Showing papers on "Cellular network published in 2019"
TL;DR: The Rel-16 features and outlook towards Rel-17 and beyond are discussed and new features to further expand the applicability of the 5G System to new markets and use cases are introduced.
Abstract: The 5G System is being developed and enhanced to provide unparalleled connectivity to connect everyone and everything, everywhere. The first version of the 5G System, based on the Release 15 (“Rel-15”) version of the specifications developed by 3GPP, comprising the 5G Core (5GC) and 5G New Radio (NR) with 5G User Equipment (UE), is currently being deployed commercially throughout the world both at sub-6 GHz and at mmWave frequencies. Concurrently, the second phase of 5G is being standardized by 3GPP in the Release 16 (“Rel-16”) version of the specifications which will be completed by March 2020. While the main focus of Rel-15 was on enhanced mobile broadband services, the focus of Rel-16 is on new features for URLLC (Ultra-Reliable Low Latency Communication) and Industrial IoT, including Time Sensitive Communication (TSC), enhanced Location Services, and support for Non-Public Networks (NPNs). In addition, some crucial new features, such as NR on unlicensed bands (NR-U), Integrated Access & Backhaul (IAB) and NR Vehicle-to-X (V2X), are also being introduced as part of Rel-16, as well as enhancements for massive MIMO, wireless and wireline convergence, the Service Based Architecture (SBA) and Network Slicing. Finally, the number of use cases, types of connectivity and users, and applications running on top of 5G networks, are all expected to increase dramatically, thus motivating additional security features to counter security threats which are expected to increase in number, scale and variety. In this paper, we discuss the Rel-16 features and provide an outlook towards Rel-17 and beyond, covering both new features and enhancements of existing features. 5G Evolution will focus on three main areas: enhancements to features introduced in Rel-15 and Rel-16, features that are needed for operational enhancements, and new features to further expand the applicability of the 5G System to new markets and use cases.
532 citations
TL;DR: In this article, a novel concept of three-dimensional (3D) cellular networks, that integrate drone base stations (drone-BSs) and cellular-connected drone users (Drone-UEs), is introduced.
Abstract: In this paper, a novel concept of three-dimensional (3D) cellular networks, that integrate drone base stations (drone-BS) and cellular-connected drone users (drone-UEs), is introduced. For this new 3D cellular architecture, a novel framework for network planning for drone-BSs and latency-minimal cell association for drone-UEs is proposed. For network planning, a tractable method for drone-BSs’ deployment based on the notion of truncated octahedron shapes is proposed, which ensures full coverage for a given space with a minimum number of drone-BSs. In addition, to characterize frequency planning in such 3D wireless networks, an analytical expression for the feasible integer frequency reuse factors is derived. Subsequently, an optimal 3D cell association scheme is developed for which the drone-UEs’ latency, considering transmission, computation, and backhaul delays, is minimized. To this end, first, the spatial distribution of the drone-UEs is estimated using a kernel density estimation method, and the parameters of the estimator are obtained using a cross-validation method. Then, according to the spatial distribution of drone-UEs and the locations of drone-BSs, the latency-minimal 3D cell association for drone-UEs is derived by exploiting tools from an optimal transport theory. The simulation results show that the proposed approach reduces the latency of drone-UEs compared with the classical cell association approach that uses a signal-to-interference-plus-noise ratio (SINR) criterion. In particular, the proposed approach yields a reduction of up to 46% in the average latency compared with the SINR-based association. The results also show that the proposed latency-optimal cell association improves the spectral efficiency of a 3D wireless cellular network of drones.
388 citations
TL;DR: It will be illustrated that the best strategy depends on the specific environment in which the nodes are deployed, and guidelines to inform the optimal choice as a function of the system parameters are given.
Abstract: The millimeter wave (mmWave) frequencies offer the availability of huge bandwidths to provide unprecedented data rates to next-generation cellular mobile terminals. However, mmWave links are highly susceptible to rapid channel variations and suffer from severe free-space pathloss and atmospheric absorption. To address these challenges, the base stations and the mobile terminals will use highly directional antennas to achieve sufficient link budget in wide area networks. The consequence is the need for precise alignment of the transmitter and the receiver beams, an operation which may increase the latency of establishing a link, and has important implications for control layer procedures, such as initial access, handover and beam tracking. This tutorial provides an overview of recently proposed measurement techniques for beam and mobility management in mmWave cellular networks, and gives insights into the design of accurate, reactive and robust control schemes suitable for a 3GPP NR (NR) cellular network. We will illustrate that the best strategy depends on the specific environment in which the nodes are deployed, and give guidelines to inform the optimal choice as a function of the system parameters.
367 citations
TL;DR: In this paper, the authors investigate the potential of massive MIMO while addressing practical deployment issues to deal with the increased back/fronthauling overhead deriving from the signal co-processing.
Abstract: Since the first cellular networks were trialled in the 1970s, we have witnessed an incredible wireless revolution. From 1G to 4G, the massive traffic growth has been managed by a combination of wider bandwidths, refined radio interfaces, and network densification, namely increasing the number of antennas per site. Due its cost-efficiency, the latter has contributed the most. Massive MIMO (multiple-input multiple-output) is a key 5G technology that uses massive antenna arrays to provide a very high beamforming gain and spatially multiplexing of users and hence increases the spectral and energy efficiency (see references herein). It constitutes a centralized solution to densify a network, and its performance is limited by the inter-cell interference inherent in its cell-centric design. Conversely, ubiquitous cell-free Massive MIMO refers to a distributed Massive MIMO system implementing coherent user-centric transmission to overcome the inter-cell interference limitation in cellular networks and provide additional macro-diversity. These features, combined with the system scalability inherent in the Massive MIMO design, distinguish ubiquitous cell-free Massive MIMO from prior coordinated distributed wireless systems. In this article, we investigate the enormous potential of this promising technology while addressing practical deployment issues to deal with the increased back/front-hauling overhead deriving from the signal co-processing.
331 citations
TL;DR: This survey presents a detailed survey on wireless evolution towards 6G networks, characterized by ubiquitous 3D coverage, introduction of pervasive AI and enhanced network protocol stack, and related potential technologies that are helpful in forming sustainable and socially seamless networks.
Abstract: While 5G is being commercialized worldwide, research institutions around the world have started to look beyond 5G and 6G is expected to evolve into green networks, which deliver high Quality of Service and energy efficiency. To meet the demands of future applications, significant improvements need to be made in mobile network architecture. We envision 6G undergoing unprecedented breakthrough and integrating traditional terrestrial mobile networks with emerging space, aerial and underwater networks to provide anytime anywhere network access. This paper presents a detailed survey on wireless evolution towards 6G networks. In this survey, the prime focus is on the new architectural changes associated with 6G networks, characterized by ubiquitous 3D coverage, introduction of pervasive AI and enhanced network protocol stack. Along with this, we discuss related potential technologies that are helpful in forming sustainable and socially seamless networks, encompassing terahertz and visible light communication, new communication paradigm, blockchain and symbiotic radio. Our work aims to provide enlightening guidance for subsequent research of green 6G.
324 citations
TL;DR: This paper proposes an efficient method to verify its feasibility via checking the connectivity between two given vertices on an equivalent graph, and obtains useful structural results on the optimal UAV trajectory, based on which two efficient methods are proposed to find high-quality approximate trajectory solutions by leveraging the techniques from graph theory and convex optimization.
Abstract: Integrating the unmanned aerial vehicles (UAVs) into the cellular network is envisioned to be a promising technology to significantly enhance the communication performance of both UAVs and existing terrestrial users. In this paper, we first provide an overview on the two main research paradigms in cellular UAV communications, namely, cellular-enabled UAV communication with UAVs as new aerial users served by the ground base stations (GBSs), and UAV-assisted cellular communication with UAVs as new aerial communication platforms serving the terrestrial users. Then, we focus on the former paradigm and study a new UAV trajectory design problem subject to practical communication connectivity constraints with the GBSs. Specifically, we consider a cellular-connected UAV in the mission of flying from an initial location to a final location that are given, during which it needs to maintain reliable communication with the cellular network by associating with one of the available GBSs at each time instant that has the best line-of-sight channel (or shortest distance) with it. We aim to minimize the UAV’s mission completion time by optimizing its trajectory, subject to a quality-of-connectivity constraint of the GBS-UAV link specified by a minimum receive signal-to-noise ratio target, which needs to be satisfied throughout its mission. To tackle this challenging non-convex optimization problem, we first propose an efficient method to verify its feasibility via checking the connectivity between two given vertices on an equivalent graph. Next, by examining the GBS-UAV association sequence over time, we obtain useful structural results on the optimal UAV trajectory, based on which two efficient methods are proposed to find high-quality approximate trajectory solutions by leveraging the techniques from graph theory and convex optimization. The proposed methods are analytically shown to be capable of achieving a flexible tradeoff between complexity and performance, and yielding a solution in polynomial time with the performance arbitrarily close to that of the optimal solution. Numerical results further validate the effectiveness of our proposed designs against benchmark schemes. Finally, we make concluding remarks and point out promising directions for future work.
315 citations
TL;DR: This paper proposes a cooperative UAV sense-and-send protocol to enable the UAV-to-X communications, and forms the subchannel allocation and UAV speed optimization problem to maximize the uplink sum-rate and shows that the proposed ISASOA can upload 10% more data than the greedy algorithm.
Abstract: In this paper, we consider a single-cell cellular network with a number of cellular users (CUs) and unmanned aerial vehicles (UAVs), in which multiple UAVs upload their collected data to the base station (BS). Two transmission modes are considered to support the multi-UAV communications, i.e., UAV-to-network (U2N) and UAV-to-UAV (U2U) communications. Specifically, the UAV with a high signal-to-noise ratio (SNR) for the U2N link uploads its collected data directly to the BS through U2N communication, while the UAV with a low SNR for the U2N link can transmit data to a nearby UAV through underlaying U2U communication for the sake of quality of service. We first propose a cooperative UAV sense-and-send protocol to enable the UAV-to-X communications, and then formulate the subchannel allocation and UAV speed optimization problem to maximize the uplink sum-rate. To solve this NP-hard problem efficiently, we decouple it into three sub-problems: U2N and cellular user (CU) subchannel allocation, U2U subchannel allocation, and UAV speed optimization. An iterative subchannel allocation and speed optimization algorithm (ISASOA) is proposed to solve these sub-problems jointly. The simulation results show that the proposed ISASOA can upload 10% more data than the greedy algorithm.
314 citations
TL;DR: A reinforcement learning approach is proposed to achieve the maximum long-term overall network utility while guaranteeing the quality of service requirements of user equipments (UEs) in the downlink of heterogeneous cellular networks.
Abstract: Heterogeneous cellular networks can offload the mobile traffic and reduce the deployment costs, which have been considered to be a promising technique in the next-generation wireless network. Due to the non-convex and combinatorial characteristics, it is challenging to obtain an optimal strategy for the joint user association and resource allocation issue. In this paper, a reinforcement learning (RL) approach is proposed to achieve the maximum long-term overall network utility while guaranteeing the quality of service requirements of user equipments (UEs) in the downlink of heterogeneous cellular networks. A distributed optimization method based on multi-agent RL is developed. Moreover, to solve the computationally expensive problem with the large action space, multi-agent deep RL method is proposed. Specifically, the state, action and reward function are defined for UEs, and dueling double deep Q-network (D3QN) strategy is introduced to obtain the nearly optimal policy. Through message passing, the distributed UEs can obtain the global state space with a small communication overhead. With the double-Q strategy and dueling architecture, D3QN can rapidly converge to a subgame perfect Nash equilibrium. Simulation results demonstrate that D3QN achieves the better performance than other RL approaches in solving large-scale learning problems.
296 citations
TL;DR: The proposed deep reinforcement learning algorithm, based on echo state network (ESN) cells, achieves better wireless latency per UAV and rate per ground user (UE) while requiring a number of steps that are comparable to a heuristic baseline that considers moving via the shortest distance toward the corresponding destinations.
Abstract: In this paper, an interference-aware path planning scheme for a network of cellular-connected unmanned aerial vehicles (UAVs) is proposed. In particular, each UAV aims at achieving a tradeoff between maximizing energy efficiency and minimizing both wireless latency and the interference caused on the ground network along its path. The problem is cast as a dynamic game among UAVs. To solve this game, a deep reinforcement learning algorithm, based on echo state network (ESN) cells, is proposed. The introduced deep ESN architecture is trained to allow each UAV to map each observation of the network state to an action, with the goal of minimizing a sequence of time-dependent utility functions. Each UAV uses the ESN to learn its optimal path, transmission power, and cell association vector at different locations along its path. The proposed algorithm is shown to reach a subgame perfect Nash equilibrium upon convergence. Moreover, an upper bound and a lower bound for the altitude of the UAVs are derived thus reducing the computational complexity of the proposed algorithm. The simulation results show that the proposed scheme achieves better wireless latency per UAV and rate per ground user (UE) while requiring a number of steps that are comparable to a heuristic baseline that considers moving via the shortest distance toward the corresponding destinations. The results also show that the optimal altitude of the UAVs varies based on the ground network density and the UE data rate requirements and plays a vital role in minimizing the interference level on the ground UEs as well as the wireless transmission delay of the UAV.
279 citations
TL;DR: A novel user pairing scheme is developed so that more than two users can be grouped in a cluster to exploit the NOMA technique and an iterative penalty function-based beamforming scheme is presented to obtain the BF weight vectors and power coefficients with fast convergence.
Abstract: In this paper, we propose a joint optimization design for a non-orthogonal multiple access (NOMA)-based satellite-terrestrial integrated network (STIN), where a satellite multicast communication network shares the millimeter wave spectrum with a cellular network employing NOMA technology. By assuming that the satellite uses multibeam antenna array and the base station employs uniform planar array, we first formulate a constrained optimization problem to maximize the sum rate of the STIN while satisfying the constraint of per-antenna transmit power and quality-of-service requirements of both satellite and cellular users. Since the formulated optimization problem is NP-hard and mathematically intractable, we develop a novel user pairing scheme so that more than two users can be grouped in a cluster to exploit the NOMA technique. Based on the user clustering, we further propose to transform the non-convex problem into an equivalent convex one, and present an iterative penalty function-based beamforming (BF) scheme to obtain the BF weight vectors and power coefficients with fast convergence. Simulation results confirm the effectiveness and superiority of the proposed approach in comparison with the existing works.
273 citations
TL;DR: In this article, a deep learning-based method was proposed to improve the performance of SAGINs, where the authors analyzed several main challenges of SagINs and explained how these problems can be solved by AI.
Abstract: It is widely acknowledged that the development of traditional terrestrial communication technologies cannot provide all users with fair and high quality services due to scarce network resources and limited coverage areas. To complement the terrestrial connection, especially for users in rural, disaster-stricken, or other difficult-to-serve areas, satellites, UAVs, and balloons have been utilized to relay communication signals. On this basis, SAGINs have been proposed to improve the users' QoE. However, compared with existing networks such as ad hoc networks and cellular networks, SAGINs are much more complex due to the various characteristics of three network segments. To improve the performance of SAGINs, researchers are facing many unprecedented challenges. In this article, we propose the AI technique to optimize SAGINs, as the AI technique has shown its predominant advantages in many applications. We first analyze several main challenges of SAGINs and explain how these problems can be solved by AI. Then, we consider the satellite traffic balance as an example and propose a deep learning based method to improve traffic control performance. Simulation results evaluate that the deep learning technique can be an efficient tool to improve the performance of SAGINs.
TL;DR: A novel deep learning architecture, namely Spatial–Temporal Cross-domain neural Network (STCNet), to effectively capture the complex patterns hidden in cellular data to enhance knowledge reuse.
Abstract: Machine (deep) learning-enabled accurate traffic modeling and prediction is an indispensable part for future big data-driven intelligent cellular networks, since it can help autonomic network control and management as well as service provisioning. Along this line, this paper proposes a novel deep learning architecture, namely Spatial–Temporal Cross-domain neural Network (STCNet), to effectively capture the complex patterns hidden in cellular data. By adopting a convolutional long short-term memory network as its subcomponent, STCNet has a strong ability in modeling spatial–temporal dependencies. Besides, three kinds of cross-domain datasets are actively collected and modeled by STCNet to capture the external factors that affect traffic generation. As diversity and similarity coexist among cellular traffic from different city functional zones, a clustering algorithm is put forward to segment city areas into different groups, and consequently, a successive inter-cluster transfer learning strategy is designed to enhance knowledge reuse. In addition, the knowledge transferring among different kinds of cellular traffic is also explored with the proposed STCNet model. The effectiveness of STCNet is validated through real-world cellular traffic datasets using three kinds of evaluation metrics. The experimental results demonstrate that STCNet outperforms the state-of-the-art algorithms. In particular, the transfer learning based on STCNet brings about 4%~13% extra performance improvements.
TL;DR: This article provides a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such as channel modeling, resource management, positioning, and security.
Abstract: Unmanned aerial vehicles (UAVs) will be an integral part of the next generation wireless communication networks. Their adoption in various communication-based applications is expected to improve coverage and spectral efficiency, as compared to traditional ground-based solutions. However, this new degree of freedom that will be included in the network will also add new challenges. In this context, the machine-learning (ML) framework is expected to provide solutions for the various problems that have already been identified when UAVs are used for communication purposes. In this article, we provide a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such as channel modeling, resource management, positioning, and security.
TL;DR: Following-Me Cloud applies a Markov-decision-process-based algorithm for cost-effective performance-optimized service migration decisions, while two alternative schemes to ensure service continuity and disruption-free operation are proposed, based on either software defined networking technologies or the locator/identifier separation protocol.
Abstract: The trend towards the cloudification of the 3GPP LTE mobile network architecture and the emergence of federated cloud infrastructures call for alternative service delivery strategies for improved user experience and efficient resource utilization. We propose Follow-Me Cloud (FMC), a design tailored to this environment, but with a broader applicability, which allows mobile users to always be connected via the optimal data anchor and mobility gateways, while cloud-based services follow them and are delivered via the optimal service point inside the cloud infrastructure. Follow-Me Cloud applies a Markov-decision-process-based algorithm for cost-effective performance-optimized service migration decisions, while two alternative schemes to ensure service continuity and disruption-free operation are proposed, based on either software defined networking technologies or the locator/identifier separation protocol. Numerical results from our analytic model for follow-me cloud, as well as testbed experiments with the two alternative follow-me cloud implementations we have developed, demonstrate quantitatively and qualitatively the advantages it can bring about.
TL;DR: A novel DT prototype to analyze the requirements of communication in a mission-critical application such as mobile networks supported remote surgery and necessary cybersecurity technologies that will help in developing the DT architecture are developed.
Abstract: The concept of digital twin (DT) has emerged to enable the benefits of future paradigms such as the industrial Internet of Things and Industry 4.0. The idea is to bring every data source and control interface description related to a product or process available through a single interface, for auto-discovery and automated communication establishment. However, designing the architecture of a DT to serve every future application is an ambitious task. Therefore, the prototyping systems for specific applications are required to design the DT incrementally. We developed a novel DT prototype to analyze the requirements of communication in a mission-critical application such as mobile networks supported remote surgery. Such operations require low latency and high levels of security and reliability and therefore are a perfect subject for analyzing DT communication and cybersecurity. The system comprised of a robotic arm and HTC vive virtual reality (VR) system connected over a 4G mobile network. More than 70 test users were employed to assess the system. To address the cybersecurity of the system, we incorporated a network manipulation module to test the effect of network outages and attacks; we studied state of the art practices and their utilization within DTs. The capability of the system for actual remote surgery is limited by capabilities of the VR system and insufficient feedback from the robot. However, simulations and research of remote surgeries could be conducted with the system. As a result, we propose ideas for communication establishment and necessary cybersecurity technologies that will help in developing the DT architecture. Furthermore, we concluded that developing the DT requires cross-disciplinary development in several different engineering fields. Each field makes use of its own tools and methods, which do not always fit together perfectly. This is a potentially major obstacle in the realization of Industry 4.0 and similar concepts.
TL;DR: A novel multimodal DL framework for encrypted TC is proposed, named MIMETIC, able to capitalize traffic data heterogeneity (by learning both intra- and inter-modality dependences), overcome performance limitations of existing (myopic) single- modality DL-based TC proposals, and support the challenging mobile scenario.
TL;DR: This work derives the expressions of the outage probabilities and the ergodic rates and analyze the corresponding diversity orders and slopes for both backscatter-NOMA and SR systems and provides the numerical results to verify the theoretical analysis and demonstrate the interrelationship between the cellular networks and the IoT networks.
Abstract: Non-orthogonal multiple access (NOMA) is envisioned as a key technology to enhance the spectrum efficiency for 5G cellular networks. Meanwhile, ambient backscatter communication is a promising solution to the Internet of Things (IoT), due to its high spectrum efficiency and power efficiency. In this paper, we are interested in a symbiotic system of cellular and IoT networks and propose a backscatter-NOMA system, which incorporates a downlink NOMA system with a backscatter device (BD). In the proposed system, the base station (BS) transmits information to two cellular users according to the NOMA protocol, while a BD transmits its information over the BS signals to one cellular user using the passive radio technology. In particular, if the BS only serves the cellular user that decodes BD information, the backscatter-NOMA system turns into a symbiotic radio (SR) system. We derive the expressions of the outage probabilities and the ergodic rates and analyze the corresponding diversity orders and slopes for both backscatter-NOMA and SR systems. Finally, we provide the numerical results to verify the theoretical analysis and demonstrate the interrelationship between the cellular networks and the IoT networks.
TL;DR: In this article, the authors provide an in-depth analysis of user and network-level performance of a cellular network that serves both UAVs and ground users in the downlink.
Abstract: The growing use of aerial user equipments (UEs) in various applications requires ubiquitous and reliable connectivity for safe control and data exchange between these devices and ground stations. Key questions that need to be addressed when planning the deployment of aerial UEs are whether the cellular network is a suitable candidate for enabling such connectivity and how the inclusion of aerial UEs might impact the overall network efficiency. This paper provides an in-depth analysis of user and network-level performance of a cellular network that serves both unmanned aerial vehicles (UAVs) and ground users in the downlink. Our results show that the favorable propagation conditions that UAVs enjoy due to their height often backfire on them, as the increased load-dependent co-channel interference received from neighboring ground base stations (BSs) is not compensated by the improved signal strength. When compared with a ground user in an urban area, our analysis shows that a UAV flying at 100 m can experience a throughput decrease of a factor 10 and a coverage drop from 76% to 30%. Motivated by these findings, we develop UAV and network-based solutions to enable an adequate integration of UAVs into cellular networks. In particular, we show that an optimal tilting of the UAV antenna can increase the coverage from 23% to 89% and throughput from 3.5 to 5.8 b/s/Hz, outperforming ground UEs. Furthermore, our findings reveal that depending on the UAV altitude and its antenna configuration, the aerial user performance can scale with respect to the network density better than that of a ground user. Finally, our results show that network densification and the use of microcells limit the UAV performance. Although UAV usage has the potential to increase the area spectral efficiency (ASE) of cellular networks with a moderate number of cells, they might hamper the development of future ultradense networks.
TL;DR: In this paper, the authors expose the wireless and security challenges that arise in the context of UAV-based delivery systems, UAVbased real-time multimedia streaming, and UAVenabled intelligent transportation systems.
Abstract: Cellular-connected UAVs will inevitably be integrated into future cellular networks as new aerial mobile users. Providing cellular connectivity to UAVs will enable a myriad of applications ranging from online video streaming to medical delivery. However, to enable reliable wireless connectivity for the UAVs as well as secure operation, various challenges need to be addressed such as interference management, mobility management and handover, cyber-physical attacks, and authentication. In this article, the goal is to expose the wireless and security challenges that arise in the context of UAV-based delivery systems, UAV-based real-time multimedia streaming, and UAV-enabled intelligent transportation systems. To address such challenges, ANN-based solution schemes are introduced. The introduced approaches enable UAVs to adaptively exploit wireless system resources while guaranteeing secure operation in real time. Preliminary simulation results show the benefits of the introduced solutions for each of the aforementioned cellular-connected UAV application use cases.
TL;DR: A Deep-Q Network (DQN) based task offloading and resource allocation algorithm for the MEC system is proposed and extensive numerical results show that the proposed DQN-based approach can achieve the near-optimal performance.
TL;DR: This paper integrates the D2D communications with MEC to further improve the computation capacity of the cellular networks, where the task of each device can be offloaded to an edge node and a nearby D1D device.
Abstract: The future 5G wireless networks aim to support high-rate data communications and high-speed mobile computing. To achieve this goal, the mobile edge computing (MEC) and device-to-device (D2D) communications have been recently developed, both of which take advantage of the proximity for better performance. In this paper, we integrate the D2D communications with MEC to further improve the computation capacity of the cellular networks, where the task of each device can be offloaded to an edge node and a nearby D2D device. We aim to maximize the number of devices supported by the cellular networks with the constraints of both communication and computation resources. The optimization problem is formulated as a mixed integer non-linear problem, which is not easy to solve in general. To tackle it, we decouple it into two subproblems. The first one minimizes the required edge computation resource for a given D2D pair, while the second one maximizes the number of supported devices via optimal D2D pairing. We prove that the optimal solutions to the two subproblems compose the optimal solution to the original problem. Then, the optimal algorithm to the original problem is developed by solving two subproblems, and some insightful results, such as the optimal transmit power allocation and the task offloading strategy, are also highlighted. Our proposal is finally tested by extensive numerical simulation results, which demonstrate that combining D2D communications with MEC can significantly enhance the computation capacity of the system.
TL;DR: This article introduces a distributed Vehicular edge computing solution named the autonomous vehicular edge (AVE), which makes it possible to share neighboring vehicles' available resources via vehicle-tovehicle (V2V) communications, and extends this concept to a more general online solution called the hybrid vehicle edge cloud (HVC), which enables the efficient sharing of all accessible computing resources.
Abstract: As an enabling technology for the Internet of Vehicles (IoV), mobile edge computing (MEC) provides potential solutions for sharing the computation capabilities among vehicles, in addition to other accessible resources. In this article, we first introduce a distributed vehicular edge computing solution named the autonomous vehicular edge (AVE), which makes it possible to share neighboring vehicles' available resources via vehicle-tovehicle (V2V) communications. We then extend this concept to a more general online solution called the hybrid vehicular edge cloud (HVC), which enables the efficient sharing of all accessible computing resources, including roadside units (RSUs) and the cloud, by using multiaccess networks. We also demonstrate the impact of these two decentralized edge computing solutions on the task execution performance. Finally, we discuss several open problems and future research directions.
TL;DR: This article considers two application cases of UAVs in conjunction with safeguarding the exchange of confidential messages, and demonstrates physical layer security mechanisms via two case studies to ensure security, and sheds light on new opportunities in the emerging network architecture.
Abstract: Wireless communications can leverage UAVs to provide ubiquitous connectivity to different device types. Recently, integrating UAVs into a macro cell network is drawing unprecedented interest for supplementing terrestrial cellular networks. Compared with communications with fixed infrastructure, a UAV has salient attributes, such as easy-to-deploy, higher capacity due to dominant LoS communication links, and additional design degree-of-freedom with the controlled mobility. While UAV communication offers numerous benefits, it also faces security challenges due to the broadcasting nature of the wireless medium. Thus, information security is one of the fundamental requirements. In this article, we first consider two application cases of UAVs (i.e., a UAV as a flying base station and a UAV as an aerial node) in conjunction with safeguarding the exchange of confidential messages. Then, we demonstrate physical layer security mechanisms via two case studies to ensure security, and numerically show superior performance gains. Finally, we shed light on new opportunities in the emerging network architecture that can serve as a guide for future research directions.
TL;DR: This paper considers the uplink transmission from a UAV to cellular BSs, under spectrum sharing with the existing ground users, and proposes a centralized and decentralized ICIC schemes that achieve a near-optimal performance and draw important design insights based on practical system setups.
Abstract: The line-of-sight (LoS) air-to-ground channel brings both opportunities and challenges in cellular-connected unmanned aerial vehicle (UAV) communications. On one hand, the LoS channels make more cellular base stations (BSs) visible to a UAV as compared to the ground users, which leads to a higher macro-diversity gain for UAV-BS communications. On the other hand, they also render the UAV to impose/suffer more severe uplink/downlink interference to/from the BSs, thus requiring more sophisticated inter-cell interference coordination (ICIC) techniques with more BSs involved. In this paper, we consider the uplink transmission from a UAV to cellular BSs, under spectrum sharing with the existing ground users. To investigate the optimal ICIC design and air-ground performance trade-off, we maximize the weighted sum-rate of the UAV and existing ground users by jointly optimizing the UAV’s uplink cell associations and power allocations over multiple resource blocks. However, this problem is non-convex and difficult to be solved optimally. We first propose a centralized ICIC design to obtain a locally optimal solution based on the successive convex approximation (SCA) method. As the centralized ICIC requires global information of the network and substantial information exchange among an excessively large number of BSs, we further propose a decentralized ICIC scheme of significantly lower complexity and signaling overhead for implementation, by dividing the cellular BSs into small-size clusters and exploiting the LoS macro-diversity for exchanging information between the UAV and cluster-head BSs only. Numerical results show that the proposed centralized and decentralized ICIC schemes both achieve a near-optimal performance, and draw important design insights based on practical system setups.
TL;DR: This paper proposes to apply the non-orthogonal multiple access (NOMA) technique to the uplink communication from a UAV to cellular BSs, under spectrum sharing with the existing ground users, and investigates the optimal design of cooperative NOMA and air-ground performance tradeoff.
Abstract: Aerial–ground interference mitigation is a challenging issue in the emerging cellular-connected unmanned aerial vehicle (UAV) communications. Due to the strong line-of-sight (LoS) air-to-ground (A2G) channels, the UAV may impose/suffer more severe uplink/downlink interference to/from the cellular base stations (BSs) as compared to the ground users. To tackle this challenge, we propose in this paper to apply the non-orthogonal multiple access (NOMA) technique to the uplink communication from a UAV to cellular BSs, under spectrum sharing with the existing ground users. However, for our considered system, traditional NOMA with only local interference cancellation (IC) at individual BSs, termed non-cooperative NOMA, may provide very limited gain compared to the orthogonal multiple access (OMA). This is because there are a large number of co-channel BSs due to the LoS A2G channels, and thus, the rate performance of the UAV is severely limited by the BS with the worst channel condition with the UAV. To mitigate the UAV's uplink interference without significantly compromising its achievable rate, a new cooperative NOMA scheme is proposed in this paper by exploiting the existing backhaul links among BSs. Specifically, some BSs with better channel conditions are selected to decode the UAV's signals first, and then forward the decoded signals to their backhaul-connected BSs for IC. To investigate the optimal design of cooperative NOMA and air-ground performance tradeoff, we maximize the weighted sum-rate of the UAV and ground users by jointly optimizing the UAV's rate and power allocations over multiple resource blocks as well as their associated BSs. However, this problem is difficult to be solved optimally. To obtain useful insights, we first consider two special cases with egoistic and altruistic transmission strategies of the UAV, respectively, and solve their corresponding problems optimally. Next, we consider the general case and propose an efficient suboptimal solution by applying the alternating optimization and successive convex approximation techniques. Numerical results show that the proposed cooperative NOMA scheme yields significant throughput gains than those by the traditional OMA as well as the non-cooperative NOMA benchmark.
TL;DR: An overview of HO management in long-term evolution (LTE) and 5G new radio (NR) to highlight the main differences in basic HO scenarios and a detailed literature survey on radio access mobility in LTE, heterogeneous networks (HetNets) and NR is provided.
Abstract: To satisfy the high data demands in future cellular networks, an ultra-densification approach is introduced to shrink the coverage of base station (BS) and improve the frequency reuse. The gain in capacity is expected but at the expense of increased interference, frequent handovers (HOs), increased HO failure (HOF) rates, increased HO delays, increase in ping pong rate, high energy consumption, increased overheads due to frequent HO, high packet losses and bad user experience mostly in high-speed user equipment (UE) scenarios. This paper presents the general concepts of radio access mobility in cellular networks with possible challenges and current research focus. In this article, we provide an overview of HO management in long-term evolution (LTE) and 5G new radio (NR) to highlight the main differences in basic HO scenarios. A detailed literature survey on radio access mobility in LTE, heterogeneous networks (HetNets) and NR is provided. In addition, this paper suggests HO management challenges and enhancing techniques with a discussion on the key points that need to be considered in formulating an efficient HO scheme.
TL;DR: A brief survey of the challenges and opportunities of THz band operation in wireless communication, along with some potential applications and future research directions is provided.
Abstract: With 5G Phase 1 finalized and 5G Phase 2 recently defined by 3GPP, the mobile communication community is on the verge of deciding what will be the Beyond-5G (B5G) system. B5G is expected to further enhance network performance, for example, by supporting throughput per device up to terabits per second and increasing the frequency range of usable spectral bands significantly. In fact, one of the main pillars of 5G networks has been radio access extension to the millimeter-wave bands. However, new envisioned services, asking for more and more throughput, require the availability of one order of magnitude more spectrum chunks, thus suggesting moving the operations into the THz domain. This move will introduce significant new multidisciplinary research challenges emerging throughout the wireless communication protocol stacks, including the way the mobile network is modeled and deployed. This article, therefore, provides a brief survey of the challenges and opportunities of THz band operation in wireless communication, along with some potential applications and future research directions.
TL;DR: In this proposed network architecture, control information is transmitted through the control plane at sub-6 GHz, and the user equipment (UE) data are forwarded through the user plane in millimeter-wave (mm-wave) bands.
Abstract: Multiband cooperative networking is being recognized as a new enabling scheme to provide superior user experience and greatly scale up system capacity for 5G cellular networks. This article presents a control/user plane split (CUPS)-based multiband cooperative network architecture. To satisfy the diversified requirements of future 5G applications, the separation of the control and user planes is applied in both the core network and radio access network (RAN). In this proposed network architecture, control information is transmitted through the control plane at sub-6 GHz, and the user equipment (UE) data are forwarded through the user plane in millimeter-wave (mm-wave) bands.
TL;DR: A novel hierarchical network architecture enabled by software defined networking is proposed, which integrates cross-layer high and low altitude platforms into conventional terrestrial cellular networks to inject additional capacity and expand the coverage for underserved areas in a flexible, seamless, and cost-effective manner.
Abstract: UAVs are expected to be an important complementary component for 5G (and beyond) communication systems to achieve the goal of global access to the Internet for all. To fully exploit the benefits of the distinct features of various UAVs, this article proposes a novel hierarchical network architecture enabled by software defined networking, which integrates cross-layer high and low altitude platforms into conventional terrestrial cellular networks to inject additional capacity and expand the coverage for underserved areas in a flexible, seamless, and cost-effective manner. Specifically, we first present a comprehensive comparison and review of different types of UAVs for communication services. Then, we propose an integrated airground heterogeneous network architecture and outline its characteristics and potential advantages. Next, several key enabling techniques for the integrated system are discussed in detail. In addition, we identify the potential application scenarios where the system can further enhance the performance of traditional terrestrial networks, followed by a case study to demonstrate the effectiveness of the proposed architecture. Finally, the discussions on challenges and open research issues are given.
01 Oct 2019
TL;DR: The proposed DeepSlice model will be able to make smart decisions and select the most appropriate network slice, even in case of a network failure, utilizing in-network deep learning and prediction.
Abstract: Existing cellular communications and the upcoming 5G mobile network requires meeting high-reliability standards, very low latency, higher capacity, more security, and high-speed user connectivity. Mobile operators are looking for a programmable solution that will allow them to accommodate multiple independent tenants on the same physical infrastructure and 5G networks allow for end-to-end network resource allocation using the concept of Network Slicing (NS). Data-driven decision making will be vital in future communication networks due to the traffic explosion and Artificial Intelligence (AI) will accelerate the 5G network performance. In this paper, we have developed a ‘DeepSlice’ model by implementing Deep Learning (DL) Neural Network to manage network load efficiency and network availability, utilizing in-network deep learning and prediction. We use available network Key Performance Indicators (KPIs) to train our model to analyze incoming traffic and predict the network slice for an unknown device type. Intelligent resource allocation allows us to use the available resources on existing network slices efficiently and offer load balancing. Our proposed DeepSlice model will be able to make smart decisions and select the most appropriate network slice, even in case of a network failure.