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The radio access network (RAN) landscape is undergoing a transformative shift from traditional, communication-centric infrastructures towards converged compute-communication platforms. This article introduces AI-RAN which integrates both RAN and artificial intelligence (AI) workloads on the same infrastructure. By doing so, AI-RAN not only meets the performance demands of future networks but also improves asset utilization. We begin by examining how RANs have evolved beyond mobile broadband towards AI-RAN and articulating manifestations of AI-RAN into three forms: AI-for-RAN, AI-on-RAN, and AI-and-RAN. Next, we identify the key requirements and enablers for the convergence of communication and computing in AI-RAN. We then provide a reference architecture for advancing AI-RAN from concept to practice. To illustrate the practical potential of AI-RAN, we present a proof-of-concept that concurrently processes RAN and AI workloads utilizing NVIDIA Grace-Hopper GH200 servers. Finally, we conclude the article by outlining future work directions to guide further developments of AI-RAN., Comment: 7 pages, 5 figures

The 3rd Generation Partnership Project (3GPP) evolution of mobile communication technologies from 5G to 6G has been a transformative journey spanning a decade, shaped by six releases from Release 15 to Release 20. This article provides a retrospective of this evolution, highlighting the technical advancements, challenges, and milestones that have defined the transition from the foundational 5G era to the emergence of 6G. Starting with Release 15, which marked the birth of 5G and its New Radio (NR) air interface, the journey progressed through Release 16, where 5G was qualified as an International Mobile Telecommunications-2020 (IMT-2020) technology, and Release 17, which expanded 5G into new domains such as non-terrestrial networks. Release 18 ushered in the 5G-Advanced era, incorporating novel technologies like artificial intelligence. Releases 19 and 20 continue this momentum, focusing on commercially driven enhancements while laying the groundwork for the 6G era. This article explores how 3GPP technology evolution has shaped the telecommunications landscape over the past decade, bridging two mobile generations. It concludes with insights into learned lessons, future challenges, and opportunities, offering guidelines on 6G evolution for 2030 and beyond., Comment: 16 pages, 6 figures, 2 tables, submitted for possible publication

The rise of generative artificial intelligence (GenAI) is transforming the telecom industry. GenAI models, particularly large language models (LLMs), have emerged as powerful tools capable of driving innovation, improving efficiency, and delivering superior customer services in telecom. This paper provides an overview of GenAI for telecom from theory to practice. We review GenAI models and discuss their practical applications in telecom. Furthermore, we describe the key technology enablers and best practices for applying GenAI to telecom effectively. We highlight the importance of retrieval augmented generation (RAG) in connecting LLMs to telecom domain specific data sources to enhance the accuracy of the LLMs' responses. We present a real-world use case on RAG-based chatbot that can answer open radio access network (O-RAN) specific questions. The demonstration of the chatbot to the O-RAN Alliance has triggered immense interest in the industry. We have made the O-RAN RAG chatbot publicly accessible on GitHub., Comment: 7 pages, 6 figures, submitted for possible publication

As 5G deployments continue throughout the world, concerns regarding its energy consumption have gained significant traction. This article focuses on radio access networks (RANs) which account for a major portion of the network energy use. Firstly, we introduce the state-of-the-art 3GPP and O-RAN standardization work on enhancing RAN energy efficiency. Then we highlight three unique ways for enabling energy optimization in telecommunication networks, including full stack acceleration, network functions consolidation, and shared infrastructure between communication and artificial intelligence. These network design strategies not only allow for considerable overall reduction in the energy footprint, but also deliver several added benefits including improved throughput, reduced cost of ownership, and increased revenue opportunities for telcos., Comment: 8 pages, 6 figures

The 3rd generation partnership project (3GPP) initiated 5G-Advanced in Release 18, laying a solid foundation for the further evolution of 5G-Advanced. Release 19-the next wave of 5G-Advanced-will primarily focus on commercial deployment needs while serving as a bridge toward 6G. In this article, we provide an in-depth overview of the 5G-Advanced evolution in 3GPP Release 19. We not only delve into the key technology components and their corresponding use cases in 5G-Advanced evolution but also shed light on initial 3GPP studies toward 6G., Comment: 8 pages, 5 figures, submitted for publication

This letter considers the transceiver design in frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems for high-quality data transmission. We propose a novel deep learning based framework where the procedures of pilot design, channel feedback, and hybrid beamforming are realized by carefully crafted deep neural networks. All the considered modules are jointly learned in an end-to-end manner, and a graph neural network is adopted to effectively capture interactions between beamformers based on the built graphical representation. Numerical results validate the effectiveness of our method., Comment: 5 pages, 4 figures, acccpted by IEEE Communication Letters

Air interface is a fundamental component within any wireless communication system. In Release 18, the 3rd Generation Partnership Project (3GPP) delves into the possibilities of leveraging artificial intelligence (AI)/machine learning (ML) to improve the performance of the fifth-generation (5G) New Radio (NR) air interface. This endeavor marks a pioneering stride within 3GPP's journey in shaping wireless communication standards. This article offers a comprehensive overview of the pivotal themes explored by 3GPP in this domain. Encompassing a general framework for AI/ML and specific use cases such as channel state information feedback, beam management, and positioning, it provides a holistic perspective. Moreover, we highlight the potential trajectory of AI/ML for the NR air interface in 3GPP Release 19, a pathway that paves the journey towards the sixth generation (6G) wireless communication systems that will feature integrated AI and communication as a key usage scenario., Comment: 7 pages, 5 figures, submitted for possible publication

Radio access networks (RAN) are going through a paradigm shift towards interoperable, intelligent, software-defined, and cloud-native open RAN solutions. A key challenge towards the adoption and deployment of open RAN at scale is performance. Hence, it is critical to leverage the power of hardware acceleration to offload compute-heavy RAN workloads to specialized hardware devices to enable accelerated compute for open RAN deployments. In this article, we provide a state-of-the-art overview of hardware acceleration for open RAN in the fifth generation (5G) wireless networks. We also present a practical implementation of inline hardware acceleration for open RAN layer 1 processing and identify several areas for future exploration towards the sixth generation (6G) wireless networks., Comment: 8 pages, 6 figures

Modern cellular networks are multi-cell and use universal frequency reuse to maximize spectral efficiency. This results in high inter-cell interference. This problem is growing as cellular networks become three-dimensional with the adoption of unmanned aerial vehicles (UAVs). This is because the strength and number of interference links rapidly increase due to the line-of-sight channels in UAV communications. Existing interference management solutions need each transmitter to know the channel information of interfering signals, rendering them impractical due to excessive signaling overhead. In this paper, we propose leveraging deep reinforcement learning for interference management to tackle this shortcoming. In particular, we show that interference can still be effectively mitigated even without knowing its channel information. We then discuss novel approaches to scale the algorithms with linear/sublinear complexity and decentralize them using multi-agent reinforcement learning. By harnessing interference, the proposed solutions enable the continued growth of civilian UAVs., Comment: 13 pages, 5 figures. Accepted for publication in the IEEE Communications Magazine

Industries worldwide are being transformed by artificial intelligence (AI), and the telecom industry is no different. Standardization is critical for industry alignment to achieve widespread adoption of AI in telecom. The 3rd generation partnership project (3GPP) Release 18 is the first release of 5G-Advanced, which includes a diverse set of study and work items dedicated to AI. This article provides a holistic overview of the state of the art in the 3GPP work on AI in 5G-Advanced, by presenting the various 3GPP Release-18 activities on AI as an organic whole, explaining in detail the design aspects, and sharing various design rationales influencing standardization., Comment: 7 pages; 6 figures; submitted for publication

Since the start of 5G work in 3GPP in early 2016, tremendous progress has been made in both standardization and commercial deployments. 3GPP is now entering the second phase of 5G standardization, known as 5G-Advanced, built on the 5G baseline in 3GPP Releases 15, 16, and 17. 3GPP Release 18, the start of 5G-Advanced, includes a diverse set of features that cover both device and network evolutions, providing balanced mobile broadband evolution and further vertical domain expansion and accommodating both immediate and long-term commercial needs. 5G-Advanced will significantly expand 5G capabilities, address many new use cases, transform connectivity experiences, and serve as an essential step in developing mobile communications towards 6G. This paper provides a comprehensive overview of the 3GPP 5G-Advanced development, introducing the prominent state-of-the-art technologies investigated in 3GPP and identifying key evolution directions for future research and standardization., Comment: IEEE Journal on Selected Areas in Communications, Special Issue on 3GPP Technologies: 5G-Advanced and Beyond, Editorial/Tutorial Paper

At the dawn of the next-generation wireless systems and networks, massive multiple-input multiple-output (MIMO) has been envisioned as one of the enabling technologies. With the continued success of being applied in the 5G and beyond, the massive MIMO technology has demonstrated its advantageousness, integrability, and extendibility. Moreover, several evolutionary features and revolutionizing trends for massive MIMO have gradually emerged in recent years, which are expected to reshape the future 6G wireless systems and networks. Specifically, the functions and performance of future massive MIMO systems will be enabled and enhanced via combining other innovative technologies, architectures, and strategies such as intelligent omni-surfaces (IOSs)/intelligent reflecting surfaces (IRSs), artificial intelligence (AI), THz communications, cell free architecture. Also, more diverse vertical applications based on massive MIMO will emerge and prosper, such as wireless localization and sensing, vehicular communications, non-terrestrial communications, remote sensing, inter-planetary communications., Comment: 7 pages, 5 figures. This work has been submitted to the IEEE for possible publication

Digital twin networks (DTNs) are real-time replicas of physical networks. They are emerging as a powerful technology for design, diagnosis, simulation, what-if-analysis, and artificial intelligence (AI)/machine learning (ML) driven real-time optimization and control of the sixth-generation (6G) wireless networks. Despite the great potential of what digital twins can offer for 6G, realizing the desired capabilities of 6G DTNs requires tackling many design aspects including data, models, and interfaces. In this article, we provide an overview of 6G DTNs by presenting prominent use cases and their service requirements, describing a reference architecture, and discussing fundamental design aspects. We also present a real-world example to illustrate how DTNs can be built upon and operated in a real-time reference development platform - Omniverse., Comment: 7 pages, 5 figures, submitted for publication

Artificial intelligence (AI) has emerged as a powerful technology that improves system performance and enables new features in 5G and beyond. Standardization, defining functionality and interfaces, is essential for driving the industry alignment required to deliver the mass adoption of AI in 5G-Advanced and 6G. However, fragmented efforts in different standards bodies, such as the third generation partnership project (3GPP) and the open radio access network (O-RAN) Alliance, can lead to confusion and uncertainty about which standards to follow and which aspects of the standards to embrace. This article provides a joint 3GPP and O-RAN perspective on the state of the art in AI adoption in mobile communication systems, including the fundamentals of 5G architecture and its evolution towards openness and intelligence, AI for 5G-Advanced evolution, and a case study on AI-enabled traffic steering. We also identify several areas for future exploration to accelerate AI adoption on the path towards 6G., Comment: 8 pages, 4 figures, submitted for publication

The 3rd generation partnership project (3GPP) radio access network (RAN) plenary recently approved a work package for its Release 18, representing a major evolution and branded as the first release of 5G Advanced. The work package includes diverse study or work items that will significantly boost 5G performance and address a wide variety of new use cases. In particular, 3GPP Release 18 will embrace artificial intelligence and machine learning technologies to provide data-driven, intelligent network solutions. This article provides an overview of the 5G Advanced evolution in 3GPP Release 18, which is anticipated to trigger a paradigm shift and have a profound impact on future wireless networks., Comment: 8 pages, 5 figures, submitted for publication

Fast and reliable connectivity is essential to enhancing situational awareness and operational efficiency for public safety mission-critical (MC) users. In emergency or disaster circumstances, where existing cellular network coverage and capacity may not be available to meet MC communication demands, deployable-network-based solutions such as cells-on-wheels/wings can be utilized swiftly to ensure reliable connection for MC users. In this paper, we consider a scenario where a macro base station (BS) is destroyed due to a natural disaster and an unmanned aerial vehicle carrying BS (UAV-BS) is set up to provide temporary coverage for users in the disaster area. The UAV-BS is integrated into the mobile network using the 5G integrated access and backhaul (IAB) technology. We propose a framework and signalling procedure for applying machine learning to this use case. A deep reinforcement learning algorithm is designed to jointly optimize the access and backhaul antenna tilt as well as the three-dimensional location of the UAV-BS in order to best serve the on-ground MC users while maintaining a good backhaul connection. Our result shows that the proposed algorithm can autonomously navigate and configure the UAV-BS to improve the throughput and reduce the drop rate of MC users., Comment: This work has been submitted to the IEEE for possible publication

Cellular networks, such as 5G systems, are becoming increasingly complex for supporting various deployment scenarios and applications. Embracing artificial intelligence (AI) in 5G evolution is critical to managing the complexity and fueling the next quantum leap in 6G cellular networks. In this article, we share our experience and best practices in applying AI in cellular networks. We first present a primer on the state of the art of AI in cellular networks, including basic concepts and recent key advances. Then we discuss 3GPP standardization aspects and share various design rationales influencing standardization. We also present case studies with real network data to showcase how AI can improve network performance and enable network automation., Comment: 7 pages, 5 figures, 1 table; submitted for publication

Public safety operations require fast and reliable mission critical communications under various scenarios, in which the availability of wireless connectivity can be a question of life or death. To provide connectivity when and where it counts, we have witnessed a growing demand for deployable networks for public safety in natural disasters or emergency situations. This article investigates the opportunities of using the 5th generation (5G) new radio (NR) standard for designing flexible and reliable deployable networks. We describe use cases and provide an overview of deployable 5G network concepts, including architecture options, system performance analysis, and coexistence aspects. We also identify technical challenges that can be considered in the evolution of 5G NR to unlock the full potential of deployable 5G networks., Comment: 8 pages, 6 figures, accepted by IEEE Communications Standards Magazine

Evolving 5G New Radio (NR) to support non-terrestrial networks (NTNs), particularly satellite communication networks, is under exploration in 3GPP. The movement of the spaceborne platforms in NTNs may result in large timing varying Doppler shift that differs for devices in different locations. Using orthogonal frequency-division multiple access (OFDMA) in the uplink, each device will need to apply a different frequency adjustment value to compensate for the Doppler shift. To this end, the 3GPP Release-17 work on NTNs assumes that an NTN device is equipped with a global navigation satellite system (GNSS) chipset and thereby can determine its position and calculate the needed frequency adjustment value using its position information and satellite ephemeris data. This makes GNSS support essential for the NTN operation. However, GNSS signals are weak, not ubiquitous, and susceptible to interference and spoofing. We show that devices without access to GNSS signals can utilize reference signals in more than one frequency position in an OFDM carrier to estimate the Doppler shift and thereby determine the needed frequency adjustment value for pre-compensating the Doppler shift in the uplink. We analyze the performance, elaborate on how to utilize the NR reference signals, and present simulation results. The solution can reduce the dependency of NTN operation on GNSS with reasonable complexity and performance trade-off., Comment: To appear in the 2021 IEEE Global Communications Conference (GLOBECOM)

Offering space-based Internet services with mega-constellations of low Earth orbit (LEO) satellites is a promising solution to connecting the unconnected. It can complement the coverage of terrestrial networks to help bridge the digital divide. However, there are challenges from operational obstacles to technical hurdles facing the development of LEO satellite access. This article provides an overview of state of the art in LEO satellite access, including the evolution of LEO satellite constellations and capabilities, critical technical challenges and solutions, standardization aspects from 5G evolution to 6G, and business considerations. We also identify several areas for future exploration to realize a tight integration of LEO satellite access with terrestrial networks in 6G., Comment: Accepted by the IEEE Communications Magazine - Feature Topic on Digital Divide: Closing the Gap

Satellite communication is experiencing a new dawn thanks to low earth orbit mega constellations being deployed at an unprecedented speed. Fueled by the renewed interest in non-terrestrial networks (NTN), the Third Generation Partnership Project (3GPP) is preparing 5G NR, NB-IoT and LTE-M for NTN operation. This article is focused on LTE-M and the essential adaptations needed for supporting satellite communication. Specifically, the major challenges facing LTE-M NTN at the physical and higher layers are discussed and potential solutions are outlined., Comment: 7 pages, 3 figures

We provide an overview of the 3rd generation partnership project (3GPP) work on evolving the 5G wireless technology to support non-terrestrial satellite networks. Adapting 5G to support non-terrestrial networks entails a holistic design spanning across multiple areas from radio access network to services and system aspects to core and terminals. In this article, we describe the main topics of non-terrestrial networks, explain in detail the design aspects, and share various design rationales influencing standardization., Comment: 8 pages, 5 figures, 1 table, submitted for publication

The large-scale and ever-growing use of unmanned aerial vehicles (UAVs) in a wide range of applications is foreseen to be a major part of beyond 5G and 6G wireless networks in the next decade. The effective support of such massive deployment of UAVs requires offering reliable, secure, and cost-effective wireless connectivity. In this regard, cellular networks play essential roles in serving UAVs acting as flying user equipments. While the cellular networks provide promising connectivity solutions for UAVs, enabling robust UAV operations faces several challenges. In this paper, an overview of key barriers and design considerations of widespread commercial use of flying UAVs are presented along with their potential solutions. In addition, we discuss how cellular networks can support UAVs by relying on their advanced features, network intelligence, key enabling technologies for beyond 5G and 6G, and exploiting new tools from machine learning. Finally, we shed light on offering wireless services to high altitudes and the integration of non-terrestrial networks with terrestrial networks towards limitless connectivity in 6G., Comment: This work has been submitted to the IEEE for possible publication

Mobile communication via high-altitude platforms operating in the stratosphere is an idea that has been on the table for decades. In the past few years, however, with recent advances in technology and parallel progress in standardization and regulatory bodies like 3GPP and ITU, these ideas have gained considerable momentum. In this article, we present a comprehensive overview of HIBS - High Altitude Platform Stations as IMT Base Stations. We lay out possible use cases and summarize the current status of the development, from a technological point of view as well as from standardization in 3GPP, and regarding spectrum aspects. We then present preliminary system level simulation results to shed light on the performance of HIBS. We conclude with pointing out several directions for future research., Comment: 7 pages, 5 figures

A non-terrestrial network (NTN), a term coined by the 3rd Generation Partnership Project (3GPP), refers to a network utilizing airborne or spaceborne payload for communication. The use of NTN has the potential of facilitating providing connectivity to underserved areas. This has motivated the work in 3GPP on evolving the fifth generation (5G) wireless access technology, known as new radio (NR), to support NTN. The broadband opportunities promised by NTN with low Earth orbit (LEO) satellites have attracted much attention, but the performance of LEO NTN using 5G NR has not been well studied. In this paper, we address this gap by analyzing and evaluating the throughput and capacity performance of LEO NTN. The evaluation results show that the downlink capacity of a LEO satellite in S band with 30 MHz bandwidth serving handheld terminal is about 600 Mbps and the downlink capacity of a LEO satellite in Ka band with 400 MHz bandwidth serving very small aperture terminal (VSAT) is about 7 Gbps. For a LEO NTN similar to the Kuiper project proposed by Amazon, we find that, due to the large cell sizes in the LEO NTN, the area capacity density is moderate: 1-10 kbps/km$^2$ in the S band downlink and 14-120 kbps/km$^2$ in the Ka band downlink depending on latitude., Comment: 6 pages, 5 figures, accepted by IEEE Globecom 2020

Direct air-to-ground (A2G) communications leveraging the fifth-generation (5G) new radio (NR) can provide high-speed broadband in-flight connectivity to aircraft in the sky. A2G network deployment entails optimizing various design parameters such as inter-site distances, number of sectors per site, and the up-tilt angles of sector antennas. The system-level design guidelines in the existing work on A2G network are rather limited. In this paper, a novel deep learning-based framework is proposed for efficient design and optimization of a 5G A2G network. The devised architecture comprises two deep neural networks (DNNs): the first DNN is used for approximating the 5G A2G network behavior in terms of user throughput, and the second DNN is developed as a function optimizer to find the throughput-optimal deployment parameters including antenna up-tilt angles and inter-site distances. Simulation results are provided to validate the proposed model and reveal system-level design insights.

The Narrowband Internet of Things (NB-IoT) is a cellular access technology developed by the Third Generation Partnership Project (3GPP) to provide wide area connectivity for the Internet of Things. Since its introduction in 3GPP Release 13, NB-IoT has in a few years achieved a remarkable market presence and is currently providing coverage in close to 100 countries. To further extend the reach of NB-IoT and to connect the unconnected, 3GPP Release 17 will study the feasibility of adapting NB-IoT to support non-terrestrial networks (NTNs). In this article, we review the fundamentals of NB-IoT and NTN and explain how NB-IoT can be adapted to support satellite communication through a minimal set of modifications.

The growing deployment of drones in a myriad of applications relies on seamless and reliable wireless connectivity for safe control and operation of drones. Cellular technology is a key enabler for providing essential wireless services to flying drones in the sky. Existing cellular networks targeting terrestrial usage can support the initial deployment of low-altitude drone users, but there are challenges such as mobility support. In this paper, we propose a novel handover framework for providing efficient mobility support and reliable wireless connectivity to drones served by a terrestrial cellular network. Using tools from deep reinforcement learning, we develop a deep Q-learning algorithm to dynamically optimize handover decisions to ensure robust connectivity for drone users. Simulation results show that the proposed framework significantly reduces the number of handovers at the expense of a small loss in signal strength relative to the baseline case where a drone always connect to a base station that provides the strongest received signal strength., Comment: Under review

The fifth generation (5G) wireless access technology, known as New Radio (NR), features flexibility to support a variety of usage scenarios. One of the basic concepts in 5G NR is bandwidth part (BWP), which is, at a high level, a set of contiguous resource blocks configured inside a channel bandwidth. BWP spans across many 5G NR specifications developed by the 3rd Generation Partnership Project. Understanding how BWP operates is vital to understanding 5G NR. This article provides an overview of the essentials of BWP in the NR technical specifications. We describe fundamental BWP concepts, BWP configuration methods, and BWP switch mechanisms. We also discuss user equipment capabilities in terms of BWP support and share our thoughts on use cases of BWP for NR deployments., Comment: 7 pages, 3 figures, 2 tables

Today, mobile operators are starting to deploy Fifth-Generation (5G) networks to expand the coverage ubiquity of broadband wireless service. In contrast, in-flight connectivity remains limited and its quality of service does not always meet the expectations. Embracing 5G New Radio (NR) in Air-to-Ground (A2G) communication systems can help narrow the gap between airborne and ground connectivity. In this article, we focus on 5G NR based direct A2G communications. We first provide an overview of the existing A2G systems which are based on earlier generations of mobile technologies. Then we confirm the feasibility of NR A2G systems with a performance study in a range of bands from below 7 GHz to millimeter wave frequencies. The results show that NR A2G systems can provide significantly improved data rates for in-flight connectivity. We also identify the major challenges associated with NR A2G communications, discuss enhancements to counteract the challenges, and point out fruitful avenues for future research., Comment: 7 pages, 5 figures

Flying drones can be used in a wide range of applications and services from surveillance to package delivery. To ensure robust control and safety of drone operations, cellular networks need to provide reliable wireless connectivity to drone user equipments (UEs). To date, existing mobile networks have been primarily designed and optimized for serving ground UEs, thus making the mobility support in the sky challenging. In this paper, a novel handover (HO) mechanism is developed for a cellular-connected drone system to ensure robust wireless connectivity and mobility support for drone-UEs. By leveraging tools from reinforcement learning, HO decisions are dynamically optimized using a Q-learning algorithm to provide an efficient mobility support in the sky. The results show that the proposed approach can significantly reduce (e.g., by 80%) the number of HOs, while maintaining connectivity, compared to the baseline HO scheme in which the drone always connects to the strongest cell.

New Radio (NR) is a new radio air interface developed by the 3rd Generation Partnership Project (3GPP) for the fifth generation (5G) mobile communications system. With great flexibility, scalability, and efficiency, 5G is expected to address a wide range of use-cases including enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine type communications (mMTC). The physical downlink control channel (PDCCH) in NR carries Downlink Control Information (DCI). Understanding how PDCCH operates is key to developing a good understanding of how information is communicated over NR. This paper provides an overview of the 5G NR PDCCH by describing its physical layer structure, monitoring mechanisms, beamforming operation, and the carried information. We also share various design rationales that influence NR standardization., Comment: 8 pages, 5 figures, 1 table, submitted for publication

New radio (NR) is a new wireless access technology developed as part of the fifth-generation (5G) of mobile communications to support a wide range of services, devices, and deployments. NR features spectrum flexibility, ultra-lean design, forward compatibility, low latency support, and advanced antenna technologies. There has been excitement about NR, sometimes clouded by confusion. This article is an attempt to summarize and overview the key features of NR by debunking seven of the more popular myths and revealing what NR really is. The seven topics include spectrum, flexible waveform and multiple access, LTE-NR interworking and coexistence, low latency support, massive machine type communications, non-terrestrial communications, and beyond radio., Comment: 8 pages, 4 figures, 1 table, submitted for publication

The 3rd generation partnership project (3GPP) completed the first global 5th generation (5G) new radio (NR) standard in its Release 15, paving the way for making 5G a commercial reality. So, what is next in NR evolution to further expand the 5G ecosystem? Enabling 5G NR to support satellite communications is one direction under exploration in 3GPP. There has been a resurgence of interest in providing connectivity from space, stimulated by technology advancement and demand for ubiquitous connectivity services. The on-going evolution of 5G standards provides a unique opportunity to revisit satellite communications. In this article, we provide an overview of use cases and a primer on satellite communications. We identify key technical challenges faced by 5G NR evolution for satellite communications and give some preliminary ideas for how to overcome them., Comment: 8 pages, 5 figures, submitted for publication

Initial cell search and selection is one of the first few essential steps that a mobile device must perform to access a mobile network. The distinct features of 5G bring new challenges to the design of initial cell search and selection. In this paper, we propose a load-aware initial cell search and selection scheme for 5G networks. The proposed scheme augments the existing pure received power based scheme by incorporating a new load factor broadcast as part of system information. We then formulate a throughput optimization problem using the optimal stopping theory. We characterize the throughput optimal stopping strategy and the attained maximum throughput. The results show that the proposed cell search and selection scheme is throughput optimal with a carefully optimized connection threshold., Comment: 10 pages, 6 figures, submitted for publication

The 5th generation (5G) wireless access technology, known as new radio (NR), will address a variety of usage scenarios from enhanced mobile broadband to ultra-reliable low-latency communications to massive machine type communications. Key technology features include ultra-lean transmission, support for low latency, advanced antenna technologies, and spectrum flexibility including operation in high frequency bands and inter-working between high and low frequency bands. This article provides an overview of the essentials of the state of the art in 5G wireless technology represented by the 3GPP NR technical specifications, with a focus on the physical layer. We describe the fundamental concepts of 5G NR, explain in detail the design of physical channels and reference signals, and share the various design rationales influencing standardization., Comment: 8 pages, 5 figures, submitted for publication

The emerging, practical and observed issue of how to detect rogue drones that carry terrestrial user equipment (UEs) on mobile networks is addressed in this paper. This issue has drawn much attention since the rogue drones may generate excessive interference to mobile networks and may not be allowed by regulations in some regions. In this paper, we propose a novel machine learning approach to identify the rogue drones in mobile networks based on radio measurements. We apply two classification machine learning models, Logistic Regression, and Decision Tree, using features from radio measurements to identify the rogue drones. We find that for high altitudes the proposed machine learning solutions can yield high rogue drone detection rate while not mis-classifying regular ground based UEs as rogue drone UEs. The detection accuracy however degrades at low altitudes., Comment: Submitted to Globecom 2018

Cellular connectivity to low altitude unmanned aerial vehicles (UAVs) has received significant interest recently which has led to a 3GPP study on enhanced LTE support for connected UAVs in Release 15. The objective of the study is to investigate the capability of long-term evolution (LTE) networks for providing connectivity to UAVs. In this article, we provide an overview of the key findings of the 3GPP Release-15 study-item phase. We first introduce UAV connectivity requirements and performance evaluation scenarios defined in the study. We then discuss radio channel models and the key identified challenges of using LTE networks to provide connectivity to UAVs. We summarize potential solutions to address the challenges including interference detection and mitigation techniques, mobility enhancements, and UAV identification. Finally, we also shed light on the key features standardized during the Release-15 work-item phase., Comment: 7 pages, 6 figures, submitted for publication

Beyond visual line-of-sight connectivity is key for use cases of unmanned aerial vehicles (UAVs) such as package delivery, infrastructure inspection, and rescue missions. Cellular networks stand ready to support flying UAVs by providing wide-area, quality, and secure connectivity for UAV operations. Ensuring reliable connections in the presence of UAV movements is important for safety control and operations of UAVs. With increasing height above the ground, the radio environment changes. Using terrestrial cellular networks to provide connectivity to the UAVs moving in the sky may face new challenges. In this article, we share some of our findings in mobility support for cellular connected UAVs. We first identify how the radio environment changes with altitude and analyze the corresponding implications on mobility performance. We then present evaluation results to shed light on the mobility performance of cellular connected UAVs. We also discuss potential enhancements for improving mobility performance in the sky., Comment: 6 pages, 5 figures

Drones are driving numerous and evolving use cases, and creating transformative socio-economic benefits. Drone operation needs wireless connectivity for communication between drones and ground control systems, among drones, and between drones and air traffic management systems. Mobile networks are well positioned to identify, track, and control the growing fleet of drones. The wide-area, quality, and secure connectivity provided by mobile networks can enhance the efficiency and effectiveness of drone operations beyond visual line-of-sight range. In this article, we elaborate how the drone ecosystem can benefit from mobile technologies, summarize key capabilities required by drone applications, and analyze the service requirements on mobile networks. We present field trial results collected in LTE-Advanced networks to gain insights into the capabilities of the current 4G+ networks for connected drones and share our vision on how 5G networks can further support diversified drone applications., Comment: 8 pages, 3 figures, 2 tables, submitted for publication

A main challenge in providing connectivity to the low altitude unmanned aerial vehicles (UAVs) through existing cellular network arises due to the increased interference in the network. The increased altitude and favourable propagation condition cause UAVs to generate more interference to the neighbouring cells, and at the same time experience more interference from the downlink transmissions of the neighbouring base stations. The uplink interference problem may result in terrestrial UEs having degraded performance, whereas the downlink interference problem may make it challenging for a UAV to maintain connection with the network. In this paper, we propose several uplink and downlink interference mitigation techniques to address these issues. The results indicate that the proposed solutions can reduce the uplink throughput degradation of terrestrial UEs and ensure UAVs to remain in LTE coverage under the worst case scenarios when all the base stations transmit at full power., Comment: Submitted to IEEE 5G Word Forum 2018

Drones are becoming increasingly used in a wide variety of industries and services and are delivering profound socioeconomic benefits. Technology needs to be in place to ensure safe operation and management of the growing fleet of drones. Mobile networks have connected tens of billions of devices on the ground in the past decades and are now ready to connect the drones flying in the sky. In this article, we share some of our findings in cellular connectivity for low altitude drones. We first present and analyze field measurement data collected during drone flights in a commercial Long-Term Evolution (LTE) network. We then present simulation results to shed light on the performance of a network when it is serving many drones simultaneously over a wide area. The results, analysis, and design insights presented in this article help enhance the understanding of the applicability and performance of providing mobile connectivity to low altitude drones., Comment: 10 pages; 8 figures

Many use cases of unmanned aerial vehicles (UAVs) require beyond visual line-of-sight (LOS) communications. Mobile networks offer wide area, high speed, and secure wireless connectivity, which can enhance control and safety of UAV operations and enable beyond visual LOS use cases. In this article, we share some of our experience in Long-Term Evolution (LTE) connectivity for low altitude small UAVs. We first identify the typical airborne connectivity requirements and characteristics, highlight the different propagation conditions for UAVs and mobiles on the ground with measurement and ray tracing results, and present simulation results to shed light on the feasibility of providing LTE connectivity for UAVs. We also present several ideas on potential enhancements for improving LTE connectivity performance and identify fruitful avenues for future research., Comment: 8 pages, 6 figures

Many use cases in the Internet of Things (IoT) will require or benefit from location information, making positioning a vital dimension of the IoT. The 3rd Generation Partnership Project (3GPP) has dedicated a significant effort during its Release 14 to enhance positioning support for its IoT technologies to further improve the 3GPP-based IoT eco-system. In this article, we identify the design challenges of positioning support in Long-Term Evolution Machine Type Communication (LTE-M) and Narrowband IoT (NB-IoT), and overview the 3GPP's work in enhancing the positioning support for LTE-M and NB-IoT. We focus on Observed Time Difference of Arrival (OTDOA), which is a downlink based positioning method. We provide an overview of the OTDOA architecture and protocols, summarize the designs of OTDOA positioning reference signals, and present simulation results to illustrate the positioning performance., Comment: 8 pages; 7 figures; 1 table; submitted for publication