Next-generation Networks Research Articles

Going beyond RF: A survey on how AI-enabled multimodal beamforming will shape the NextG standard

Incorporating artificial intelligence and machine learning (AI/ML) methods within the 5G wireless standard promises autonomous network behavior and ultra-low-latency reconfiguration. However, the effort so far has purely focused on learning from radio frequency (RF) signals. Future standards and next-generation (nextG) networks beyond 5G will have two significant evolution over the state-of-the-art 5G implementations: (i) massive number of antenna elements, scaling up to hundreds-to-thousands in number, and (ii) inclusion of AI/ML in the critical path of the network reconfiguration process that can access sensor feeds from a variety of RF and non-RF sources. While the former allows unprecedented flexibility in ‘beamforming’, where signals combine constructively at a target receiver, the latter enables the network with enhanced situation awareness not captured by a single and isolated data modality. This survey presents a thorough analysis of the different approaches used for beamforming today, focusing on mmWave bands, and then proceeds to make a compelling case for considering non-RF sensor data from multiple modalities, such as LiDAR, Radar, and GPS for increasing beamforming directional accuracy and reducing processing time. This so called idea of multimodal beamforming will require deep learning based fusion techniques, which will serve to augment the current RF-only and classical signal processing methods that do not scale well for massive antenna arrays. The survey describes relevant deep learning architectures for multimodal beamforming, identifies computational challenges and the role of edge computing in this process, dataset generation tools, and finally, lists open research challenges that the community should tackle to realize this transformative vision of the future of beamforming.

A comprehensive survey on authentication and secure key management in internet of things: Challenges, countermeasures, and future directions

Critical infrastructures such as healthcare, intelligent transportation, and home automation adapt billions of smart IoT devices to handle real-time data effectively in the digital era. The latest development in sensory and communication technologies collaborate directly with modern applications to bridge networking services without human intervention. Most information systems enable the extension of digital technology to demonstrate the potential use of next-generation network services and management. However, recent advancements open various opportunities, including repudiation, data tampering, and security threats in digital security and privacy. Thus, this survey article chooses a systematic literature review to present a comprehensive survey on authentication and key management in IoT. To discuss the major issues involved in IoT environments, this survey categorizes the work progress into four modules: 1. Discuss the broad aspects of convergence technologies, including healthcare, smart farming, intelligent transportation, etc., to address challenges such as security and privacy in IoT; 2. Conduct a comprehensive review of state-of-the-art technologies to identify security requirements, services, and challenges associated with modern IoT applications; 3. Present a systematic approach to review various key agreement schemes based on network models and performance analysis to examine potential vulnerabilities. 4. Show a thematic analysis to determine suitable corrective measurements dealing with various security and privacy issues. Finally, this paper rationalizes significant progress, including research challenges and future directions in IoT to promote new insights.

Performance optimization of conventional RoF link by considering the effect of RF source linewidth and photonic source linewidth for the next-generation networks

Performance optimization of conventional RoF link by considering the effect of RF source linewidth and photonic source linewidth for the next-generation networks

End-to-End Delay Bound Analysis for VR and Industrial IoE Traffic Flows under Different Scheduling Policies in a 6G Network

Next-generation networks are expected to handle a wide variety of internet of everything (IoE) services, notably including virtual reality (VR) for smart industrial-oriented applications. VR for industrial environments subtends strict quality of service constraints, requiring sixth-generation terahertz communications to be satisfied. In such an environment, an additional important issue is trying to get high utilization of network and computing resources. This implies identifying efficient access techniques and methodologies to increase bandwidth utilization and enable flows related to services, with different service requirements, to coexist on the same computation node. Towards this goal, this paper addresses the problem of coexistence of the traffic flows related to different services with given delay requirements, on the same computation node arranged to execute flow processing. In such a context, a theoretical comprehensive performance analysis, to the best of the authors’ knowledge, is still missing in the literature. As a consequence, this lack strongly limits the possibility of fully capturing the performance advantages of computation node sharing among different traffic flows, i.e., services. The proposed analysis aims to give a measure of the ability of the system in accomplishing services before the expiration of corresponding deadlines. The integration of martingale bounds within the stochastic network calculus tool is provided, assuming both the first-in–first-out and the earliest deadline first scheduling policies. Finally, the validity of the analysis proposed is confirmed by the tightness emerging from the comparison between analytical predictions and simulation results.

Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks

The cooperative aerial and device-to-device (D2D) networks employing non-orthogonal multiple access (NOMA) are expected to play an essential role in next-generation wireless networks. Moreover, machine learning (ML) techniques, such as artificial neural networks (ANN), can significantly enhance network performance and efficiency in fifth-generation (5G) wireless networks and beyond. This paper studies an ANN-based unmanned aerial vehicle (UAV) placement scheme to enhance an integrated UAV-D2D NOMA cooperative network.The proposed placement scheme selection (PSS) method for integrating the UAV into the cooperative network combines supervised and unsupervised ML techniques. Specifically, a supervised classification approach is employed utilizing a two-hidden layered ANN with 63 neurons evenly distributed among the layers. The output class of the ANN is utilized to determine the appropriate unsupervised learning method-either k-means or k-medoids-to be employed. This specific ANN layout has been observed to exhibit an accuracy of 94.12%, the highest accuracy among the ANN models evaluated, making it highly recommended for accurate PSS predictions in urban locations. Furthermore, the proposed cooperative scheme allows pairs of users to be simultaneously served through NOMA from the UAV, which acts as an aerial base station. At the same time, the D2D cooperative transmission for each NOMA pair is activated to improve the overall communication quality. Comparisons with conventional orthogonal multiple access (OMA) and alternative unsupervised machine-learning based-UAV-D2D NOMA cooperative networks show that significant sum rate and spectral efficiency gains can be harvested through the proposed method under varying D2D bandwidth allocations.

Performance Analysis of GFDM System in 5G Cellular Networks

Orthogonal Frequency Division Multiplexing (OFDM) is employed in the current 4G cellular technologies. Because of its resistance to multipath propagation channels, OFDM is a popular multicarrier modulation technique. Although OFDM is one of the effective approaches for a 4G network, out-of-band radiation, inter-symbol interference (ISI), and inter-carrier interference make this method unsuitable for 5G. (ICI). A new approach or next-generation cellular network, i.e., very high data rate, low latency, greater throughput, more device connectivity, and improved device-to-device communication, should be used to deliver effective and dependable communication. The system introduces generalized frequency division multiplexing (GFDM). In GFDM, a single cyclic prefix is utilized throughout the frame and an adjustable pulse-shaping filter can be used to monitor the transmitted signal’s out-of-band emission. Using this ISI, the multipath channel can be controlled. In the proposed system, with large signal constellations, higher-order modulation schemes, and advanced receiver structure, a very high data rate can be achieved. By using various iterative receiver structures the performance of the GFDM system in 5G cellular networks can be analyzed.

Employment of Self-Adaptive Bayesian Neural Network for Systematic Antenna Design: Improving Wireless Networks Functionalities

The performance of wireless networks is related to the optimized structure of the antenna. Therefore, in this paper, a Machine Learning (ML)-assisted new methodology named Self-Adaptive Bayesian Neural Network (SABNN) is proposed, aiming to optimize the antenna pattern for next-generation wireless networks. In addition, the statistical analysis for the presented SABNN is evaluated in this paper and compared with the current Gaussian Process (GP). The training cost and convergence speed are also discussed in this paper. In the final stage, the proposed model's measured results are demonstrated, showing that the system has optimized outcomes with less calculation time.

Access Pricing and Investment in Next-Generation Networks under Duopolistic Competition

Access Pricing and Investment in Next-Generation Networks under Duopolistic Competition

An Architecture for Provisioning In-Network Computing-Enabled Slices for Holographic Applications in Next-Generation Networks

Applications such as holographic concerts are now emerging. However, their provisioning remains highly challenging. Requirements such as high bandwidth and ultra-low latency are still very challenging for the current network infrastructure. In-network computing (INC) is an emerging paradigm that enables the distribution of computing tasks across the network instead of computing on servers outside the network. It aims at tackling these two challenges. This article advocates the use of the INC paradigm to tackle holographic applications' high bandwidth and low latency challenges instead of the edge computing paradigm that has been used so far. Slicing brings flexibility to next-generation networks by enabling the deployment of applications/verticals with different requirements on the same network infrastructure. We propose an architecture that enables the provisioning of INC-enabled slices for holographic-type application deployment. The architecture is validated through a proof of concept and extensive simulations. Our experimental results show that INC significantly outperforms edge computing when it comes to these two key challenges. In addition, low jitter was maintained to preserve the hologram's stability.

On the Relationship Between Speed and Mobility Sampling Frequency in Dynamic Urban Networks

In the current era of mobile communications and next-generation networks, mobility analysis has a key role in guaranteeing the quality of service/experience in the available services. Although a vast amount of work has analyzed mobility from both analytic and stochastic points of view, much of it has focused on a time-based analysis and disregarded spectral features. In this article, we propose a method of analyzing the main features of mobility traces in the frequency domain and determining the possible relationships between typical mobility grades (in terms of average and maximum speed) and the required sampling frequencies. The collection and storage of mobility pattern samples when they are not required is impractical, and therefore, we attempt to demonstrate how mobility can be sampled to avoid information loss or oversampling (many works in the literature are based on a default sampling period of 1 s). The work also contributes with the proposal of a closed form for relating the sampling period and average moving speed with the spectral components. We conducted numerous simulations to confirm that, compared with classical sampling approaches that provide static behavior, it is possible to obtain a gain of about 35%–65% in the collected samples, with a negligible loss of accuracy in the reconstructed signal.

Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless Networks

Deep reinforcement learning (DRL) algorithms have recently gained wide attention in the wireless networks domain. They are considered promising approaches for solving dynamic radio resource management (RRM) problems in next-generation networks. Given their capabilities to build an approximate and continuously updated model of the wireless network environments, DRL algorithms can deal with the multifaceted complexity of such environments. Nevertheless, several challenges hinder the practical adoption of DRL in commercial networks. In this article, we first discuss two key practical challenges that are faced but rarely tackled when developing DRL-based RRM solutions. We argue that it is inevitable to address these DRL-related challenges for DRL to find its way to RRM commercial solutions. In particular, we discuss the need to have safe and accelerated DRL-based RRM solutions that mitigate the slow convergence and performance instability exhibited by DRL algorithms. We then review and categorize the main approaches used in the RRM domain to develop safe and accelerated DRL-based solutions. Finally, a case study is conducted to demonstrate the importance of having safe and accelerated DRL-based RRM solutions. We employ multiple variants of transfer learning (TL) techniques to accelerate the convergence of intelligent radio access network (RAN) slicing DRL-based controllers. We also propose a hybrid TL-based approach and sigmoid function-based rewards as examples of safe exploration in DRL-based RAN slicing.

Analysis of Adversarial Jamming From a Quantum Game Theoretic Perspective

Over recent years, quantum communication systems have demarcated themselves as promising candidates for deployment in next-generation communication networks (6G and beyond). Several recent experimental demonstrations of such complex systems have been highly successful and have been instrumental in transitioning this field from the theoretical to the practical domain. In this article, we investigate the application of quantum game theory for the modeling and analysis of jamming in the context of quantum networks. We begin with a general model of jamming based on the Colonel Blotto game and generalize it to the context of quantum networks. We provide an in-depth analysis of the two-person quantum Colonel Blotto game (QCBG) in relation to classical versus classical, classical versus quantum, and quantum versus quantum strategies. We also investigate the Nash equilibria for such games via a multiagent adversarial reinforcement learning-based system. Finally, we discuss further optimizations on this model and outline several open problems for further research along these lines.

Research of In-Vehicle Ethernet Information Fault Detection Based on IEEE 802.1Qci

With features such as the high data transfer rate of 1G/bps and strong compatibility, in-vehicle Ethernet can become the backbone network of next-generation in-vehicle bus network systems. Currently, in-vehicle Ethernet is mainly used in audio and video entertainment systems, to further improve the network security performance of in-vehicle Ethernet, the information security of the data layer and physical layer in the in-vehicle bus network system must be ensured. Based on the characteristics of in-vehicle Ethernet data frame structure, this paper introduces IEEE 802.1 Qci protocol, proposes an information fault detection method through data shifting, detection, and policing, and verifies it by simulation with OMNET++ simulation system. The experimental results show that the accuracy rate of in-vehicle Ethernet data during transmission can reach 99.61%, which further improves the network security performance of in-vehicle Ethernet.

Predictive Quality of Service in Cellular Networks: Challenges, Framework, and Application in Vehicular Communications

Predictive quality of service will allow next-generation cellular networks to improve the management of the existing services and support more demanding use cases. In this article, we discuss the main challenges on the way to practical realizations of predictive quality of service, from fundamental considerations related to prediction horizons and handling uncertainty to practical aspects on data collection and implications of the architectural choice. We also present a general methodology for predictive quality of service that addresses some of the main challenges and may be used to realize predictive quality of service at scale. As a case study, we apply the framework to a vehicular communications setting using a data-set acquired from a deployed private LTE network.

Curriculum Learning for Goal-Oriented Semantic Communications With a Common Language

Goal-oriented semantic communication will be a pillar of next-generation wireless networks. Despite significant recent efforts in this area, most prior works are focused on specific data types (e.g., image or audio), and they ignore the goal and effectiveness aspects of semantic transmissions. In contrast, in this paper, a holistic goal-oriented semantic communication framework is proposed to enable a speaker and a listener to cooperatively execute a set of sequential tasks in a dynamic environment. A common language based on a hierarchical belief set is proposed to enable semantic communications between speaker and listener. The speaker, acting as an observer of the environment, utilizes the beliefs to transmit an initial description of its observation (called event) to the listener. The listener is then able to infer on the transmitted description and complete it by adding related beliefs to the transmitted beliefs of the speaker. As such, the listener reconstructs the observed event based on the completed description, and it then takes appropriate action in the environment based on the reconstructed event. An optimization problem is defined to determine the perfect and abstract description of the events while minimizing the various communication costs with constraints on the task execution time and belief efficiency. Then, a novel bottom-up curriculum learning (CL) framework based on reinforcement learning is proposed to solve the optimization problem and enable the speaker and listener to gradually identify the structure of the belief set and the perfect and abstract description of the events. Simulation results show that the proposed CL method outperforms classical RL and CL without inference scheme in terms of convergence time, task execution cost and time, reliability, and belief efficiency.

Multiaccess Point Coordination for Next-Gen Wi-Fi Networks Aided by Deep Reinforcement Learning

Wi-Fi in the enterprise - characterized by overlapping Wi-Fi cells - constitutes the design challenge for next-generation networks. Standardization for recently started IEEE 802.11be (Wi-Fi 7) Working Groups has focused on significant medium access control layer changes that emphasize the role of the access point (AP) in radio resource management (RRM) for coordinating channel access due to the high collision probability with the distributed coordination function (DCF), especially in dense overlapping Wi-Fi networks. This paper proposes a novel multi-AP coordination system architecture aided by a centralized AP controller (APC). Meanwhile, a deep reinforcement learning channel access (DLCA) protocol is developed to replace the binary exponential backoff mechanism in DCF to enhance the network throughput by enabling the coordination of APs. First-Order Model-Agnostic Meta-Learning further enhances the network throughput. Subsequently, we also put forward a new greedy algorithm to maintain proportional fairness (PF) among multiple APs. Via the simulation, the performance of DLCA protocol in dense overlapping Wi-Fi networks is verified to have strong stability and outperform baselines such as Shared Transmission Opportunity (SH-TXOP) and Request-to-Send/Clear-to-Send (RTS/CTS) in terms of the network throughput by 10% and 3% as well as the network utility considering proportional fairness by 28.3% and 13.8%, respectively.

BPPS:Blockchain-Enabled Privacy-Preserving Scheme for Demand-Response Management in Smart Grid Environments

With the ongoing revolutionary growth of the industrial Internet of Things and smart grid networks, smart grid (SG) communication has been acknowledged as a next-generation network for intelligent and efficient electric power transmission. In SG networks, smart meters (SMs) generally send requests for electricity demand to service providers (SPs), which deal with the requests for efficient energy distribution. However, SGs experience many security issues with the deployed SMs and untrusted wireless communication. To tackle these security issues, we propose a privacy-preserving authentication scheme for demand response management in SGs, called BPPS. It can resist various attacks and achieve secure mutual authentication with key agreement; moreover, it provides integrity of demand-response data using blockchain. Moreover, we perform the informal and formal (mathematical) security analysis to confirm that BPPS is secure against various attacks and achieves session key security, respectively. Furthermore, we conduct the performance and simulation analysis for SGs using NS3 and Ethereum testnet. Consequently, BPPS provides high-level security and can be applied to actual SG networks.

Deep next-generation proteomics and network analysis reveal systemic and tissue-specific patterns in Fabry disease

Fabry disease (FD) is an X-linked lysosomal rare disease due to a deficiency of α-galactosidase A activity. The accumulation of glycosphingolipids mainly affects the kidney, heart, and central nervous system, considerably reducing life expectancy. Although the accumulation of undegraded substrate is considered the primary cause of FD, it is established that secondary dysfunctions at the cellular, tissue, and organ levels ultimately give rise to the clinical phenotype. To parse this biological complexity, a large-scale deep plasma targeted proteomic profiling has been performed. We analyzed the plasma protein profiles of FD deeply phenotyped patients (n = 55) compared to controls (n = 30) using next-generation plasma proteomics including 1463 proteins. Systems biology and machine learning approaches have been used. The analysis enabled the identification of proteomic profiles that unambiguously separated FD patients from controls (615 differentially expressed proteins, 476 upregulated, and 139 downregulated) and 365 proteins are newly reported. We observed functional remodeling of several processes, such as cytokine-mediated pathways, extracellular matrix, and vacuolar/lysosomal proteome. Using network strategies, we probed patient-specific tissue metabolic remodeling and described a robust predictive consensus protein signature including 17 proteins CD200, SPINT1, CD34, FGFR2, GRN, ERBB4, AXL, ADAM15, PTPRM, IL13RA1, NBL1, NOTCH1, VASN, ROR1, AMBP, CCN3, and HAVCR2. Our findings highlight the pro-inflammatory cytokines' involvement in FD pathogenesis along with extracellular matrix remodeling. The study shows a tissue-wide metabolic remodeling connection to plasma proteomics in FD. These results will facilitate further studies to understand the molecular mechanisms in FD to pave the way for better diagnostics and therapeutics.

Enhancing the Security of Software Defined Mobile Networks (SDMN) based on Blockchain Technology

The blockchain involves future developments and new technologies. The emergence of a blockchain is a challenge for the conventional social organization and mode of activity. Data latency and mobile network capacity would no longer be a limitation for mobile users in next-generation networks of Software Defined Mobile Networks (SDMN). But there are many benefits to Software Defined Mobile Networking, it also contributes to certain security problems like DDoS / DoS attacks, unauthorized access, and single data point error. To enhance the security and privacy of the SDMN control plane, this paper proposes a new SDMN-based “Simplified Byzantine Fault Tolerance (SBFT)” algorithm to send signals between controllers and also set up an analysis study to investigate SBFT's security and results. However, there are many benefits of Software Defined Mobile Networking (SDMN), and then it helps to resolve other security concerns including DDoS / DoS attacks, unauthorized access, and single point failure. Blockchain, an evolving revolutionary technology, will offer creative approaches to address security issues in Software-Defined Mobile networks in an efficient way. This study proposes an SDMN-based Simplified Byzantine Fault Tolerance (SBFT) for enhancing the mobile network's security and privacy.

Quality of Service (QoS) Performance Analysis in a Traffic Engineering Model for Next-Generation Wireless Sensor Networks

Quality of Service (QoS) refers to techniques that function on a network to dependably execute high-priority applications and traffic reliably run high-priority applications and traffic even when the network’s capacity is limited. It is expected that data transmission over next-generation WSNs (Wireless Sensor Networks) 5G (5th generation) and beyond will increase significantly, especially for multimedia content such as video. Installing multiple IoT (Internet of Things refers to the network of devices that are all connected to each other) nodes on top of 5G networks makes the design more challenging. Maintaining a minimal level of service quality becomes more challenging as data volume and network density rise. QoS is critical in modern networks because it ensures critical performance metrics and improves end-user experience. Every client attempts to fulfill QoS access needs by selecting the optimal access device(s). Controllers will then identify optimum routes to meet clients’ core QoS needs in their core network. QoS-aware delivery is one of the most important aspects of wireless communications. Various models are proposed in the literature; however, an adaptive buffer size according to service type, priority, and incoming communication requests is required to ensure QoS-aware wireless communication. This article offers a hybrid end-to-end QoS delivery method involving customers and controllers and proposes a QoS-aware service delivery model for various types of communication with an adaptive buffer size according to the priority of the incoming service requests. For this purpose, this paper evaluates various QoS delivery models devised for service delivery in real time over IP networks. Multiple vulnerabilities are outlined that weaken QoS delivery in different models. Performance optimization is needed to ensure QoS delivery in next-generation WSN networks. This paper addresses the shortcomings of the existing service delivery models for real-time communication. An efficient queuing mechanism is adopted that assigns priorities based on input data type and queue length. This queuing mechanism ensures QoS efficiency in limited bandwidth networks and real-time traffic. The model reduces the over-provisioning of resources, delay, and packet loss ratio. The paper contributes a symmetrically-designed traffic engineering model for QoS-ensured service delivery for next-generation WSNs. A dynamic queuing mechanism that assigns priorities based on input data type and queue length is proposed to ensure QoS for wireless next-generation networks. The proposed queuing mechanism discusses topological symmetry to ensure QoS efficiency in limited bandwidth networks with real-time communication. The experimental results describe that the proposed model reduces the over-provisioning of resources, delay, and packet loss ratio.