Showing papers in "IEEE Communications Magazine in 2021"

Reconfigurable Intelligent Surfaces for 6G Systems: Principles, Applications, and Research Directions

Abstract: Reconfigurable intelligent surfaces (RISs) or intelligent reflecting surfaces (IRSs) are regarded as one of the most promising and revolutionizing techniques for enhancing the spectrum and/ or energy efficiency of wireless systems. These devices are capable of reconfiguring the wireless propagation environment by carefully tuning the phase shifts of a large number of low-cost passive reflecting elements. In this article, we aim to answer four fundmental questions: 1) Why do we need RISs? 2) What is an RIS? 3) What are RIS's applications? 4) What are the relevant challenges and future research directions? In response, eight promising research directions are pointed out.

Digital Twin for 5G and Beyond

Abstract: Although many countries have started the initial phase of rolling out 5G, it is still in its infancy with researchers from both academia and industry facing the challenges of developing it to its full potential. With the support of artificial intelligence, development of digital transformation through the notion of a digital twin has been taking off in many industries such as smart manufacturing, oil and gas, construction, bio-engineering, and automotive. However, digital twins remain relatively new for 5G/6G networks, despite the obvious potential in helping develop and deploy the complex 5G environment. This article looks into these topics and discusses how digital twin could be a powerful tool to fulfill the potential of 5G networks and beyond.

From Semantic Communication to Semantic-Aware Networking: Model, Architecture, and Open Problems

Abstract: Existing communication systems are mainly built based on Shannon's information theory, which deliberately ignores the semantic aspects of communication. The recent iteration of wireless technology, 5G and beyond, promises to support a plethora of services enabled by carefully tailored network capabilities based on contents, requirements, as well as semantics. This has sparked significant interest in semantic communication, a novel paradigm that involves the meaning of messages in communication. In this article, we first review classic semantic communication frameworks and then summarize key challenges that hinder its popularity. We observe that some semantic communication processes such as semantic detection, knowledge modeling, and coordination can be resource-consuming and inefficient, especially for communication between a single source and a destination. We therefore propose a novel architecture based on federated edge intelligence for supporting resource-efficient semantic-aware networking. Our architecture allows each user to offload computationally intensive semantic encoding and decoding tasks to edge servers and protect its proprietary model-re-lated information by coordinating via intermediate results. Our simulation result shows that the proposed architecture can reduce resource consumption and significantly improve communication efficiency.

Reconfigurable Intelligent Surfaces for Rich Scattering Wireless Communications: Recent Experiments, Challenges, and Opportunities

Abstract: Recent advances in the fabrication and experimentation of reconfigurable intelligent surfaces (RISs) have motivated the concept of the smart radio environment, according to which the propagation of information-bearing waveforms in the wireless medium is amenable to programmability. Although the vast majority of recent experimental research on RIS-empowered wireless communications gravitates around narrowband beamforming in quasi-free space, RISs are foreseen to revolutionize wideband wireless connectivity in dense urban as well as indoor scenarios, which are usually characterized as strongly reverberant environments exhibiting severe multipath conditions. In this article, capitalizing on recent physics-driven experimental explorations of RIS-empowered wave propagation control in complex scattering cavities, we identify the potential of the spatio-temporal control offered by RISs to boost wireless communications in rich scattering channels via two case studies. First, an RIS is deployed to shape the multipath channel impulse response, which is shown to enable higher achievable communication rates. Second, the RIS-tunable propagation environment is leveraged as an analog multiplexer to localize non-cooperative objects using wave fingerprints, even when they are outside the line of sight. Future research challenges and opportunities in the algorithmic design and experimentation of smart rich scattering wireless environments enabled by RISs for sixth generation wireless communications are discussed.

The Road to 6G: Ten Physical Layer Challenges for Communications Engineers

Abstract: While the deployment of 5G cellular systems will continue well into the next decade, much interest is already being generated toward technologies that will underlie its successor, 6G. Undeniably, 5G will have a transformative impact on the way we live and communicate, but it is still far away from supporting the Internet of Everything, where upward of 1 million devices/km3 (both terrestrial and aerial) will require ubiquitous, reliable, low-latency connectivity. This article looks at some of the fundamental problems that pertain to key physical layer enablers for 6G. This includes highlighting challenges related to intelligent reflecting surfaces, cell-free massive MIMO, and THz communications. Our analysis covers theoretical modeling challenges, hardware implementation issues, and scalability, among others. The article concludes by delineating the critical role of signal processing in the new era of wireless communications.

Position Location for Futuristic Cellular Communications: 5G and Beyond

Abstract: With vast mmWave spectrum and narrow beam antenna technology, precise position location is now possible in 5G and future mobile communication systems. In this article, we describe how centimeter-level localization accuracy can be achieved, particularly through the use of map-based techniques. We show how data fusion of parallel information streams, machine learning, and cooperative localization techniques further improve positioning accuracy.

Opportunities of Federated Learning in Connected, Cooperative, and Automated Industrial Systems

Abstract: Next-generation autonomous and networked industrial systems (i.e., robots, vehicles, drones) have driven advances in ultra-reliable low-laten-cy communications (URLLC) and computing. These networked multi-agent systems require fast, communication-efficient, and distributed machine learning (ML) to provide mission-crit-ical control functionalities. Distributed ML techniques, including federated learning (FL), represent a mushrooming multidisciplinary research area weaving together sensing, communication, and learning. FL enables continual model training in distributed wireless systems: rather than fusing raw data samples at a centralized server, FL leverages a cooperative fusion approach where networked agents, connected via URLLC, act as distributed learners that periodically exchange their locally trained model parameters. This article explores emerging opportunities of FL for the next-generation networked industrial systems. Open problems are discussed, focusing on cooperative driving in connected automated vehicles and collaborative robotics in smart manufacturing.

Reconfigurable, Intelligent, and Sustainable Wireless Environments for 6G Smart Connectivity

Abstract: Various visions of the forthcoming sixth generation (6G) networks point toward flexible connect-and-compute technologies to support future innovative services and the corresponding use cases. 6G should be able to accommodate ever evolving and heterogeneous applications, future regulations, and diverse user-, service-, and location-based requirements. A key element toward building smart and energy sustainable wireless systems beyond 5G is the reconfigurable intelligent surface (RIS), which offers programmable control and shaping of the wireless propagation environment. Capitalizing on this technology potential, in this article we introduce two new concepts: i) wireless environment as a service, which leverages a novel RIS-empowered networking paradigm to trade off diverse, and usually conflicting, connectivity objectives; and ii) performance-boosted areas enabled by RIS-based connectivity, representing competing service provisioning areas that are highly spatially and temporally focused. We discuss the key technological enablers and research challenges with the proposed networking paradigm, and highlight the potential profound role of RISs in the recent Open Radio Access Network architecture.

High Altitude Platform Station Based Super Macro Base Station Constellations

Abstract: High altitude platform station (HAPS) systems have recently attracted renewed attention. While terrestrial and satellite technologies are well established for providing connectivity services, they face certain shortcomings and challenges, which could be addressed by complementing them with HAPS systems. In this article, we envision a HAPS as a super macro base station, to which we refer as HAPS-SMBS, to provide connectivity in a plethora of applications. Unlike a conventional HAPS, which targets broad coverage for remote areas or disaster recovery, we envision next-generation HAPS-SMBS to have the necessary capabilities to address the high capacity, low latency, and computing requirements, especially for highly populated metropolitan areas. This article focuses mainly on the potential opportunities, target use cases, and challenges that we expect to be associated with the design and implementation of the HAPS-SMBS-based future wireless access architecture.

NOMA for Next-Generation Massive IoT: Performance Potential and Technology Directions

Abstract: Broader applications of the Internet of Things (loT) are expected in the forthcoming 6G system, although massive loT is already a key scenario in 5G, predominantly relying on physical layer solutions inherited from 4G LTE and primarily using orthogonal multiple access (OMA). In 6G loT, supporting a massive number of connections will be required for diverse services of the vertical sectors, prompting fundamental studies on how to improve the spectral efficiency of the system. One of the key enabling technologies is non-or-thogonal multiple access (NOMA). This article consists of two parts. In the first part, finite block length theory and the diversity order of multi-user systems are used to show the significant potential of NOMA compared to traditional OMA. The supremacy of NOMA over OMA is particularly pronounced for asynchronous contention-based systems relying on imperfect link adaptation, which are commonly assumed for massive loT systems. To approach these performance bounds, in the second part of the article, several promising technology directions are proposed for 6G massive loT, including linear spreading, joint spreading and modulation, multi-user channel coding in the context of various techniques for practical uncoordinated transmissions, cell-free operations, and so on, from the perspective of NOMA.

LEO Satellite Constellations for 5G and Beyond: How Will They Reshape Vertical Domains?

Abstract: The rapid development of communication technologies in the past decades has provided immense vertical opportunities for individuals and enterprises. However, conventional terrestrial cellular networks have unfortunately neglected the huge geographical digital divide, since high-bandwidth wireless coverage is concentrated in urban areas. To meet the goal of “connecting the unconnected,” integrating low Earth orbit (LEO) satellites with the terrestrial cellular networks has been widely considered as a promising solution. In this article, we first introduce the development roadmap of LEO satellite constellations (SatCons), including early attempts in LEO satellites with the emerging LEO constellations. Further, we discuss the unique opportunities of employing LEO SatCons for the delivery of integrating 5G networks. Specifically, we present their key performance indicators, which offer important guidelines for the design of associated enabling techniques, and then discuss the potential impact of integrating LEO SatCons with typical 5G use cases, where we engrave our vision of various vertical domains reshaped by LEO SatCons. Technical challenges are finally provided to specify future research directions.

Six Key Challenges for Beam Management in 5.5G and 6G Systems

Abstract: Future cellular networks will increasingly rely on the millimeter-wave bands to increase capacity. Migrating to ever higher carrier frequencies will require increasingly directional beamforming to establish and maintain the link. Intelligent beam management (BM) protocols will be critical for establishing and maintaining connections between the base station and user equipment in a dynamic channel. This article first provides a brief overview of the BM protocol in Release 15 of 5G New Radio, then identifies six major challenges to BM for later 5G releases that will likely persist into 6G. We describe the trends and issues behind each of the six challenges, and provide recommendations and suggested research directions to address them.

Aerial Platforms with Reconfigurable Smart Surfaces for 5G and Beyond

Abstract: Aerial platforms are expected to deliver enhanced and seamless connectivity in the fifth generation (5G) wireless networks and beyond (B5G). Alternatively, reconfigurable smart surfaces (RSS), which smartly exploit/recycle signal reflections in the environment, are increasingly being recognized as a new wireless communication paradigm to improve communication links. In fact, their reduced cost, low power use, light weight, and flexible deployment make them an attractive candidate for integration with 5G/B5G technologies. In this article, we discuss comprehensive approaches to the integration of RSS in aerial platforms. First, we present a review of RSS technology, its operations, and types of communication. Next, we describe how RSS can be used in aerial platforms, and we propose a control architecture workflow. Then several potential use cases are presented and discussed. Finally, associated research challenges are identified.

Intelligence and Learning in O-RAN for Data-Driven NextG Cellular Networks

Abstract: Next generation (NextG) cellular networks will be natively cloud-based and built on programmable, virtualized, and disaggregated architectures. The separation of control functions from the hardware fabric and the introduction of standardized control interfaces will enable the definition of custom closed-control loops, which will ultimately enable embedded intelligence and real-time analytics, thus effectively realizing the vision of autonomous and self-optimizing networks. This article explores the disaggregated network architecture proposed by the O-RAN Alliance as a key enabler of NextG networks. Within this architectural context, we discuss the potential, the challenges, and the limitations of data-driven optimization approaches to network control over different timescales. We also present the first large-scale integration of O-RAN-compliant software components with an open source full-stack softwarized cellular network. Experiments conducted on Colosseum, the world's largest wireless network emulator, demonstrate closed-loop integration of real-time analytics and control through deep reinforcement learning agents. We also show the feasibility of radio access network (RAN) control through xApps running on the near-real-time RAN intelligent controller to optimize the scheduling policies of coexisting network slices, leveraging the O-RAN open interfaces to collect data at the edge of the network.

Data Collection in MI-Assisted Wireless Powered Underground Sensor Networks: Directions, Recent Advances, and Challenges

Abstract: Wireless underground sensor networks (WUSNs) facilitate remote monitoring and control of various underground environments, which are suffering from a significant reliability problem. To address this problem and relieve the ongoing networking challenges, we propose a new concept, called the magnetic induction (MI)-assisted wireless powered underground sensor network (MI-WPUSN), which integrates the advantages of MI communication techniques with those of wireless power transfer mechanisms. MI-WPUSN offers a unique platform consisting of seven envisioned devices and four distinct communication modes to provide significant reliability potential, which is constrained by its complex and challenging data collection. To unlock the potential of MI-WPUSN, we provide a systematic research roadmap for MI-WPUSN data collection, spreading from sensor deployment to multiple channel access control and to frequency-selective routing establishment. Specifically, we first summarize the state-of-the-art research advances on sensor deployment, based on which we propose a bidirectional projection-based deployment topology that achieves high network reliability and discuss the sensor numerical determination problem resorting to weighted Voronoi diagrams. We then introduce the principles for multiple channel access control and review the existing research. Finally, after introducing kernel regression methods that can easily handle the frequency-selective property of MI-WPUSN, we develop a modified Q-learning-based routing protocol. In particular, we list and analyze the underlying challenges and related future issues in each direction.

5Growth: An End-to-End Service Platform for Automated Deployment and Management of Vertical Services over 5G Networks

Abstract: This article introduces the key innovations of the 5Growth service platform to empower vertical industries with an AI-driven automated 5G end-to-end slicing solution that allows industries to achieve their service requirements. Specifically, we present multiple vertical pilots (Industry 4.0, transportation, and energy), identify the key 5G requirements to enable them, and analyze existing technical and functional gaps as compared to current solutions. Based on the identified gaps, we propose a set of innovations to address them with: (i) support of 3GPP-based RAN slices by introducing a RAN slicing model and providing automated RAN orchestration and control; (ii) an AI-driven closed-loop for automated service management with service level agreement assurance; and (iii) multi-domain solutions to expand service offerings by aggregating services and resources from different provider domains and also enable the integration of private 5G networks with public networks.

MagicNet: The Maritime Giant Cellular Network

Abstract: Recently, the development of marine industries has increasingly attracted attention from all over the world. A wide-area and seamless maritime communication network has become a critical supporting approach. In this article, we propose a novel architecture named the maritime giant cellular network (MagicNet) relying on seaborne floating towers deployed in a honeycomb topology. The tower-borne giant-cell base stations are capable of providing wide-area seamless coverage for maritime users and can construct multihop line-of-sight (LoS) links connecting to terrestrial networks. Then the MagicNet-aided maritime network architecture is expounded in terms of five dimensions (i.e., space, air, shore, surface, and underwater), which is compatible with existing systems including maritime satellite networks and maritime Internet of Things, and supports a range of compelling industrial applications. Moreover, we introduce a joint multicast beamforming and relay system for the sake of supporting high-speed and low-cost information services for near-shore areas, as well as a three-tier space-air-surface hybrid network in order to provide reliable wide-area communications for deep offshore areas. Finally, we discuss the open issues and future works on MagicNet.

Semantics-Empowered Communication for Networked Intelligent Systems

Abstract: Wireless connectivity has traditionally been regarded as an opaque data pipe carrying messages, whose context-dependent meaning and effectiveness have been ignored. Nevertheless, in emerging cyber-physical and autonomous networked systems, acquiring, processing, and sending excessive amounts of distributed real-time data, which ends up being stale or useless to the end user, will cause communication bottlenecks, increased latency, and safety issues. We envision a communication paradigm shift, which makes the semantics of information (i.e., the significance and usefulness of messages) the foundation of the communication process. This entails a goal-orient-ed unification of information generation, transmission, and reconstruction, by taking into account process dynamics, signal sparsity, data correlation, and semantic information attributes. We apply this structurally new, synergetic approach to a communication scenario where the destination is tasked with real-time source reconstruction for the purpose of remote actuation. Capitalizing on semantics-empowered sampling and communication policies, we show significant reduction in both reconstruction error and cost of actuation error, as well as in the number of uninformative samples generated.

Toward Intelligent Cyber-Physical Systems: Digital Twin Meets Artificial Intelligence

Abstract: Industry 4.0 aims to support smarter and autonomous processes while improving agility, cost efficiency, and user experience. To fulfill its promises, properly processing the data of the industrial processes and infrastructures is required. Artificial intelligence (AI) appears as a strong candidate to handle all generated data, and to help in the automation and smartification process. This article overviews the digital twin as a true embodiment of a cyber-physical system (CPS) in Industry 4.0, showing the mission of AI in this concept. It presents the key enabling technologies of the digital twin such as edge, fog, and 5G, where the physical processes are integrated with the computing and network domains. The role of AI in each technology domain is identified by analyzing a set of AI agents at the application and infrastructure levels. Finally, movement prediction is selected and experimentally validated using real data generated by a digital twin for robotic arms with results showcasing its potential.

1-Bit Massive MIMO Transmission: Embracing Interference with Symbol-Level Precoding

Abstract: The deployment of large-scale antenna arrays for cellular base stations (BSs), called massive MIMO, has been a key enabler for meeting the ever increasing capacity requirement for 5G communication systems and beyond. Despite their promising performance, fully digital massive MIMO systems require a large number of hardware components including radio frequency chains, power amplifiers, digital-to-analog converters (DACs), and so on, resulting in a huge increase in terms of the total power consumption and hardware costs for cellular BSs. Toward both spectrally-efficient and energy-efficient massive MIMO deployment, a number of hardware limited architectures have been proposed, including hybrid analog-digital structures, constant-envelope transmission, and the use of low-resolution DACs. In this article, we overview the recent advances in improving the error rate performance of massive MIMO systems with 1-bit DACs through precoding at the symbol level. This line of research goes beyond traditional interference suppression or cancellation techniques by managing interference on a symbol-by-symbol basis. This provides unique opportunities for interference-aware precoding tailored for practical massive MIMO systems. In this article, we first explain the concept of constructive interference (CI) and elaborate on how CI can benefit the 1-bit signal design by exploiting the traditionally undesired multi-user interference as well as the interference from imperfect hardware components. We then overview several solutions for 1-bit signal design to illustrate the gains achievable by exploiting CI. Finally, we identify some challenges and future research directions for 1-bit massive MIMO systems that have yet to be explored.

AI-Assisted MAC for Reconfigurable Intelligent-Surface-Aided Wireless Networks: Challenges and Opportunities

Abstract: Recently, significant research attention has been devoted to the study of reconfigurable intelligent surfaces (RISs), which are capable of reconfiguring the wireless propagation environment by exploiting the unique properties of metamaterials-based integrated large arrays of inexpensive antennas. Existing research demonstrates that RISs significantly improve physical layer performance, including wireless coverage, achievable data rate, and energy efficiency. However, the medium access control (MAC) of multiple users accessing an RIS-enabled channel is still in its infancy, while many open issues remain to be addressed. In this article, we present four typical RIS-aided multi-user scenarios with special emphasis on the MAC schemes. We then propose and elaborate on centralized, distributed, and hybrid artificial-in-telligence-assisted MAC architectures in RIS-aid-ed multi-user communications systems. Finally, we discuss some challenges, perspectives, and potential applications of RISs as they are related to MAC design.

Metamaterial-Based Reconfigurable Intelligent Surface: 3D Meta-Atoms Controlled by Graphene Structures

Abstract: The upcoming high-speed wireless communication systems will be hosted by millimeter- and sub-millimeter-wave frequency bands. At these frequencies, electromagnetic waves suffer from severe propagation losses and non-line-of-sight scenarios. A new wireless communication paradigm has arrived to resolve this situation through the use of reconfigurable intelligent surfaces (RISs). These metadevices are designed to reconfigure the wireless environment in a smart way. Traditional RIS designs based on the implementation of 2D configurations have been considered up to now. However, 3D structures enable an extra degree of freedom in the design that can be taken as an advantage for the development of improved RIS structures with advanced functionalities. This article proposes the implementation of a novel electronically reconfigurable RIS based on the use of 3D graphene meta-atoms. The reconfigurability lies in the graphene conductivity, easily tunable with a biasing voltage. Different conductivity values vary the meta-atom electromagnetic response, modifying the RIS functionality. A multi-objective optimization framework determines the optimal phase state of each meta-atom to accomplish the desired RIS performance. The operation of the RIS as an efficient beam steerer/ splitter, absorber, and polarization selector is validated with full-wave results.

The Role of Digital Twin in Optical Communication: Fault Management, Hardware Configuration, and Transmission Simulation

Abstract: Optical communication is developing rapidly in the directions of hardware resource diversification, transmission system flexibility, and network function virtualization. Its proliferation poses a significant challenge to traditional optical communication management and control systems. Digital twin (DT), a technology that utilizes data, models, and algorithms and integrates multiple disciplines, acts as a bridge between the real and virtual worlds for comprehensive connectivity. In the digital space, virtual models are established dynamically to simulate and describe the states, behaviors, and rules of physical objects in the physical space. DT has been significantly developed and widely applied in the industrial and military fields. This study introduces the DT technology to optical communication through interdisciplinary crossing and proposes a DT framework suitable for optical communication. The intelligent fault management model, flexible hardware configuration model, and dynamic transmission simulation model are established in the digital space with the help of deep learning algorithms to ensure the high-reliability operation and high-ef-ficiency management of optical communication systems and networks.

Terahertz Line-of-Sight MIMO Communication: Theory and Practical Challenges

Abstract: A relentless trend in wireless communications is the hunger for bandwidth, and fresh bandwidth is only to be found at ever higher frequencies. While 5G systems are seizing the mmWave band, the attention of researchers is shifting already to the terahertz range. In that distant land of tiny wavelengths, antenna arrays can serve for more than power-enhancing beamforming. Defying lower-frequency wisdom, spatial multiplexing becomes feasible even in line-of-sight conditions. This article reviews the underpinnings of this phenomenon, and it surveys recent results on the ensuing information-theoretic capacity. Reconfigurable array architectures are put forth that can closely approach such capacity, practical challenges are discussed, and supporting experimental evidence is presented.

IoTrace: A Flexible, Efficient, and Privacy-Preserving IoT-Enabled Architecture for Contact Tracing

Abstract: Contact tracing promises to help fight the spread of COVID–19 via an early detection of possible contagion events. To this end, most existing solutions share the following architecture: smartphones continuously broadcast random beacons that are intercepted by nearby devices and stored into their local contact logs. In this article, we propose an IoT-enabled architecture for contact tracing that relaxes the smartphone-centric assumption, and provides a solution that enjoys the following features: it reduces the overhead on the end user to the bare minimum - the mobile device only broadcasts its beacons; it provides the user with a degree of privacy not achieved by competing solutions - even in the most privacy adverse scenario, the solution provides $k$ -anonymity; and it is flexible: the same architecture can be configured to support several models - ranging from fully decentralized to fully centralized ones - and the system parameters can be tuned to support the tracing of several social interaction models. What is more, our proposal can also be adopted to tackle future human-proximity transmissible diseases. Finally, we also highlight open issues and discuss a number of future research directions at the intersection of IoT and contact tracing.

Toward a 6G AI-Native Air Interface

Abstract: Each generation of cellular communication systems is marked by a defining disruptive technology of its time, such as OFDM for 4G or Massive MIMO for 5G. Since AI is the defining technology of our time, it is natural to ask what role it could play for 6G. While it is clear that 6G must cater to the needs of large distributed learning systems, it is less certain if AI will play a defining role in the design of 6G itself. The goal of this article is to paint a vision of a new air interface that is partially designed by AI to enable optimized communication schemes for any hardware, radio environment, and application.

Advancements in Vehicular Communication Technologies: C-V2X and NR-V2X Comparison

Abstract: Cellular vehicle-to-everything (C-V2X) is one of the key enabling vehicular communication technologies endorsed by the vehicular industry, scientists, and researchers. Introduced by the 3GPP, LTE device-to-device in Release 12 evolved into C-V2X with mode 3 and mode 4. In mode 4, semi-persistent -scheduling-based sensing is performed by vehicles to select resources. However, when the density of vehicles increases, the performance of C-V2X mode 4 degrades. To address this challenge, 3GPP Release 16 introduced New Radio (NR) for V2X services, also known as NR-V2X. Modes 1 and 2 along with four additional sub-modes were considered in NR-V2X to achieve high reliability and higher throughput with low latency. This article discusses and evaluates the advancements introduced in NR-V2X by comparative analysis with C-V2X. To compare the performance, an NR-V2X simulator based on a network simulator (ns-3) for sub-6 GHz band is also introduced. In addition, feasible practical solutions to resolve resource selection problems are also discussed.

Enhancing RF Sensing with Deep Learning: A Layered Approach

Abstract: In recent years, radio frequency (RF) sensing has gained increasing popularity due to its pervasiveness, low cost, non-intrusiveness, and privacy preservation. However, realizing the promises of RF sensing is highly nontrivial, given typical challenges such as multipath and interference. One potential solution leverages deep learning to build direct mappings from the RF domain to target domains, hence avoiding complex RF physical modeling. While earlier solutions exploit only simple feature extraction and classification modules, an emerging trend adds functional layers on top of elementary modules for more powerful generalizability and flexible applicability. To better understand this potential, this article takes a layered approach to summarize RF sensing enabled by deep learning. Essentially, we present a four-layer framework: physical, backbone, generalization, and application. While this layered framework provides readers a systematic methodology for designing deep interpreted RF sensing, it also facilitates making improvement proposals and hints at future research opportunities.

IEEE 802.11be: Wi-Fi 7 Strikes Back

Abstract: As hordes of data-hungry devices challenge its current capabilities, Wi-Fi strikes back with 802.11be, alias Wi-Fi 7. This brand new amendment promises a (r)evolution of unlicensed wireless connectivity as we know it. To appreciate its foreseen impact, we start by overviewing the latest Wi-Fi-related news, with emphasis on the recently launched Wi-Fi 6E certification program. We then provide an updated digest of 802.11be essential features, vouching for multi-AP coordination as a must-have for critical and latency-sensitive applications. We finally get down to the nitty-gritty of one of its most enticing implementations – coordinated beamforming – for which our standard-compliant simulations confirm near-tenfold reductions in worst case delays.