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With the dawn of 5G network, a new set of requirements for site spectrum monitoring, cellular planning are emerging, all of which are relying on fine-grained signal map. Although with significant importance, the traditional signal map construction could be time-consuming and labor-intensive. The state-of-the arts usually employ crowdsourcing scheme and matrix completion algorithm to solve the dilemma. However, the crowdsourcing scheme usually suffers from uneven distributed and inadequate participants. To this end, in this paper, we study how to effectively reconstruct and update the signal map in the case of partially measured signal maps with smaller cost and propose a GAN-based active signal map reconstruction method (GSMAC). Our method is mainly innovative in two parts: GSMC, GAN-based signal map construction, and ACS, an active crowdsourcing scheme. Specifically, GSMC can effectively update the signal map with only a small number of observations while updating the signal map online. ACS consists of a reinforce learning-based active query mechanism which quantitatively evaluates the most valuable measurement site for reconstruction. The simulation results and real implemented data-driven experiments demonstrate the advantages and effectiveness of our approach in both accuracy and cost.

In recent years, mobile edge computing (MEC), as a key technology that facilitates the 5G mobile network, has raised a number of new challenges for app vendors, including the Edge User Allocation (EUA) problem. EUA aims to allocate as many app users as possible in an MEC system to minimum edge servers in the system. In non-orthogonal multiple access (NOMA)-based MEC system, multiple app users can be allocated to the same subchannel on an edge server through transmit power allocation based on their intra-cell and inter-cell interference. However, allocating excessive app users to the same subchannel may result in severe interference and consequently impact app users data rates. In addition, in an MEC system, app users join and depart randomly, and thus need to be allocated in an online manner. Existing EUA approaches suffer from poor performance in dynamic real-world NOMA-based MEC systems because they allocate app users in an offline manner and do not consider the complication caused by NOMA. In this paper, we propose OL-EUA, an online approach for solving dynamic EUA problem in NOMA-based MEC systems. Its performance is theoretically analyzed and experimentally evaluated against a baseline approach and two state-of-the-art approaches on a widely-used real-world dataset.

Mobile live video streaming is expected to become mainstream in the fifth generation (5G) mobile networks. To boost the Quality of Experience (QoE) of streaming services, the integration of Scalable Video Coding (SVC) with Mobile Edge Computing (MEC) becomes a natural candidate due to its scalability and the reliable transmission supports for real-time interactions. However, it still takes efforts to integrate MEC into video streaming services to exploit its full potentials. We find that the efficiency of the MEC-enabled cellular system can be significantly improved when the requests of users can be redirected to proper MEC servers through optimal user associations. In light of this observation, we jointly address the caching placement, video quality decision and user association problem in the live video streaming service. Since the proposed nonlinear integer optimization problem is NP-hard, we firstly develop a two-step approach from a Lagrangian optimization under the dual pricing specification. Further, to have a computation-efficient solution and less performance loss, we provide a one-step Lagrangian dual pricing algorithm by the convex transformation of non-convex constraints. The simulations show that the service quality of live video streaming can be remarkably enhanced by the proposed algorithms in the MEC-enabled cellular system.

The 5G network presents a new horizon with tremendous opportunities for future generation wireless networks. Mobile edge computing (MEC), non-orthogonal multiple access (NOMA), and network slicing (NS) are some of the key enablers for 5G. MEC reduces the latency to a great extent for a wireless network, while NOMA gives access to more users with resource constraints. NS provides users with a better quality of service and network operators with more flexibility. In this work, we propose an NS technique enabled with NOMA for a MEC network. The proposed NS technique improves service latency for MEC users and reduces the unnecessary allocation of radio resources in NOMA. The saved resources can be leveraged to accommodate more users, thus increasing the spectral efficiency of the network. We consider different types of services based on the task completion time of users in this work. The primary focus is to optimize the total energy consumption for wireless uplink transmission for the NOMA-enabled sliced MEC network. We also propose a heuristic algorithm as an alternative to reduce the time and computational complexities of the optimization algorithm and simulate the results extensively to show the effectiveness of our proposed algorithm.

5G and beyond wireless networks are the upcoming evolution for the current cellular networks to provide the essential requirement of future demands such as high data rate, low energy consumption, and low latency to provide seamless communication for the emerging applications. Heterogeneous cloud radio access network (H-CRAN) is envisioned as a new trend of 5G that uses the advantages of heterogeneous and cloud radio access networks to enhance both spectral and energy efficiency. In this paper, building on the notion of effective capacity (EC), we propose a framework in orthogonal frequency division multiple access (OFDMA)- non-orthogonal multiple access (NOMA) based H-CRAN to meet these demands simultaneously. Our proposed approach is to maximize the effective energy efficiency (EEE) while considering spectrum and power cooperation between a macro base station (MBS) and radio remote heads (RRHs). To solve the formulated problem and to make it more tractable, we transform the original problem into an equivalent subtractive form via Dinkelbach algorithm. Afterward, the combinational framework of distributed stable matching and successive convex algorithm (SCA) is then adopted to obtain the solution of the equivalent problem. Hereby, we propose an efficient resource allocation scheme to maximize energy efficiency while maintaining the delay quality of service (QoS) requirements for all users. The simulation results show that the proposed algorithm can provide a non-trivial trade-off between delay and energy efficiency in OFDMA-NOMA based H-CRAN systems in terms of EC and EEE and the spectrum and power cooperation improves EEE of the proposed network. Moreover, our proposed solution complexity is much lower than the optimal solution and it suffers a very limited gap compared to the optimal method.

The utilization of information from the radio channel is useful in detecting the spoofing attacksin 5G wireless communications. This concept has been used for a wide range of uses for the Internet of Things (IoT) environment by users and their IoT devices. But how these tasks can minimise the effects of cyber threats in genuinely complex networks has not yet been sufficiently addressed. These are seriously exposed, as is the case with 5G broadband networks due to a wide variety of technology at various abstract stages. As 5G IoT is the 5G environment, including the IoT background, in this article, we are an artificial intelligence (AI) intended to minimise the impact of 5G IoT threats, when extended to the participants involved on a number of levels.This paper uses Support Vector Machine (SVM) based PHY-layer authentication algorithm to detect the possible security attacks in 5G wireless communication at physical layer. It is utilized in increasing the rate of authentication with test features. The detection rate is improved further with test statistic features. The model is implemented on multiple-input multiple-output (MIMO) channel. The simulation results shows that the proposed method yield the high detection rate on all attacks.

Wireless Sensor Networks (WSNs) plays an indispensable role in different application and development in new generation technology. In 5G technology, one of the essential features is the connectivity with registered and unregistered networks to promote remote automation. By which the technology can be harnessed at its best and scaled in multi-dimensional applications. These applications are also enabled for diverse motion detection and object tracking applications. In this presented work, an underwater object tracking technique has been proposed, which enable the prediction of the mobile sensor node. The method involves a simulation which demonstrates a 2D-UASN architecture of underwater scenario and in this network using a different number of nodes using random positioning static sensor nodes are deployed. Finally, a mobile sensor node with random mobility is introduced into the network. The approach follows statistical analysis to compute the next possible position of the node. Based on experiments, the accuracy of the predictive methodology is measured in two different scenarios. In the first scenario, the number of nodes is variable, and the simulation area kept fixed size, i.e., 1000X1000. Additionally, in the second scenario, the number of nodes remains fixed, and the simulation area is variable. The results demonstrate that when we deploy sensor nodes randomly in a large area and network node density is less, the detection accuracy significantly affects and demonstrates low accuracy compared to dense area networks. Secondly, the prediction trend from dense to less dense area demonstrates the reducing accuracy.

Mobile edge computing (MEC) allows edge servers to be placed at cellular base stations. App vendors like Uber and YouTube can rent computing resources and deploy latency-sensitive applications on edge servers for their users to access. Non-orthogonal multiple access (NOMA) is an emerging technique that facilitates the massive connectivity of 5G networks, further enhancing the capability of MEC. The edge user allocation (EUA) problem faces new challenges in 5G NOMA-based MEC systems. In this study, we investigate the EUA problem in a multi-cell multi-channel downlink power-domain NOMA-based MEC system. The main objective is to help mobile app vendors maximize their benefit by allocating maximum users to edge servers in a specific area at the lowest computing resource and transmit power costs. To this end, we introduce a decentralized game-theoretic approach to effectively select a channel and edge server for each user while fulfilling their resource and data rate requirements. We theoretically and experimentally evaluate our solution, which significantly outperforms various state-of-the-art and baseline approaches.

Web-based Deep Neural Networks (DNNs) enhance the ability of object recognition and has attracted considerable attention in mobile Web AR and other services. However, neither performing the DNN inference on mobile Web browsers locally nor offloading computations to the cloud can strike a balance between accuracy and efficiency; generally, rude methods are often accompanied by unsatisfactory accuracy. Collaborative approaches seem to fill this gap by coordinating the distributed hierarchical computing resources, especially in the 5G era, but it still faces challenges in the current solutions, such as the lack of (1) full use of 5G resources for the one point DNN computation partitioning schemes; (2) fine-grained branching mechanism; (3) efficient partitioning method; and (4) multi-objective optimization. To this end, we present the fine-grained elastic computation partitioning mechanism for distributed DNN in 5G networks. First, we elaborate two collaborative scenarios. Second, we study the DNN branching mechanism at layer granularity. Next, we propose a DNN computation partitioning algorithm based on deep reinforcement learning. Finally, we develop a mobile Web AR application as a proof of concept. The experiments were conducted in an actually deployed 5G trial network, and the results show the superiority of this collaborative approach.

Fog computing is a promising option for time sensitive vehicular over-the-air (OTA) updates, as it can offer enhanced network durability and lower communication delays, versus the cloud. Fog node utilization is non-deterministic, largely owing to vehicular traffic patterns. The resultant over provisioning of resources manifests itself in increased communication and handover delays. Based on an analysis of regional traffic patterns, our proposed algorithm determines the optimal number of fog nodes required for OTA updates. The efficacy of our proposed approach is demonstrated using a case study that considers handover delay, propagation delay, transmission rate and vehicular mobility to predict the OTA update time. We employ a machine learning model for predicting the communication delay between fog devices and vehicles. Using European WiFi hotspot signal strength NYC dataset and 5G dataset, we observe that the proposed approach increases net reserve fog resources by 26.57% on average, and reduces the OTA update time by 5.34%. We test the scalability of our approach by analyzing the performance in terms of average throughput while varying the number of vehicles and OTA update size. The performance of the proposed OTA update scheme on simulations has been corroborated by implementation on a real-world testbed.

Vehicular ad-hoc network (VANET) is an emerging aspect of the 5G vertical application. Network function virtualization (NFV) is the key enabling technology of 5G and beyond 5G (B5G) networks. In NFV-enabled vehicular and 5G networks, all underlying nodes (e.g. vehicular, edge, core) can be completely virtualized and easy to be managed and allocated. Network service providers can implement each dynamically requested virtual network service (VNS), having arbitrary topology and customized resource demands, on top of the NFV-enabled networks. However, network elements (e.g. nodes and links) may come into failures accidentally. Consequently, it will lead to the performance degradation of implemented VNSs that run on top of the failed network elements. It is vital to guarantee the survivable services even though the network elements fail accidentally. Therefore, we propose the dynamic virtual resource allocation mechanism in this paper. Firstly, we introduce the business model and formulate the dynamic virtual resource allocation in NFV-enabled networks. Secondly, we detail all modules of our proposed mechanism. Especially, the initial resource allocation and re-allocation modules of achieving the survivable network services are detailed. Finally, we execute the comprehensive simulations by comparing with the typical virtual resource allocation mechanisms. The simulation results are discussed so as to highlight the merits of the proposed mechanism.

Recently, decentralization has been extensively explored by researchers. Blockchain, as a representation of decentralized technology, has attracted attention with its unique characteristics, such as irrevocability and security. Consequently, herein, we introduce cutting-edge blockchain technologies from four directions: blockchain system, consensus algorithms, smart contract, and scalability. Subsequently, we analyze the current lack of consensus mechanism, fault tolerance, and block capacity of the blockchain, and the integration of blockchain into 5G/6G mobile communication. Furthermore, we discuss the possible applications of blockchain in intellectual property protection, the Internet of Things, digital twins, standardization, and epidemic prevention and control. Finally, explore the impacts and solutions of blockchain on human society beyond technology.

Multi-user mobile Augmented Reality (AR) has been successfully used in various fields as a novel visual interaction technology. But current mainstream wearable device-based and app-based solutions are still facing cross-platform, real-time communication, and intensive computing requirements. Mobile Web technology is envisioned to be a promising supporting technology for cross-platform application of mobile AR especially in 5G networks, which provide pervasive communication and computing resources thereby forming a formidable framework for the practical application of multi-user mobile Web AR. However, the problem of how to use these new techniques properly to achieve efficient communication and computing collaboration is obviously paramount in order for multi-user mobile Web AR to be realized in 5G networks. In this article, we propose the first edge-assisted multi-user collaborative framework for mobile Web AR. Firstly, we propose a heuristic mechanism for efficient communication planning, which allows multi-user interaction synchronization to be achieved. Secondly, we introduce a motion-aware key frame selection mechanism to optimize the computational efficiency of the edge system, and simultaneously alleviate the initialization problem by collaborating with nearby mobile devices using the Device-to-Device communication technique. Experiments are conducted in a real-world 5G network, and the results demonstrate the superiority of our proposed collaborative framework.

Providing high-speed Internet services using satellite networks have attracted researchers from both academia and industry mainly due to the characteristics of Low Earth Orbit (LEO) satellite networks such as global coverage, scalability, and lower transmission delays. Implementation of Software-Defined Networking (SDN) in Non-Terrestrial Networks (NTNs) can help to achieve the goals set for 5G and beyond networks. Since satellite networks have a specific architecture, some of the legacy protocols no longer remain useful. Therefore, to satisfy the diverse Quality of Service (QoS) requirements for a variety of applications, we propose a novel and centralized QoS-aware routing algorithm, called fybrrLink in which the global view of the network in SDN is utilized. We implement a modified Bresenham’s algorithm and Dijkstra’s algorithm to find the optimal path in a significantly reduced computation time. Also, taking advantage of the deterministic satellite constellation, we propose a flow rule transfer algorithm and a topology monitoring algorithm. Further, fybrrLink is evaluated with multiple NS3 simulations, and results confirm that our approach outperforms other state-of-the-art algorithms.

5G communications system is on the evolution path with present focus on enabling advanced features and new service capabilities. Multicast and Broadcast Services (MBS) are being considered as one of the most promising use cases of 5G. As a late entrant to 3rd Generation Partnership Project (3GPP) 5G standards, MBS work item is presently being developed over existing 5G framework, and is targeted to enhance 5G New Radio and 5G Core Network capabilities for a reliable, low latency, resource efficient, and massive deployment of a wide array of multicast and broadcast services. MBS development entails enhancements for network and user equipment side on architectural, protocol and transmission aspects in order to address a new set of multicast and broadcast service requirements in 5G. In this article, we present an introduction to MBS standardization and outline newly introduced technical features, and their use cases.

To achieve higher throughputs in cellular networks, 3GPP proposes to use unlicensed frequency bands and develops technologies - the latest one is NR-U - allowing a cellular base station to transmit in unlicensed bands, which are already occupied by Wi-Fi networks. To enable fair channel sharing between two technologies, the base station uses a sort of CSMA/CA with binary exponential backoff similar to Wi-Fi. However, the base station can start data transmission only at strictly periodic time moments. Many papers propose sending a reservation signal between the end of the backoff procedure and such a moment to prevent nearby devices from accessing the channel. However, this approach significantly reduces Wi-Fi performance. The paper proposes a novel method CR-LBT of transmitting a reservation signal that greatly decreases channel resource waste caused by collisions and improves channel resource sharing fairness. With developed analytical models and simulations, it is shown that CR-LBT may simultaneously increase the throughput of both NR-U and Wi-Fi networks. The effect is more noticeable for the Wi-Fi network, the throughput of which may rise three times compared with the traditional method of sending the reservation signal. Finally, the influence of various factors on CR-LBT performance is studied.

With the evolution of wireless technologies, 5G and Beyond (5GB) communication is paving a way for efficient, ultra-reliable, low-latent, and high converging services for the Internet of Things (IoT). Along with efficient communication, the security of messages is one of the concerns which must be maintained throughout the operations. Backhaul forms an essential part of 5GB with an ability to enhance the coverage and quality of service for IoT. However, conventional wired backhaul connection would cost operators thousands of dollars in the construction of 5GB infrastructure considering the ultra-dense nature of IoT. As a result, wireless backhaul is quickly becoming a feasible alternative to address 5GB’s direction towards network densification without affecting its other provisions. Wireless backhaul is expected to increase the landscape, covering from islands to mountains, which were difficult to access in the existing network generation. Moreover, it can effectively respond to the situation where the data traffic tremendously increased. Despite such provisioning, the wireless backhaul poses relatively various security threats and vulnerabilities due to the characteristics of wireless technologies. Several studies have been conducted to address the security problems; however, existing protocols do not support dynamic security policy and key management in a decentralized structure as well as secure handover in a specific scenario where Terminals (TMs) are moving. Motivated by this, we proposed the Mobile Terminal Handover Security Protocol (MoTH) to provide secure handover of mobile terminals between hubs. To solve the problem of existing protocols, a new entity called Backhaul Management Function (BMF) is introduced to support distributed and dynamic security policy and key management in each serving network of 5GB backhaul environment. The proposed protocol satisfies security requirements including authentication and key management, confidentiality, integrity, and perfect forward secrecy. Additionally, it supports policy and key update services, and optimized handover. The security and correctness of the proposed protocol are thoroughly verified using the two formal security analysis tools, BAN logic and Scyther. Additionally, the performance evaluation shows that the proposed protocol is efficient.

The satellite component is recognized as a promising solution to complement and extend the coverage of future Internet of things (IoT) terrestrial networks (TNs). In this context, a study item to integrate satellites into narrowband-IoT (NB-IoT) systems has been approved within the 3rd Generation Partnership Project (3GPP) standardization body. However, as NB-IoT systems were initially conceived for TNs, their basic design principles and operation might require some key modifications when incorporating the satellite component. These changes in NB-IoT systems, therefore, need to be carefully implemented in order to guarantee a seamless integration of both TN and non-terrestrial network (NTN) for a global coverage. This paper addresses this adaptation for the random access (RA) step in NB-IoT systems, which is in fact the most challenging aspect in the NTN context, for it deals with multi-user time-frequency synchronization and timing advance for data scheduling. In particular, we propose an RA technique which is robust to typical satellite channel impairments, including long delays, significant Doppler effects, and wide beams, without requiring any modification to the current NB-IoT RA waveform. Performance evaluations demonstrate the proposal’s capability of addressing different NTN configurations recently defined by 3GPP for the 5G new radio system.

The sixth generation (6G) era is expected to provide even higher levels of massive connectivity/Internet of Things (IoT), tremendous data rate and low latency than the 5G communication. Therefore, the future 6G wireless networks would confront with much more severe spectrum scarcity problems, which makes the new radio in unlicensed spectrum (NR-U) technology attractive. Meanwhile, the high densely deployed massive IoT devices lead to fearful inter-and intra-cell interference issues. To address these challenges, a coordinated multi-point (CoMP) technology can be adopted. In this work, we introduce a CoMP based NR-U network, particularly for 6G enabled massive IoT scenarios. Under the proposed network, we introduce a spatial listen-before-talk (LBT) scheme to control mobile network operators (MNOs) to coexist with the incumbent WiFi devices orthogonally, which can significantly improve the spectrum efficiency. To suppress the strong co-channel interference among multiple MNOs, we investigate a joint beamforming coordination and user selection problem, which is NP-hard. To deal with this problem, we first adopt a fractional programming and an integer replacement method to transform the objective problem into a tractable one. Then, we introduce a semi-distributed alternating direction method of multipliers and block coordinate update (ADMM-BCU) algorithm to find a suboptimal solution, which only requires limited information exchange via a CoMP server and can help reduce energy consumption. Theoretical analysis and numerical results demonstrate the effectiveness of the proposed algorithm for large-scale 6G IoT scenarios.

One of the main application scenario that the upcoming 5G systems are expected to support is Internet of Things (IoT) traffic backhauling. With the exponential increase of devices, it follows the critical challenge of handling the huge data volume that they produce. Terrestrial Networks may not be sufficient for that, or not always available. Satellites then come into play, and they can provide the ubiquitous coverage targeted by 5G. On the other side, aggregation can be an efficient strategy to reduce the amount of traffic, and use network resources in an efficient manner. Building on that, in this work we consider an hybrid IoT-satellite network architecture for collecting MQTT traffic. We design an advanced filter namely MQTT-MFA that performs MQTT topics aggregation in close proximity of MQTT Publishers before data is sent over the satellite link. The developed MQTT-MFA implementation leverages on MQTT bridge, and it is compliant with Mosquitto, an open source MQTT broker. Extensive simulation results have demonstrated that MQTT-MFA requires only 10% of the TCP/IP overhead generated in a baseline architecture (where the bridge is not set up) and 1/4 number of bytes to transmit the same amount of information, when compression of the data payload is also enabled.

The emerging technologies, such as smart sensors, 5G/6G wireless communication, artificial intelligence, etc., have being maturing the future Internet of Things (IoT) by connecting massive number of devices, which are expected to consistently collect and transmit real-time data to support business intelligence in an efficient and privacy-preserving way. The IoT can afford businesses predictive maintenance, improve field service, asset tracking, and further enhance customer satisfaction and facility management in industrial sectors. However, the privacy concern in IoT is a big challenge in IoT applications and services. This work proposed a lightweight privacy-preserving scheme based on homomorphic encryption in the context of the IoT, in which we investigated and analysed the privacy issues between the data owners, untrustworthy third-part cloud servers, and the data users. Meanwhile, computationally-efficient homomorphic algorithms are proposed to guarantee the privacy protection for the data users. Experimental results demonstrates that the proposed scheme can effectively prevent privacy breaches in IoT.

Recent technical innovation has driven the evolution of autonomous vehicles. To improve safety as well as on-road vehicular experience, vehicles should be connected with each other or to vehicular networks. Some specification groups, e.g., IEEE and 3GPP, have studied and released vehicular communication requirements and architecture. IEEE's Wireless Access in Vehicular Environment focuses on dedicated and short-range communication, while 3GPP's New Radio V2X supports not only sidelink but also uplink communication. The 3GPP Release 16, which supports 5G New Radio, offers evolved functionalities such as network slice, Network Function Virtualization, and Software-Defined Networking. In this paper, we define and design a vehicular network architecture compliant with 5G core networks to enable and support autonomous driving. As a validation example, a high-definition map needs to contain the context of trajectory for localization and planning of autonomous driving vehicles. We also propose new methods by which autonomous vehicles can push and pull map content efficiently, without causing bottlenecks on the network core. We evaluate the performance of the proposed method via network simulations and our autonomous driving vehicle on the road. Experimental results indicate that the proposed method improves the performance of vehicular content delivery in real-world road environments.

In the past few years, the industry and academia have been working hard to establish and standardise vehicle-to-everything (V2X) communications, which is one of the vital emerging services for next generation wireless networks. Therewith, to control radio resource properly with balanced implementation complexity, a full-duplex V2V discovery mechanism in a 5G-V2X network based on a random backoff procedure is proposed to better utilize radio resources on which nearby vehicles establish their links. The main aims are to reduce complexity, latency, improve throughput and guarantee stability for V2V communication. The unemployed cellular network channel operates in full-duplex mode and leaves the channel as soon as they are informed that someone is in place through latency and throughput analysis technique. The channel sensing and identification are done in a cooperative manner before transmission. Furthermore, two effective resource distribution algorithms are proposed that grant the optimum resource distribution for V2V and V2I-Users. The detection and the throughput of full-duplex V2V communication in the proposed scheme have been formulated and the performance of the proposed scheme and validity of corresponding analysis are verified through simulation experiments.

Industry 5.0 is emerging as a result of the advancement in networking and communications technologies, Artificial Intelligent (AI), distributed computing, and Beyond 5G (B5G). Among the important enabling technologies, federated Learning, industrial edge computing, and Byzantine-tolerant Machine Learning (ML) are key accelerators in Industry 5.0. We propose a framework to integrate these key components. Recent works have designed various Byzantine-tolerant ML algorithms for a datacenter or a cluster. However, these algorithms are difficult to be applied to industrial edge computing paradigms. In this paper, a novel Byzantine-tolerant federated learning algorithm, CRACAU, is designed for the popular three-level edge computing architecture. In this algorithm, edge devices jointly learn an ML model using the data collected at each device, and their private data is never shared with others. Under standard assumptions, we formally prove that CRACAU converges to the optimal point, i.e., CRACAU finds the optimal parameters of the ML model. We also implement CRACAU in the MXNet framework and evaluate it on the popular benchmark MNIST and CIFAR-10 image classification datasets. Experimental results show that CRACAU achieves satisfying accuracy.

The benefit of real-time joint dynamic route guidance and signal control (DRG-SC) is usually compromised by a centralized framework since it naturally leads to an un-timely solution with the growing data-processing needs and problem-solving complexity. Mobile edge computing (MEC) pushes the data storage and computation from the remote cloud to local infrastructures and hence reduces response time and improves network bandwidth when further combined with 5G. As such, our study first develops a novel distributed framework to facilitate DRG-SC in connected vehicle (CV) environment with clarifying the MEC's vital role. The method captures the interaction of vehicles' routing and signal control, wherein we use a more realistic and accurate way to define the relationship between travel time and traffic volume. Vehicles make route decisions and cooperate to reach user optimal (UO) or system optimal (SO) traffic state. In tandem, the developed adaptive signal control (ASC) adjusts the signal timing plan with considering both the adjacent intersections' traffic volume and the vehicles' waiting time. Our method achieves significant reductions in vehicles' average departure delay, waiting time and travel time when justified by a comprehensive case study implemented in SUMO. Moreover, the effectiveness of adopting such a distributed framework in saving computation time is verified. Overall, our study provides valuable and practical insights into the intelligent operation and control.

The distribution of large-size data block in the Internet of Vehicles (IoV), especially in the urban IoV with dense vehicles, is still a challenge issue. Though the methods based on 5G-cellular network commonly can efficiently distribute the large-size file in IoVs, they also have obvious defects, such as occupying scarce 5G resources, generating communication fees and limited service coverage. This paper focuses on distributing large-size data block in IoVs based on dedicated vehicular ad hoc network to reduce the relying on cellular (5G/6G) resources. A heterogeneous vehicular network (HetVNET), in which a short-range OFDM wideband communication (SOWC) with ultra-high data rate and the original IEEE 802.11p protocol are included, is first proposed. To match with the proposed HetVNET, a content-centric data distribution scheme based on edge caching is designed. The content-centric data distribution architecture for distributing large-size file, the cache node selection and the process of data distribution and control based on the HetVNET are studied. The 5G/6G communication can be enabled in the scheme to further enhance the engineering stability of the massive infrastructure-to-vehicle (I2V) broadcast in IoVs. The evaluation results show that the proposed approach has low delivery delay and high penetration ratio.

Reconfigurable intelligent surface (RIS) has emerged as important transmission technology to improve the spectral/energy efficiency in the next-generation (beyond 5G (B5G) and 6G) wireless communication network and has numerous applications in the areas of Internet of Things (IoT) and vehicular communication systems. Thus, in this paper, we investigate the application of RIS in unmanned aerial vehicle (UAV) enabled vehicular communication system with infinite and finite block length codes, where UAV communicates with the single antenna ground vehicle in the presence of several interfering vehicles on the road. We have obtained the approximate closed-form statistics of received SINR at ground vehicle in the presence of multiple nonidentical interference links. Furthermore, we analyze the performance of the considered system in terms of the coverage probability, bit-error-rate, block error rate (BLER) and goodput. It has been shown through the numerical results that the deployment of RIS significantly improves the performance of UAV-enabled vehicular communication network, even in the presence of the direct link between the UAV and the ground vehicle. Additionally, we also investigate the impact of various channel and system parameters like practical reflection coefficients of RIS, number of RIS reflecting elements, and number of interfering vehicles on the system performance. The analytical results are corroborated with Monte Carlo simulations.

5G and subsequent cellular network generations aim to extend ubiquitous connectivity of billions of Internet-of-Things (IoT) for their consumers. Security is a prime concern in this context as adversaries have evolved to become smart and often employ new attack strategies. Network defenses can be enhanced against attacks by employing behavior models for devices to detect misbehavior. One example is Abusive Modeling (AM) that is inspired by financial technologies to defend adversaries operating with unlimited resources who have no intention of self-profit apart from harming the system. This article investigates behavior modeling against abusive adversaries in the context of 5G and beyond security functions for IoT. Security threats and countermeasures are discussed to understand AM. A complexitysecurity trade-off enables a better understanding of the limitations of state-based behavior modeling and paves the way as a future direction for developing more robust solutions against AM. Postprint

The 5G networks promise to use advanced access techniques and modulation formats to achieve new requirements of the huge number of users such as higher data, high speed, and low latency. Millimeter-wave (MMW) signal and new modulation schemes such as universal filtered multicarrier (UFMC) and filtered bank multicarrier (FBMC) are playing a fundamental role to accomplish these requirements. UFMC and FBMC are classified under multicarrier modulation formats which are considered as a suitable modulation scheme in 5G and replacing a well-known modulation format of filtered orthogonal frequency division multiplexing (OFDM) which was used in 4G. This interest in using UFMC and FBMC in 5G wireless systems is the reduction of out-of-band (OOB) spectrum in UFMC and reduction of sidelobe suppression in FBMC. The contribution of this paper is to propose a model of wavelength conversion using a semiconductor optical amplifier (SOA) for both schemes UFMC and FBMC- off-set quadrature amplitude (OQAM). In addition, we analyze and compare their performance in terms of bit error rate (BER) and error vector magnitude (EVM) for a wavelength conversion application. Finally, we investigate the main disadvantage of OFDM which is the high Peak to Average Power Ratio (PAPR) for both schemes, and compare between them. Photonic switching of a 4 Gbps 16-QAM UFMC and FBMC-OQAM signal centered at 50 GHz is performed. The optical single-sideband (OSSB) signal is generated with 18.85 dB of sideband suppression ratio (SSR). The results show that the best values of BER and EVM can be obtained when the injection current (IC) is 0.7–0.9A. For converted signal using FBMC-OQAM, it has power penalty ∼ 2.5 dB at BER threshold 1 × 10 -3 and has also ∼ 2 dB power penalty compared to the UFMC scheme at EVM threshold 5.6%. In addition, the wavelength switching system with FBMC and UFMC modulation schemes achieves the same PAPR.

The large amount of driving data can help intelligent vehicles make decisions to drive safely, improve vehicular services and enhance driving experience. In traditional vehicular networks, data sharing needs to be done with roadside units (RSUs). However, RSUs cannot be entirely trusted and the data stored in the RSUs may be tampered with. In addition, the deployment of RSUs along roads consumes a large amount of social resources. Further, data sharing between vehicles lacks a trusted environment, and vehicles may be unwilling to share data with others because of data security and privacy concerns. Moreover, in the event of unauthorized data sharing, the source of the leaked data is difficult to trace. In this study, we exploit consortium blockchain technology to achieve traceable and anonymous vehicle-to-vehicle (V2V) data sharing, effectively preventing second-hand sharing of data. The combination of 5G and blockchain makes it possible to share data without using RSUs. We design an enhanced delegated proof-of-stake consensus algorithm to make it more suitable for applications in the distributed Internet of Vehicles (IoV). A comprehensive analysis shows that the proposed scheme is secure and efficient.

Future telecommunication systems in beyond 5G/6G networks, will include a massive amount of devices and a high variation of applications, many of which with steep processing requirements and strict latency limitations. To satisfy such demands, Multi-access Edge Computing will play a key role in the future of cloud systems. Users can offload their applications to edge cloud servers, capable of processing their tasks and responding with an output quickly. However, for this to become a reality, it is important to carefully choose a server for each user. This decision is complicated by user mobility and how users could alternatively connect to the remote cloud or execute applications locally. In this paper, we propose a heuristic algorithm for determining the best server for each user in a multiple mobile users, multiple servers, multi-tiered scenario. Our proposal considers the time needed for transmitting and processing tasks when minimizing the total service delay as well as the time needed for service setup and migration of data between servers. Moreover, our method attempts to mimic as faithfully as possible real-life scenarios. Finally, analysis shows that our proposal is vastly superior to benchmark methods and even improves upon a solution commonly used in the literature.

This paper focuses on designing high-data-rate wireless communications for drone networks in the mmWave and THz frequency bands. MmWave/THz-band communications has been envisioned as a key technology to achieve ultra broadband wireless links through beamforming in 5G and beyond networks. However, a main challenge with these frequency bands is that the narrow-beam directional wireless links can be easily disconnected because of the beam misalignment in mobile environments. To address this challenge, in this paper we design a new beam control scheme called LeBeam, with the objective of maximizing the expected capacity of the mmWave/THz-band links by determining the optimal beamwidth dynamically under drone mobility uncertainties. In LeBeam, an ESN is adopted to capture the mobility uncertainties of the drones dynamically and predict the optimal beamwidth based on the first- and second-order moments of the drone mobility. The ESN has been trained based on real drone flight traces. The performance of LeBeam is evaluated over UBSim, a newly developed trace-driven Universal Broadband Simulator for integrated aerial and ground wireless networking.

Internet of Things (IoT) and 5G network are fundamental building blocks for Industrial Internet of Things (IIoT). IoT has enabled real-time monitoring and actuation in industrial floors and machinery,aimed at improving the efficiency and safety of industrial activities and processes. On the other hand,5G networks will provide ultra-reliable and low-latency communication for the wireless integration of autonomous industrial machinery,mobile vehicles,and robots,and management systems,aimed at the real-time control and management of industrial machinery towards smart factories. In IIoT,machine learning (ML) will also play a fundamental role in handling complex tasks at industrial machinery and 5G networks management,configuration,and control. However,ML suffers from the cold-start problem and needs a large amount of highly accurate data samples for model training,which is costly and difficult to obtain in IIoT applications. In this paper,we shed light on the design of transfer learning (TL)-based systems for IIoT. We discuss how TL can overcome the demand for high-quality large data samples required to training ML models in IIoT. We also highlight the work principles and daunting challenges faced during the TL systems for IIoT. Furthermore,we categorize the TL systems for IIoT into TL for IIoT machinery level and for IIoT networking level and provide an in-depth discussion of the design building blocks and challenges of TL systems in each proposed class. Finally,we point out some future research directions for the design of novel TL-based systems for envisioned 5G-enabled IIoT applications.

This paper presents a quick block transport system for scalable Hyperledger Fabric (HLF) blockchain over device-todevice (D2D)-assisted 5G networks. The objective of the proposed system is to increase the transaction throughput and further enable the application of the blockchain to delay-sensitive 5G services. To this end, the system rapidly disseminates the block data to all the peers on the blockchain network. The blockchain network is organized with mobile edge cloud (MEC) server and mobile peers in the 5G network. To ensure a scalable and reliable blockchain architecture, the core functionalities of the HLF blockchain are appropriately distributed among the mobile edge cloud and each mobile peer. The MEC server handles core functionalities for the blockchain network, such as block generation and membership management. In addition, it is responsible for gathering the network status of all peers and organizing the overlay structure to be utilized to rapidly propagate the block with a low network cost. On the other hand, the mobile peers handle the endorsing and validating functionalities to prevent block data tampering in the blockchain network. Furthermore, locality-aware device-to-device networking is utilized in 5G network coverage to achieve faster block dissemination. The proposed system is designed to work seamlessly in an existing HLF blockchain framework with consideration of scalability and compatibility. It is fully implemented using the open-source HLF and OpenAirInterface. To verify the effectiveness of the proposed block transport system, it was evaluated in an actual 5G network as well as 802.11 network. The experimental results verified its feasibility.

Cellular networks are envisioned to be a cornerstone in future industrial IoT (IIoT) wireless connectivity in terms of fulfilling the industrial-grade coverage, capacity, robustness, and ultra-responsiveness requirements. This vision has led to verticals-centric service-based architecture in 5G radio access and core networks, with the capabilities to include 5G-AI-Edge ecosystem for computing, intelligence, and flexible deployment and integration options (e.g., centralized and distributed, physical and virtual) while eliminating the privacy/security concerns of mission-critical systems. In this paper, driven by the industrial interest in enabling large-scale wireless IIoT deployments for operational agility, flexible, and cost-efficient production, we present the state-of-the-art 5G architecture, transformative technologies, and recent design trends, which we also selectively supplemented with new results. We also identify several research challenges in these promising design trends that beyond-5G systems must overcome to support rapidly unfolding transition in creating value-centric industrial wireless networks.

Small cells are deployed in 5G networks to complement the macro cells for improving coverage and capacity. Small cell nodes (SCNs) equipped with edge servers can support emerging computing services such as virtual reality which impose low-latency and precise contextual requirements. With the proliferation of wireless devices, there is an increasing demand for offloading tasks to SCNs. Given limited computation and communication resources, the fundamental problem for a small cell network is how to select computing tasks to maximize effective rewards in an uncertain and stochastic environment. To this end, we propose an online learning framework, LFSC, which has the performance guarantee to guide task offloading in a small cell network. LFSC balances between reward and constraint violations, and it consists of three subroutines: i) a randomized algorithm which calculates selection probability of each task based on task weights; ii) a greedy assignment algorithm which cooperatively allocates tasks among different SCNs based on the selection probability; iii) an update algorithm which exploits the multi-armed bandit technique to update task weights according to the feedback. Our theoretical analysis shows both the regret and violations metrics of LFSC have the sub-linear property. Extensive simulation studies show LFSC outperforms many state-of-the-art algorithms.

Millimeter wave (mmWave) communication has attracted considerable attention as a key technology for the next-generation wireless communications thanks to its exceptional advantages. MmWave leads the way to achieve a high transmission quality with directed narrow beams from source to multiple destinations by adopting different antenna beamforming (BF) techniques, which have a pivotal role in establishing and maintaining robust links. However, realizing such BF gains in practice requires overcoming several challenges, such as severe signal deterioration, hardware constraints, and design complexity. The elevated complexity of configuring mmWave BF vectors encourages researchers to leverage relevant machine learning (ML) techniques for better BF configurations deployment in 5G and beyond. In this article, we summarize mmWave BF strategies employed for future wireless networks. Then, we provide a comprehensive overview of ML techniques plus its applications and promising contributions toward efficient mmWave BF deployment. Furthermore, we discuss mmWave BF’s future research directions and challenges. Finally, we discuss a single and concurrent mmWave BF case study by applying multiarmed bandit to confirm the superiority of ML-based methods over conventional ones.

Along with the commercialization of evolved multimedia broadcast multicast services (eMBMS), the number of mobile broadcasting users is growing notably. Previous works reveal that the accuracy of mobile channel estimation will significantly impact the quality of broadcasting services. Motivated by this fact, we apply machine learning (ML) to the fifth-generation Radio Access Network (5G RAN) slicing in this paper for the estimation and the prediction of the channel status in mobile scenarios. More specifically, a cascaded convolutional neural network (CNN)-long short term memory network (LSTM) architecture is developed to achieve channel estimation for mobile broadcasting users. The energy efficiency of the base station (BS) is modeled mathematically, and the sub-optimal solution is achieved by deep Q-Network (DQN) based on the available channel status. Finally, we present the simulation results to justify the performance of our proposed schemes.

The industrial Internet of Things (IIoT) and 5G have been served as the key elements to support the reliable and efficient operation of Industry 4.0. By integrating burgeoning network function virtualization technology with cloud computing and mobile edge computing, an NFV-enabled cloud-edge collaborative IIoT (CECIIoT) architecture can efficiently provide flexible service for the massive IIoT traffic in the form of a service function chain (SFC). However, though edge and cloud resources can be jointly utilized in this architecture, it is hard to balance resource consumption and the quality of multiple IIoT services. To overcome this challenge, a multi-objective SFC deployment model is designed to characterize the diverse service requirements and specific network environment. Then, a deep Q-learning based online SFC deployment algorithm is presented, which can efficiently learn the relationship between the SFC deployment scheme and its performance through the iterative training.The simulation results validate the execution effectiveness.

The emerging uplink (UL) and downlink (DL) decoupled radio access networks (RAN) has attracted a lot of attention due to the significant gains in network throughput, load balancing and energy consumption, etc. However, due to the diverse vehicular service requirements in different vehicle-to-everything (V2X) applications, how to provide customized cellular V2X services with diversified requirements in the UL/DL decoupled 5G and beyond cellular V2X networks is challenging. To this end, we investigate the feasibility of UL/DL decoupled RAN framework for cellular V2X communications, including the vehicle-to-infrastructure (V2I) communications and relay-assisted cellular vehicle-to-vehicle (RAC-V2V) communications. We propose a two-tier UL/DL decoupled RAN slicing approach. On the first tier, the deep reinforcement learning (DRL) soft actor-critic (SAC) algorithm is leveraged to allocate bandwidth to different base stations. On the second tier, we model the QoS metric of RAC-V2V communications as an absolute-value optimization problem and solve it by the alternative slicing ratio search (ASRS) algorithm with global convergence. The extensive numerical simulations demonstrate that the UL/DL decoupled access can significantly promote load balancing and reduce C-V2X transmit power. Meanwhile, the simulation results show that the proposed solution can significantly improve the network throughput while ensuring the different QoS requirements of cellular V2X.

The proliferation of 5th generation networks (5G) towards Vehicle-to-Everything (V2X) communication has paved the way for driverless Autonomous Vehicles (AVs) in vehicular Critical Energy Infrastructures (CEI). Though technological advancements improve AVs, the Safety-Critical Messages (SCMs) still play a vital role in reducing crashes, preventing injuries, and saving lives. Therefore, in this paper, a Software-Defined Networking (SDN)-assisted continuous clustering approach called Migrating Consignment Region (MiCR) based on Federated K-means algorithm is proposed for disseminating SCMs to the AVs via 5G-V2X communication. Unlike other methods that create clusters for every instance, MiCR continuously holds moving clusters for disseminating SCMs to AVs with ultra-high reliability and low latency. The proposed MiCR approach has been simulated under real-time highway road maps and compared with other methods. The simulation results prove the superiority of MiCR in terms of network overload, SCM delivery ratio, latency, dissemination efficiency, and collision rate compared with the existing methods.

With the rapid development of machine-type communications (MTC), the future communication architecture needs to provide services for both human-type communications (HTC) and MTC with unique characteristics. The huge connections from MTC bring serious challenges to the existing wireless network. Ultra-dense network (UDN), a promising candidate technology, can support massive device access through dense deployment of small base stations (SBSs). Different from the resource management in traditional wireless network with single base station (BS), the resource allocation problem at BS level is more prominent in UDN, and the diversity of devices will make this problem more complicated. In view of this, we investigate the joint optimization of massive access and resource management in the UDN where HTC and MTC coexist. Considering the computational complexity and scalability, we propose a multi-agent deep reinforcement learning based SBS state selection scheme, in which each SBS acts as an agent and selects the optimal state between active and idle by continuously interacting with the environment. In addition, we adopt the power-domain non-orthogonal multiple access to further improve system throughput, and use grant-based and grant-free access manners for HTC and MTC respectively, so as to meet their unique characteristics. Extensive numerical results demonstrate the superior performances of proposed scheme in multiple perspectives.

Motivated by Industry 4.0, 5G-enabled unmanned aerial vehicles (UAVs; also known as drones) are widely applied in various industries. However, the open nature of 5G networks threatens the safe sharing of data. In particular, privacy leakage can lead to serious losses for users. As a new machine learning paradigm, federated learning (FL) avoids privacy leakage by allowing data models to be shared instead of raw data. Unfortunately, the traditional FL framework is strongly dependent on a centralized aggregation server, which will cause the system to crash if the server is compromised. Unauthorized participants may launch poisoning attacks, thereby reducing the usability of models. In addition, communication barriers hinder collaboration among a large number of cross-domain devices for learning. To address the above issues, a blockchain-empowered decentralized horizontal federated learning (BE-DHFL) framework is proposed. The authentication of cross-domain UAVs is accomplished through multisignature smart contracts. Global model updates are computed by using these smart contracts instead of a centralized server. Extensive experimental results show that the proposed scheme achieves high efficiency of cross-domain authentication and good accuracy.

Cooperative Intelligent Transport Systems (C-ITS) enable information to be shared wirelessly between vehicles and infrastructure in order to improve transport safety and efficiency. Delivering C-ITS services using existing cellular networks offers both financial and technological advantages, not least since these networks already offer many of the features needed by C-ITS, and since many vehicles on our roads are already connected to cellular networks. Still, C-ITS pose stringent requirements in terms of availability and latency on the underlying communication system; requirements that will be hard to meet for currently deployed 3G, LTE, and early-generation 5G systems. Through a series of experiments in the MONROE testbed (a cross-national, mobile broadband testbed), the present study demonstrates how cellular multi-access selection algorithms can provide close to 100% availability, and significantly reduce C-ITS transaction times. The study also proposes and evaluates a number of low-complexity, low-overhead single-access selection algorithms, and shows that it is possible to design such solutions so that they offer transaction times and availability levels that rival those of multi-access solutions.

Edge computing-enhanced Internet of Vehicles (EC-IoV) enables ubiquitous data processing and content sharing among vehicles and terrestrial edge computing (TEC) infrastructures (e.g., 5G base stations and roadside units) with little or no human intervention, plays a key role in the intelligent transportation systems. However, EC-IoV is heavily dependent on the connections and interactions between vehicles and TEC infrastructures, thus will break down in some remote areas where TEC infrastructures are unavailable (e.g., desert, isolated islands and disaster-stricken areas). Driven by the ubiquitous connections and global-area coverage, space-air-ground integrated networks (SAGINs) efficiently support seamless coverage and efficient resource management, represent the next frontier for edge computing. In light of this, we first review the state-of-the-art edge computing research for SAGINs in this article. After discussing several existing orbital and aerial edge computing architectures, we propose a framework of edge computing-enabled space-air-ground integrated networks (EC-SAGINs) to support various IoV services for the vehicles in remote areas. The main objective of the framework is to minimize the task completion time and satellite resource usage. To this end, a pre-classification scheme is presented to reduce the size of action space, and a deep imitation learning (DIL) driven offloading and caching algorithm is proposed to achieve real-time decision making. Simulation results show the effectiveness of our proposed scheme. At last, we also discuss some technology challenges and future directions.