated by huge antenna array (AA) size and required channel state information (CSI) exchange, which is time consuming for vehicle user equipment (UE). Knowledge of transmitter and receiver location, UE or gNodeB (gNB), could significantly alleviate directional link establishment and space division multiple access (SDMA) implementation. Background of SDMA is in efficient maintenance of affordable level of interference, and the purpose of this research is in signal-to-interference ratio (SIR) evaluation in various 5G UDN scenarios with LAB. The method, used to evaluate SIR, is link level simulation, and results are obtained from publicly released open-source simulator. Contribution of research includes substantiation of allowable UE density, working with LAB. Practical implications include recommendations on terrestrial and angular separation of two UE in 5G UDN scenarios. Full article

One major influence on the future deployment of cellular networks will be a continuous increase in traffic inside mobile broadband systems. Moreover, traditional macrocell-based mobile communication networks will struggle to keep up with the enormous expansion in the demand for communications services in the future. Densification of networks is required if we are to meet the comprehensive needs for end terminals for a wide range of applications. One of the leading concepts in this competitive environment is the Ultra-Dense Network (UDN) where the access nodes and/or the communication links per unit area are densified, with the aim of improving overall network performance. The location of the UDN nodes meets the criteria for symmetry with a high probability. Ultra-dense cell deployment aims to reduce the physical distance between the transmitter and receiver in order to boost system performance and generally optimize the values of a wide variety of key performance indicators (KPIs). This paper aims to provide a taxonomy of UDNs and specifically of UDN-related KPIs. Initially, we address the complex questions “What is the current understanding of what ultra-dense networks are and what they should be, and how can we measure their performance?” by shedding light on the fundamental characteristics of UDNs. Full article

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. Full article

Beyond Fifth Generation (B5G) networks are expected to be the most efficient cellular wireless networks with greater capacity, lower latency, and higher speed than the current networks. Key enabling technologies, such as millimeter-wave (mm-wave), beamforming, Massive Multiple-Input Multiple-Output (M-MIMO), Device-to-Device (D2D), Relay Node (RN), and Heterogeneous Networks (HetNets) are essential to enable the new network to keep growing. In the forthcoming wireless networks with massive random deployment, frequency re-use strategies and multiple low power nodes, severe interference issues will impact the system. Consequently, interference management represents the main challenge for future wireless networks, commonly referred to as B5G. This paper provides an overview of the interference issues relating to the B5G networks from the perspective of HetNets, D2D, Ultra-Dense Networks (UDNs), and Unmanned Aerial Vehicles (UAVs). Furthermore, the existing interference mitigation techniques are discussed by reviewing the latest relevant studies with a focus on their methods, advantages, limitations, and future directions. Moreover, the open issues and future directions to reduce the effects of interference are also presented. The findings of this work can act as a guide to better understand the current and developing methodologies to mitigate the interference issues in B5G networks. Full article

The rapid growth of mobile data traffic will lead to the deployment of Ultra–Dense Networks (UDN) in the near future. Various networks must overlap to meet the massive demands of mobile data traffic, causing an increase in the number of handover scenarios. This will subsequently affect the connectivity, stability, and reliability of communication between mobile and serving networks. The inclusion of Unmanned Aerial Vehicles (UAVs)—based networks will create more complex challenges due to different mobility characterizations. For example, UAVs move in three–dimensions (3D), with dominant of line–of–sight communication links and faster mobility speed scenarios. Assuring steady, stable, and reliable communication during UAVs mobility will be a major problem in future mobile networks. Therefore, this study provides an overview on mobility (handover) management for connected UAVs in future mobile networks, including 5G, 6G, and satellite networks. It provides a brief overview on the most recent solutions that have focused on addressing mobility management problems for UAVs. At the same time, this paper extracts, highlights, and discusses the mobility management difficulties and future research directions for UAVs and UAV mobility. This study serves as a part of the foundation for upcoming research related to mobility management for UAVs since it reviews the relevant knowledge, defines existing problems, and presents the latest research outcomes. It further clarifies handover management of UAVs and highlights the concerns that must be solved in future networks. Full article

With the deployment of the fifth generation (5G) mobile network systems and the envisioned heterogeneous ultra-dense networks (UDNs), both small cell (SmC) and distributed antenna system (DAS) technologies are required by mobile network operators (MNOs) and venue owners to support multiple spectrum bands, multiple radio access technologies (RATs), multiple optical central offices (COs), and multiple MNOs. As a result, the neutral host business model representing a third party responsible for managing the network enterprise on behalf of multiple MNOs has emerged as a potential solution, mainly influenced by the desire to provide a high user experience without significantly increasing the total cost of ownership (TCO). However, designing a sustainable business model for a neutral host is a nontrivial task, especially when considered in the context of 5G and beyond (5GB) UDNs. In this paper, under an integrated optical wireless network infrastructure, we review how SmC and DAS technologies are evolving towards the adoption of the neutral host business model and identify key challenges and requirements for 5GB support. Thus, we explore recent candidate advancements in heterogeneous network integration technologies for the realization of an efficient 5GB neutral host business model design capable of accommodating both SmC and DAS. Furthermore, we propose a novel design architecture that relies on virtual radio access network (vRAN) to enable real-time dynamic resource allocation and radio over Ethernet (RoE) for flexible and reconfigurable fronthaul. The results from our simulations using MATLAB over two real-life deployment scenarios validate the feasibility of utilizing switched RoE considering end-to-end delay requirements of 5GB under different switching schemes, as long as the queuing delay is kept to a minimum. Finally, the results show that incorporating RoE and vRAN technologies into the neutral host design results in substantial TCO reduction by about 81% in an indoor scenario and 73% in an outdoor scenario. Full article

Mobile edge computing (MEC) has the potential to realize intensive applications in 5G networks. Through migrating intensive tasks to edge servers, MEC can expand the computing power of wireless networks. Fifth generation networks need to meet service requirements, such as wide coverage, high capacity, low latency and low power consumption. Therefore, the network architecture of MEC combined with ultra-dense networks (UDNs) will become a typical model in the future. This paper designs a MEC architecture in a UDN, which is our research background. First, the system model is established in the UDN, and the optimization problems is proposed. Second, the action classification (AC) algorithm is utilized to filter the effective action in Q-learning. Then, the optimal computation offloading strategy and resource allocation scheme are obtained using a deep reinforcement learning-based AC algorithm, which is known as the DQN-AC algorithm. Finally, the simulation experiments show that the proposed DQN-AC algorithm can effectively reduce the system weighted cost compared with the full local computation algorithm, full offloading computation algorithm and Q-learning algorithm. Full article

In the current smart era of 5G, cellular devices and mobile data have increased exponentially. The conventional network deployment and protocols do not fulfill the ever-increasing demand for mobile data traffic. Therefore, ultra-dense networks have widely been suggested in the recent literature. However, deploying an ultra-dense network (UDN) under macro cells leads to severe interference management challenges. Although various centralized and distributed clustering methods have been used in most research work, the issue of increased interference persists. This paper proposes a joint small cell power control algorithm (SPC) and interference-managed hybrid clustering (IMHC) scheme, to resolve the issue of co-tier and cross-tier interference in the small cell base station cluster tiers. The small cell base stations (SBSs) are categorized based on their respective transmitting power, as high-power SBSs (HSBSs) and low-power SBSs (LSBSs). The simulation results show that by implementing the IMHC algorithm for SBSs in a three-tier heterogeneous network, the system throughput is improved with reduced interference. Full article

The ultra-dense network (UDN) is one of the key technologies in fifth generation (5G) networks. It is used to enhance the system capacity issue by deploying small cells at high density. In 5G UDNs, the cell selection process requires high computational complexity, so it is considered to be an open NP-hard problem. Internet of Vehicles (IoV) technology has become a new trend that aims to connect vehicles, people, infrastructure and networks to improve a transportation system. In this paper, we propose a machine-learning and IoV-based cell selection scheme called Artificial Neural Network Cell Selection (ANN-CS). It aims to select the small cell that has the longest dwell time. A feed-forward back-propagation ANN (FFBP-ANN) was trained to perform the selection task, based on moving vehicle information. Real datasets of vehicles and base stations (BSs), collected in Los Angeles, were used for training and evaluation purposes. Simulation results show that the trained ANN model has high accuracy, with a very low percentage of errors. In addition, the proposed ANN-CS decreases the handover rate by up to 33.33% and increases the dwell time by up to 15.47%, thereby minimizing the number of unsuccessful and unnecessary handovers (HOs). Furthermore, it led to an enhancement in terms of the downlink throughput achieved by vehicles. Full article