Next-generation Mobile Networks Research Articles

Deep Deterministic Policy Gradient-Based Resource Allocation Considering Network Slicing and Device-to-Device Communication in Mobile Networks.

Next-generation mobile networks, such as those beyond the 5th generation (B5G) and 6th generation (6G), have diverse network resource demands. Network slicing (NS) and device-to-device (D2D) communication have emerged as promising solutions for network operators. NS is a candidate technology for this scenario, where a single network infrastructure is divided into multiple (virtual) slices to meet different service requirements. Combining D2D and NS can improve spectrum utilization, providing better performance and scalability. This paper addresses the challenging problem of dynamic resource allocation with wireless network slices and D2D communications using deep reinforcement learning (DRL) techniques. More specifically, we propose an approach named DDPG-KRP based on deep deterministic policy gradient (DDPG) with K-nearest neighbors (KNNs) and reward penalization (RP) for undesirable action elimination to determine the resource allocation policy maximizing long-term rewards. The simulation results show that the DDPG-KRP is an efficient solution for resource allocation in wireless networks with slicing, outperforming other considered DRL algorithms.

Joint wireless energy transmission and dynamic power management for full-duplex relay system

The rapid deployment of fifth-generation (5G) mobile communication systems demands sustainable power solutions. This paper introduces a novel joint design for wireless energy transmission and dynamic power management in full-duplex relay systems, aiming to ensure a continuous power supply for relay nodes. Our approach integrates energy harvesting from RF signals and dynamic battery management, focusing on optimizing throughput via energy allocation and precoding design. Simulation results demonstrate that our scheme significantly outperforms conventional non-battery half-duplex schemes in system throughput. Notably, we observe performance increments in throughput compared to all mentioned traditional methods, highlighting the efficacy of our design in enhancing 5G network sustainability and efficiency. This innovative approach presents a promising solution for effective power management in next-generation mobile networks.

Review of Routing Protocols for Mobile Ad Hoc Networks

Research on Mobile Ad Hoc Networks is of great significance in various fields including military, emergency response, Internet of Things (IoT), vehicular networking, aviation, and scientific research. This research contributes to the advancement and application of mobile communication technologies. Both domestic and international researchers have conducted in-depth studies on routing protocol design, which encompasses various aspects such as protocol design, topology management, link quality estimation, security, and energy efficiency. These areas represent current research hotspots and challenges. With the continuous development of wireless communication technologies and increasing demands for application, research on routing in Mobile Ad Hoc Networks remains highly significant with broad prospects for development. Therefore, research on routing strategies for Mobile Ad Hoc Networks has garnered widespread attention in academic circles worldwide, leading to numerous valuable research outcomes. This paper comprehensively analyzes the requirements related to the design of routing protocols for mobile self-organizing networks and comprehensively investigates the existing routing protocols, and classifies and investigates the routing protocols for mobile self-organizing networks in order to promote the research of next-generation mobile self-organizing network routing in China, which provides a reference.

Load-aware continuous-time optimization for multi-agent systems: toward dynamic resource allocation and real-time adaptability

In the realm of next-generation mobile communication networks, characterized by dynamic and evolving workloads, the efficient resource allocation becomes paramount for achieving optimal performance. This paper addresses the intricate challenges of multiagent resource management through the integration of stochastic learning automata and continuous-time optimization techniques, presenting an innovative solution for real-time adaptability and dynamic load balancing. The central aim is to achieve optimal values for local time-varying cost functions while taking into account resource constraints and the feasibility of local allocation. In the context of multi-agent resource allocation systems, where conditions vary over time, the algorithm exhibits a dynamic adaptability, continuously adjusting to the evolving optimal solutions tailored for each agent. Utilizing stochastic learning automata, the algorithm effectively addresses the time-varying load-balancing function in response to dynamic user demands. Furthermore, we introduced an scalable solution for mobility robustness optimization based on deep reinforcement learning (DRL-MRO). This method dynamically identifies network-wide optimal parameter values across the entire network to accommodate diverse mobility patterns, ensuring the effectiveness of load balancing parameters that seamlessly adapt to the dynamic configuration of the network. The overarching goal is to maintain a consistent quality of service level for each agent, thereby enhancing the overall system performance. Simulation outcomes unequivocally affirm the superior performance of the proposed load balancing approach, rooted in continuous-time optimization and stochastic learning automata, surpassing existing schemes across diverse system configurations.

Dual wavelength signal generation with four wave mixing based on directly modulated laser

Dual wavelength (DW) lasers are crucial for producing petahertz (PHz) signals, which are higher value terahertz (THz) signals. The infrared and visible light sub-bands are valuable frequency range for a number of applications, including networking, multiple access techniques, next-generation mobile networks (6G), future wireless networks, and new PHz communication systems. The importune need to the high frequency signal such as THz and PHz signal for 5 and 6 next generation wireless cellular mobile networks motivate us to design an optical system to provide these signals rather than an electronic circuits. They are also beneficial for a variety of medical testing. The wavelength spacing ranging from 14 nm to 7 nm is proposed and demonstrated a dual-wavelength generation with FWM based on DML is realized at 1554 and 1548 nm. It is essential to make use of the fiber's non-linearity characteristics to ensure synchronization for continuous wave (CW) laser and directly modulated laser (DML) signals and the creation of the four wave mixing (FWM) components. With the help of OptiSystem version 14.0, the system is designed and investigated then, the desired THz signal is created as pulse amplitude modulation. The position of the FWM components within the frequency range between the spectrums of the two lasers affects the synthesis of the required signal. At the DML frequency equal to or below the CW Laser (1520 nm), neither FWM frequency components nor dual wavelength pulses exist. On the other hand, the FWM frequency components and dual wavelength pulses can only be seen when the DML frequency is greater than 1520 nm. The CW laser frequency is the main controlling factor in the generation THz signal. If the CW laser frequency is set at or below 1520 nm, the dual wavelength and THz signal are accessible. Because these frequencies are so far from the reference fiber frequency, it is impossible to produce dual wavelength pulses at frequencies higher than those mentioned above.

Quantum-Secure Signalling Model for L1/L2 Next-Gen Interconnect and Roaming Networks Over IPX for NB-IoT Traffic: A Review

Signalling security is critical for next-generation mobile (NGN) networks to ensure integrity, privacy, and confidentiality of communication protocols. However, recent research on existing 5G standards has uncovered vulnerabilities that could be exploited by emerging surveillance threats such as HiddenArt and Harvest Now Decrypt Later (HNDTL), as well as the prospect of quantum computing threatening to break classical cryptographic techniques. This systematic literature review (SLR) investigates quantum-based signalling solutions to mitigate these threats in interconnect and roaming 5G networks (IRN) over IP eXchange for Narrowband Internet of Things (NB-IoT) traffic. A database search was conducted, studies were selected and compared based on methods, approaches, techniques, and limitations. The SLR found that quantum key distribution (QKD) combined with quantum teleportation (QT) can protect and mitigate threats and attacks on signalling networks when protocols interact and interoperate in carrier networks. QKD techniques can be used for L1 protocols, while secure direct quantum communication can be used for L2 protocols. This study concludes that further studies are needed to integrate different techniques and protocols for L1/L2 signalling networks to create a robust quantum-secure signalling model for global interconnect and roaming NGN.

Multi-objective Service Function Chain placement in 5G cellular networks based on meta-heuristic approach

With the emergence of new applications driven by the popularization of mobile devices, the next generation of mobile networks faces challenges to meet different requirements. Virtual Network Functions (VNFs) have been deployed to minimize operational costs and make network management more flexible. In this sense, strategies for VNF placement can impact different metrics of interest. Invoking and visiting VNFs in a specific execution order may be required for different use cases, resulting in a complete network service called Service Function Chain (SFC). The SFC placement problem is to define a feasible path in the physical infrastructure whose nodes and edges meet the computational and bandwidth requirements for the VNFs and virtual links, respectively. It has already been proved that this process is NP-hard and it is difficult to find an optimal solution to this problem. Therefore, in this paper, we propose the use of meta-heuristics to solve the SFC placement problem in cellular networks. We consider a triathlon competition leading to different mobility patterns. We collected real data about the competitors to simulate their movements through the scenario as well as the measured signal quality of the network. We formulate the SFC placement problem as a multi-objective problem where we try to minimize the placement cost and the total SFC delay. To solve the problem, we propose the use of two algorithms, NSGA-II and GDE3, which compare two different greedy approaches that prioritize the different optimization metrics considered in this work. Our results show that the meta-heuristics provide better results for each of the metrics. For all competition stages, GDE3 presented a slightly lower placement costs than NSGA-II, while NSGA-II had a lower delay in some scenarios.

A Comprehensive Review of UAV-Assisted FSO Relay Systems

The evolving requirements of next-generation mobile communications networks can be met by leveraging vertically deployed Unmanned Aerial Vehicle (UAV) platforms integrated with Free Space Optical communications (FSO). This integration offers a flexible and scalable architecture capable of delivering high-rate communication without requiring licenses while aligning with the multi-gigabit paradigm. In recent times, the increasing availability of commercial aerial platforms has facilitated experimental demonstrations of UAV-enabled FSO systems, which play a crucial role in proposed backhaul networks and point-to-point communications by overcoming Line-of-Sight (LOS) challenges. These systems can be rapidly deployed to meet sudden demand scenarios. This document provides a comprehensive review of relevant field demonstrations of UAV-enabled FSO relay systems, with a particular focus on commercially available, free-flying platforms that are driving advancements in this domain. It categorizes the different platforms by considering the operational altitudes of these systems and their payload actuation capacity, which determines their adaptability to variables. The analysis aims to distill the design considerations that lead to optimal performance regarding communications throughput and other relevant metrics. Moreover, it also attempts to highlight areas where design choices have fallen short, indicating gaps in current research efforts toward the widespread adoption of UAV-enabled FSO relay systems. Finally, this work endeavors to outline effective design considerations, guidelines, and recommendations to bridge these identified gaps. It serves as a valuable reference guide for researchers involved in developing UAV-enabled FSO relay systems, enabling them to make informed decisions and pave the way for the successful implementation of such systems.

Digital twin framework for smart greenhouse management using next-gen mobile networks and machine learning

Due to the increase in world population, arable land has been reduced. Consequently, the concept of urban greenhouses is on the rise. Smart greenhouses need to monitor physical parameters for the healthy growth of plants from remote locations. A digital twin is a representation of physical assets in the digital world, and this emerging technology has opened up opportunities for efficient system development for Industry 4.0. The digital twin receives real-time operational data to monitor the asset in the digital domain. It performs real-time processing, data analysis, and machine learning to predict optimized decisions. In the era of next-generation mobile networks, IoT devices can communicate and perform their remote operations in a timely manner. In smart greenhouse technology, the digital twin could be a revolutionary substitute for real-time remote monitoring and process management. However, there has been limited work on digital twin-driven smart greenhouse technology. In this paper, a process management framework is developed that can be interpreted as a machine learning and cloud-based data-driven digital twin for smart greenhouses. The proposed framework consists of three layers: the physical, fog, and cloud layers. The physical greenhouse measurements are monitored using a highly immersive cloud-based, real-time 3D environment. We present an example architecture using commercial cloud and open-source tools to verify the proof of concept. Additionally, different ML techniques are utilized to predict the operational requirements for smart greenhouses.

Emerging Directions for Blockchainized 6G

The next generation of mobile networks, i.e., sixth generation (6G), is expected by 2030, with already burgeoning research efforts towards this goal. Along with various other candidate technologies, blockchain is envisioned to enable and enhance numerous key functionalities of 6G. Thus, the main objective of this paper is threefold: 1) to categorize the different aspects of 6G into four emerging directions that anticipate significant advancements leveraging blockchain, 2) to discuss the potential role of blockchainized 6G under each key emerging direction, 3) to expound on the technical challenges in blockchaining 6G along with possible solutions.

Data-driven decision-making framework for optical fronthaul slice resizing in 6G networks

The third-generation partnership introduced three main types of slices: enhanced mobile broadband, massive machine-type communication, and ultrareliable low-latency communication. To accommodate these services, the next generation of mobile networks will require architecture with distinct requirements and network slices. To implement these services on an optical fronthaul, the slices will be hosted using lightpaths. Such lightpaths will have to accommodate latency and bandwidth constraints to keep radio units (RUs) and baseband units (BBUs) synchronized. However, the traffic in a slice may vary, and the resources allocated to a long-established lightpath could be out of date, leading to waste or lack of resources. For example, a lack of bandwidth can cause desynchronization between BBUs and RUs. Therefore, the slice must be resized regularly to meet the variable demands. This work proposes a data-driven decision-making (DDDM) framework to resize the fronthaul slices while mitigating the consequences of a lack of bandwidth. The framework uses long short-term memory to implement its analytical stage and integer linear programming (ILP) to reconfigure the entire network when it is required. The results show that the DDDM-based framework outperforms the state-of-the-art ILP-based heuristic by up to 15% in terms of radio blocking mitigation.

New developments and trends in 5G technologies: applications and concepts

Fifth-generation (5G) wireless technology is the most up-to-date iteration of mobile data networks. This research analyzes the effectiveness of next-generation mobile networks in tandem with mobile communication technologies. Various difficulties encountered at each stage are discussed. With the advent of 5G networks, users may connect to the internet at lightning speeds from almost any location. 5G is one of a kind because of its new characteristics, which allow it to link people and enable them to operate gadgets, machines, and things. 5G mobile technology’s varying speed and capabilities will allow for new user experiences and link new businesses. Companies must know where they can best put 5G to use. This research paper examines and analyzes various topics in great detail, demonstrating how mmWave, massive multiple-input and multiple-output (massive MIMO), microcells, mobile edge computing (MEC), beamforming, diverse antenna technologies, and so on can all work together to improve cellular networks. The primary goals of this article are to demonstrate some of the most recent technical developments and to analyze potential future research directions for the 5G mobile system.

Multi-Slice Privacy-Aware Traffic Forecasting at RAN Level: A Scalable Federated-Learning Approach

Next-generation mobile networks are expected to meet the requirements of a wide range of new vertical services. Hence, the network slicing concept has been introduced, in which Mobile Virtual Network Operators (MVNOs) are allowed to provide various types of services over the same physical infrastructure, owned by an Infrastructure Provider (InP). To cope with an ever-changing traffic demand, MVNOs seek to pre-allocate/reconfigure the resources at the base stations in an anticipatory manner, based on traffic demand predictions. Ideally, conducting per-slice traffic forecasting requires information that is likely to disclose MVNO confidential information (i.e., business strategy or private user data). To secure data ownership while conducting traffic forecasting, we propose the Federated Proximal Long Short-Term Memory (FPLSTM) framework, which allows MVNOs to train their local models with their private dataset at each base station; subsequently, an associated InP global model can be updated through the aggregation of the local models. The results obtained by training the models on a real-world dataset indicate that the forecasting performance of our proposed approach is as accurate as state-of-the-art centralized solutions, while improving data privacy. To enable scalability, we further propose the Information-based Clustering FPLSTM (IC-FPLSTM) and Random Clustering FPLSTM (RC-FPLSTM) frameworks, dealing with large-scale cellular networks. These solutions demonstrate computation and communication cost efficiency significantly above the state-of-the-art.

Fundamental Detection Probability vs. Achievable Rate Tradeoff in Integrated Sensing and Communication Systems

Integrating sensing functionalities is envisioned as a distinguishing feature of next-generation mobile networks, which has given rise to the development of a novel enabling technology – <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Integrated Sensing and Communication (ISAC)</i> . Portraying the theoretical performance bounds of ISAC systems is fundamentally important to understand how sensing and communication functionalities interact (e.g., competitively or cooperatively) in terms of resource utilization, while revealing insights and guidelines for the development of effective physical-layer techniques. In this paper, we characterize the fundamental performance tradeoff between the detection probability for target monitoring and the user’s achievable rate in ISAC systems. To this end, we first discuss the achievable rate of the user under sensing-free and sensing-interfered communication scenarios. Furthermore, we derive closed-form expressions for the probability of false alarm (PFA) and the successful probability of detection (PD) for monitoring the target of interest, where we consider both communication-assisted and communication-interfered sensing scenarios. In addition, the effects of the unknown channel coefficient are also taken into account in our theoretical analysis. Based on our analytical results, we then carry out a comprehensive assessment of the performance tradeoff between sensing and communication functionalities. Specifically, we formulate a power allocation problem to minimize the transmit power at the base station (BS) under the constraints of ensuring a required PD for perception as well as the communication user’s quality of service requirement in terms of achievable rate. It indicates that, on the one hand, there exists an intrinsic tradeoff between sensing and communication performance under the mutual-interfered scenarios; On the other hand, with prior knowledge of the baseband waveform, these two functionalities might mutually assist each other to enhance the performance. Finally, simulation results corroborate the accuracy of our theoretical analysis and the effectiveness of the proposed power allocation solutions showing the advantages of the ISAC system over the conventional radar and communication coexistence counterpart.

Reconfigurable optical crosshaul architecture for 6G radio access networks

The radio access network (RAN) architecture is undergoing a significant evolution to support the next-generation mobile networks and their emerging applications. To realize scalable and sustainable deployment and operations, RAN needs to consider the requirements of 6G and beyond wireless technologies such as ultra densification of cells, higher data rates, ubiquitous coverage, and new radio spectrum in the millimeter-wave band. This calls for a careful redesign of every aspect of RAN, including its crosshaul. The crosshaul is an important network segment in future RAN, capable of transporting diverse traffic types with varying stringent requirements within RAN. The crosshaul towards 6G is envisioned to be highly intelligent, reconfigurable, and adaptable to dynamic service requirements and network conditions. To this end, we propose a software defined network (SDN)-enabled reconfigurable optical crosshaul architecture (ROCA) that supports heterogeneous crosshaul transport technologies and dynamic functional splittings. ROCA enables efficient and intelligent control of the crosshaul data plane. The proposed architecture with a set of the next-generation RAN (NG-RAN) transport interfaces is evaluated using network models built on the ns-3 network simulator. Simulation results demonstrate the strengths and weaknesses of different crosshaul interfaces in agreement with the understanding of respective NG-RAN interfaces from the literature, which validates the modeling accuracy. We then demonstrate the reconfigurability of the architecture using a dynamic scenario with different reconfiguration strategies for meeting the user and network demands. The results indicate that ROCA serves as a scalable and flexible foundation for supporting high-capacity delay-stringent RAN that can be used in 6G and beyond wireless technologies.

Digital Twins for Next-Generation Mobile Networks: Applications and Solutions

Digital Twins (DTs) create fully-synchronized virtual representations of real-world systems, which can serve as interactive counterparts for artificial intelligence (AI) and machine learning (ML) algorithms, and hold significant importance for the upcoming 6G mobile networks. In this paper, we argue that DTs can improve all phases of the intelligent networks' workflow, due to their adaptability and scalability properties that would allow them to transparently integrate new AI/ML algorithms faster, more scalably, and more precisely. Our contribution is two-fold: first, we propose three specific application scenarios of DT-enhanced network architectures in the context of 6G. Second, using open-source tools, we implement and evaluate in detail one of them. Our results demonstrate that our DT reflects the characteristics of the physical object, successfully and scalably twinning it, and adapting to changing contextual conditions.

Energy-Efficient Hierarchical Resource Allocation in Uplink–Downlink Decoupled NOMA HetNets

The dense deployment of small cell networks is a key feature of next-generation mobile networks aimed at providing the necessary capacity increase. It is noteworthy that small cell networks employ high-capacity backhaul links on millimeter-wave bands to develop multi-hop topologies in order to mitigate data transmission costs. The current static backhaul infrastructures cannot control severe fluctuating network traffic. To resolve this problem, this paper proposed a novel adaptive backhaul topology with the ability to adapt to different traffic patterns. Based on the graph theory, the adaptive system dynamically allows changes to the hybrid millimeter-wave backhaul architecture, and it also provides the possibility of effective channel allocation to each backhaul link to meet capacity and QoS demands. Also, regarding the importance of green networking in integrated-access-and-backhaul networks we proposed a dynamic optimization model which minimizes the overall energy consumption of UL/DL Decoupled NOMA heterogeneous networks in addition to providing the essential coverage and capacity. The proposed model optimizes user association/power utilization and presents an effective modular and scalable framework for analytical technology-oriented modeling of integrated multi-hop backhauls. The numerical results proved that the joint power optimization and hybrid backhaul architecture can increase the total network throughput by 18 percent compared to the current optimized static architectures. It can also reduce the energy consumption level by 30 percent, and enhance users’ quality satisfaction by 24.5 percent with respect to user distribution patterns.

Assessing the Spectrum Needs for Network-Wide Ultra-Reliable Communication With Meta Distributions

Identifying and assessing the spectrum needs for ultra-reliable communication are key to enabling ultra-reliable communication services in the next generation of mobile networks. This article proposes a framework to assess the spectrum required to support ultra-reliable communication services network-wide. Using meta distributions, we derive expressions that reveal a simple relationship between spectrum needs and reliability requirements. Broadly speaking, each “nine” in reliability (e.g., five-nines in 99.999%) maps onto one order of magnitude of additional bandwidth. Numerical evaluations indicate that the bandwidth required for ultra-reliable communication can be up to hundreds of gigahertz, beyond what is available in today’s networks.

Cross-Tier Interference Mitigation for RIS-Assisted Heterogeneous Networks

With the development of the next generation of mobile networks, new research challenges have emerged, and new technologies have been proposed to address them. On the other hand, reconfigurable intelligent surface (RIS) technology is being investigated for partially controlling wireless channels. RIS is a promising technology for improving signal quality by controlling the scattering of electromagnetic waves in a nearly passive manner. Heterogeneous networks (HetNets) are another promising technology that is designed to meet the capacity requirements of the network. RIS technology can be used to improve system performance in the context of HetNets. This study investigates the applications of reconfigurable intelligent surfaces (RISs) in heterogeneous downlink networks (HetNets). Due to the network densification, the small cell base station (SBS) interferes with the macrocell users (MUEs). In this paper, we utilise RIS to mitigate cross-tier interference in a HetNet via directional beamforming by adjusting the phase shift of the RIS. We consider RIS-assisted heterogeneous networks consisting of multiple SBS nodes and MUEs that utilise both direct paths and reflected paths. Therefore, the aim of this study is to maximise the sum rate of all MUEs by jointly optimising the transmit beamforming of the macrocell base station (MBS) and the phase shift of the RIS. An efficient RIS reflecting coefficient-based optimisation (RCO) is proposed based on a successive convex approximation approach. Simulation results are provided to show the effectiveness of the proposed scheme in terms of its sum rate in comparison with the scheme HetNet without RIS and the scheme HetNet with RIS but with random phase shifts.

Fully-Decoupled Radio Access Networks: A Flexible Downlink Multi-Connectivity and Dynamic Resource Cooperation Framework

To enable flexible base stations (BS) association and dynamic resource management for personalized user equipment (UE) download service provision in the next-generation mobile communication network (6G), in this paper, we investigate the downlink (DL) transmission scenario in an origin fully-decoupled radio access network (FD-RAN) architecture. Considering the unique fully-decoupled UL/DL access feature, we propose an efficient two-stage DL channel estimation method in the FD-RAN. We formulate a novel multi-connectivity and dynamic resource cooperation problem with joint multiple-BS and multiple-UE association and coordinated beamforming, aiming at maximizing the weighted sum achievable rate in DL FD-RAN. By leveraging the many-to-many swap-matching theory and fractional relaxation approach, we solve the dynamic UE scheduling problem with multiple-BS and multiple-UE association and the coordinated beamforming problem, respectively. Extensive simulation results based on standard 3GPP 36.873 urban micro channel demonstrate that the proposed framework can improve the average spectral efficiency by 34.9% as compared to the traditional maximum ratio transmission beamforming method.