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The active and dynamic control of the TeraHertz (THz) waves is highly demanded due to the rapid development of wireless communication systems. Recently, reconfigurable intelligent surface (RIS) has gained significant attention due to their tremendous potential in controlling the electromagnetic waves. Particularly, RIS-based wireless communications are promising for improving the system���s performance by properly designing the reflection coefficient of the unit cell. In this work, we investigate a graphene-based RIS for active and dynamic control of THz waves. The RIS design consists of rectangular graphene meta-atoms periodic array placed over a metallic grounded silicon substrate. An equivalent circuit modeling of the RIS design and its solution is provided. The RIS performance is numerically analyzed. The graphene-based RIS achieves nearly 100% reflection in the operational frequency ranging from 0.1 to 4 THz. The perfect reflection is insensitive to the polarization and the incident angles. Moreover, 100% absorption in the graphene-based RIS is achieved by electrically reconfiguring the metaatom response via the chemical potential of the graphene. The graphene RIS also achieves the anomalous reflection performance by controlling the number of unit cells and phase gradient of RIS. In view of the effective THz wireless communication environment, this paper finally presents a simple RIS-aided communication model to study the impact of the proposed graphene-based RIS on the signal-to-noise ratio. The results reveal that the proposed THz RIS with graphene meta-atoms is promising for efficient and intelligent THz wireless communications., �� 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

THz frequencies offer enormous amounts of bandwidth, which could solve the current speed bottleneck for next-generation wireless communications. Recent reports show sub-THz links offering capacities of hundreds of Gbit/s, finally approaching those of state-of-the-art optical transmission channels. Non-etheless, generation, transport, detection and processing of signals in the THz range is far from being a trivial task. Even though the recent evolution of integrated technology is starting to indicate that chip-scale THz technology could gradually close the so-called “THz gap,” much work still needs to be done to enable functional systems, in particular in terms of efficiency. Photonics can be of help, thanks to its extremely low loss and broad bandwidth. Yet, a particularly critical aspect hindering the deployment of THz technology is that state-of-the-art photonics devices generally do not offer sufficient electro-optical bandwidth to process THz signals. Plasmonics, by focusing electromagnetic surface waves at sub-wavelength scales, can play a key role in this quest, as it finally enables the realization of electro-optical devices such as modulators and detectors displaying sufficient compactness and speeds to reach the THz range. This paper overviews recent achievements on plasmonic-based modulators displaying characteristics of speed, efficiency and linearity that enable high-performance access to this much desired frequency range.

Reconfigurable intelligent surface (RIS) has been shown as a promising technique to increase the channel randomness for secret key generation (SKG) in low-entropy channels (e.g., static or line-of-sight (LoS)), without small-scale fading. In this letter, we show that even with the aid of RIS, collaborative eavesdroppers (Eves) can still estimate the legitimate Alice-Bob channel and erode their secret key rates (SKRs), since the RIS induced randomness is also reflected in the Eves’ observations. Conditioned on Eves’ observations, if the entropy of RIS-combined legitimate channel is zero, Eves are able to estimate it and its secret key. Leveraging this, we design a multi-Eve scheme against the RIS-secured LoS dominated scenarios, by using the multiple Eves’ observations to reconstruct the RIS-combined legitimate channel. We further deduce a closed-form secret key leakage rate under our designed multi-Eve scheme, and demonstrate the results via simulations. Engineering and Physical Sciences Research Council (EPSRC): EP/V026763/1

Peer reviewed: True, Active modification of the polarization state is a key feature for the next-generation of wireless communications, sensing, and imaging in the THz band. The polarization modulation performance of an integrated metamaterial/graphene device is investigated via a modified THz time domain spectroscopic system. Graphene’s Fermi level is modified through electrostatic gating, thus modifying the device’s overall optical response. Active tuning of ellipticity by >0.3 is reported at the resonant frequency of 0.80 THz. The optical activity of transmitted THz radiations is continuously modified by >21.5o at 0.71 THz. By carefully selecting the transmitted frequency with the relative angle between the incoming linear polarization and the device’s symmetry axis, active circular dichroism and optical activity are almost independently exploited. Finally, this all-electronically tuneable versatile polarization device can be used in all applications requiring an ultrafast modulation such as polarization spectroscopy, imaging, and THz wireless generation.

—In this paper, a de-noising approach in conjunction with channel estimation (CE) algorithm for OFDM systems using singular spectrum analysis (SSA) is presented. In the proposed algorithm, the initial CE is computed with the aid of traditional linear minimum mean square error (LMMSE) algorithm, and then further channel is evaluated by considering the low rank eigenvalue approximation of channel correlation matrix related to channel using SSA. Simulation results on bit error rate (BER) revealed that the method attains an improvement of 7 dB, 5 dB and 3 dB compared to common LSE, MMSE and SVD based methods respectively. With the help of statistical correlation coefficient (C) and kurtosis (k), the SSA method utilized to de-noise the received OFDM signal in addition to CE. In the process of denoising, the received OFDM signal will be decomposed into different empirical orthogonal functions (EOFs) based on the singular values. It was established that the correlation coefficients worked well in identifying useful EOFs only up to moderate SNR  12dB. For low SNR

The ever-increasing demand for applications with stringent constraints in device density, latency, user mobility, or peak data rate has led to the appearance of the last generation of mobile networks (i.e., 5G). However, there is still room for improvement in the network spectral efficiency, not only at the waveform level but also at the Radio Resource Management (RRM). Up to now, solutions based on multicast transmissions have presented considerable efficiency increments by successfully implementing subgrouping strategies. These techniques enable more efficient exploitation of channel time and frequency resources by splitting users into subgroups and applying independent and adaptive modulation and coding schemes. However, at the RRM, traditional multiplexing techniques pose a hard limit in exploiting the available resources, especially when users' QoS requests are unbalanced. Under these circumstances, this paper proposes jointly applying the subgrouping and Non-Orthogonal Multiple Access (NOMA) techniques in 5G to increase the network data rate. This study shows that NOMA is highly spectrum-efficient and could improve the system throughput performance in certain conditions. In the first part of this paper, an in-depth analysis of the implications of introducing NOMA techniques in 5G subgrouping at RRM is carried out. Afterward, the validation is accomplished by applying the proposed approach to different 5G use cases based on vehicular communications. After a comprehensive analysis of the results, a theoretical approach combining NOMA and time division is presented, which improves considerably the data rate offered in each use case. This work was supported in part by the Italian Ministry of University and Research (MIUR), within the Smart Cities framework, Project Cagliari2020 ID: PON04a2_00381; in part by the Basque Government under Grant IT1234-19; and in part by the Spanish Government [Project PHANTOM under Grant RTI2018-099162-B-I00 (MCIU/AEI/FEDER, UE)].

Sustainable urban mobility refers to the sustainable transportation mode in terms of social, environmental, and climate impacts. Cycling has emerged as one of the most sustainable means of urban travel due to its flexibility, low costs, reduced carbon emissions, improved traffic, and mobility in cities. Vehicle-to-vehicle (V2V) communication is becoming a reality standard. Non-motorized vehicles, such as bikes, are expected to participate in V2V and Vehicle-To-Everything (V2X) networking alongside cars and trucks, although they have gotten significantly less attention. In this paper, we analyze and experimentally evaluate Bike-to-Bike (Bi2Bi) wireless communication in different urban scenarios, considering full topo-morphological characteristics of urban environments by means of deterministic 3D Ray Launching hybrid simulations. Communication and data exchange between bikes relies on the IEEE 802.15.4 standard, and specifically, on ZigBee. We analyze Bi2Bi communication and cooperation towards sustainable smart mobility, following a holistic approach providing a tool based on the city platform to monitor and understand the impact of mobility at both city and citizen level and to provide accurate information on carbon footprint reduction, for multiple city/system stakeholders.

Advanced technologies are required in future mobile wireless networks to support services with highly diverse requirements in terms of high data rate and reliability, low latency, and massive access. Deep Learning (DL), one of the most exciting developments in machine learning and big data, has recently shown great potential in the study of wireless communications. In this article, we provide a literature review on the applications of DL in the physical layer. First, we analyze the limitations of existing signal processing techniques in terms of model accuracy, global optimality, and computational scalability. Next, we provide a brief review of classical DL frameworks. Subsequently, we discuss recent DL-based physical layer technologies, including both DL-based signal processing modules and end-to-end systems. Deep neural networks are used to replace a single or several conventional functional modules, whereas the objective of the latter is to replace the entire transceiver structure. Lastly, we discuss the open issues and research directions of the DL-based physical layer in terms of model complexity, data quality, data representation, and algorithm reliability.

This paper presents a novel physical-layer security approach to protect the information exchanged in a wireless communication system based on OFDM. In this method, QAM symbols that are fed into the IFFT block are split into two subsets. The first subset of symbols is placed on non-scrambled (indexing) subcarriers, whereas the remaining symbols are transmitted on scrambled (data) subcarriers. Based on the bits placed on the indexing subcarriers, a permutation matrix that defines the (data-based) scrambling sequence of the data subcarriers is determined using an algorithm that is known a priori between the transmitter and receiver. The mapping between indexing bits and scrambling sequences is designed to minimize error propagation when there are erroneous received indexing bits (i.e. Gray-mapped sequences). Closed form formulas that approximate the Bit Error Probability (BEP) of the baseline (non-scrambled) and proposed (scrambled) OFDM transmissions are determined for different link configurations. The impact of the proposed physical-layer security scheme on the BEP is minimal, while increasing notably the number of combinations that an eavesdropper must check in order to execute a brute-force search attack., This work received funding from the Spanish ministry of science and innovation under project IRENE PID2020-115323RB-C31 (AEI/FEDER,UE) and from the Deanship of Scientific Research at Jordan University of Science and Technology, Irbid, Jordan

The Internet of Vehicles (IoV) paradigm aims to improve road safety and provide a comfortable driving experience for Internet-connected vehicles, by transmitting early warning and infotainment signals to Internet-connected vehicles in the network. The unique characteristics of the IoV, such as their mobility and pervasive Internet connectivity, expose such networks to many cyberattacks. In particular, jamming attacks represent a considerable risk to their performance, as they can significantly affect vehicles’ functionality, possibly leading to collisions in dense networks. This paper presents a new scheme enabling the detection and localization of jamming attacks carried out within an IoV network. We consider several scenarios, e.g., where the Internet-connected vehicles and the jammer are statically positioned, as when parked on a street, moving in the same direction and with variable speeds, and moving in opposite directions. We leverage the physical-layer characteristics of the received signals, particularly the Received Signal Strength (RSS), and devise a solution minimizing the jammer localization error based on a set of antennas deployed on the vehicle. Specifically, we compute the power emitted by the jammer and received by the arrays of omnidirectional antennas and we use such values to estimate the location of the jammer in the previous-cited scenarios. Through an extensive simulation campaign, we provide a thorough study of our algorithm, evaluating the effect of several system and channel parameters on the measurement error. The results obtained for all scenarios show a significant localization accuracy, i.e., ranging from 0.23 meters to 13 meters, depending on the channel conditions. This publication was supported by Qatar University Graduate Assistantship.

Unmanned aerial vehicle (UAV)-empowered communications have gained significant attention in recent years due to the promise of agile coverage provision for a large number of various mobile nodes on the ground and in three-dimensional (3D) space. Consequently, there is a need for efficient spectrum utilization in these dense aerial networks, which is characterized through radio environment maps (REMs), the construction of which is an important research area. Nevertheless, due to the difficult collection of radio frequency (RF) data, there are limited works that are based on real-world measurement campaigns. This paper presents a novel experimental setup that includes a constellation of three UAVs, the communication signals of which are measured by a software-defined radio (SDR) mounted on a separate UAV. It follows a trajectory that defines the REM’s two-dimensional (2D) area on a plane, executed at four altitudes, to extend the REM to 3D. The measurements are then processed and their features (received mean power level, average difference of the mean power, percentage of meaningful correlations) are analyzed in the temporal, spatial, and frequency domains to determine the utilization of a 20 MHz band in the 2.4 GHz spectrum, as well as their variation with altitude. This analysis provides a base for research in reducing the amount of measurements (by identifying the regions of low and of high interest) and spectrum occupancy prediction for UAV-based communication coexistence.

A study of the behavior of NB-IoT wireless communication in an industrial indoor environment was conducted in this paper. With Wireless Insite software, a scenario in the industrial sector was simulated and modeled. Our research examined how this scenario or environment affected the communication parameters of NB-IoT’s physical layer. In this context, throughput levels among terminals as well as between terminals and transceiver towers, the power received at signal destination points, signal-to-noise ratios (SNRs) in the environment, and distances between terminals and transceivers are considered. These simulated results are also compared with the calculated or theoretical values of these parameters. The results show the effect of the industrial setting on wireless communication. The differences between the theoretical and simulated values are also established.

The huge spreading of COVID-19 viral outbreak to several countries motivates many of the research institutions everywhere in numerous disciplines to try decreasing the spread rate of this pandemic. Among these researches are the robotics with different payloads and sensory devices with wireless communications to remotely track patients’ diagnosis and their treatment. That is, it reduces direct contact between the patients and the medical team members. Thus, this paper is devoted to design and implement a prototype of wireless medical robot (MR) that can communicate between patients and medical consultants. The prototype includes the modelling of a four-wheeled MR using systems' identification methodology, from which the model is utilized in control design and analysis. The required controller is designed using the proportional-integral-derivative (PID) and Fuzzy logic (FLC) techniques. The MR is equipped onboard with some medical sensors and a camera to acquire vital signs and physical parameters of patients. The MR model is obtained via an experimental test with input/output signals in open-loop configuration as single–input–single–output from which the estimation and validation results demonstrate that the identified model possess about 89% of the output variation/dynamics. This model is used for controllers' design with PID and FLC, the response of which is good for heading angle tracking. Concerning the medical measurements, more than two thousand real recorded Photo-plethysmography (PPG) signals and Blood Pressure (BP) are used to find the appropriate BP estimation model. Towards this objective, some experiments are designed and conducted to measure the PPG signal. Finally, the BP is estimated with mean absolute error of about 4.7 mmHg in systolic and 4.8 mmHg in diastolic using Artificial Neural Network.

International audience; Due to highly variant traffic in downlink (DL) and uplink (UL) in heterogeneous networks (HetNets), dynamic time-division duplexing (TDD) is proposed to dynamically allocate UL and DL resources. Under the same circumstances, downlink and uplink decoupled access (DUDA) is introduced to balance between UL and DL transmissions and to further improve the system performance. Rather than belonging to a specific cell, a mobile user can receive the downlink traffic from one base station (BS) and send uplink traffic through another BS. In this article, we analytically investigate a joint TDD and DUDA statistical model with multiple small cells deployment. This model is based on a geometric probability approach. Taking all possible TDD subframes combinations between the macro and small cells, coupled and decoupled cell associations strategies are investigated in details. We derive analytical expressions for the capacity and the interference, considering a network of one macro cell and multiple small cells. We build on the derived capacity expressions to measure the decoupling gain and thus, identify the location of the interferer small cell where the decoupled mode maintains a higher gain in both DL and UL. Monte-Carlo simulations results are presented to validate the accuracy of the statistical model.

Sustainability and sustainable development are topics that are increasingly addressed in the ICT sector including wireless networks. Yet, their interpretations vary a great deal depending on the person without a commonly agreed approach. This paper presents an overview of sustainability and sustainable development in the context of ICTs and wireless networks highlighting the urgency of introducing sustainability principles at all levels and stages of ICT technology development. The paper presents a way forward to adopt sustainability principles into future technology design considering the triple bottom line of economic, social and environmental sustainability perspectives. A change in mindset is urgently needed and all stakeholders need to act in their own capacity as well as collaboratively to make the wireless networks and their use truly sustainable without green-washing. New metrics need to be defined and measured to respond to growing concerns as today’s metrics, such as total consumed mobile data are far from sustainable. Time to act is now as the design criteria for the future sustainable 6G are defined in the next years.

In recent years, the Smart City concept has grown in popularity, and a significant number of cities around the world have adopted smart city strategies. Smart and sustainable cities are an emerging urban development approach due to their immense potential to improve environmental sustainability. The Smart City concept is based on collecting, analyzing, and displaying a large amount of data and information concerning urban systems and subsystems. As time goes by, the capacity of smart cities for generating digital information has grown exponentially. However, this digital information is heterogeneous, massive, collected from different sources, generated in different formats, and in most cases not structured, which exacerbates the situation of extracting valuable knowledge from data. Therefore, it is fundamental to handle the significant volumes of heterogeneous sensed data and to integrate such data along with information and analysis tools into a comprehensive platform. This can ensure the security, efficiency, and performance of the different Smart City tasks. A comprehensive software platform could provide services such as facilities for application development, integration of heterogeneous data sources, deployment, and management to ease the construction of sophisticated Smart Cities’ applications. In this context, the work begins with a concise description of the concept of smart city and the technologies involved in it. It addresses the development of an urban data platform along with how to obtain and integrate information from sensors and other data sources, in order to provide aggregated and intelligent views of raw data to support various domains within the city; in our case, smart mobility. The platform architecture is implemented following a five-layer model that considers elements from perception, sensing to data management, processing, and visualization. With the aim of evaluating the efficiency of the developed platform, three different use cases are described and analyzed, which have been implemented in the city of Pamplona, Spain, as vertical services linked to the platform: intelligent urban mobility-bike handling, bike-2-bike communication, and restricted vehicle access zone control system. Ultimately, this work provides an experiment to assess different long-range wireless communication technologies to enable their implementation within an urban environment. This work was partially supported by the European Union’s Horizon 2020 Research and Innovation Programme (STARDUST-Holistic and Integrated Urban Model for Smart Cities) under Grant 774094, by the Ministerio de Ciencia, Innovación y Universidades, Gobierno de España (Agencia Estatal de Investigación, Fondo Europeo de Desarrollo Regional-FEDER, European Union) under Grant RTI2018-095499-B-C31 IoTrain, and by Gobierno de Navarra under Grant PC183-184 (Securing information and applications in V2G environments, SECV2G). Programa de Doctorado en Tecnologías de las Comunicaciones, Bioingeniería y de las Energías Renovables (RD 99/2011) Bioingeniaritzako eta Komunikazioen eta Energia Berriztagarrien Teknologietako Doktoretza Programa (ED 99/2011)

The 3rd Generation Partnership Project (3GPP) narrowband Internet of Things (NB-IoT) over non-terrestrial networks (NTN) is the most promising candidate technology supporting 5G massive machine-type communication. Compared to geostationary earth orbit, low earth orbit (LEO) satellite communication has the advantage of low propagation loss, but suffers from high Doppler shift. The 3GPP proposes Doppler shift pre-compensation for each beam region of the satellite. However, user equipment farther from the beam center has significant residual Doppler shifts even after pre-compensation, which degrades link performance. This study proposes residual Doppler shift compensation by adding demodulation reference signal symbols and reducing satellite beam coverage. The block error rate (BLER) data are obtained using link-level simulation with the proposed technique. Since the communication time provided by a single LEO satellite moving fast is short, many LEO satellites are necessary for seamless 24-h communication. Therefore, with the BLER data, we analyze the link budget for actual three-dimensional orbits with a maximum of 162 LEO satellites. We finally investigate the effect of the proposed technique on performance metrics such as the per-day total service time and maximum persistent service time, considering the number of satellites and the satellite spacing. The results show that a more prolonged and continuous communication service is possible with significantly fewer satellites using the proposed technique.

In the era of Internet of Things (IoT), billions of small but smart wireless devices work together to make our cities more intelligent and sustainable. One challenge is that many IoT devices do not have human interfaces and are very difficult for humans to manage. This creates sustainability and security issues. Enabling automatic secret sharing across heterogeneous devices for cryptography purposes will provide the needed security and sustainability for the underlying IoT infrastructure. Therefore, wireless secret sharing is crucial to the success of smart cities. One secret sharing method is to utilize the effect of the randomness of the wireless channel in the data link layer to generate the common secret between legitimate users. This paper models this secret sharing mechanism from the perspective of game theory. In particular, we formulate a non-cooperative zero-sum game between the legitimate users (Alice and Bob) and an eavesdropper (Eve). Alice and Bob’s strategy is deciding how to exchange packets to protect the secret, and Eve’s strategy is choosing where to stay to better intercept the secret. In a symmetrical game where Eve has the same probability of successfully receiving a packet from Alice and Bob when the transmission distance is the same, we show that both pure and mixed strategy Nash equilibria exist. In an asymmetric game where Eve has different probabilities of successfully receiving a packet from Alice and Bob, a pure strategy may not exist; in this case, we show how a mixed strategy Nash equilibrium can be found. We run simulations to show that our results are better than other approaches.

The spectrum allocation in any auctioned wireless service primarily depends upon the necessity and the usage of licensed primary users (PUs) of a certain band of frequencies. These frequencies are utilized by the PUs as per their needs and requirements. When the allocated spectrum is not being utilized in the full efficient manner, the unused spectrum is treated by the PUs as white space without believing much in the concept of spectrum scarcity. There are techniques invented and incorporated by many researchers, such as cognitive radio technology, which involves software-defined radio with reconfigurable antennas tuned to particular frequencies at different times. Cognitive radio (CR) technology realizes the logic of the utility factor of the PUs and the requirements of the secondary users (SU) who are in queue to utilize the unused spectrum, which is the white space. The CR technology is enriched with different frequency allocation engines and with different strategies in different parts of the world, complying with the regulatory standards of the FCC and ITU. Based on the frequency allocation made globally, the existing CR technology understands the nuances of static and dynamic spectrum allocation and also embraces the intelligence in time allocation by scheduling the SUs whenever the PUs are not using the spectrum, and when the PUs pitch in the SUs have to leave the band without time. This paper identifies a few of the research gaps existing in the earlier literature. The behavioral aspects of the PUs and SUs have been analyzed for a period of 90 days with some specific spectrum ranges of usage in India. The communal habits of utilizing the spectrum, not utilizing the spectrum as white space, different time zones, the requisites of the SUs, the necessity of the applications, and the improvement of the utility factor of the entire spectrum have been considered along with static and dynamic spectrum usage, the development of the spectrum policy engine aligned with cooperative and opportunistic spectrum sensing, and access techniques indulging in artificial intelligence (AI). This will lead to fine-tuning the PU and SU channel mapping without being hindered by predefined policies. We identify the cognitive radio transmitter and receiver parameters, and resort to the same in a proposed channel adaption algorithm. We also analyze the white spaces offered by spectrum ranges of VHF, GSM-900, and GSM-1800 by a real-time survey with a spectrum analyzer. The identified parameters and white spaces are mapped with the help of a swotting algorithm. A sample policy has been stated for ISM band 2.4 GHz where such policies can be excited in a policy server. The policy engine is suggested to be configured over the 5G CORE spectrum management function.

Although airborne base stations (ABSs) mounted on drones show a significant potential to enhance network capacity and coverage due to their flexible deployment, the system performance is severely limited by the endurance of the on-board battery. To overcome this key shortcoming, we are exploring robotic airborne base station (RABS) with energy neutral grasping end-effectors able to autonomously perch at tall urban landforms. This paper studies the optimal deployment (fly to another grasping location or remain in the same one) and operation (active or sleep at an epoch) of RABS based on the spatio-temporal characteristics of underlying traffic demand from end-users. Specifically, an integer linear programming (ILP) is formulated by exploiting the coupling between these two decisions, that is, the RABS only needs to visit the locations where it is active. A Lagrangian heuristic algorithm is then proposed by exploiting the totally unimodular structure of the ILP formulation. A wide set of numerical investigations reveal that thanks to its mobility, a single robotic aerial small cell is able to outperform five (5) fixed small cells in terms of served user generated traffic within a 16 to 41 hours period.

International Journal of VLSI design & Communication Systems (VLSICS)is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas ofVLSI Design & Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & communication concepts and establishing new collaborations in these areas. Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the VLSI design & Communications.

International Journal of VLSI design & Communication Systems (VLSICS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of VLSI Design & Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & communication concepts and establishing new collaborations in these areas. Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the VLSI design & Communications.

IoT applications rely strongly on the performance of wireless communication networks. There is a wide variety of wireless IoT technologies and choosing one over another depends on the specific use case requirements—be they technical, implementation-related or functional factors. Among the technical factors, latency, error rate and stability are the main parameters that affect communication reliability. In this work, we present the design, development and validation of a Universal Testbed to experimentally measure these parameters, abstracting them from the wireless IoT technology protocols and hardware platforms. The Testbed setup, which is based on a Raspberry Pi 4, only requires the IoT device under test to have digital inputs. We evaluate the Testbed’s accuracy with a temporal characterisation—accumulated response delay—showing an error less than 290 µs, leading to a relative error around 3% for the latencies of most IoT wireless technologies, the latencies of which are usually on the order of tens of milliseconds. Finally, we validate the Testbed’s performance by comparing the latency, error and stability measurements with those expected for the most common IoT wireless technologies: 6LoWPAN, LoRaWAN, Sigfox, Zigbee, Wi-Fi, BLE and NB-IoT.

In this paper, the bandwidth density of misaligned free space optical interconnects (FSOIs) system with and without coding under a fixed bit error rate is considered. In particular, we study the effect of using error correction codes of various codeword lengths on the bandwidth density and misalignment tolerance of the FSOIs system in the presence of higher order modes. Moreover, the paper demonstrates the use of the fill factor of the detector array as a design parameter to optimize the bandwidth density of the communication. The numerical results demonstrate that the bandwidth density improves significantly with coding and the improvement is highly dependent on the used codeword length and code rate. In addition, the results clearly show the optimum fill factor values that achieve the maximum bandwidth density and misalignment tolerance of the system.

Due to the progression growth of multiservice wireless communication systems in a single device, multiband bandpass filter has attract a great attention to the end user. Therefore, multiband bandpass filter is a crucial component in the multiband transceivers systems which can support multiple services in one device. This paper presents a design of dual-band bandpass filter at 2.4 GHz and 3.5 GHz for WLAN and WiMAX applications. Firstly, the wideband bandpass filter is designed at a center frequency of 3 GHz based on quarter-wavelength short circuited stub. Three types of defected microstrip structure (DMS) are implemented to produce a wide notch band, which are T-inversed shape, C-shape, and U- Shape. Based on the performance comparisons, U-shaped DMS is selected to be integrated with the bandpass filter. The designed filter achieved two passbands centered at 2.51 GHz and 3.59 GHz with 3 dB bandwidth of 15.94 % and 15.86 %. The proposed design is very useful for wireless communication systems and its applications such as WLAN and WiMAX

The physical layer security (PLS) performance of a wireless communication link through a large reflecting surface (LRS) with phase errors is analyzed. Leveraging recent results that express the \ac{LRS}-based composite channel as an equivalent scalar fading channel, we show that the eavesdropper's link is Rayleigh distributed and independent of the legitimate link. The different scaling laws of the legitimate and eavesdroppers signal-to-noise ratios with the number of reflecting elements, and the reasonably good performance even in the case of coarse phase quantization, show the great potential of LRS-aided communications to enhance PLS in practical wireless set-ups., This work has been submitted to the IEEE for publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Machine Learning brings intelligence services to IoT systems, with Edge Computing contributing for edge nodes to be part of these services, allowing data to be processed directly in the nodes in real time. This paper introduces a new way of creating a self-configurable IoT node, in terms of communications, supported by machine learning and edge computing, in order to achieve a better efficiency in terms of power consumption, as well as a comparison between regression models and between deploying them in edge or cloud fashions, with a real case implementation. The correct choice of protocol and configuration parameters can make the difference between a device battery lasting 100 times more. The proposed method predicts the energy consumption and quality of signal using regressions based on node location, distance and obstacles and the transmission power used. With an accuracy of 99.88% and a margin of error of 1.504 mA for energy consumption and 98.68% and a margin of error of 1.9558 dBm for link quality, allowing the node to use the best transmission power values for reliability and energy efficiency. With this it is possible to achieve a network that can reduce up to 68% the energy consumption of nodes while only compromising in 7% the quality of the network. Besides that, edge computing proves to be a better solution when energy efficient nodes are needed, as less messages are exchanged, and the reduced latency allows nodes to be configured in less time. info:eu-repo/semantics/publishedVersion

This preface-style article contains a brief account of the creators of radio - Lee de Forest, inventor of the triode; Edwin Howard Armstrong, inventor of regeneration, the superheterodyne, and wide-band FM; and David Sarnoff, head of RCA and champion of electronic television - their influences, and the digital age they helped to bring about.

The huge available bandwidth in Terahertz (THz) frequency band is recently contemplated to achieve high data rate wireless communications. Consequently, THz communications are attractive candidates to fulfill the continuous ever–increasing requirements of future wireless networks. Numerous beyond 5G applications are highly considered for those systems such as high capacity backhaul, enhanced hotspot booths as well as short–range device–to–device (D2D) communications. Wireless communications systems that deploy unmanned aerial vehicles (UAVs) promise to achieve cost–effective wireless connectivity for devices without any need to pay for infrastructure coverage. When compared to terrestrial communications, wireless systems with UAVs are generally faster and more flexible to deploy or reconfigure. In addition, systems deploying UAVs are likely to have much better communication channels due to their high mobility capabilities. Accordingly, the presence of short–range line–of–sight (LOS) links prevail. In this paper, we consider a single–cell cellular network with a UAV deployed as a decode-and-forward (DF) relay in the full-duplex (FD) mode in order to assist a base station (BS) and extend its coverage over THz channel. A joint power allocation and trajectory optimization scheme that minimizes the outage probability of the link between the BS and a mobile device (MD) is derived in the presence of the interference of the D2D devices that share the same THz frequency band. Furthermore, the optimum powers of the MD and the UAV that maximize the achievable rate at the BS are obtained. The performance of the proposed schemes is compared with the fixed power allocation schemes which distribute the power equally among users. Numerical results show that the outage probability and the achievable rate at the BS using the proposed schemes are remarkably superior compared to the fixed power allocation schemes.

The ability to recognize the medium access control protocol employed by a network can facilitate the incorporation of a cognitive radio into an existing network by elucidating an integral aspect of network behavior. Since the way in which users access the electromagnetic spectrum is one of the most prominent distinctions between reservation based and contention based medium access control protocols, the first part of this work exploits the regular timing of transmissions from networks utilizing reservation based time-division multiple access (TDMA) protocols to differentiate between transmissions governed by TDMA and by contention based carrier sense multiple access (CSMA) protocols. Our approach leverages modular arithmetic to identify periodicity in transmission timings and an unsupervised $k$ -means algorithm to generate distinct TDMA and CSMA clusters. Several supervised machine learning algorithms are explored to build a protocol classifier. We then present a method of distinguishing between transmissions from multi-channel frequency division multiple access (FDMA) based networks and single channel networks. This method uses an automated machine learning clustering algorithm to obtain an estimate of the actual center frequencies of channels utilized by a network. Such information can be used to determine whether the network is employing an FDMA protocol to access the electromagnetic spectrum.

Emerging wireless networks possess the potential to achieve levels of connectivity and Quality-of-Service (QoS) that are orders of magnitude higher than today’s networks. Realizing the potential of these networks will require flexible, low cost, and accurate Digital Signal Processing (DSP). Supervised Learning (SL) models employing unknown parameter estimation and classification techniques have experienced widespread use in physical (PHY) layer wireless communication systems since they can achieve low costs via inexpensive forward-pass computations, attain flexible operations due to trainable parameters, and yield accurate results based on the universal approximator attribute. In this survey and tutorial paper, we present a methodical explanation of how SL can be applied to unknown parameter estimation and classification across several different PHY layer components of a wireless communications system. Additionally, via a survey and comparison of popular methods, this paper provides insights on how to perform weight training, weight initialization, loss function regularization, data pre-processing, input feature design, ensemble training, and hyper-parameter validation. In our review of state-of-the-art works, we found significant use of SL algorithms in the following PHY layer applications: Dynamic Spectrum Access (DSA), channel corrections, Automatic Gain Control (AGC), Multiple Input Multiple Output (MIMO) control, Analog to Digital (ADC) conversion, and Automatic Coding and Modulation (ACM). The overarching goal of this survey and tutorial paper is to assist the reader in understanding the motivation and methodologies associated with various SL algorithms applied to PHY layer DSP operations, as well as to provide the reader with the necessary tools and techniques needed for addressing open challenges to be experienced by future wireless networks.

We propose a general framework for noncoherent communication using techniques from the field of quantum error correction (QEC). We first propose an approach for analyzing a classical communication channel as a quantum channel, and develop an extension of the QEC conditions to the classical case. We derive a quantum analogue of the noncoherent multiantenna wireless channel. Restricting to the case in which the number of transmitter and receiver antennas are equal and a power of two, we use the framework to develop a family of space-time block codes for noncoherent multiple-input multiple-output (MIMO) communication. Under a Rayleigh fading assumption, we derive the optimal decoder and bound the probability of symbol detection error. We compare our performance to comparable coherent and noncoherent approaches and achieve competitive performance for various antenna geometries and rates.

In the current digital era, where Unmanned Aerial Vehicles (UAVs), Artificial intelligence (AI), and Internet of Everything (IoE) can be well integrated, more global connectivity and automated solutions can be witnessed. This paper aims to develop an intelligent cellular structure design for a UAV wireless communication topology using an AI framework. The proposed AI framework includes Self Organizing Maps (SOMs) and an NN fitting tool that can be simulated using the Graphical User Interface (GUI) toolbox in MATLAB. The proposed framework is validated in a proof-of-concept scenario, where various parameters of link budget and cellular structure design have been tuned to achieve an efficient and optimized automatic design. The obtained results show high levels of adaptable wireless communication predictions without human intervention, which is a noticeable shift from existing work in the literature.

This paper presents the technical development and subsequent testing of a Real-Time Locating System based on Ultra-Wideband signals, with the aim to appraise its potential implementation in a real industrial case. The system relies on a commercial Radio Indoor Positioning System, called Qorvo MDEK1001, which makes use of UWB RF technology to determine the position of RF-tags placed on an item of interest, which in turn is located in an area covered by specific fixed antennas (anchors). Testing sessions were carried out both in an Italian laboratory and in a real industrial environment, to determine the best configurations according to some selected performance indicators. The results support the adoption of the proposed solution in industrial environments to track assets and work in progress. Moreover, most importantly, the solution developed is cheap in nature: indeed, normally tracking solutions involve a huge investment, quite often not affordable above all by small-, medium- and micro-sized enterprises. The proposed low-cost solution instead, as demonstrated by the economic assessment completing the work, justifies the feasibility of the investment. Hence, results of this paper ultimately constitute a guidance for those practitioners who intend to adopt a similar system in their business.

In this paper, the problem of trajectory design for energy harvesting unmanned aerial vehicles (UAVs) is studied. In the considered model, the UAV acts as a moving base station to serve the ground users, while collecting energy from the charging stations located at the center of a user group. For this purpose, the UAV must be examined and repaired regularly. In consequence, it is necessary to optimize the trajectory design of the UAV while jointly considering the maintenance costs, the reward of serving users, the energy management, and the user service time. To capture the relationship among these factors, we first model the completion of service and the harvested energy as the reward, and the energy consumption during the deployment as the cost. Then, the deployment profitability is defined as the ratio of the reward to the cost of the UAV trajectory. Based on this definition, the trajectory design problem is formulated as an optimization problem whose goal is to maximize the deployment profitability of the UAV. To solve this problem, a foraging-based algorithm is proposed to find the optimal trajectory so as to maximize the deployment profitability and minimize the average user service time. The proposed algorithm can find the optimal trajectory for the UAV with low time complexity at the level of polynomial. Fundamental analysis shows that the proposed algorithm achieves the maximal deployment profitability. Simulation results show that, compared to Q-learning algorithm, the proposed algorithm effectively reduces the operation time and the average user service time while achieving the maximal deployment profitability.

International Journal of VLSI design & Communication Systems (VLSICS)is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas ofVLSI Design & Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & communication concepts and establishing new collaborations in these areas. Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the VLSI design & Communications.

This paper presents a compact and planar reconfigurable filtenna for application in Wireless systems. The filtenna is composed of a UWB circular antenna and a filter with two trapezoidal resonators. The filter is integrated on the antenna feed line and has a band-pass frequency response. The filtenna frequency response reconfiguration is achieved by changing the capacitance of a varactor diode, placed between filter resonators; the capacitance varies continuously from 1.32 pF to 4.09 pF according to an applied bias voltage. The varactor capacitance analog variation defines the filtenna frequency of operation, which can be varied from 2.2 GHz to 3.4 GHz with an average bandwidth of 200 MHz, while maintaining the radiation characteristics of the UWB antenna. Simulated and measured filtenna results are shown according to varactor diode bias, demonstrating multiband operation using a compact planar design. © 2021 SBMO/SBMag.

Reliability and determinism of Wi-Fi can be tangibly improved by means of seamless redundancy, to the point of making this technology suitable for industrial environments. As pointed out in recent papers, the most benefits can be achieved when no phenomena can simultaneously affect transmissions on all channels of a redundant link. In this paper, several aspects are analyzed which, if not properly counteracted, may worsen seamless redundancy effectiveness. Effects they cause on communication have been experimentally evaluated in real testbeds, which rely on commercial Wi-Fi devices. Then, practical guidelines are provided, which aim at preventing joint interference through a careful system design. Results show that measured communication quality can be made as good as expected in theory., Comment: preprint, 13 pages

This paper presents a self-contained factorization for the delay Vandermonde matrix (DVM), which is the super class of the discrete Fourier transform, using sparse and companion matrices. An efficient DVM algorithm is proposed to reduce the complexity of radio-frequency (RF) $N$-beam analog beamforming systems. There exist applications for wideband multi-beam beamformers in wireless communication networks such as 5G/6G systems, system capacity can be improved by exploiting the improvement of the signal to noise ratio (SNR) using coherent summation of propagating waves based on their directions of propagation. The presence of a multitude of RF beams allows multiple independent wireless links to be established at high SNR, or used in conjunction with multiple-input multiple-output (MIMO) wireless systems, with the overall goal of improving system SNR and therefore capacity. To realize such multi-beam beamformers at acceptable analog circuit complexities, we use sparse factorization of the DVM in order to derive a low arithmetic complexity DVM algorithm. The paper also establishes an error bound and stability analysis of the proposed DVM algorithm. The proposed efficient DVM algorithm is aimed at implementation using analog realizations. For purposes of evaluation, the algorithm can be realized using both digital hardware as well as software defined radio platforms., Comment: 25 pages, 2 figures

Given the importance of line-of-sight in mmWave communications, a strategic adversary can harm a transmission by obstructing the receiver, which in turn can react by trying to move around this hurdle. To expand on this point, we study one such scenario from the perspective of game theory, considering a mobile mmWave receiver and an adversary interacting strategically as players in a zero-sum game, where they want to maximize, or respectively minimize, the spectral efficiency of the communication. To do so, the adversary attempts at screening the receiver's line of sight as an obstacle, while the receiver can move around so as to avoid the blockage. We consider preset distances and the choices available to the players are to change their angular coordinates to go around each other. This is framed as a static game of complete information, for which we numerically find the Nash equilibrium in mixed strategies, drawing some interesting conclusions such as connecting it with the beamforming pattern of the transmitter., Comment: 8 pages, 6 figures. Published on MedComNet 2022

The increasing applications for nomadic computing have experienced enormous development over the preceding decade. This has eventually caused the lack of bandwidth. Therefore, to accomplish the need of consumers, compact antennas shall be designed for mmWave wireless communications. Consequently, this paper presents a novel negative meander line based microstrip antenna system being composed of inductors (L) and capacitors (C). A detailed impedance analysis of the configuration is reported. The effects of changing the radiating element’s width and length on the resonant frequency have been studied. The finalized arrangement involved 243 sq. mm area and functioned at 28 GHz with a bandwidth of 2.16 GHz. At resonant frequency, the system exhibited gain and efficiency values of 8.40 dBi and 83.51%, respectively. Furthermore, the proposed design demonstrated better bandwidth and gain capabilities in comparison with the conventional microstrip patch antenna and meander line antenna.

IoT is emerging as the future evolution of the Internet, aiming to provide connectivity for everyone and everything. Since IoT is expected to carry important and private information, a high level of PHY security is critical for wireless communications in IoT, as a complement for traditional security techniques that are employed at high layers. In this overview, we examine the recent interest in energy-efficient and cost-efficient PHY solutions for securing downlink IoT transmission through interference exploitation. This exciting line of research departs from conventional interference cancellation, and judiciously employs the inherent interference as a useful element for LUs while obstructing the eavesdropping of information. We first discuss the concept of CI, and then elaborate the fundamental CI signal design that employs the traditionally undesired interference as a constructive element to LUs while ensuring they are destructive to potential Eves. Subsequently, we illustrate several low-hardware-cost techniques to inherit the advantage of CI in an energy- and cost-efficient manner, from the perspective of HBF and DM. This family of techniques brings a disruptive vision of interference management for securing wireless communications with an eye on low-cost and hardware-constrained devices tailored for IoT systems.

Wireless communications is nowadays an important aspect of robotics. There are many applications in which a robot must move to a certain goal point while transmitting information through a wireless channel which depends on the particular trajectory chosen by the robot to reach the goal point. In this context, we develop a method to generate optimum trajectories which allow the robot to reach the goal point using little mechanical energy while transmitting as much data as possible. This is done by optimizing the trajectory (path and velocity profile) so that the robot consumes less energy while also offering good wireless channel conditions. We consider a realistic wireless channel model as well as a realistic dynamic model for the mobile robot (considered here to be a drone). Simulations results illustrate the merits of the proposed method., IEEE Transactions on Robotics, Vol. 36, No. 2, pp. 431-442, April 2020

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Wireless communications play a central role in our modern connected lives; at the same time, they constitute a very broad and deep area of research. The elements that make wireless communications possible are a transmitter, which sends information through electromagnetic waves; a medium that is able to transport these waves; and, finally, a receiver, which extracts the information from the-usually very small-amount of energy it is able to collect from the medium.

In this article, a series of convolutional-type predictive neural networks are proposed for the issue of fading channel forecasting for orthogonal frequency-division multiplexing (OFDM) transmission systems in a multiple-input and multiple-output (MIMO) mode via a noisy channel. The proposed neural networks all employ convolutional connections that operate in a translation-invariant manner in the frequency domain of the time-varying channel transfer function, which effectively tackles the essential challenges of high dimensionality and denoising. Each of the proposed convolutional-type neural networks is built on a specific overall network architecture and functions as an independent predictor that offers advantages regarding a specific aspect such as accuracy over a certain prediction span or computational effort. Comparative evaluations against common prediction methods such as the Kalman filtering scheme and the standard long-short term memory units (LSTMs) are provided on the basis of transmission simulations over dispersive fading channels with Rayleigh components according to the well-established 3GPP Long Term Evolution (LTE) standards.

The use of Unmanned Aerial Vehicles (UAVs) for wireless networks is rapidly growing as key enablers of new applications, including: surveillance and monitoring, military, delivery of medical supplies, telecommunications, etc. In particular, due to their unique proprieties such as flexibility, mobility, and adaptive altitude, UAVs can act as mobile base stations to improve capacity, coverage, and energy efficiency of wireless networks. On the other hand, UAVs can operate as mobile terminals to enable many applications such as item delivery and real-time video streaming. In such context, data-driven Deep Learning-assisted (DL) approaches are gaining a growing interest to not only exploit the huge amount of generated data, but also to optimize the network operations, and hence ensure the QoS requirements of these emerging wireless networks. However, UAVs are resource-constrained devices especially in terms of computing and power resources, and traditional DL-assisted schemes are cloud-centric, which require UAVs' data to be sent and stored in a centralized server. This represents a critical issue since it generates a huge network communication overhead to send raw data towards the centralized entity, and hence may lead to network bandwidth and energy inefficiency of UAV devices. In addition, the transferred data may contain personnel data such as UAVs' localization and identity, which can directly affect UAVs' privacy concerns. As a solution, Federated Deep Learning (FDL), or distributed DL, was introduced, where the basic idea is to keep raw data where it is generated, while sending only users' local trained DL models to the centralized entity for aggregation. Due to its privacy-preserving and low communication overhead and latency, FDL is much more adequate for many UAVs-enabled wireless applications. In this work, we provide a general introduction of FDL application for UAV-enabled wireless networks. We first introduce the FDL concept and its fundamentals. Then, we highlight the possible applications of FDL in UAVs-enabled wireless networks by addressing the suitability and how to use FDL to deal with target challenges. Finally, we discuss about key technical challenges, open issues, and future research directions on FDL-based approaches in such context.

Advanced means of transportation such as flying vehicles provide as many possibilities as challenges to be solved before they reach the sky. One of the challenges is to improve air to ground communications. Maximizing the received power and availability results in a better air to ground link. The trajectory of the flying vehicle influences the performance of this communication link. Our contribution to the field is to propose a path planner based on evolutionary algorithms with the improvement of the air to ground channel quality as one of the objectives. The path planner efficiently explores the search space and proposes a trajectory that fulfils objectives such as; maximum air to ground channel quality, availability, and minimum flight time, among others. The path planner is tested in different situations. These situations include different terrain conditions for different channel behaviours. With the aim of testing other optimisation objectives, elements such as no fly zones are included.