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Connected and automated vehicles (CAVs), integrated with sensors, cameras, and communication networks, are transforming the transportation industry and providing new opportunities for consumers to enjoy personalized and seamless experiences. The fast proliferation of connected vehicles on the road and the growing trend of autonomous driving create vast amounts of data that need to be analyzed in real time. Anomaly detection in CAVs refers to identifying any unusual or unforeseen behavior in the data generated by vehicles' various sensors and components. Anomaly detection aims to identify any unusual behavior that might indicate a problem or a malfunction in the vehicle. To identify and detect anomalies efficiently, a method must deal with noisy data, missing data, dynamic frequency data, and low- and high-magnitude data, and it must be accurate enough to detect anomalies in a dynamic sensor streaming environment. Therefore, this paper proposes a fast and efficient hard-voting-based technique named FT-HV, comprising three fine-tuned machine learning algorithms to detect and classify anomaly behavior in CAVs for single and mixed sensory datasets. In experiments, we evaluate our approach on the benchmark Sensor Anomaly dataset that contains data from various vehicle sensors at low and high magnitudes. Further, it contains single and mixed anomaly types that are challenging to detect and identify. The results reveal that the proposed approach outperforms existing solutions for detecting single anomaly types at low magnitudes and detecting mixed anomaly types in all settings. Furthermore, this research is envisioned to help detect and identify anomalies early and efficiently promote safer and more resilient CAVs. [ABSTRACT FROM AUTHOR]

Some passive sensors can provide only relative angles of a signal source. To obtain the signal source location, multiple passive sensors can be constructed into a passive sensor network through communication links. This paper investigates the source localization problem with angle-only measurements in three-dimensional space. First, we present an intersection localization method, which estimates the target position by minimizing the sum of distances between lines formed by angle-only measurements. It has the same target position estimate as the widely used least-squares (LS) method, but with a lower computational cost. Furthermore, considering the differences in measurement accuracy of sensors, the weighted least-squares (WLS) algorithm can achieve better localization performance than the LS method. Unfortunately, since the coefficient matrix and the noise vector are correlated, the WLS method is biased. The bias-compensation WLS (BCWLS) method is also presented in this paper to reduce the bias by estimating the correlation between the coefficient matrix and the pseudolinear noise vector. To evaluate the performance of the presented algorithms, numerical simulations are conducted, indicating that the superiority of the intersection localization method in computational cost and the superiority of the BCWLS method in localization accuracy. [ABSTRACT FROM AUTHOR]

This paper considers model-based fault detection of large-scale (possibly rank-deficient) dynamic systems. Assuming only global (and not local) observability over a sensor network, we introduce a single time-scale networked estimator/observer. Sensors take local outputs/measurements of system states with partial observability and share their information (including estimation and/or output) over a communication network, and gain distributed observability. We define the conditions on the network structure ensuring distributed observability and stabilising the error dynamics. However, system outputs are prone to faults and uncertainties, which affect the state estimation of all sensors as a consequence of communicating (possibly) faulty data. From the cyber-physical-systems (CPS) perspective, such faults add bias to the data transferred from the physical layer (dynamic system) to the cyber layer (sensor network). In this work, we propose a localised fault detection and isolation (FDI) mechanism at sensors to secure distributed estimation. This protocol enables every sensor to locally identify the possible fault at the sensor measurement, and, via local detection and isolation, to prevent the spread of biased/faulty information over the network. This distributed isolation and localisation of fault follows from our partial observability assumption instead of full observability at every sensor. Then, other sensors can estimate/track the system by using observationally-equivalent output information to recover for possible loss of observability. In particular, we study rank-deficient systems as they are known to demand more information-sharing, and thus, are more vulnerable to the spread of possible faults over the network. One challenge is the detection of faults in the presence of system/output noise without making (simplifying and unrealistic) upper-bound assumptions on the noise support. We resolve this by adopting probabilistic threshold designs on the residuals. Further, we show that additive faults at rank-deficiency-related outputs affect the residuals at all sensors, a consequence that mandates more constraints on the (distributed) FDI strategy. We address this problem by constrained LMI design of the feedback gain matrix. Finally, we design q-redundant distributed estimators, resilient to isolation/removal of up to q number of faulty sensors, and further, we consider thresholding residual history over a sliding time-window, known as the stateful FDI. [ABSTRACT FROM AUTHOR]

Cybersecurity is becoming an increasingly important aspect in ensuring maritime data protection and operational continuity. Ships, ports, surveillance and navigation systems, industrial technology, cargo, and logistics systems all contribute to a complex maritime environment with a significant cyberattack surface. To that aim, a wide range of cyberattacks in the maritime domain are possible, with the potential to infect vulnerable information and communication systems, compromising safety and security. The use of navigation and surveillance systems, which are considered as part of the maritime OT sensors, can improve maritime cyber situational awareness. This survey critically investigates whether the fusion of OT data, which are used to provide maritime situational awareness, may also improve the ability to detect cyberincidents in real time or near-real time. It includes a thorough analysis of the relevant literature, emphasizing RF but also other sensors, and data fusion approaches that can help improve maritime cybersecurity. [ABSTRACT FROM AUTHOR]

Copyright of Ingeniería (0121-750X) is the property of Ingenieria and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, the set-membership filtering problem is investigated for a class of polytopic uncertain two-dimensional shift-varying systems with delays and sensor saturation. The measurement information is transmitted between the sensors and the filter through a shared communication network. To avoid data congestions, the Round-Robin (R-R) protocol is employed to regulate the data transmission order. This paper aims to design a robust set-membership filter such that the real system states are always contained in the state estimate ellipsoid in the presence of the state delays, the sensor saturation, the parameter uncertainties, the R-R protocol and the unknown-but-bounded external perturbations. By using the mathematical induction, sufficient condition for the existence of the desired robust set-membership filter is given in the form of recursive linear matrix inequalities, and a convex optimisation method is also proposed to determine the optimal state estimation ellipsoid in the trace sense. One numerical example is also given to illustrate effectiveness of the proposed filtering strategy. [ABSTRACT FROM AUTHOR]

Many applications of wireless sensor networks (WSNs) require deploying sensors in hostile environments, where an adversary may eavesdrop communications. To secure communications in WSNs, the $q$ -composite key predistribution scheme has been proposed in the literature. In this paper, we investigate secure $k$ -connectivity in WSNs operating under the $q$ -composite scheme, in consideration of the unreliability of wireless links. Secure $k$ -connectivity ensures that any two sensors can find a path in between for secure communication, even when $k-1$ sensors fail. We present conditions on how to set the network parameters such that the network has secure $k$ -connectivity asymptotically almost surely. The result is given in the form of a sharp zero–one law. [ABSTRACT FROM AUTHOR]

In this paper, passive wireless sensor network technology is used to conduct in-depth research and analysis on the monitoring of the in-transit health status of railway transportation. Safety detection sensors, train communication networks, and other onboard devices organically constitute a train sensor network with comprehensive state sensing, information aggregation, and business collaboration. First, this paper analyzes the three-layer architecture of the sensing layer, network layer, and application layer of the train operation status safety detection sensor network. Based on analyzing the feasibility of Ethernet application in an onboard communication network, ring, multiring, and ladder-type sensor network structure schemes are designed. Next, the real-time performance and reliability of various sensor network structures are analyzed. First, the characteristics of bandwidth demand, priority, and importance of information transmission of each monitoring object are analyzed; then, the system utility function is established according to the bandwidth usage efficiency and communication demand of information transmission of each monitoring object; finally, the bandwidth allocation optimization model is solved by using a particle swarm optimization algorithm, and the remaining bandwidth resources are dynamically rationed on demand while ensuring the complete transmission of basic information of each monitoring object. Rail transit technology has profoundly affected and changed the travel and lifestyle of residents in the new era and will provide strong infrastructure support for the smooth advancement of new urbanization construction and development strategies. The remaining bandwidth resources are dynamically allocated on the premise of ensuring the complete transmission of basic information of each monitoring object. In summary, this paper presents a complete study on the optimal allocation of resources for wireless sensor networks dedicated to rail transit condition monitoring, starting from the perspectives of routing protocol optimization, data fusion rate optimization, and bandwidth dynamic allocation optimization to achieve the efficient use of limited resources. The achieved research results enrich the research content in the field of wireless monitoring of rail transit system conditions at home and abroad in a certain sense and will also provide theoretical and empirical support for practical practitioners, managers, and scholars of service condition monitoring of rail transit systems. [ABSTRACT FROM AUTHOR]

The internet of vehicles needs mobile communication systems for efficient communications. On the physical downlink control channel (PDCCH) of 5G, blind detection is performed to identify the downlink control information (DCI). Due to the uncertainty of DCI formats and PDCCH formats, the existing two-stage blind detection schemes separately decode every PDCCH code block under different DCI formats, leading to a high decoding complexity in the first stage. To reduce the detection complexity, this paper proposes an efficient DCI detection method, where a significance-test strategy is designed to detect the DCI formats. The significance-test strategy can forecast the DCI lengths without separately decoding the whole codeword, thus reducing the decoding complexity by several times. Moreover, the accuracy of the DCI detection is analyzed, with which the test scope of the test statistic is optimized for minimizing the decoding complexity on condition of meeting the detection accuracy requirements. Simulation results show that the proposed method significantly reduces the decoding complexity relatively to the existing two-stage detection schemes. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Information Technologies & Communications / Revista de Tecnologías de la Información & Comunicaciones is the property of ECORFAN-Mexico S.C. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

There has been a recent surge in interest for optical satellite communication (SatCom) utilizing lasers. It is clear to see why, as optical SatCom is capable of higher speed, lighter weight, higher directionality, and higher efficiency versus their radio-based counterparts. Research into optical SatCom has focused on devices operating in the short-wave infrared (SWIR), which is due to the maturity and commercial availability of such component’s thanks to significant development in terrestrial telecommunications networks. However, SWIR performs poorly in fog and heavy weather, prompting investigations into longer mid-wave and long-wave infrared bands for optical communication instead due to reduced atmospheric losses. This paper provides a comprehensive review of laser transmitters, detectors, and the science behind selecting longer wavelengths for optical SatCom to boost optical SatCom between ground stations and low earth orbit satellite constellations being deployed. [ABSTRACT FROM AUTHOR]

This paper studies the temporal characteristics of the intelligent reflecting surface (IRS)-based visible light communication (VLC) channel using radiometric concepts. Throughout this study, we account for the delays experienced by the transmitted power along the continuum of paths originating at the source, passing through the IRS, reaching the detector. Then, we derive the impulse response of multi-element phase-tunable metasurface and orientation-tunable mirror array-based reflector setups for a general setting of source, reflector, and detector dimensions and relative positions. In addition, we derive simpler expressions for the two special cases, namely, the point source and the large-source small-reflector. Moreover, we present the exact expression for the delay spread and derive lower, upper bounds and asymptotic expressions when the number of reflecting elements increases for both reflector types. Finally, we study the impact of several system parameters on the temporal characterization of the two IRS-based VLC systems. [ABSTRACT FROM AUTHOR]

This paper deals with the practical aspects of development of multisatellite communication system. Specifically, the problem of data detection in multisatellite communication system is discussed in this paper. All satellites want to communicate to a particular earth station (ES). A novel interference cancellation method is used to obtain the data of different satellites at the ES. Based on the standard moment generating function approach, the proposed system is evaluated by analyzing the symbol error rate, diversity order, and average capacity. It is shown through simulation and analysis that the number of antennas at the ES should be sufficiently large as compared to the number of satellites in order to achieve good performance and improved diversity gain and average capacity. It is also shown that the diversity order of the considered system does not depend upon the shadowing environment. By placing more antennas at the ES, the effect of channel (shadowing) can be compensated. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Information Technologies & Communications / Revista de Tecnologías de la Información & Comunicaciones is the property of ECORFAN-Mexico S.C. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we extend the bilinear generalized approximate message passing (BiG-AMP) approach, originally proposed for high-dimensional generalized bilinear regression, to the multi-layer case for the handling of cascaded problem such as matrix-factorization problem arising in relay communication among others. Assuming statistically independent matrix entries with known priors, the new algorithm called ML-BiGAMP could approximate the general sum-product loopy belief propagation (LBP) in the high-dimensional limit enjoying a substantial reduction in computational complexity. We demonstrate that, in large system limit, the asymptotic MSE performance of ML-BiGAMP could be fully characterized via a set of simple one-dimensional equations termed state evolution (SE). We establish that the asymptotic MSE predicted by ML-BiGAMP’ SE matches perfectly the exact MMSE predicted by the replica method, which is well-known to be Bayes-optimal but infeasible in practice. This consistency indicates that the ML-BiGAMP may still retain the same Bayes-optimal performance as the MMSE estimator in high-dimensional applications, although ML-BiGAMP's computational burden is far lower. As an illustrative example of the general ML-BiGAMP, we provide a detector design that could estimate the channel fading and the data symbols jointly with high precision for the two-hop amplify-and-forward relay communication systems. [ABSTRACT FROM AUTHOR]

The article describes a modern system of control and communication integrated in an in-situ soil sensor to make it autonomous in operation, energy and data transfer at low cost. Network of such sensors can monitor continuously soil moisture over a catchment with low maintenance. A recent industrial standard of radio communication, Long Range Wide Area Network (LoRaWAN) is presented. It permits rapid deployment of a sensor in a remote observatory, and the transfer in real time of sensor data to a central web-accessed database, via a LoRaWAN receiver, or gateway, 12 km away. In this situation transmission loss can reach a rate of 30%. Good quality antenna can lower it below 5% without extensive cost. Double messaging and other transmission algorithms are a possibility, taking into account consumption. Currently, sensors measuring one point every ten minutes can last seven months with four cheap AA alkaline batteries. Further gains would increase lifetime in same conditions by 30%. [ABSTRACT FROM AUTHOR]

Signal acquisition and sensing in underwater systems and applications is typically a challenging issue due to the small signal strength within the background noise. Here, we present a ring vector sensor communication receiver that can significantly improve signal acquisition, by utilizing the underwater acoustic vector field components, compared to the scalar component. The vector sensor receiver is a multichannel receiver that measures particle velocities, which are vector components of the underwater acoustic field, in addition to the scalar field component. According to the combination of our measured experimental data with our signal acquisition performance analysis, the introduced ring vector sensor receiver exhibits higher signal acquisition probabilities for the vector components compared to the scalar component. This can be attributed to certain characteristics of the vector field components. Another advantage of this multichannel receiver is that combining all of its channels can further increase the signal acquisition and packet detection probability in underwater communication systems compared to a single-channel approach. [ABSTRACT FROM AUTHOR]

In the era of the fifth generation (5G) communication networks, massive multiple-input multiple-output (MIMO) systems demand even lower computation complexity and power consumption while catching up with good detection performance. In this paper, a low-complexity nonlinear detection algorithm is proposed for massive MIMO systems, which is based on partial tree search and successive interference cancellation (SIC). The proposed scheme allows us to expedite the detection process by coping with the transmit symbols group by group. As compared to vertical-Bell laboratories layered space time (V-BLAST), the major breakthrough of computation reduction lies in the fact that the partial tree search can assist the detection process to avoid the inversion of the detection matrix required in each recursion of the SIC process. Both computational complexity analysis and simulation results show that our proposed algorithm not only significantly reduces computational complexity, but also has better bit error rate (BER) performance. [ABSTRACT FROM AUTHOR]

In this paper, we consider non-coherent detection schemes for molecular communication systems in the presence of inter-symbol-interference. In particular, we study non-coherent detectors based on memory-bits-based thresholds in order to achieve low bit-error-ratio (BER) transmission. The main challenge of realizing detectors based on memory-bits-based thresholds is to obtain the channel state information based only on the received signals. We tackle this issue by reformulating the thresholds through intermediate variables, which can be obtained by clustering multi-dimensional data from the received signals, and by using the K-means clustering algorithm. In addition to estimating the thresholds, we show that the transmitted bits can be retrieved from the clustered data. To reduce clustering errors, we propose iterative clustering methods from one-dimensional to multi-dimensional data, which are shown to reduce the BER. Simulation results are presented to verify the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Ambient backscatter communication (AmBC), a promising solution to future Internet of Things (IoT), has attracted increasing attention from both academia and industry in recent years. AmBC systems enable communications between RF-powered devices (e.g., tags and sensors) and AmBC receivers by utilizing ambient RF source signals from existing communication systems. Thus it offers a low-cost and energy-efficient means of communications for future IoT applications. In the current AmBC research, the impact of using channel coding on existing primary networks in terms of the bit-error-rate (BER) performance of the AmBC systems are mostly ignored. Low-density parity-check (LDPC) codes that offer fast encoding and decoding process have been proposed as promising channel codes in the fifth-generation (5 G) networks. In this paper, we investigate the BER performance of an AmBC system that uses LDPC-coded RF source signals. We have analytically derived the BER upper and lower bounds of both the RF source and tag signals for our proposed AmBC system. Simulation results show that the BERs of both signals decrease significantly, compared to conventional AmBC systems with uncoded RF source signals. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has become an addition of humans. IoT assists in the security work to increase the quality of people life. This paper presents an intruder alert system using IoT-sensing through cloud service network. The proposed intruder alert system is divided into 4 sections, which are (1) intruder monitoring section, (2) communication network, (3) data processing unit and (4) user interface. Each composition works according to its function effectively. The intruder monitoring device was contained Ultrasonic and Passive Infrared (PIR) Sensors to analyze its performance. NETPIE work as graphic user interface (GUI) on cloud server was serviced to act as an intermediary to distribute information to other devices through internet network. Both sensors were evaluated the detection sensing test according to various distances. The obtained results based on individual test were analyzed in terms of percentage form, which showed that the Ultrasonic and PIR sensors show its different responses. The Ultrasonic stables in the distance test of 5–55 cm and after 55 cm distance it reacts unstable for detection. While, the PIR sensor swings in the distance of 5–30 cm and more stable after distance of 35 to 100 cm. Accordance the graph visually, it was found that the both sensors work in the opposite way. Therefore, the results of the experiment were again analyzed by statistical program. The finding presented that the Ultrasonic and PIR sensors accurately detected intruder at 99.94% and 99.56% respectively, with at statistically significant level of 95%. [ABSTRACT FROM AUTHOR]

This paper investigates the symbol detection of the multiple antenna ambient backscatter communication systems. In particular, to overcome the problem of the noise uncertainty, the maximum-eigenvalue detector is derived from the general likelihood ratio test, and the approximate bit error rate (BER) performance is characterized. The analytical results show that the BER of the maximum-eigenvalue detector converges to a fixed number in large signal-to-noise ratio or number of receiving antennas $M$ regime. Finally, numerical results are presented to confirm the correctness of theoretical analysis, and reveal that the maximum-eigenvalue detector outperforms the energy detector regarding the BER performance. [ABSTRACT FROM AUTHOR]

In this paper, we address a multiple-input multiple-output (MIMO) communication system based on generalized spatial modulation (GSM) and dual-polarized (DP) antennas in sub-TeraHertz (sub-THz) bands to improve the Spectral Efficiency (SE) while reducing the spatial correlation effect and the space occupancy. Moreover, the joint Maximum Likelihood (ML) detector and a Modified Ordered Block Minimum Mean-Squared Error (MOB-MMSE) detector algorithm are proposed to detect jointly the complex symbols and the virtual bits conveyed by the activated polarization and the transmit antenna combination index. MOB-MMSE can achieve near-ML performance with low complexity. The performance of DP-GSM system over correlated Rayleigh/Rician fading and indoor sub-THz channels is studied. Provided results show that DP-GSM system is robust to the spatial correlation effects under Rician fading channel. In addition, an average bit-error probability (ABEP) upper bounding is derived and analyzed for the DP-GSM system over correlated Rayleigh/Rician fading channels. Since the sub-THz band suffers from many technological limitations and severe RF-impairments such as low transmit output power and important phase noise (PN), the DP-GSM system is studied by considering the sub-THz impairments. Moreover, the Dual Polarized Spatial Multiplexing (DP-SMX) and the Uni-Polarized GSM (UP-GSM) are compared with the DP-GSM system. The results reveal that DP-GSM with low order modulation schemes as QPSK outperforms the UP-GSM. Besides, DP-GSM and DP-SMX provide a good performance up to medium PN, which is interesting for sub-THz bands, but DP-GSM can reduce the transceiver cost by using a lower number of RF chains when the used transmitter architecture is based on RF-switching. [ABSTRACT FROM AUTHOR]

In modern distributed cyber-physical systems (CPS), information fusion often plays a key role in automate and self-adaptive decision making process. However, given the heterogeneous and distributed nature of modern CPSs, it is a great challenge to operate CPSs with the compromised data integrity and unreliable communication links. In this paper, we study the distributed state estimation problem under the false data injection attack (FDIA) with probabilistic communication networks. We propose an integrated ”detection + fusion” solution, which is based on the Kullback-Leibler divergences (KLD) between local posteriors and therefore does not require the exchange of raw sensor data. For the FDIA detection step, the KLDs are used to cluster nodes in the probability space and to partition the space into secure and insecure subspaces. By approximating the distribution of the KLDs with a general χ2 distribution and calculating its tail probability, we provide an analysis of the detection error rate. For the information fusion step, we discuss the potential risk of double counting the shared prior information in the KLD-based consensus formulation method. We show that if the local posteriors are updated from the shared prior, the increased number of neighbouring nodes will lead to the diminished information gain. To overcome this problem, we propose a near-optimal distributed information fusion solution with properly weighted prior and data likelihood. Finally, we present simulation results for the integrated solution. We discuss the impact of network connectivity on the empirical detection error rate and the accuracy of state estimation. [ABSTRACT FROM AUTHOR]

Orthogonal frequency-division multiplexing (OFDM) is a promising technology for communication systems. This paper investigates the impacts of unknown noise variance on the popular spectrum sensing scheme, i.e., energy detection, for OFDM cognitive radios over multipath fading channels. To study the effects of unknown parameters on the energy detector, a new maximum-likelihood estimation of noise and signal powers employing the cyclic prefix of OFDM is presented in this paper. The mean values and Cram $\acute{\text{e}}$ r–Rao lower bounds of the estimation are obtained. Furthermore, the performances of the energy detector for both hypotheses, i.e., false-alarm rate and detection probability, under the influence of unknown noise variance are validated by both simulation and analytical results. The assessment on the required number of samples for the proposed energy detector is conducted, which indicates that an amount of 40–50% samples can be saved compared to the conventional energy detector. [ABSTRACT FROM AUTHOR]

A significant technological transformation has recently occurred in the agriculture sector. Precision agriculture is one among those transformations that largely focus on the acquisition of the sensor data, identifying the insights, and summarizing the information for better decision-making that would enhance the resource usage efficiency, crop yield, and substantial quality of the yield resulting in better profitability, and sustainability of agricultural output. For continuous crop monitoring, the farmlands are connected with various sensors that must be robust in data acquisition and processing. The legibility of such sensors is an exceptionally challenging task, which needs energy-efficient models for handling the lifetime of the sensors. In the current study, the energy-aware software-defined network for precisely selecting the cluster head for communication with the base station and the neighboring low-energy sensors. The cluster head is initially chosen according to energy consumption, data transmission consumption, proximity measures, and latency measures. In the subsequent rounds, the node indexes are updated to select the optimal cluster head. The cluster fitness is assessed in each round to retain the cluster in the subsequent rounds. The network model's performance is assessed against network lifetime, throughput, and network processing latency. The experimental findings presented here show that the model outperforms the alternatives presented in this study. [ABSTRACT FROM AUTHOR]

In this paper, we propose a generalized space-time block coded spatial modulation (GSTBC-SM) scheme for open-loop massive multiple-input and multiple-output (MIMO) downlink communication systems. Specifically, we firstly partition the information bits into multiple groups with each group modulated by the spatial modulation (SM), where the SM symbols are invoked for orthogonal STBC (OSTBC) and quasi-orthogonal STBC (Q-OSTBC) structures. Then, message passing (MP) and block minimum mean square equalization (B-MMSE) detectors are designed for our GSTBC-SM systems, to achieve near-optimal performance with significantly reduced complexity in massive MIMO configurations. Finally, we derive the theoretical average bit error probability (ABEP) of the proposed scheme. The main contribution is that the propose scheme achieves high transmission rate and diversity gain even with small number of radio frequency (RF) chains at the transmitter. Simulation results verify the theoretical derivations and show that the proposed GSTBC-SM scheme provides near 20 dB gain over the conventional GSTBC scheme under massive MIMO configurations. [ABSTRACT FROM AUTHOR]

Direct conversion radios are widely recognized as the most appealing approach for reducing the hardware cost as well as power consumption in upcoming communication systems. However, such radios are known to entail gain and phase uncertainties along the analog inphase/quadrature (I/Q) paths. In this paper, we address the effects of the transmitter (TX) and receiver (RX) I/Q errors or mismatches on the improperness of the transmitted and received signals, respectively. We analytically show how the properness of a transmitted signal and the receiver thermal noise can be destroyed, respectively, under the transmitter and receiver I/Q errors, given that the corresponding ideal signals are proper under perfect I/Q balance. Then, we address the spectrum sensing problem in cognitive radio systems through modeling it as a composite binary hypothesis testing task, and apply the likelihood ratio test approach to solve it. To this end, we propose three impropriety-based multiantenna spectrum sensing algorithms under the transmitter and receiver I/Q uncertainties. The principle of invariance is exploited to examine the potential constant false alarm rate (CFAR) behavior of the proposed detectors. We analytically prove that all the proposed sensing methods possess CFAR behavior against the noise variance uncertainty, while only two of them have CFAR property against the receiver I/Q mismatch values. The achievable sensing performance of the proposed methods is then analyzed through extensive numerical experiments, and the devised alternative detectors are mutually compared. Finally, analytical solutions are derived to quantify the improvement/degradation in the effective received signal SNR under I/Q imbalanced radios. [ABSTRACT FROM AUTHOR]

Quadrature spatial modulation (QSM) using frequency modulated diverse retrospective array (FMD-RDA) is put forward to achieve green transmission paradigm shift from phased-array-based arrangement. Up to date, the QSM techniques have employed phased-array, but only angular focusing direction is possible without range focusing direction. On the other hand, millimeter-wave (mmWave) scheme is suggested as an alternative approach for fifth-generation because of large bandwidth spectrum. Therefore, this paper studies green secure communication range-angle focusing QSM FMD-RDA in mmWave wireless communication. In FMD-RDA, a small frequency increment is included across the RDA elements to decouple range-angle focusing. More importantly, an attractive advantage of using FMD-RDA is the automatic self tracking feature which alleviate transmitter/receiver's channel state information (CSI) requirement. To ensure secure transmission, we exploit physical layer security to degrade the channel quality of the eavesdropper. Furthermore, low complexity greedy detector is devised to retrieved the information bits at the receiver. We also derived pair-wise error probability, and analyze secrecy sum rate and energy transfer capability. The reported simulation results demonstrate that the proposed scheme is effective in improving the spectral efficiency with low complexity and no CSI requirement. [ABSTRACT FROM AUTHOR]

Index modulation (IM) is an attractive concept for next generation communication systems. However, as an emerging technique, there are still some challenges need to be tackled in this frontier. In this paper, we consider two aspects of IM, which are diversity and detection complexity. Specifically, we propose a linear precoding assisted index modulation (LPIM) scheme for orthogonal frequency division multiplexing (OFDM) systems. We commence by analyzing the diversity and coding gains of the proposed scheme. Moreover, a detailed codebook design criterion is proposed. Then, our LPIM codebook is designed based on the maximum diversity and coding gain criteria. In contrast to the signaling model of the existing full diversity precoder designed for OFDM, we introduce a modeling method to link the zero-valued IM symbols to the origin of a Lattice for implementing our full diversity precoder designed for OFDM-IM. Both analytical and computer simulation results are provided for characterizing the attainable performance of OFDM-LPIM, demonstrating that it is capable of achieving full diversity as well as an attractive coding gain. However, the maximum-likelihood (ML) detection complexity of OFDM-LPIM is excessive, hence a low-complexity generalized iterative residual check detector (GIRCD) is proposed, which is inspired by the existing sparse recovery algorithms. Finally, computer simulation results are provided for demonstrating that GIRCD can provide a beneficial trade-off between bit error ratio performance and complexity. [ABSTRACT FROM AUTHOR]

For the ultra-wideband chaotic radio pulses an analytical model of an energy detector is designed and closed-form expressions for the optimal threshold and the detection probabilities are derived. The analytical solution is compared with the results of the numerical simulation. The model proves to be well-suited for the chaotic radio pulses with large base B (or processing gain, or the number of freedom degrees), namely, B ≥ 100. Potential applications areas of the proposed model are ultra-wideband communications with chaotic pulse carriers, e.g., ultra-wideband systems of 802.15.x standards, and multi-element chaotic communication systems. [ABSTRACT FROM AUTHOR]

Up-conversion single-photon detectors (UCSPD) are based on sum-frequency generation of the telecom band single-photons to near-infrared wavelengths at which efficient and low-noise silicon single-photon detectors are available. Moreover, because of high dynamic range of silicon single-photon detectors, UCSPD is suitable for high-speed quantum communication. UCSPDs reported to date, however, have a very narrow fixed window of detectable wavelengths, severely limiting their applications in wavelength-multiplexed quantum networks. In this work, we report a tunable UCSPD module that covers the complete telecom C band, making it suitable for quantum communication networks based on sharing wavelength-multiplexed entangled photons. [ABSTRACT FROM AUTHOR]

Vehicular networks are vulnerable to various attacks such as Sybil, denial-of-service (DoS) and false alert generation attacks. Cryptographic methods can provide some protection to vehicular networks from external attacks but are found to be vulnerable to internal attacks. A misbehavior detection system (MDS) can be deployed to detect and prevent internal attacks. In this paper, we propose a machine learning and reputation based MDS to enhance the detection accuracy as well as to ensure the reliability of both vehicles and messages. Proposed MDS is trained using datasets generated through extensive simulation based on the realistic vehicular network environment. To improve the accuracy of the detection, we have employed the Dempster-Shafer (DS) theory-based collaborative misbehavior detection system. In the proposed scheme, the reputation score of each vehicle is used as a belief value for Dempster-Shafer based feedback combination. In addition, we propose a beta distribution based reputation update and revocation scheme. Moreover, we show that our proposed scheme is better compared to previous methods in terms of accurately identifying various misbehaviors. [ABSTRACT FROM AUTHOR]

Faster-than-Nyquist signaling (FTNS) is capable of improving the spectral efficiency (SE) of communication systems. However, for conventional single-carrier FTNS (SC-FTNS) in which only symbol interval is reduced, the increase of SE is very limited due to the presence of inter-symbol interference (ISI) introduced by the FTNS. To deal with this problem, this paper proposes a new time-frequency compressed SC-FTNS (TFC-SC-FTNS) scheme that includes the conventional FTNS as a special case, to improve the SE via two dimensions simultaneously: time dimension by stacking symbols closer; frequency dimension by precoding to make the FTN signal spectrum more compact. Further, an optimization subject to a spectral mask constraint is performed on the precoder to suppress the ISI, according to a mean-square-error criterion, but the optimization problem is non-convex. A nontrivial contribution in the new scheme is that the non-convex problem is transformed into a convex one by a change of variable and an addition of admissibility constraint. Simulation results demonstrate that the proposed scheme significantly outperforms the conventional FTNS in terms of achievable SE or, equivalently, reception performance at a given SE. Further, with larger constellations applied, the gains of the TFC-FTNS increase. [ABSTRACT FROM AUTHOR]

Summary: This paper presents the design, testing, and validation of a ultra‐wideband (UWB) wireless solution to replace wired intra‐spacecraft communications with wireless communications. The system requirements are provided in the framework of the Eu:CROPIS mission and the wired intra‐spacecraft communication system is replaced with a two‐segment wireless network. As such, distinct sets of UWB gateways and nodes are prototyped to interface with the on‐board entities and properly handle the data transmission in the resulting spacecraft and payload wireless networks. To accomplish these tasks, a custom radio module integrated into the prototypes was designed to provide the appropriate UWB RF front‐end and run a custom‐built communication stack. The viability of the solution was tested in laboratory conditions. For the test scenarios, sensor data are acquired in the payload network and forwarded as SpaceWire packets over the spacecraft network. Experimental test results indicate the suitability of the proposed solution in terms of delay and data integrity for implementing intra‐spacecraft wireless communications. [ABSTRACT FROM AUTHOR]

Industry 4.0 focuses on continuous interconnection services, allowing for the continuous and uninterrupted exchange of signals or information between related parties. The application of messaging protocols for transferring data to remote locations must meet specific specifications such as asynchronous communication, compact messaging, operating in conditions of unstable connection of the transmission line of data, limited network bandwidth operation, support multilevel Quality of Service (QoS), and easy integration of new devices. The Message Queue Telemetry Transport (MQTT) protocol is used in software applications that require asynchronous communication. It is a light and simplified protocol based on publish-subscribe messaging and is placed functionally over the TCP/IP protocol. It is designed to minimize the required communication bandwidth and system requirements increasing reliability and probability of successful message transmission, making it ideal for use in Machine-to-Machine (M2M) communication or networks where bandwidth is limited, delays are long, coverage is not reliable, and energy consumption should be as low as possible. Despite the fact that the advantage that MQTT offers its way of operating does not provide a serious level of security in how to achieve its interconnection, as it does not require protocol dependence on one intermediate third entity, the interface is dependent on each application. This paper presents an innovative real-time anomaly detection system to detect MQTT-based attacks in cyber-physical systems. This is an online-semisupervised learning neural system based on a small number of sampled patterns that identify crowd anomalies in the MQTT protocol related to specialized attacks to undermine cyber-physical systems. [ABSTRACT FROM AUTHOR]

Viterbi detectors are widely used in all communication systems dealing with transmission and storage. In this paper, we develop the theory and design architecture for a 2-D separable iterative soft-output Viterbi detector (SISOVD) for applications in 2-D storage channels extending the original work from Wu et al. (IEEE Trans. Magn., vol. 39, no. 4, pp. 2115–2120, Jul. 2003). The contribution of this paper is twofold. First, we design a non-binary soft Viterbi detector that will be used as the core engine for the SISOVD and show its equivalence to the max-log-MAP algorithm implementation. We also study the performance of the algorithm through simulations under various design parameter constraints toward a systems architecture. Second, we propose a novel digital circuit that incorporates non-uniform quantization within a sliding block design framework. The proposed detector system architecture is tested within an field programmable gate array platform and proved to be efficient in terms of resource utilization, timing, and power compared with a conventional uniform quantizer with no signal-to-noise ratio performance degradation. It shows a 13.28% reduction in the total number of slice registers, 30.26% reduction in the slice lookup tables, 21.23% reduction in the critical path delay, and 14.84% reduction in the power consumption compared with the uniform quantizer. [ABSTRACT FROM AUTHOR]

Mobile crowd sensing has emerged as a novel data collection paradigm by leveraging pervasive mobile sensing devices to enable various applications. To obtain good quality of service, incentive mechanisms are indispensable for attracting enough users. Most of the existing mechanisms focus on the offline scenario in which all users submit profiles in advance. However, the online scenario often appears in the real world wherein users arrive one by one in random order. In this paper, we investigate the frugal online incentive problem based on an online auction model, where users report their strategic profiles to the crowdsourcer in an online mode, and the crowdsourcer selects users before a deadline to complete a specific number of tasks while minimizing the total payment. We design two online mechanisms, namely, Frugal-OMZ and Frugal-OMG, satisfying computational efficiency, individual rationality, truthfulness, consumer sovereignty, and constant frugality under the zero arrival–departure interval model and the general interval model, respectively. Extensive simulations verify the desirable properties of our mechanisms. [ABSTRACT FROM AUTHOR]

In the last period (last 3 years) the maritime domain, especially the sea transportation and fossil resources exploitation became the most important aspects of the international politics and our lives. Because of the COVID19 situation and now the conflict between Russia and Ukraine, the maritime domain was pushed to its limits. What a few years ago was a safety area of navigation, now this one can be transformed very quickly in a dangerous one. It is important to develop fast, systems for monitoring sea areas and platforms of intervention in case of distress situations. To have a real-time system of monitoring and intervention on sea it is mandatory to have redundant maritime communications. The article presents the most relevant publications related to maritime offshore applications and communications that support them. [ABSTRACT FROM AUTHOR]

Today, due to the impossibility of further reducing the dimensions, electronic devices are faced with fundamental challenges in parameters such as speed, frequency, and power consumption for CMOS technology circuit. One solution is to replace CMOS technology with other technologies such as Quantum-dot Cellular Automata (QCA) technology. Extensive researches have been done for design digital circuits in QCA technology. Phase-frequency detector (PFD) is one of the main blocks in electrical and communication systems. In this paper, a structure is presented for a PFD in QCA technology for the first time. In the proposed structure, a D flip-flop (D-FF) with reset ability based on a new inverter gate is used. The new inverter gate of this D-FF compared to previous inverters, has output signal with high polarization level. Also, the proposed PFD has 199 cells, 0.22 μm2 occupied area and two clock cycles latency that is smaller compared with PFD which is designed with the conventional inverter. [ABSTRACT FROM AUTHOR]

Massive multiple-input multiple-output (mMIMO) is a promising technique to realize the ever-increasing demand for high-speed data, quality of service (QoS), and energy efficiency for 5G and beyond wireless systems. However, the increased number of users in mMIMO systems significantly affects performance of the existing approximate matrix inversion based and matrix inversion less iterative symbol detection techniques. Conventional detection algorithms cannot learn the inter-relations of input-output parameters based on available data without having specific mathematical models of communication scenarios. Moreover, existing deep learning (DL) based symbol detection models lack in-network compression, resulting in large training time and high computational load while expected to be deployed in a low latency communication system. In this article, a sparse refinement architecture is proposed for symbol detection in uplink mMIMO. The proposed DL architecture requires less trainable parameters as compared to a conventional fully connected detection network and refines the estimated symbol vector in each layer. Convergence of the proposed symbol detection technique is analytically justified. An expression for the approximate upper bound on the BER is derived which is supported by simulations. The obtained results prove viability of the proposed symbol detection model as compared to the several existing state-of-art uplink mMIMO detection techniques, in terms of superior the error performance and low computational complexity. [ABSTRACT FROM AUTHOR]

Due to the increasing demand for spectrum resources, the co-existence of communications and radar systems has been proposed that allows radar and communications systems to operate in the same frequency band. On the other hand, deep learning has shown great potential in revolutionizing communications systems. In this work, we investigate the use of deep learning in communications systems subject to interference from radar systems. Specifically, we consider a single-carrier communications system. Linear frequency-modulated (LFM) and frequency-modulated continuous-wave (FMCW) are considered for radar. Several important system parameters, including the level of noise and interference, the radar interference coverage, the symbol duration, feature extraction methods and the number of hidden layers are investigated for the performance of the detector. Fully connected deep neural network (FCDNN) and long short-term memory (LSTM) detectors are implemented, where principle component analysis (PCA) is applied to preprocess the observed signals for the FCDNN detector. Numerical results show that the learning-based detector achieves comparable performance in the radar-communication system to the traditional detector but without interference cancellation. Preprocessing the received signals with PCA can improve the performance of FCDNN when interference is strong. Also, LSTM shows more robust performance than FCDNN when the channel has time-related distortion. [ABSTRACT FROM AUTHOR]

This paper presents a cooperative, combinatorial search model for a multi-mobile sensor, multi-mobile target scenario in a two dimensional grid space. In the proposed model, sensors and targets act as searchers and hiders of a search game. A search game is a game between searcher and hider, modeled on a graph. In this paper, the identified problem states that multiple targets enter into a grid from the same or different sides and try to exit on the opposite directions of their respective entry sides. Alternatively, multiple mobile sensors are randomly deployed in the grid and try to maximize the number of target searched and to minimize search time before the targets leave the grid. This problem fits into several real life scenarios such as international border security, terrorist searching in a specified area, finding spies etc. The novelty of the proposed model is interpreting the problem as a search game and formulating strategies for the mobile sensors to optimize their moves to maximize the number of target searched and minimize search time. In this paper, shortest path and random walk strategies for the targets are analyzed by applying suitable mathematical models and on the other hand, advantages of cooperative over non-cooperative strategies for the mobile sensors are also shown with the help of analytical methods and simulation results. The significant mathematical concepts applied in the proposed model are as follows-computing shortest path, random walk, Markov chain, probability graph, overlapping of multiple probability graphs, cooperative and non-cooperative strategies etc. [ABSTRACT FROM AUTHOR]

Nowadays, smartphones have become ubiquitous and one of the main communication resources for human beings. Their widespread adoption was due to the huge technological progress and to the development of multiple useful applications. Their characteristics have also experienced a substantial improvement as they now integrate multiple sensors able to convert the smartphone into a flexible and multi-purpose sensing unit. The combined use of multiple smartphones endowed with several types of sensors gives the possibility to monitor a certain area with fine spatial and temporal granularity, a procedure typically known as crowdsensing. In this paper, we propose using smartphones as environmental noise-sensing units. For this purpose, we focus our study on the sound capture and processing procedure, analyzing the impact of different noise calculation algorithms, as well as in determining their accuracy when compared to a professional noise measurement unit. We analyze different candidate algorithms using different types of smartphones, and we study the most adequate time period and sampling strategy to optimize the data-gathering process. In addition, we perform an experimental study comparing our approach with the results obtained using a professional device. Experimental results show that, if the smartphone application is well tuned, it is possible to measure noise levels with a accuracy degree comparable to professional devices for the entire dynamic range typically supported by microphones embedded in smartphones, i.e., 35-95 dB. [ABSTRACT FROM AUTHOR]

For the first time, two-arm planar sinuous antenna is demonstrated to realize the emerging concept of integrating RFID functionalities along with sensors and other wireless applications for "green" electronics. In-depth, parametric analysis is performed for the proposed antenna which is fabricated on a paper substrate using revolutionary inkjet printing technology to develop a system-level solution for ultra-low-cost mass production of multipurpose wireless tags in an approach that could be easily expanded to other microwave and wireless "cognition" applications. The proposed antenna exhibits excellent performance throughout several RFID ISM bands and for other wireless applications in its operational range from 0.8 to 3.0 GHz. [ABSTRACT FROM AUTHOR]

In this paper, we investigate differential amplitude/phase modulation (APM) schemes for correlated Rayleigh fading channels. Considering the noncoherent maximum-likelihood receiver, a theoretical pairwise error probability (PEP) formula for differential signal vectors with arbitrary power values is derived. The design criterion for the signal set of differential vectors is then proposed based on the asymptotic analysis of the PEP. For both uncoded and coded APM schemes, the design criteria and optimization procedures for bit labeling rules are investigated. The designed differential APM schemes are shown to provide significant improvement in bit-error-rate performance, particularly in the error-floor regions. [ABSTRACT FROM PUBLISHER]

With the advent of the Internet of things, massive machine-type communications (mMTC) have become one of the most important requirements for next generation communication systems. In the mMTC scenarios, grant-free nonorthogonal multiple access on the transmission side and compressive sensing-based multi-user detection (CS-MUD) on the reception side are a promising solution because many users sporadically transmit small data packets at low rates. In this paper, we propose a novel CS-MUD algorithm for the active user and data detection in the mMTC systems. The proposed scheme consists of the maximum a posteriori probability (MAP) based active user detector (MAP-AUD) and the MAP-based data detector (MAP-DD). By exchanging the extrinsic information between MAP-AUD and MAP-DD, the proposed algorithm improves the active user detection performance and the reliability of the data detection. In addition, we extend the proposed algorithm to exploit group sparsity. By jointly processing the multiple received data with common activity, the proposed algorithm dramatically enhances the active user detection performance. We show by numerical experiments that the proposed algorithm achieves a substantial performance gain over existing algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a cross-layer optimization framework for the wireless sensor networks. In a wireless sensor network, each sensor makes a local observation of the underlying physical phenomenon and sends a quantized version of the observation to a central location via wireless links. As the sensor observations are often partial and correlated, the network performance is a complicated and nonseparable function of individual data rates at each sensor. In addition, due to the shared nature of wireless medium, nearby transmissions often interfere with each other. Thus, the traditional "bit-pipe" model for network link capacity no longer holds. This paper deals with the joint optimization of source quantization, routing, and power control in a wireless sensor network. We follow a separate source and channel coding approach and show that the overall network optimization problem can be naturally decomposed into a source coding subproblem at the application layer and a wireless power control subproblem at the physical layer. The interfaces between the layers are precisely the dual optimization variables. In addition, we introduce a novel source coding model at the application layer, which allows the efficient design of practical source quantization schemes at each sensor. Finally, we propose a dual algorithm for the overall network optimization problem. The dual algorithm, when combined with a column-generation method, allows an efficient solution for the overall network optimization problem. [ABSTRACT FROM AUTHOR]

In this paper, we design the uplink transceiver for a new multiuser (MU) massive spatial modulation (SM) multiple-input–multiple-output (SM-MIMO) system over frequency-selective fading channels, where the base station (BS) is equipped with massive antennas, and user equipment (UE) has multiple transmit antennas (TAs) but only one radio-frequency (RF) chain. UE transmit data bits to the BS simultaneously by cyclic-prefix single-carrier (CP-SC) SM. For the uplink MU detection (MUD), we construct a low-complexity generalized approximate message passing detector (GAMPD), which can exploit both the sparsity and prior probability distribution of the transmitted signal and is suitable for hardware implementation because its most complex operation is only matrix–vector multiplication. Its mean square error (MSE) and uncoded bit error rate (BER) performances are also analyzed based on the state evolution (SE). Compared with stagewised linear detectors, GAMPD shows orders-of-magnitude lower complexity. Moreover, simulation results indicated that GAMPD approaches to the performance of maximum-likelihood (ML) detection and outperforms minimum MSE (MMSE) significantly. Finally, to design energy-efficient massive SM-MIMO, we propose a practical algorithm to optimize the key system parameters (e.g., the transmission power, the numbers of the BS antennas or UE, or the TAs at the UE). Numerical results indicate that low BS circuit power consumption and long channel coherence time are the two key prerequisites for the success of massive SM-MIMO. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a detector, based on the compressive sensing (CS) principles, for multiple-access spatial modulation (SM) channels with a large-scale antenna base station (BS). Particularly, we exploit the use of a large number of antennas at the BSs and the structure and sparsity of the SM transmitted signals to improve the performance of conventional detection algorithms. Based on the above, we design a CS-based detector that allows the reduction of the signal processing load at the BSs particularly pronounced for SM in large-scale multiple-input–multiple-output (MIMO) systems. We further carry out analytical performance and complexity studies of the proposed scheme to evaluate its usefulness. The theoretical and simulation results presented in this paper show that the proposed strategy constitutes a low-complexity alternative to significantly improve the system's energy efficiency against conventional MIMO detection in the multiple-access channel. [ABSTRACT FROM AUTHOR]