Next-generation Networks Research Articles

Artificial Intelligence in 6G Wireless Networks: Opportunities, Applications, and Challenges

Wireless technologies are growing unprecedentedly with the advent and increasing popularity of wireless services worldwide. With the advancement in technology, profound techniques can potentially improve the performance of wireless networks. Besides, the advancement of artificial intelligence (AI) enables systems to make intelligent decisions, automation, data analysis, insights, predictive capabilities, learning, and adaptation. A sophisticated AI will be required for next-generation wireless networks to automate information delivery between smart applications simultaneously. AI technologies, such as machines and deep learning techniques, have attained tremendous success in many applications in recent years. Hances, researchers in academia and industry have turned their attention to the advanced development of AI-enabled wireless networks. This paper comprehensively surveys AI technologies for different wireless networks with various applications. Moreover, we present various AI-enabled applications that exploit the power of AI to enable the desired evolution of wireless networks. Besides, the challenges of unsolved research in this area, which represent the future research trends of AI-enabled wireless networks, are discussed in detail. We provide several suggestions and solutions that help wireless networks be more intelligent and sophisticated to handle complicated problems. In summary, this paper can help researchers deeply understand the up-to-the-minute wireless network designs based on AI technologies and identify interesting unsolved issues to be pursued in their research in a fast way.

Hybrid cell handover strategy for O-RAN-based campus networks

In recent years, the concept of Open Radio Access Network (O-RAN) has become a prominent and ongoing research area in mobile communications, as mobile operators strive to enhance the intelligence, efficiency, and vendor independence of their RAN (Radio Access Network) architectures and components. O-RAN aims to virtualize and improve RAN functions in software and therefore it supports virtualized RANs where disaggregated network components of multiple vendors are connected via open interfaces. O-RAN is a concept based on interoperability and standardization of RAN elements including a unified interconnection standard for white-box hardware and open-source software elements from different vendors. Architecture integrates a modular base station software stack on off-the-shelf hardware which allows baseband and radio unit components from different suppliers to operate seamlessly together. Analyzing and examining the O-RAN architecture in terms of various network scenarios and different network policies is crucial for next-generation networks. This study begins by discussing the O-RAN architecture and its advantages in detail. Subsequently, a campus network has been proposed for the analysis and simulation of the O-RAN architecture, and performance analyses have been performed within the scope of different scenarios and network policies. As a result of these analysis, a new hybrid algorithm for cell handovers in the O-RAN architecture has been developed and compared with traditional cell handover methods based on Reference Signal Received Power (RSRP) and Quality of Service (QoS). The results indicate that the proposed hybrid handover algorithm for O-RAN architecture yields better outcomes.

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.

ABRSL: AI based bilateral RAT selection framework for next-generation wireless networks

aBRSL: AI based bilateral RAT selection framework for next-generation wireless networks

Visual Communication Design Method in Folk Art Based on Multimedia Data Transmission Technology

With the rapid development of science and technology and China's economy, the internet, big data, computers, and multimedia technology are widely used in all walks of life to promote the application and improvement of visual communication design concepts, the gradual implementation of multimedia technology, and the continuous improvement of innovation. In this paper, from the multimedia transmission technology based on network coding, some key technologies in the network communication of data and video streams are studied in depth and, based on the transmission-quality assessment model, the working effect of the jitter-buffering algorithm and WebRTC jitter-buffering algorithm is compared in different network environments. The experimental results show that the jitter-buffering algorithm proposed in this paper has better working effect. This research is of great significance for the realization of multimedia transmission technology in next-generation networks.

Privacy-Preserving Serverless Edge Learning With Decentralized Small-Scale Mobile Data

In next-generation (i.e., 6G) networking systems, the data-driven approach will play an essential role, being an efficient tool for networking system management and bringing popular user applications. With those unprecedented and novel usages, existing frameworks fail to consider the complex nature of the next-generation networking system and consequently fail to be applied to future communication systems directly. Moreover, existing frameworks also fail to support popular privacy-preserving learning strategies efficiently by presenting special designs to respond to the resource-demanding nature of the aforementioned strategies. To fill this gap, this paper extends conventional serverless platforms with serverless edge learning architectures, providing a mature and efficient distributed training framework by fully exploiting limited wireless communication and edge computation resources in the considered networking system with the following three features. Firstly, this framework dynamically orchestrates resources among heterogeneous physical units to efficiently fulfill privacy-preserving learning objectives. The design jointly considers learning task requests and underlying infrastructure heterogeneity, including last-mile transmissions, computation abilities of edge and cloud computing centers, and loading status of infrastructure. Secondly, the proposed framework can easily work with data-driven approaches to improve network management efficiency, realizing AI for network promise of next-generation networking systems to provide efficient network automation. Lastly, to significantly reduce distributed training overheads, small-scale data training is proposed by integrating with a general, simple data classifier. This low-load enhancement can seamlessly work with various distributed deep models in the proposed framework to improve communications and computation efficiencies during the training phase. Based on the above innovations, open challenges, and future research directions encourage the research community to develop efficient privacy-preserving learning techniques.

DHT-based GFDM and STC-GFDM systems for beyond 5G wireless networks

This manuscript endeavours an exploration of Generalized Frequency Division Multiplexing (GFDM) as a promising alternative to the Orthogonal Frequency Division Multiplexing (OFDM) in next-generation cellular networks. GFDM’s exceptional performance in minimizing out-of-band emissions (OOBE) and maximizing spectral efficiency (SE) makes it a compelling candidate, especially when integrated with Multi-Input Multi-Output (MIMO) technology to enhance reliability through spatial diversity. However, ensuring GFDM’s resilience against frequency-selective channels (FSCs), especially in low-latency scenarios, is crucial.This research investigates Discrete Hartley Transform (DHT) based GFDM and Space-Time Coded GFDM (STC-GFDM) systems, proposing a comprehensive framework to enhance performance metrics such as Bit Error Rate (BER) and Achievable Rate (AR). Further enhancements are achieved through a Minimum BER-Power Allocation (MBER-PA) strategy, optimizing BER and AR in QAM-based GFDM and STC-GFDM systems employing a DHT precoding scheme. Analytical expressions for BER and AR are derived and validated via Monte Carlo simulations. Additionally, a comparative analysis against traditional GFDM systems is conducted, evaluating metrics including Peak-to-Average Power Ratio (PAPR), BER, and AR.

Handover algorithm based on Bayesian-optimized LSTM and multi-attribute decision making for heterogeneous networks

Heterogeneous networks (HetNets) which can satisfy the diverse requirements of future terminal tasks through providing different network access services for terminals, have emerged as a vital technology for the development of next-generation networks. However, frequent access to different networks often leads to high handover delay and handover failure, resulting in a degradation of user experience. Therefore, maintaining seamless network connectivity has become a challenging problem for applications with high real-time and reliability requirements in HetNets. To address this problem, we propose a handover algorithm based on Bayesian-optimized Long Short-Term Memory (BO-LSTM) and multiple attribute decision making (HO-BLM) to reduce handover latency and failure, ensuring seamless network connectivity. To reduce handover delay, we designed a handover decision prediction algorithm based on BO-LSTM to trigger handover in advance by predicting the network state of users in the future. Additionally, we propose a preselection mechanism based on current network status to further reduce the calculation delay. Moreover, a multi-attribute decision making (MADM)-based selection approach is designed to select the best alternative network for a specific user, where the utility value of the best alternative network is calculated by weighing the users’ preferences for network parameters of different business types and the actual status of the candidate networks. Simulation results show that the proposed algorithm decreases the handover failure by up to 42.1% and the handover delay by up to 68% compared with traditional handover approaches.

Securing Service Instantiation on Next-generation Networks

The Next Generation of Software Defined Networks (NG-SDN) enables a whole new set of possibilities for innovation in the data plane (e.g., computing, monitoring, load-balancing). In parallel, we have been witnessing the increasing adoption of virtualized services running in light but efficient containers, which brings forth the capability to flexibly deploy services at the network edge, while providing dynamic scaling and management. As a result, operators have tap in a lot of computational real-estate as part of the adoption of telco cloud concepts, as well as to support the foreseen edge and fog computing scenarios. However, exposing the infrastructure to third parties and realizing complex services over a multi-stakeholder environment raises challenges at different levels (e.g., security, reliability, SLA guarantees). Reconfiguring the data plane for flow monitoring and handling service requests directly on the data plane could address these challenges to support the joint communication and computation vision. This is where this paper contributes by proposing a secure and reliable system for dynamic instantiation of third-party services. A proof-of-concept prototype was developed using P4 and Kubernetes showcasing the feasibility of the proposed approach.

Enhancing the power amplifier performance of an optical-OTFS modulation for optical communication system

Abstract Future wireless networks extensively employ the two-dimensional Optical-Orthogonal Time Frequency Space (O-OTFS) modulation system due to its increased dependability, flexibility, and data throughput. When there is a lot of movement, OTFS offers improved connectivity performance. While OFDM functions in the time-frequency domain, O-OTFS modulation acts in the delay-Doppler domain. This study provides an overview of how OTFS functions in the delay-Doppler domain, as well as its benefits and drawbacks. Next-generation networks utilize this modulation method in various applications, such as vehicle-to-vehicle communication, communication in mountainous areas, high-speed mobility, and train communication. This paper provides an easy-to-understand overview of the PAPR algorithms and its reduction in O-OTFS modulation.

Intelligent Seamless Handover in Next-Generation Networks

Intelligent Seamless Handover in Next-Generation Networks

Performance comparison of Duobinary, AMI, CNRZ and CSRZ for next generation FSO communication system

Abstract FSO, known as free-space optics, is a competent technology in wireless communication for providing license free spectrum, bandwidth, and high efficiency in next-generation networks. In this paper, an experimental approach is used to deploy FSO channel communication using different intensity modulation schemes such as CSRZ, chirped NRZ, duobinary and AMI. The performance of FSO link is adversely affected by weather conditions like haze, rain, and fog. By using different techniques, it is possible to overcome the effect of atmospheric turbulences. Therefore, four different modulation formats are compared and analyzed by testing different parameters like laser power, range, attenuation, and data rates. Quality of received signal is judged by taking Q-factor, bit error rate (BER), and eye-diagrams. An operating wavelength of 850 nm has been used to simulate the system designs. This paper aims to conclude the best intensity modulation scheme for its use in next generation networks such as 5G where direct deployment of optical fiber is not possible or feasible.

Enhancing Programmability in Next-Generation Networks: An Innovative Simulation Approach

Enhancing Programmability in Next-Generation Networks: An Innovative Simulation Approach

Smart Resource Allocation in Mobile Cloud Next-Generation Network (NGN) Orchestration with Context-Aware Data and Machine Learning for the Cost Optimization of Microservice Applications

Mobile cloud computing (MCC) provides resources to users to handle smart mobile applications. In MCC, task scheduling is the solution for mobile users’ context-aware computation resource-rich applications. Most existing approaches have achieved a moderate service reliability rate due to a lack of instance-centric resource estimations and task offloading, a statistical NP-hard problem. The current intelligent scheduling process cannot address NP-hard problems due to traditional task offloading approaches. To address this problem, the authors design an efficient context-aware service offloading approach based on instance-centric measurements. The revised machine learning model/algorithm employs task adaptation to make decisions regarding task offloading. The proposed MCVS scheduling algorithm predicts the usage rates of individual microservices for a practical task scheduling scheme, considering mobile device time, cost, network, location, and central processing unit (CPU) power to train data. One notable feature of the microservice software architecture is its capacity to facilitate the scalability, flexibility, and independent deployment of individual components. A series of simulation results show the efficiency of the proposed technique based on offloading, CPU usage, and execution time metrics. The experimental results efficiently show the learning rate in training and testing in comparison with existing approaches, showing efficient training and task offloading phases. The proposed system has lower costs and uses less energy to offload microservices in MCC. Graphical results are presented to define the effectiveness of the proposed model. For a service arrival rate of 80%, the proposed model achieves an average 4.5% service offloading rate and 0.18% CPU usage rate compared with state-of-the-art approaches. The proposed method demonstrates efficiency in terms of cost and energy savings for microservice offloading in mobile cloud computing (MCC).

A Federated Learning-Based Resource Allocation Scheme for Relaying-Assisted Communications in Multicellular Next Generation Network Topologies

Growing and diverse user needs, along with the need for continuous access with minimal delay in densely populated machine-type networks, have led to a significant overhaul of modern mobile communication systems. Within this realm, the integration of advanced physical layer techniques such as relaying-assisted transmission in beyond fifth-generation (B5G) networks aims to not only enhance network performance but also extend coverage across multicellular orientations. However, in cellular environments, the increased interference levels and the complex channel representations introduce a notable rise in the computational complexity associated with radio resource management (RRM) tasks. Machine and deep learning (ML/DL) have been proposed as an efficient way to support the enhanced user demands in densely populated environments since ML/DL models can relax the traffic load that is associated with RRM tasks. There is, however, in these solutions the need for distributed execution of training tasks to accelerate the decision-making process in RRM tasks. For this purpose, federated learning (FL) schemes are considered a promising field of research for next-generation (NG) networks’ RRM. This paper proposes an FL approach to tackle the joint relay node (RN) selection and resource allocation problem subject to power management constraints when in B5G networks. The optimization objective of this approach is to jointly elevate energy (EE) and spectral efficiency (SE) levels. The performance of the proposed approach is evaluated for various relaying-assisted transmission topologies and through comparison with other state-of-the-art ones (both ML and non-ML). In particular, the total system energy efficiency (EE) and spectral efficiency (SE) can be improved by up to approximately 10–20% compared to a state-of-the-art centralized ML scheme. Moreover, achieved accuracy can be improved by up to 10% compared to state-of-the-art non-ML solutions, while training time is reduced by approximately 50%.

Dynamic Telemetry and Deep Neural Networks for Anomaly Detection in 6G Software-Defined Networks

With the increasing availability of computational power, contemporary machine learning has undergone a paradigm shift, placing a heightened emphasis on deep learning methodologies. The pervasive automation of various processes necessitates a critical re-evaluation of contemporary network implementations, specifically concerning security protocols and the imperative need for swift, precise responses to system failures. This article introduces a meticulously crafted solution designed explicitly for 6G software-defined networks (SDNs). The approach employs deep neural networks for anomaly detection within network traffic, contributing to a more robust security framework. Furthermore, the paper delves into the realm of network monitoring automation by harnessing dynamic telemetry, providing a specialized and forward-looking strategy to tackle the distinctive challenges inherent in SDN environments. In essence, our proposed solution aims to elevate the security and responsiveness of 6G mobile networks. By addressing the intricate challenges posed by next-generation network architectures, it seeks to fortify these networks against emerging threats and dynamically adapt to the evolving landscape of next-generation technology.

EDFA with power pump laser in WDM Network design of 10GBPS transmission

Wavelength Division Multiplexing (WDM) that enabled optical networks are currently widely used in current telecommunications infrastructures and are anticipated to play a significant role in next-generation networks and the future Internet, supporting a wide range of services with a wide range of bandwidth, latency, reliability, and other feature requirements. In WDM networks, the input power of an optical transmitter is crucial; as a result, this results in a good output signal at the receiver side. The purpose of this paper is to design a WDM Optical Network in terms of length and pump power. The system is simulated using Opti-System software to achieve bit error rate (BER), quality factor, and output power of EDFA through optimized fiber length and pump power. In this analysis, the BER value, Q-factor and output of the system have been evaluated between 1550 and 1552nm with 0 dBm input signal power. EDFA length of 5km, power pump value of 500mv and 980nm frequency was set as reference in this study. This system was analysed using Opti-System simulation software at 10 Gigabits per second (10GBPS) transmission system. Result have shown that, min BER value shows a very significant increase at a 9-meter length and the Q-factor was rapidly decrease. The min BER value was in range 10-11 and 10-19 and Q-factor range 6.5 to 9.1 for 200mW to 8000mW power pump.

NLOS Transmission Analysis for Mobile SLIPT Using Resonant Beam

Simultaneous lightwave information and power transfer (SLIPT) is a potential way to meet the demands of sustainable power supply and high-rate data transfer in next-generation networks. Although resonant beam-based SLIPT (RB-SLIPT) can realize high-power energy transfer, high-rate data transfer, human safety, and self-alignment simultaneously, mobile transmission channel (MTC) analysis under non-line-of-sight (NLOS) propagation has not been investigated. In this paper, we propose analytical models and simulation tools for reflector-assisted NLOS transmission of RB-SLIPT, where transmission loss and accurate beam field profile of NLOS MTC can be obtained with a receiver at arbitrary positions and attitude angles. We establish analytical models relying on full diffraction theory for beam propagation between tilted or off-axis planes. Then, we provide three numerical methods (i.e., NUFFT-based, cubic interpolation-based, and linear interpolation-based methods) in simulations. Moreover, to deal with the contradiction between limited computing memory and high sampling requirements for long-range transmission analysis, we propose a multi-hop sliding window approach, which can reduce the sampling number by a factor of thousands. Finally, numerical results demonstrate that RB-SLIPT can achieve 3W charging power and 10bit/s/Hz data rate over a 2m distance in NLOS scenarios.

Joint Beamforming Design in Reconfigurable Intelligent Surface-Assisted Rate Splitting Networks

Reconfigurable Intelligent Surface (RIS) is an emerging technology that can improve the spectrum and energy efficiency of next-generation wireless networks. However, attaining accurate channel state information (CSI) for the cascaded RIS channel is particularly challenging. Imperfect CSI is a major bottleneck to achieving the spectral efficiency benefit of RIS-assisted networks. Rate splitting (RS), a promising multiple access technology, has been shown to be able to achieve an improved spectrum efficiency and be robust to channel uncertainties. This paper investigates the interplay between RIS and RS by considering a RIS-assisted RS beamforming problem. Active beamforming at the base station (BS) and passive beamforming at the RIS are jointly considered to maximize the minimum user rate under both the perfect CSI case and the imperfect CSI case. A block coordinate descent (BCD) algorithm is developed to solve this non-convex problem. Compared with the conventional semi-definite relaxation (SDR) approach, the proposed method does not require that the covariance matrix of the common beamforming vector be rank-one. Moreover, we present theoretical results to help reveal the impact of the system parameters and explain the performance gain resulting from the integration of RIS and RS. Extensive simulation results are also provided to show that RIS-assisted RS can improve the max-min rate performance significantly compared with conventional multiple access technologies, such as non-orthogonal multiple access (NOMA) and space division multiple access (SDMA) with/without RIS, especially in overloaded systems. With the proposed method, RS shows great potential in combating realistic CSI errors in RIS-assisted networks.

Latest advances in VCSEL technology for next-generation data center network [Invited

Latest advances in VCSEL technology for next-generation data center network [Invited