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Welcome to Volume 4 of IEEE Journal of Microwaves! In this issue, we bring you 11 quality papers: two invited and nine regular submissions. We also celebrate six of our Outstanding Reviewers of 2023 and announce our IEEE Journal of Microwaves 2022 Best Paper prize. Our author and reader pools continue to expand and our usage counts per article published now exceed 1530. All of our 232 published articles from 2021 to 2023 now appear on Clarivate's Web of Science and we are looking forward to possibly receiving an impact factor this July. Remember to take a look at our solicitation for papers that can fit into our Microwaves in Climate Change special issue, which we intend to release before the end of 2024.

Welcome to Volume 3 of IEEE Journal of Microwaves! In this issue we combine a complete set of regular contributions with Part 1 of a Microwave Theory and Technology Society 70th Anniversary commemoration, consisting of some twenty-one specially invited papers embodying this major milestone in the history of the organization. Several additional articles, including some major historic overviews, are to be released in our spring issue of JMW which contains Part 2 of this 70th Anniversary celebration. In order to make it possible to bring up all the anniversary issue papers contained in both parts one and two using a single database search, every special issue paper contains the index term: “MTT 70th Anniversary Special Issue.” The full set (covering two issues) of 70th anniversary papers can be accessed using a simple parenthesis-delineated search of the five-word phrase. Plans are also in place to combine all the on-line papers into a full-color print issue for distribution upon request. In addition to this very nice collection of quality papers, we celebrate six of our Outstanding Reviewers of 2021 and 2022 and announce our first IEEE Journal of Microwaves Best Paper prize.

The article presents the discussion on considerable aspects of face-to-face human interactions remain difficult to relay. Topics include computer's ability capturing a human's emotion called affective sensing, alternatively called emotion sensing and more recently emotional artificial intelligence (emotional AI); and use of emotions in crowd and traffic management; to relaying emotions to and from the metaverse.

The article presents the discussion on sustainable urban development as populations rise and cities draw ever-larger populations. Topics include industrial Internet and Internet of Vehicles, smart energy systems, building management systems, cyber security; and presenting the technological advancement and challenges of smart cities and smart villages from the system design and implementation point of view.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

The article discusses the telecommunication industry has evolved to provide services for new types of Internet of Things (IoT) devices in addition to basic mobile phone or internet services. Topics include the extended reality devices, sensors, and ground and aerial robots, to name a few. With the deployment of these new services, it is difficult for the wireless network to support ubiquitous connections with diverse quality-of-service (QoS) requirements.

The article reports that from the late 1970's onward, the global telecommunication industry has gone through several waves of regulatory and technological changes. Today, the very definition of telecommunication has changed. First, the traffic is no longer confined to voice and limited data services, but has become a fundamental pillar of the digital transformation that is affecting all aspects of our contemporary life.

The article offers information on the journal's call for feature topic (FT) proposals to complement its Series and Open Call publication tracks.

This is Part II of the double-part Special Issue (SI) on Integrated Sensing and Communication (ISAC). This SI aims at bringing together contributions from both academia and industry to highlight the recent progress of ISAC, where sensing and communication (S $\$ $ C) functionalities are jointly designed to utilize wireless/hardware resources efficiently and to assist each other for mutual benefits. The 32 accepted articles of this SI are arranged into six groups, namely, 1) Fundamental Performance Bounds and Optimization, 2) Time-Frequency Signal Processing, 3) Spatial Signal Processing, 4) Networking and Resource Allocation, 5) ISAC With Emerging Communications Technologies, and 6) ISAC Applications. We kindly refer readers to Part I of this SI for a comprehensive overview written by the Guest Editorial Team, which provides both a bird’s eye view and technical details regarding state-of-the-art ISAC innovations. The contributions made by the papers in Part II are summarized as follows, which correspond to paper groups 4), 5), and 6). [ABSTRACT FROM AUTHOR]

The explosive growth of Internet-of-Things (IoT) applications such as smart cities and Industry 4.0 have led to drastic increase in demand for wireless bandwidth, hence motivating the rapid development of new techniques for enhancing spectrum utilization needed by new generation wireless communication technologies. Among others, dynamic spectrum access (DSA) is one of the most widely accepted approaches. In this paper, as an enhancement of existing works, we take into consideration of inter-node collaborations in a dynamic spectrum environment. Typically, in such distributed circumstances, intelligent dynamic spectrum access almost invariably relies on self-learning to achieve dynamic spectrum access improvement. Whereas, this paper proposes a DSA scheme based on deep reinforcement learning to enhance spectrum and access efficiency. Unlike traditional Q-learning-based DSA, we introduce the following to enhance the spectrum efficiency in dynamic IoT spectrum environments. First, deep double Q-learning is adopted to perform local self-spectrum-learning for IoT terminals in order to achieve better dynamic access accuracy. Second, to accelerate learning convergence, federated learning (FL) in edge nodes is used to improve the self-learning. Third, multiple secondary users, who do not interfere with each other and have similar operation condition, are clustered for federated learning to enhance the efficiency of deep reinforcement learning. Comparing with the traditional distributed DSA with deep learning, the proposed scheme has faster access convergence speed due to the characteristic of global optimization for federated learning. Based on this, a framework of federated deep reinforcement learning (FDRL) for DSA is proposed. Furthermore, this scheme preserves privacy of IoT users in that FDRL only requires model parameters to be uploaded to edge servers. Simulations are performed to show the effectiveness of theproposed FDRL-based DSA framework. [ABSTRACT FROM AUTHOR]

Blockchain technology is becoming the cornerstone for the development and deployment of other technologies like Federated Learning (FL) and the Internet of Things (IoT), as it plays a critical role in data sharing and incentives. Blockchains supports decentralization, data-privacy protection, security, and reliability. Assuring secure data sharing in mobile computing and FL is challenging because of untrustworthy participants and unknown data quality. Blockchain provides trust in decentralized environments without requiring trusted third parties. By using smart contracts, blockchain has been able to supporting rich decentralized applications. However, the scalability of blockchain is a challenge that prevents its wide adoption by high-performance applications. To address the blockchain scalability issue, various blockchain sharding technologies and off-chain solutions have been proposed. To improve the network throughput, blockchain sharding divides the entire network into several smaller parallel groups and exploits fast consensus algorithms in blockchain shards. Off-chain solutions, such as payment channel networks (PCNs), transfer the slow on-chain transactions to the off-chain environment, in which transactions can be accelerated. Without consensus and on-chain expensive operations, off-chain scalable solutions significantly reduce transaction costs and increase transaction throughput. This special issue aims to provide a forum for the presentation of state-of-the-art research approaches that advance the construction of intelligent blockchain systems. A total of 27 articles were accepted after a two-round rigorous review process. Based on their topics, we have grouped the accepted articles into four categories: blockchain-based federated learning systems, blockchain and the IoT, blockchain scalability, and high-performance blockchains. In what follows, we introduce these articles and their contributions. [ABSTRACT FROM AUTHOR]

In the future era of intelligent networks, communication technology and network architecture need to be further developed to provide users with high-quality services. The Space-Air-Ground Integrated Networks (SAGIN) is seen as a potential architecture to provide ubiquitous communication and drive the era of the intelligent global network. The space and air segments in SAGIN can assist in offloading traffic from the ground segment. However, in a highly dynamic and heterogeneous network like SAGIN, offloading decisions are easily affected by the incorporated/malicious nodes. How to ensure security and improve network performance becomes a critical problem. In this paper, we address the above problem by jointly using blockchain and federated reinforcement learning (FRL). Firstly, we propose a blockchain-based secure federated learning framework that combines topology information chain and model chain to assist traffic offloading. Then, we propose a node security evaluation and an enhanced practical byzantine fault tolerance (EPBFT) algorithm to secure the traffic offloading process. Furthermore, we describe the traffic offloading problem as a Markov decision problem (MDP) and employ the Blockchain-based Federated Asynchronous Advantage Actor-Critic (BFA3C) algorithm to solve this problem. Finally, the simulation results show that the BFA3C-based algorithm used in SAGIN with/without malicious nodes achieves superior performance in terms of latency and security. [ABSTRACT FROM AUTHOR]

The testing of vehicular communication technologies, the study of urban mobility patterns, the evaluation of new traffic policies cannot dispense from vehicle mobility simulation. As is often the case, the larger the dataset, the better. Indeed, in recent years, many projects in the fields of mobility or vehicular communication have sought new traffic simulators with extended areas of investigation, possibly covering a whole city and its suburbs. In this spirit, we have modeled an urban traffic simulation in a 600-Km2 area in and around the Municipality of Turin, leveraging the SUMO tool. This paper aims at reporting in detail the methodology we followed in the creation of this dataset. Our results demonstrate that a complete modeling of such a wide area is possible at the expense of minor simplifications, reaching a very good level of approximation. [ABSTRACT FROM AUTHOR]

Space-division multiplexing (SDM) technology is a promising candidate to achieve massive parallelism in optical fiber cables and overcome the capacity limitation of the conventional single-mode fiber (SMF). Among the several kinds of SDM fiber, a weakly coupled multicore fiber (MCF) is advantageous in that it can be easily upgraded for use in existing equipment and requires no complex signal processing for optical multi-input multi-output (MIMO). In this article, we discuss the progress and standardization activity of MCF technologies from the viewpoint of accelerating the practical application of SDM technology. We first describe the possible application areas of SDM fibers and then show the recent progress of the essential technologies to construct the MCF link: the MCF itself, its splicing and connectorization, and the input/output techniques. We also discuss the availability and deployability of MCF as a large-capacity transmission medium and detail the progress on establishing a new international standard for SDM technology, which includes both historical progress and more recent endeavors on the standardization of optical fiber cables and SDM technology in International Telecommunication Union Telecommunication Standardization Sector (ITU-T) and International Electrotechnical Commission (IEC). We close with a summary of MCF technology and its standardization activity with an eye to next-generation transmission systems and the long-term development of ultralarge capacity SDM transmission technology. [ABSTRACT FROM AUTHOR]

Recently, reconfigurable intelligent surface (RIS) has drawn much attention due to its capability of improving coverage and communication performance. The development of RIS-assisted communication technology relies on deep understanding of RIS channel characteristics. However, most of the existing investigations focus on the impact of RIS, ignoring the existence of local scatterers in the real propagation environment. The ideal propagation condition limits the accuracy and application of the developed channel model. In this paper, we aim to analyze the channel characteristics of RIS-assisted near-filed communication by considering different conditions of local scatterers. Specifically, we propose a three-dimensional (3D) RIS-assisted multiple-input multiple-output (MIMO) channel model based on a three-dimensional cylinder model, which considers line-of-sight, single-bounced at RIS (SBR) and double-bounced (DB) modes where radio waves reflect from RIS and scatterers near receiver. Based on the proposed channel model, we investigate some key statistical properties for SBR and DB propagation, including the time-frequency-space correlation function and Doppler power spectrum. From the simulation results, it is found that RIS element number, RIS horizontal and vertical locations, distribution of surrounding scatterers, and movement of Rx have significant impacts on channel characteristics. Finally, channel capacities of SBR and DB modes are calculated, where the large size and appropriate locations of RIS significantly improve the channel capacity of RIS-assisted communication. These numerical results and observations can be used for design of RIS-assisted near-field communication systems. [ABSTRACT FROM AUTHOR]

Emerging communication technologies allow to reconfigure the physical network topology at runtime, enabling demand-aware networks (DANs): networks whose topology is optimized toward the workload they serve. However, today, only little is known about the fundamental algorithmic problems underlying the design of such demand-aware networks. This paper presents the first bounded-degree, demand-aware network, $\textit {cl-DAN} $ , which minimizes both congestion and route lengths. The degree bound $\Delta $ is given as part of the input. The designed network is provably (asymptotically) optimal in each dimension individually: we show that there do not exist any bounded-degree networks providing shorter routes (independently of the load), nor do there exist networks providing lower loads (independently of the route lengths). The main building block of the designed $\textit {cl-DAN} $ networks are $\textit {ego-trees}$ : communication sources arrange their communication partners in an optimal tree, individually. While the union of these ego-trees forms the basic structure of $\textit {cl-DANs}$ , further techniques are presented to ensure bounded degrees (for scalability). [ABSTRACT FROM AUTHOR]

With the explosive proliferation of connected devices and mobile users in the Internet-of-things, multiple access techniques are urged to be developed for the next generation wireless communications. Recently, rate-splitting multiple access (RSMA) has been a promising communication technology that holds advantages of strong robustness, low complexity, and high spectral efficiency, which can be integrated with the indoor visible light communication (VLC) broadcast system to compensate for the shortcomings of limited modulation bandwidth of LEDs. However, the research on the RSMA-based VLC systems is still in its infancy and there exist various problems to be explored. To benefit from the RSMA technique, this paper investigates the energy efficiency optimizations for both single-cell and multi-cell RSMA-based VLC broadcast systems. Specifically, these two systems are modeled, where the VLC broadcast channel follows Lambertian radiation model, and the splitting design and successive interference cancellation of RSMA are employed to mitigate the multi-user interference. Especially for multi-cell networks, the zero-forcing approach is adopted to eliminate the inter-cell interference. To maximize the energy efficiency, the precoding and power allocation problems are formulated for single-cell and multi-cell networks while accommodating multiple constraints including dynamic operation ranges of LEDs, QoS requirements, and interference elimination. For solving these non-convex fractional problems, two pieces of successive convex approximation (SCA)-based algorithms are proposed, in which the variable transformation and linear approximation are adopted. Simulation results indicate that the proposed schemes can achieve superior energy efficiency with fast convergence for various network loads and user deployments. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive survey of recent advances in deep learning (DL) techniques for channel coding problems. Inspired by the recent successes of DL in a variety of research domains, its applications to physical layer technologies have been extensively studied in recent years, and they are expected to be a potential breakthrough in supporting the emerging use cases of the next generation wireless communication systems such as 6G. In this paper, we focus exclusively on channel coding problems and review existing approaches that incorporate advanced DL techniques into code design and channel decoding. After briefly introducing the background of recent DL techniques, we categorize and summarize a variety of approaches, including model-free and model-based DL, for the design and decoding of modern error-correcting codes, such as low-density parity check (LDPC) codes and polar codes, to highlight their potential advantages and challenges. Finally, the paper concludes with a discussion of open issues and future research directions in channel coding.

ElectricalImpedance Myography (EIM) is an innovative, non-invasive technique offering a convenient means of localized exogenous electrophysiological recording. By measuring muscle impedance parameters, this method characterizes the physiological state of muscles, functioning as a biomarker for muscle contractility, injuries, and the progression of neuromuscular diseases. This paper provides an overview of the current state of EIM technology development, along with modeling and data analysis methods, focusing on their application requirements. It further highlights the advancements in EIM research within the realm of sports health, emphasizing its efficacy in identifying injuries and monitoring wound healing, and discusses existing technological limitations. Additionally, the paper explores future research directions. Serving as a transient biosensor during physical activity, EIM holds significant potential in sports health. It presents a promising alternative to invasive and costly clinical assessment methods, positioning itself as a viable personal monitoring tool for both professional athletes and fitness enthusiasts. Nevertheless, the resolution of technical challenges and the establishment of industry-standard implementation programs are essential prerequisites for EIM to evolve into a standard clinical assessment tool.

This is our last release in 2024 and closes off our fourth year of publications. As we enter 2025, we will be scheduled for bimonthly, rather than quarterly, issues as we take advantage of a significant increase in paper submissions after receiving our Clarivate Journal Impact Factor of 6.9. Our October issue contains 14 regular papers and one invited review article on GaN MMIC's. As an added treat in 2024, immediately following the release of our regular issue papers, we will bring you our long-awaited special issue on Microwaves in Climate Change which we have been assembling since the start of 2024. IEEE Journal of Microwaves is extremely pleased to host this special topic, which was inspired by our special editorial series article, “Making Waves: Microwaves in Climate Change,” (Siegel and Siegel 2023) that we released in July 2023. We hope that this special issue will bring additional scientists into the microwave publishing community and inspire more microwave engineers to seek out geophysicists, Earth scientists, environmentalists, climatologists, geochemical engineers, energy and power specialists, resource managers, and other technical experts who might benefit from the instrumentation, knowledge, and skillsets within our community. A more detailed introduction to the Climate Issue can be found in (Siegel 2024).

This paper explores the potential near-field beamforming (NFBF) in integrated sensing and communication (ISAC) systems with extremely large-scale arrays (XL-arrays). The large-scale antenna arrays increase the possibility of having communication users and targets of interest in the near field of the base station (BS). The paper first establishes the models of near-field spherical waves and far-field plane waves. With the models, we analyze the near-field beam focusing ability and the far-field beam steering ability by finding the gain-loss mathematical expression caused by the far-field steering vector mismatch in the near-field case. Subsequently, we analyzed the performance degradation caused by traditional far-field beamforming in the near field for both communication and sensing. We formulate the transceiver NFBF design problem as maximizing the sensing signal-to-interference-plus-noise ratio (SINR) while ensuring the required communication quality-of-service (QoS) and total power constraint. We decompose it into two subproblems and solve them using the generalized Rayleigh entropy theory and the Semi-Definite Relaxation (SDR) technique. Additionally, we prove the attainability of the optimal solution for SDR. Additionally, a low-complexity design scheme is proposed as an alternative to the SDR approach for obtaining transmit beamforming. The simulation results validate the effectiveness of the proposed NFBF scheme, demonstrating its capability to manage co-angle interference and enhance both communication and sensing performance.

This paper presents a novel variant of filter bank multicarrier (FBMC) communication, which carries additional bits through mapping the real and imaginary parts of the symbol to the active time-frequency (TF) points, called FBMC with real part and imaginary part index modulation (FBMCRIIM). Spectral efficiency is improved by doubling the index bits and the inherent interference in each index group is significantly reduced. In this paper, an efficient combination set selection strategy, which synthetically considers the average Hamming distance, average poor index number and average interference coefficient, is also designed. The BER performance comparison between the proposed and reference schemes is obtained under different index modes, spectral efficiency, modulation order and prototype filters. The enhanced performance demonstrates that the proposed scheme is a promising alternative for multicarrier transmission by outperforming the existing FBMC-IM counterpart.

Cell-free massive MIMO (CF-mMIMO) systems represent a deeply investigated evolution from the conventional multicell co-located massive MIMO (MC-mMIMO) network deployments. Anticipating a gradual integration of CF-mMIMO systems alongside pre-existing MC-mMIMO network elements, this paper considers a scenario where both deployments coexist, in order to serve a large number of users using a shared set of frequencies. The investigation explores the impact of this co-existence on the network’s downlink performance, considering various degrees of mutual cooperation, precoder selection, and power control strategies. Moreover, to take into account the effect of the proposed cooperation scenarios on the fronthaul links, this paper also provides a fronthaul-aware heuristic association algorithm between users and network elements, which allows the fulfillment of the front-haul requirement on each link. The research is finally completed by extensive simulations, shedding light on the performance outcomes associated with the various levels of cooperation and several solutions delineated in the paper.

With recent advances in wearable wrist-worn wireless sensing applications, the demand for smartwatches and wristbands is rapidly increasing due to their widespread adoption in applications such as smart health monitoring, security, and fitness tracking. Currently, these devices primarily operate in the 2.45 GHz band, leveraging the availability of Bluetooth and Wi-Fi wireless technologies. However, the use of Sub-GHz frequencies (e.g., 433 MHz, 868 MHz, 915 MHz, 923 MHz) for wearable systems has also gained interest due to the emergence of wireless technologies like long-range wide area network (LoRaWAN), narrowband-IoT (NB-IoT) and Sigfox, which offer the potential for long-range wireless communications and sensing applications. In recent times, there has been a notable surge in the commercial production of a variety of Sub-GHz wrist-worn wireless sensing devices for health monitoring and tracking applications. Nevertheless, communications at Sub-GHz frequencies present significant challenges in antenna design, primarily due to the practical size constraints of wrist-worn devices and the necessity for using electrically small antennas. This paper meticulously reviews wrist-worn Sub-GHz antennas reported in the literature, analyzing key antenna parameters such as antenna topology, size, impedance bandwidth, peak realized gain, radiation efficiency, and specific absorption rate (SAR). Additionally, it underlines antenna design challenges, limitations, current trends, and presents potential future perspectives. To the best of the author’s knowledge, there is currently no existing literature comprehensively reviewing Sub-GHz wrist-worn antennas. Therefore, this paper represents the inaugural effort to provide a comprehensive review in this specific domain.

This paper presents a rigorous new analytical derivation of the theorem on the embedded element radiation function of ideally infinite planar array antennas, along with a formula for accurately calculating the main beam direction of finite-sized array antennas based on the theorem. It validates the previously established formula of the embedded element radiation function, where the amplitude is proportional to $\sqrt {\cos \theta }$ , based on the intuitive reasoning that the effective area of an element should be proportional to its projected area in the direction of interest angle $\theta $ , provided that the array antenna has no grating lobes for the full scan, no surface waves, no losses, and active impedance matched. More importantly, the new analytical derivation can accurately predict the embedded element radiation function in cases where there are grating lobes for the array antenna with the full scan, which the intuitive area projection reasoning cannot provide. The theorem concludes that the array’s active element reflection coefficient and inter-element spacing fully determine the embedded element radiation function in all cases. Utilizing this theorem, a new formula for the element phase progression is derived to accurately steer the main beam in the desired direction of the array with a finite number of elements. Several verification cases of wide-scanning array antennas are presented, and the comparisons between numerical simulations, measurements, theoretical results, and some interesting conclusions are discussed in the paper.

Blockchain network (BCN)-based Self-Sovereign Identity (SSI) has emerged lately as an identity and access management framework that is based on Distributed Ledger Technology (DLT) and allows users to control their own data. Federated Learning (FL), on the other hand, provides a collaborative framework to update Machine Learning (ML) models without relying explicitly on data exchange between the users. This paper investigates identity management and authentication for vehicle users in the context of FL. We propose a novel approach based on blockchain-based SSI, which focuses on maintaining the authenticity and integrity of vehicle users’ identities and data exchanged between the users and the aggregation server during the execution of the FL iterations. A primary objective of this paper is to achieve shorter durations for credential operations in an FL setting as the system size scales out. Integrating BCN-based SSI into the FL framework addresses several critical FL challenges, ensuring enhanced system security and operational integrity. This synergy of BCN-based SSI with federated learning enables robust identity verification providing a solution to fundamental trustworthiness issues in FL without sacrificing the benefits of decentralized data control, improving both the performance and reliability of the FL system. Experimental results suggest that the proposed FL-based system, together with credential management on a blockchain platform, has the potential to significantly improve data integrity and ensure the authentication of users. More specifically, the results of the FL system demonstrate that it takes longer (on the order of a hundred seconds) as the number of rounds and clients increase, while the implemented Decentralized Identifier (DID) system relying on BCN-based SSI has dramatically shorter dedicated time for completing credential operations.

Over the past decade, the use of Unmanned Aerial Vehicles (UAVs) has grown significantly due to their agility, maneuverability, and rapid deployability. An important application is the use of UAV-mounted 360-degree cameras for real-time streaming of Omnidirectional Videos (ODVs), enabling immersive experiences with up to six Degrees-of-freedom (6DoF) for applications like remote surveillance and gaming. However, streaming high-resolution ODVs with low latency (below 1 second) over an air-to-ground (A2G) wireless channel faces challenges due to its inherent non-stationarity, impacting the Quality-of-experience (QoE). Limited onboard energy availability and energy consumption variability based on flight parameters add to the complexity. This paper conducts a thorough survey of challenges and research efforts in UAV-based immersive video streaming. First, we outline the end-to-end 360-degree video transmission pipeline, covering coding, packaging, and streaming with a focus on standardization for device and service interoperability. Next, we review the research on optimizing video streaming over UAV-to-ground wireless channels, and present a real testbed demonstrating 360-degree video streaming from a UAV with remote control over a 5G network. To assess performance, a high-resolution 360-degree video dataset captured from UAVs under different conditions is introduced. Encoding schemes like AVC/H.264, HEVC/H.265, VVC/H.266, VP9, and AV1 are evaluated for encoding latency and QoE. Results show that HEVC‘s hardware implementation achieves a good QoE-latency trade-off, while AV1’s software implementation provides superior QoE. The paper concludes with discussions on open challenges and future directions for efficient and low-latency immersive video streaming via UAVs.

In this paper, we consider the problem of detecting the channel activity in LoRaWAN networks. To improve the medium access performance, recent implementations of the chipsets for LoRaWAN end nodes are offering a feature called Channel Activity Detection (CAD), as the first step toward the implementation of the Carrier Sense Multiple Access (CSMA) in LoRaWAN networks. The algorithm underlying the CAD is at the moment left undisclosed. In this paper, we propose a possible implementation of the CAD algorithm; we then evaluate its performance in an additive white Gaussian noise (AWGN) channel, and we find the performance being completely satisfactory and matching the one of the undisclosed algorithm. The results we find are encouraging the community to include the CAD feature in open source implementations of a LoRaWAN receiver.

Cell-free massive MIMO (CF-mMIMO) emerges as a pivotal technology in the landscape of beyond-5G and 6G wireless networks, addressing the ever-increasing demand for seamless connectivity and unprecedented data throughput. This paper undertakes a comprehensive exploration of scalable usercentric (UC) CF-mMIMO systems, focusing on critical aspects of downlink (DL) channel state information (CSI) acquisition and its intricate interactions with both distributed and centralized precoding strategies. The paper delves into the crucial role of DL CSI acquisition, particularly in scenarios of weak channel hardening arising from sparse subsets of access points (APs) serving specific mobile stations (MS) in UC strategies, and transmission over spatially correlated multiple keyhole Ricean fading channels. The main contributions of this research work include in-depth analyses of different detection schemes under varying precoding scenarios, offering valuable insights for practical deployment. The pivotal role of DL CSI acquisition in optimizing the performance of UC CF-mMIMO networks is fully assessed, dismissing the use of DL pilot-based detection approaches and advocating for either centralized precoding architectures with statistical CSI-based decoding strategies at the MSs or distributed precoding schemes with DL blind channel estimation-based decoders at the MSs.

This paper introduces Waste Factor (W), also denoted as Waste Figure (WF) in dB, a promising new metric for quantifying energy efficiency in a wide range of circuits and systems applications, including data centers and Radio Access Networks (RANs). Creating and managing 5G and future 6G networks that are energy-efficient is of paramount importance as the wireless industry evolves to become a major consumer of energy. Also, the networks used to connect data centers and artificial intelligence (AI) computing engines with users for machine learning (ML) applications must become more power efficient. This paper illustrates the limitations of existing energy efficiency metrics that inadequately capture the intricate energy dynamics of RAN components. We show here that W provides a generalized analysis of power utilization and energy waste at both the component and system levels for any source-to-sink communication system. We delineate the methodology for applying W across various network configurations, including multiple-input single-output (MISO), single-input and multiple-output (SIMO), and multiple-input multiple-output (MIMO) systems, and demonstrate the effectiveness of W in identifying energy optimization opportunities. Our findings reveal that W not only offers nuanced insights into the energy consumption of RANs but also facilitates informed decision-making for network design and operational efficiency. Furthermore, we show how W can be integrated with other key performance indicators (KPIs) and key value indicators (KVIs) to guide the development of optimal strategies for enhancing network energy efficiency under different operational conditions. Additionally, we present simulation results for a distributed multi-user MIMO (MU-MIMO) system at 3.5, 17, and 28 GHz, demonstrating overall network power efficiency on a per square kilometer basis, and show how overall W decreases (e.g., energy efficiency increases over the entire network) with an increasing number of base stations and increasing carrier frequency. This paper shows that adopting W as a figure of merit (FoM) can enable the design of more sustainable next-generation wireless communication networks, paving the way for greener and more sustainable, energy-efficient 5G and 6G technologies.

Federated Learning (FL) emerged as a significant advancement in the field of Artificial Intelligence (AI), enabling collaborative model training across distributed devices while maintaining data privacy. As the importance of FL and its application in various areas increased, addressing trustworthiness issues in its various aspects became crucial. In this survey, we provided a comprehensive overview of the state-of-the-art research on Trustworthy FL, exploring existing solutions and key foundations relevant to Trustworthiness in FL. There has been significant growth in the literature on trustworthy centralized Machine Learning (ML) and Deep Learning (DL). However, there is still a need for more focused efforts toward identifying trustworthiness pillars and evaluation metrics in FL. In this paper, we proposed a taxonomy encompassing five main classifications for Trustworthy FL, including Interpretability and Explainability, Transparency, Privacy and Robustness, Fairness, and Accountability. Each category represents a dimension of trust and is further broken down into different sub-categories. Moreover, we addressed trustworthiness in a Decentralized FL (DFL) setting. Communication efficiency is essential for ensuring Trustworthy FL. This paper also highlights the significance of communication efficiency within various Trustworthy FL pillars and investigates existing research on communication-efficient techniques across these pillars. Our survey comprehensively addresses trustworthiness challenges across all aspects within the Trustworthy FL settings. We also proposed a comprehensive architecture for Trustworthy FL, detailing the fundamental principles underlying the concept, and provided an in-depth analysis of trust assessment mechanisms. In conclusion, we identified key research challenges related to every aspect of Trustworthy FL and suggested future research directions. This comprehensive survey served as a valuable resource for researchers and practitioners working on the development and implementation of Trustworthy FL systems, contributing to a more secure and reliable AI landscape.

Electromagnetic (EM) body models predict the impact of human presence and motions on the Radio-Frequency (RF) field originated from wireless devices nearby. Despite their accuracy, EM models are time-consuming methods which prevent their adoption in strict real-time computational imaging and estimation problems, such as passive localization, RF tomography, and holography. Physicsinformed Generative Neural Network (GNN) models have recently attracted a lot of attention thanks to their potential to reproduce a process by incorporating relevant physical laws and constraints. They can be used to simulate or reconstruct missing data or samples, reproduce EM propagation effects, approximated EM fields, and learn a physics-informed data distribution, i.e., the Bayesian prior. Generative machine learning represents a multidisciplinary research area weaving together physical/EM modelling, signal processing, and Artificial Intelligence (AI). The paper discusses two popular techniques, namely Variational Auto-Encoders (VAEs) and Generative Adversarial Networks (GANs), and their adaptations to incorporate relevant EM body diffraction methods. The proposed EM-informed GNN models are verified against classical EM tools driven by diffraction theory, and validated on real data. The paper explores emerging opportunities of GNN tools targeting real-time passive RF sensing in communication systems with dense antenna arrays. Proposed tools are also designed, implemented, and verified on resource constrained wireless devices. Simulated and experimental analysis reveal that GNNs can limit the use of time-consuming and privacy-sensitive training stages as well as intensive EM computations. On the other hand, they require hyper-parameter tuning to achieve a good compromise between accuracy and generalization.

Chirp spread spectrum (CSS) signal is the heart of long-range (LoRa) modulation, also known as CSS modulation, and is used in long-range wide area network (LoRaWAN) in Internet of things (IoT) scenarios. CSS modulation has drawn much attention from the research and industry communities in recent years. However, to the best of our knowledge, a comprehensive tutorial, investigating the mathematical foundations of CSS modulation in the LoRaWAN application, is missing in the literature. Therefore, in the first part of this paper, we provide a thorough analysis and tutorial of CSS modulation as the physical (PHY) layer of LoRaWAN, discussing various aspects such as signal generation, detection, error performance, and spectral characteristics. Moreover, a summary of key recent advances in the context of CSS modulation modifications and applications in IoT networks is presented in the second part of this paper under four main categories, i.e., transceiver configuration and design, data rate improvement, interference modeling, and synchronization algorithms. Finally, future research directions are also provided to discuss the potential issues and solutions for improving the performance of CSS modulation in LoRaWAN.

Orthogonal time frequency space (OTFS) modulation is strongly considered as a promising solution for high-mobility communications. In contrast to conventional modulation techniques, wherein information symbols are multiplexed in a one-dimensional time or frequency domain, OTFS employs a two-dimensional modulation scheme by multiplexing information symbols in the delay-Doppler domain. This paper presents a comprehensive survey of OTFS. It starts with an overview of OTFS, its advantages over conventional air interface techniques, general block diagrams, and implementations. Subsequently, the paper explores the potential integration of multiple-input multiple-output and OTFS techniques. The paper further discusses the feasibility of integrating OTFS into multiple access techniques as a solution for maintaining acceptable performance in high-mobility scenarios. Then, widespread applications of OTFS in satellite communications are highlighted. Also, the potential utilization of OTFS modulation in integrated sensing and communications paradigm is thoroughly treated. In addition, the survey covers further applications of OTFS in deep learning, index modulation, underwater acoustic, and unmanned aerial vehicle communications. The paper concludes by pointing out numerous challenging and promising directions for future OTFS research.

The efficient representation, transmission, and reconstruction of three-dimensional (3D) contents are becoming increasingly important for sixth-generation (6G) networks that aim to merge virtual and physical worlds for offering immersive communication experiences. Neural radiance field (NeRF) and 3D Gaussian splatting (3D-GS) have recently emerged as two promising 3D representation techniques based on radiance field rendering, which are able to provide photorealistic rendering results for complex scenes. Therefore, embracing NeRF and 3D-GS in 6G networks is envisioned to be a prominent solution to support emerging 3D applications with enhanced quality of experience. This paper provides a comprehensive overview on the integration of NeRF and 3D-GS in 6G. First, we review the basics of the radiance field rendering techniques, and highlight their applications and implementation challenges over wireless networks. Next, we consider the over-the-air training of NeRF and 3D-GS models over wireless networks by presenting various learning techniques. We particularly focus on the federated learning design over a hierarchical device-edge-cloud architecture, which is suitable for exploiting distributed data and computing resources over 6G networks to train large models representing large-scale scenes. Then, we consider the over-the-air rendering of NeRF and 3D-GS models at wireless network edge. We present three practical rendering architectures, namely local, remote, and co-rendering, respectively, and provide model compression approaches to facilitate the transmission of radiance field models for rendering. We also present rendering acceleration approaches and joint computation and communication designs to enhance the rendering efficiency. In a case study, we propose a new semantic communication enabled 3D content transmission design, in which the radiance field models are exploited as the semantic knowledge base to reduce the communication overhead for distributed inference. In addition, we discuss the utilization of radiance field rendering in wireless applications like radio mapping and radio imaging, in which radiance field models are used to effectively represent complex radio environments to facilitate wireless network designs. It is our hope that this paper can provide new insights on the interesting wireless integration with radiance field rendering for future 6G networks with 3D contents.

A crucial component of an interactive Virtual Reality (VR) experience is high-quality, real-time image rendering and display, which requires high-end hardware in VR headsets. Cloud VR offers a compelling alternative by offloading computationally intensive rendering tasks to remote servers. However, such a distributed operation requires a real-time reliable delivery of the VR traffic over the networks, which is a challenging task. Therefore, a thorough understanding of the VR traffic characteristics, Quality of Service (QoS) requirements, and the impact of packet service on the quality of VR is required. The paper provides insights in this area by exploring the traffic generated by a cloud VR application of an off-the-shelf Pico Neo 2 VR headset. Specifically, it identifies often-overlooked application features with a significant impact on QoS and develops a novel VR application model that accounts for these features. These findings are used in three ways. First, the paper shows that the 3GPP baseline underestimates the effective VR bitrate, i.e., the bandwidth needed to deliver VR traffic by 25%, while the developed model provides a much more accurate estimation. Second, the model is used to evaluate the benefits of intra-refresh video coding and adjusting video buffering at the VR headset according to motion-to-photon requirements. Finally, based on the revealed important features, the paper improves a VR bitrate adaptation algorithm.

A key aspect of the imaging capability of radar systems is the angular resolution, which is determined by the aperture size of the antenna array. Therefore technologies such as MIMO and especially radar networks consisting of multiple independent MIMO radar sensors seek to maximize the virtual aperture size. Depending on the range and velocity resolution of the MIMO radar network, multistatic aspects must be accounted for. So far, those multistatic effects were seen as errors, which must be compensated for in order to restore the classical DoA properties of the virtual aperture, described by the narrowband beam pattern. This paper shows that new virtual aperture designs with larger antenna spacings are possible while still preserving the angular ambiguity range of smaller antenna spacings, as long as the multistatic effects of distributed radar networks, namely radar networks whose virtual aperture is large in comparison to the range resolution, are correctly accounted for. The larger antenna element spacing enables larger aperture sizes leading to higher angular resolution. This paper illustrates that the well-known, Fourier Tranformation-based signal processing is unable to exploit this potential of distributed radar networks, and an computationally efficient approximated matched filter is proposed. This article presents a signal model for distributed radar networks, suitable signal processing, and a comparison to the well-known Fourier Transformation-based signal processing for compact radar networks. Both the signal model and the proposed signal processing are verified by measurements with a radar sensor network composed of 2 MIMO radar sensors operating in the automotive frequency range of $76 \,\mathrm{G}\mathrm{Hz}\,\mathrm{to}\, 81 \,\mathrm{G}\mathrm{Hz}$ providing 64 virtual channels with a range resolution of $0.03 \,\mathrm{m}$. The virtual aperture size of the radar network is ${\sim }0.5 \,\mathrm{m}$ with virtual antenna spacing of twice the wavelength, but the proposed signal processing still allows unambiguous DoA estimation within the full $180 \,\mathrm{^{\circ }}$ range.

This paper proposes to explore the Radio Frequency Fingerprint (RFF) identification with a virtual database generator. RFF is a unique signature created in the emitter transmission chain by the hardware impairments. These impairments may be used as a secure identifier as they cannot be easily replicated for spoofing purposes. In recent years, the RFF identification relies mainly on Deep Learning (DL), and large databases are consequently needed to improve identification in different environmental conditions. In this paper, we introduce the so-called RiFyFi_VDG, referring to Radio Frequency Fingerprint Virtual Database Generator, and explore individually the impairment impact on the classification accuracy to highlight the most relevant impairment. Different transmission scenarios are then explored, such as the impact of the data type (being a preamble or a payload) and the data size. Design rules of real databases are finally drawn for the different scenarios. We found out that the power amplifier imperfections play the biggest role in RFF accuracy and that average accuracy levels of 94% can be reached when combining the various hardware impairments at the transmitter.

Via is an essential component in the design of an Electromagnetic Band Gap (EBG) structure. This paper investigates the effects of the via in a mushroom-like EBG structure and proposes an equivalent circuit model of the via. It is revealed that when the via in the mushroom-like structure is offset, the element reflection phase will vary within 720 degrees instead of 360 degrees phase range in a conventional design. This phenomenon can be explained by a new circuit model that introduces a mutual inductance, and the corresponding electromagnetic properties of mushroom-like EBG structure can be quantitatively analyzed by this model. In addition, the applicability and error analysis of the model are discussed in this paper. The theoretical modeling and analysis enable an efficient and in-depth exploration of the via functions in similar EBG structures, and also provide more theoretical guidance and assistance for the design of reflection and transmission units based on EBG structure.

This paper presents a thorough review of the main techniques used for wireless power transfer (WPT) in implantable medical devices (IMDs) with a specific focus on the techniques that employ implantable antennas for energy harvesting (electromagnetic (EM) WPT techniques). The techniques are first analysed and compared based on the IMD application, power transfer efficiency (PTE), transfer distance, implantation depth, implant size, operating frequency, and specific absorption rate (SAR). The study provides a critical analysis of the main WPT system’s as well as implantable antennas’ design parameters that control the PTE and hence the charging rate of the IMD. The investigated design parameters include the WPT TX-RX antennas’ gain, WPT-RX size, transfer distance, and the WPT TX-RX antennas’ alignment. Tutorial simulation examples are included to showcase the impact of these design parameters on the amount of power coupled to the IMD. The paper also discusses recent techniques used for improving the amount of power received by implantable antennas, and hence higher PTE and IMDs charging rate, namely, the use of implantable MIMO WPT-RX antennas to mitigate antennas misalignment and the use of metamaterial surfaces to focus the power emitted from WPT-TX antennas towards the implantable WPT-RX antennas. The findings and observations reported in this study serve as a valuable resource for designers and researchers to comprehend the effect of various WPT TX-RX antennas design parameters on PTE. The analysis and full-wave simulation examples, included in the paper, are shown very useful in understanding the challenges associated with WPT in IMDs and in proposing potential solutions.

Traffic Classification (TC) systems are designed to identify the applications generating network traffic. Recent advancements in TC leverage Deep Learning (DL) techniques, surpassing traditional methods in complex scenarios, including those with encrypted traffic. Notably, state-of-the-art DL-based TC systems have been developed for wireless networks using Physical Layer (L1) packets. This approach overcomes the common limitation in TC research that assumes traffic flows within a wired network under a single network management domain. Despite their benefits, DL-based TC systems often demand significant computational resources, typically available only in cloud environments. Consequently, deploying models at the edge is often infeasible due to their resource-intensive nature, given their original training and optimization for high-resource environments. The inherent challenge lies in adapting these systems for edge computing scenarios, including deployment at access points. In this paper, we propose a novel methodology that exploits expert knowledge in combination with recent advances in Multi-Task Learning (MTL) and Deep Neural Network (DNN) optimization to allow spectrum-based TC systems to run on constrained devices. This paper propose a well-defined and innovative methodology for resource-efficient, spectrum-based TC to address this issue, combining MTL with DNN optimization techniques. Performance evaluations on an NVIDIA Jetson TX2 demonstrate that our most optimized MTL model, handling four TC tasks, can reduce memory requirements by a factor of 2.65x and improve execution time by 3.6x compared to sequential execution of four Single-Task Learning (STL) models in a server-grade configuration, with minimal accuracy impact (less than a 0.5% drop) and energy efficiency of 0.97 millijoules per sample at inference. Compared to other edge platforms such as the Raspberry Pi model 3B+ (RPI3B+) with a low-power Artificial Intelligence (AI)-accelerator such as the Coral Tensor Processing Unit (TPU), the NVIDIA Jetson achieves a 12-fold improvement in energy efficiency with no impact on accuracy.These are the first available results to provide a benchmark for different performance metrics (memory, computing, energy) over heterogeneous constrained devices for this type of TC system.

Channel coding plays a pivotal role in ensuring reliable communication over wireless channels. With the growing need for ultra-reliable communication in emerging wireless use cases, the significance of channel coding has amplified. Furthermore, minimizing decoding latency is crucial for critical-mission applications, while optimizing energy efficiency is paramount for mobile and the Internet of Things (IoT) communications. As the fifth generation (5G) of mobile communications is currently in operation and 5G-advanced is on the horizon, the objective of this paper is to assess prominent channel coding schemes in the context of recent advancements and the anticipated requirements for the sixth generation (6G). In this paper, after considering the potential impact of channel coding on key performance indicators (KPIs) of wireless networks, we review the evolution of mobile communication standards and the organizations involved in the standardization, from the first generation (1G) to the current 5G, highlighting the technologies integral to achieving targeted KPIs such as reliability, data rate, latency, energy efficiency, spectral efficiency, connection density, and traffic capacity. Following this, we delve into the anticipated requirements for potential use cases in 6G. The subsequent sections of the paper focus on a comprehensive review of three primary coding schemes utilized in past generations and their recent advancements: low-density parity-check (LDPC) codes, turbo codes (including convolutional codes), and polar codes (alongside Reed-Muller codes). Additionally, we examine alternative coding schemes like Fountain codes (also known as rate-less codes), sparse regression codes, among others. Our evaluation includes a comparative analysis of error correction performance and the performance of hardware implementation for these coding schemes, providing insights into their potential and suitability for the upcoming 6G era. Lastly, we will briefly explore considerations such as higher-order modulations and waveform design, examining their contributions to enhancing key performance indicators in conjunction with channel coding schemes.

Non-orthogonal multiple access (NOMA) is a powerful transmission technique that enhances the spectral efficiency of communication links, and is being investigated for 5G standards and beyond. A major drawback of NOMA is the need to apply successive interference cancellation (SIC) at the receiver on a symbol-by-symbol basis, which limits its practicality. To circumvent this, in this paper a novel constructive multiple access (CoMA) scheme is proposed and investigated. CoMA aligns the superimposed signals to the different users constructively to the signal of interest. Since the superimposed signal aligns with the data signal, there is no need to remove it at the receiver using SIC. Accordingly, SIC component can be removed at the receiver side. In this regard and in order to provide a comprehensive investigation and comparison, different optimization problems for user paring NOMA multiple-input-single-output (MISO) systems are considered. Firstly, an optimal precoder to minimize the total transmission power for CoMA subject to a quality-of-service constraint is obtained, and compared to conventional NOMA. Then, a precoder that minimizes the CoMA symbol error rate (SER) subject to power constraint is investigated. Further, the computational complexity of CoMA is considered and compared with conventional NOMA scheme in terms of total number of complex operations. The results in this paper prove the superiority of the proposed CoMA scheme over the conventional NOMA technique, and demonstrate that CoMA is an attractive solution for user paring NOMA MISO systems with low number of BS antennas, while circumventing the receive SIC complexity.

The problem of optimizing discrete phases in a reconfigurable intelligent surface (RIS) to maximize the received power at a user equipment is addressed. Necessary and sufficient conditions to achieve this maximization are given. These conditions are employed in an algorithm to achieve the maximization. New versions of the algorithm are given that are proven to achieve convergence in $N$ or fewer steps whether the direct link is completely blocked or not, where $N$ is the number of the RIS elements, whereas previously published results achieve this in $KN$ or $2N$ number of steps where $K$ is the number of discrete phases. Thus, for a discrete-phase RIS, the techniques presented in this paper achieve the optimum received power in the smallest number of steps published in the literature. In addition, in each of those $N$ steps, the techniques presented in this paper determine only one or a small number of phase shifts with a simple elementwise update rule, which result in a substantial reduction of computation time, as compared to the algorithms in the literature. As a secondary result, we define the uniform polar quantization (UPQ) algorithm which is an intuitive quantization algorithm that can approximate the continuous solution with an approximation ratio of ${\mathrm {sinc}}^{2}(1/K)$ and achieve low time-complexity, given perfect knowledge of the channel.

Non-fungible tokens (NFTs) have become an exciting technology that provides a fresh perspective on asset ownership, provenance, and value exchange. NFTs, a blockchain-based technology, are distinct and indivisible cryptographic tokens used to confirm and record the ownership of digital and physical assets in an immutable and transparent way. The fundamental block of NFT is a smart contract built on a blockchain network. This contract contains specific information about the asset it represents, such as its unique identifier, metadata, and ownership details. The information is kept private and tamper-proof due to the decentralized and distributed structure of the blockchain, boosting faith in the token’s authenticity. The NFT is gaining popularity, but it is still in the developing stage. There is a need for a comprehensive survey to guide future research and development in NFTs. Thus, this paper presents the technical components of NFTs, their features, and the minting process. Further, this survey paper describes different token standards for NFTs. It presents various applications of NFTs in healthcare, supply chain, gaming, identity verification, agriculture, intellectual property, smart cities, charity and donation, and education. The article also emphasizes the significant difficulties faced currently in implementing NFT technology from the viewpoints of ownership, governance, and property rights, as well as security, privacy, and environmental effects. This work also elucidates the future directions to overcome the challenges in adopting NFTs in various applications.