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Graphene, a 2D nanomaterial, has garnered significant attention in recent years due to its exceptional properties, offering immense potential for revolutionizing various technological applications. In the context of the Internet of Things (IoT), which demands seamless connectivity and efficient data processing, graphene's unique attributes have positioned it as a promising candidate to prevail over challenges and optimize IoT systems. This review paper aims to provide a brief sketch of the diverse applications of graphene in IoT, highlighting its contributions to sensors, communication systems, and energy storage devices. Additionally, it discusses potential challenges and prospects for the integration of graphene in the rapidly evolving IoT landscape. [ABSTRACT FROM AUTHOR]

Experimental Semiotics (ES) is the study of novel forms of communication that communicators develop in laboratory tasks whose designs prevent them from using language. Thus, ES relates to pragmatics in a "pure," radical sense, capturing the process of creating the relation between signs and their interpreters as biological, psychological, and social agents. Since such a creation of meaning-making from scratch is of central importance to language evolution research, ES has become the most prolific experimental approach in this field of research. In our paper, we report the results of a study on the scope of recent ES and evaluate the ways in which it is relevant to the study of language origins. We coded for multiple levels across 13 dimensions related to the properties of the emergent communication systems or properties of the study designs, such as type of goal (coordination versus referential), modality of communication, absence or presence of turn-taking, or the presence of vertical vs. horizontal transmission. We discuss our findings and our classification, focusing on the advantages and limitations of those trends in ES, and in particular their ecological validity in the context of bootstrapping communication and the evolution of language. [ABSTRACT FROM AUTHOR]

It is clear that 5G networks have already become integral to our present. However, a significant issue lies in the fact that current 5G communication systems are incapable of fully ensuring the required quality of service and the security of transmitted data, especially in government networks that operate in the context of the Internet of Things, hostilities, hybrid warfare, and cyberwarfare. The use of 5G extends to critical infrastructure operators and special users such as law enforcement, governments, and the military. Adapting modern cellular networks to meet the specific needs of these special users is not only feasible but also necessary. In doing so, these networks must meet additional stringent requirements for reliability, performance, and, most importantly, data security. This scientific paper is dedicated to addressing the challenges associated with ensuring cybersecurity in this context. To effectively improve or ensure a sufficient level of cybersecurity, it is essential to measure the primary indicators of the effectiveness of the security system. At the moment, there are no comprehensive lists of these key indicators that require priority monitoring. Therefore, this article first analyzed the existing similar indicators and presented a list of them, which will make it possible to continuously monitor the state of cybersecurity systems of 5G cellular networks with the aim of using them for groups of special users. Based on this list of cybersecurity KPIs, as a result, this article presents a model to identify and evaluate these indicators. To develop this model, we comprehensively analyzed potential groups of performance indicators, selected the most relevant ones, and introduced a mathematical framework for their quantitative assessment. Furthermore, as part of our research efforts, we proposed enhancements to the core of the 4G/5G network. These enhancements enable data collection and statistical analysis through specialized sensors and existing servers, contributing to improved cybersecurity within these networks. Thus, the approach proposed in the article opens up an opportunity for continuous monitoring and, accordingly, improving the performance indicators of cybersecurity systems, which in turn makes it possible to use them for the maintenance of critical infrastructure and other users whose service presents increased requirements for cybersecurity systems. [ABSTRACT FROM AUTHOR]

Scenario identification plays an important role in improving communication system performance. Considering that the scenarios of vehicle communications are dynamic due to movements of vehicles, and there are obvious differences in channel characteristics, vehicle speeds, traffic densities between various scenarios, the requirement for real-time scenario identification of vehicular communications is increasingly urgent. Vehicular communication systems can select appropriate channel models and transmission mode by correctly identifying the current scenarios to maintain an effective and reliable operating state. This paper presents a machine-learning-based scenario identification model for intelligent vehicular communications. Channel characteristics extracted from channel measurements in different scenarios form the datasets used to training, then a back-propagation neural network (BPNN) is trained, and a scenario identification model is obtained. Furthermore, the model configuration scheme is explored and presented which can make the proposed identification model achieves optimal performance. Subsequently, identification accuracy is verified by using validation data of the corresponding scenarios. The results show that the identification accuracies are all above 98 % in four typical scenarios of urban areas, highways, tunnels, and vehicle obstructions, which indicates that the model proposed in this paper shows good performance in scenario identification for intelligent vehicular communications. [ABSTRACT FROM AUTHOR]

In this paper, simple methodologies of deep learning application to conventional multiple-input multiple-output (MIMO) communication systems are presented. The deep learning technologies with deep neural network (DNN) structure, emerging technologies in various engineering areas, have been actively investigated in the field of communication engineering as well. In the physical layer of conventional communication systems, there are practical challenges of application of DNN: calculating complex number in DNN and designing proper DNN structure for a specific communication system model. This paper proposes and verifies simple solutions for the difficulty. First, we apply a basic DNN structure for signal detection of one-tap MIMO channel. Second, convolutional neural network (CNN) and recurrent neural network (RNN) structures are presented for MIMO system with multipath fading channel. Our DNN structure for one-tap MIMO channel can achieve the optimal maximum likelihood detection performance, and furthermore, our CNN and RNN structures for multipath fading channel can detect the transmitted signal properly. [ABSTRACT FROM AUTHOR]

Communication systems introduce uncertainties that directly impact power system management. Quantitative analysis of such impact is essential for power system reliability and resilience. This paper establishes rigorous relationships between communication packet delivery ratio and errors on optimal load tracking and allocation (OLTA) in DC microgrids (MGs). By modeling channel uncertainties using packet delivery ratio and communication network topologies, impact of communication packet loss on the distributed OLTA algorithm for DC MGs is studied. It is shown that communication packet loss directly affects the convergence rate of the distributed OLTA algorithm under intermittent renewable generations. The results of this paper quantitatively characterize a practical criterion for securing reliability of OLTA solutions under communication uncertainty. Simulation studies using a real-world DC MG are conducted to validate the theoretical findings. [ABSTRACT FROM AUTHOR]

The multi-user Holographic Multiple-Input and Multiple-Output Surface (MU-HMIMOS) paradigm, which is capable of realizing large continuous apertures with minimal power consumption, has been recently considered as an energy-efficient solution for future wireless networks, offering increased flexibility in impacting electromagnetic (EM) wave propagation according to the desired communication, localization, and sensing objectives. The tractable channel modeling in MU-HMIMOS wireless systems is one of the most critical research challenges, mainly due to the coupling effect induced by the excessively large number of closely spaced patch antennas. In this paper, we focus on this challenge for the downlink of multi-user MIMO communications and extend an EM-compliant channel model to multi-user case, which is expressed in the wavenumber domain using the Fourier plane wave approximation. Based on the presented channel model, we investigate the spectral efficiency of maximum-ratio transmission and Zero-Forcing (ZF) precoding schemes. We also introduce a novel hardware efficient ZF precoder, leveraging Neumann series (NS) expansion to replace the required matrix inversion operation, which is very hard to be computed in the conventional way due to the extremely large number of patch antennas in the envisioned MU-HMIMOS communication systems. In comparison with the conventional independent and identical Rayleigh fading channels that ignore antenna coupling effects, the proposed EM-compliant channel model captures the mutual couplings induced by the very small antenna spacing. Our extensive performance evaluation results demonstrate that our theoretical performance expressions approximate sufficiently well the simulated achievable spectral efficiency with the considered linear precoding schemes, even for the highly correlated cases, thus verifying the effectiveness and robustness of the presented analytical framework. In addition, it is verified that the proposed NS-based ZF precoder achieves similar performance to conventional ZF, while requiring lower hardware complexity, thus, providing a hardware efficient solution for practical design of MU-HMIMOS communications systems. [ABSTRACT FROM AUTHOR]

In this paper, a new structure of the quadrature hybrid coupler (QHC) with compact size is proposed using capacitive composite lines and meandered open stubs. The proposed coupler works at 1.6 GHz with a 0.4 GHz bandwidth, which shows 25% fractional bandwidth (FBW). The proposed QHC occupies only 15 mm × 15 mm (0.12 λ × 0.12 λ), while the typical QHC size is 32 mm × 32 mm (0.25 λ × 25 λ) at the same working frequency. In the designed structure, two symmetric meandered stubs and two symmetric π-shaped composite networks including capacitors and microstrip lines are applied together. The designed QHC has a small size and occupies only 22% of the area of the conventional QHC, resulting in a 78% size reduction. The designed prototype has been analyzed, fabricated and tested, and the experimental results verify the simulated and analysis results. The results show a better than 27 dB return loss, more than 28 dB isolation between the output ports and less than 0.4 dB insertion loss at the working frequency of 1600 MHz. With the achieved desirable specifications, the fabricated QHC is a suitable choice for wireless microwave applications. [ABSTRACT FROM AUTHOR]

In order to solve the problem of low detection rate of frequency hopping (FH) signal under low SNR, an FH signal detection technology based on deep neural network (vgg16 network) is proposed. Firstly, this paper introduces the principle of FH signal. Then, this paper presents the SSD deep neural network detection framework, and uses this framework to detect FH signal and estimate its parameters. Time-frequency distribution maps of FH signals are used as the input of deep neural network model to carry out tag learning and training, and then the trained model is used to realise FH signal detection. At the same time, aiming at the noise problem of time-frequency distribution map, the time-frequency graph correction method of K-means clustering algorithm is proposed. After modification, it is more tolerant to noise. The simulation results show that when the SNR is –4 dB, the detection rate of FH signal can reach more than 88%. Finally, the performance of parameter estimation of FH signal is analysed. [ABSTRACT FROM AUTHOR]

This paper is devoted to software-defined radio (SDR) implementation of frequency modulated antipodal chaos shift keying (FM-ACSK) transceiver and presents results of prototype testing in real conditions. This novel and perspective class of spread-spectrum communication systems employs chaotic synchronization for the acquisition and tracking of the analog chaotic spreading code and does not need resource-demanding cross-correlation. The main motivation of the given work is to assess the performance of FM-ACSK in real conditions and demonstrate that chaotic synchronization can be considered an efficient spread-spectrum demodulation method. The work focuses on the real-time implementation aspects of the modulation-demodulation algorithms, forward error correction (FEC) and symbol timing synchronization approach in MATLAB Simulink. The performance of the presented prototype is assessed via extensive testing, which includes measurement of bit error ratio (BER) in single-user and multi-user scenarios, estimation of carrier frequency offset (CFO) impact and image transmission over-the-air between two independent sites and comparison with classical frequency hopping spread spectrum (FHSS). The paper shows that the presented class of the spread spectrum communication systems demonstrates good performance in low signal-to-noise ratio (SNR) conditions and in terms of BER significantly outperforms the classic spread-spectrum modulation schemes which employ correlation-based detection. [ABSTRACT FROM AUTHOR]

This paper focuses on the design and evaluation of pragmatic schemes for delay-sensitive communication. Specifically, this contribution studies the operation of data links that employ incremental redundancy as a means to shield information bits from the degradation associated with unreliable channels. While this inquiry puts forth a general methodology, exposition centers around erasure channels because they are well suited for analysis. Nevertheless, the goal is to identify both structural properties and design guidelines that are broadly applicable. Conceptually, this paper leverages a methodology, termed sequential differential optimization, aimed at identifying near-optimal block sizes for hybrid ARQ. This technique is applied to erasure channels and it is extended to scenarios where throughput is maximized subject to a constraint on the feedback rate. The analysis shows that the impact of the coding strategy adopted and the propensity of the channel to erase symbols naturally decouple when maximizing throughput. Ultimately, block size selection is informed by approximate distributions on the probability of decoding success at every stage of the incremental transmission process. This novel perspective, which rigorously bridges hybrid automatic repeat request and coding, offers a computationally efficient framework to select code rates and blocklengths for incremental redundancy. These findings are supported through numerical results. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the coexistence between a MIMO radar and cellular base stations. We study the interfering channel estimation, where the radar is operated in the “search and track” mode, and the BS receives the interference from the radar. Unlike the conventional methods where the radar and the cellular systems fully cooperate with each other, in this paper, we consider that they are uncoordinated and the BS needs to acquire the interfering channel state information (ICSI) by exploiting the radar probing waveforms. For completeness, both the line-of-sight (LoS) and Non-LoS (NLoS) channels are considered in the coexistence scenario. By further assuming that the BS has limited a priori knowledge about the radar waveforms, we propose several hypothesis testing methods to identify the working mode of the radar, and then obtain the ICSI through a variety of channel estimation schemes. Based on the statistical theory, we analyze the theoretical performance of both the hypothesis testing and the channel estimation methods. Finally, the simulation results verify the effectiveness of our theoretical analysis and demonstrate that the BS can effectively estimate the interfering channel even with the limited information from the radar. [ABSTRACT FROM AUTHOR]

Communication-based Train Control (CBTC) systems are the burgeoning directions for developing future train control systems. With the adoption of wireless communication and network techniques, train control systems are more vulnerable to cyber-attacks. Notably, the jamming attacks, aiming at the handoff process that is the weakest part of train ground communication systems, will cause long disruption of communication. It will have a severe impact on train control operation efficiency. Current research regarding industry control system security is hard to model the impact of the jamming attacks on the train control system quantitatively, and current countermeasure schemes against jamming attacks are not designed for the operating mechanism of train control systems. This paper first builds the train control security state transition probability model under jamming attacks. A cross-layer defense scheme is then proposed from the aspect of the physical layer, the cyber layer and the management layer. In the physical layer, this paper designs a model prediction control algorithm to track dynamic target signals, in the hopes of eventually tracking the dynamic target quickly and smoothly. In the cyber layer, a multi-stage and zero-sum stochastic game model is built for the channel selection for the attack and the defense, whereby the channel selection randomized policy will be obtained. In the management layer, a dynamic train travel speed profile generation algorithm is proposed to mitigate the jamming attacks’ impact on train control systems. Extensive simulation results are shown that jamming attack impact on CBTC can be mitigated effectively with our proposed cross-layer defense scheme. [ABSTRACT FROM AUTHOR]

In underwater acoustic (UWA) adaptive communication system, due to time-varying channel, the transmitter often has outdated channel state information (CSI), which results in low efficiency. UWA channels are much more difficult to estimate and predict than terrestrial wireless channels, given the more severe multipath environments with varying propagation speeds in different locations, non-linear propagation paths, several-order higher propagation latency, mobile transceiver and obstacles in the sea, etc. To handle the complexity, this paper proposes an efficient online CSI prediction model for UWA CSI prediction considering the complicated correlationship of UWA channels in both the time and frequency domains. This paper designs a learning model called CsiPreNet, which is an integration of a one-dimensional convolutional neural network (CNN) and a long short term memory (LSTM) network. The performance is compared with the widely used recursive least squares (RLS) predictor, the approximate linear dependency recursive kernel least-squares (ALD-KRLS), and two common conventional deep neural networks (DNN) predictors, i.e., back propagation neural network (BPNN) and LSTM network using the measured data recorded in the South China Sea. To validate the efficacy of prediction, we investigate the prediction of CSI in simulated downlink UWA orthogonal frequency division multiple access (OFDMA) systems. Specifically, the measured UWA channel is used in the OFDMA system. A joint subcarrier-bit-power adaptive allocation scheme is used for resource allocation. To further improve the performance, we develop an offline-online prediction scheme, enabling the prediction results to be more stable. Simulation and experimental results show that the CsiPreNet has superior performance than the existing solutions, thanks to its capability in capturing both the temporal and frequency correlation of the UWA CSIs. [ABSTRACT FROM AUTHOR]

The paradigm known as Cognitive Radio (CR) proposes a continuous sensing of the electromagnetic spectrum in order to dynamically modify transmission parameters, making intelligent use of the environment by taking advantage of different techniques such as Neural Networks. This paradigm is becoming especially relevant due to the congestion in the spectrum produced by increasing numbers of IoT (Internet of Things) devices. Nowadays, many different Software-Defined Radio (SDR) platforms provide tools to implement CR systems in a teaching laboratory environment. Within the framework of a 'Communication Systems' course, this paper presents a methodology for learning the fundamentals of radio transmitters and receivers in combination with Convolutional Neural Networks (CNNs). [ABSTRACT FROM AUTHOR]

This paper presents the design and optimization of a dual-band polarization-dependent metasurface capable of dynamically switching transmission and reflection characteristics. The metasurface is composed of three metallic patterns, with the bottom layer governing the reflection and transmission phase for both TE-polarization and TM-polarization states. The middle and top layers are strategically employed to ensure optimal transmission and reflection performance. The results confirm that the metasurface enables the transformation of the transmission band into a complete reflection band, and vice versa, through variations in the incident wave polarization. Remarkable transmission and reflection characteristics are achieved within the frequency ranges of 6.1–6.55 GHz and 8.9–9.3 GHz, respectively. The proposed metasurface offers promising applications in advanced communication systems and radar technology, enabling dynamic manipulation of electromagnetic waves. [ABSTRACT FROM AUTHOR]

The eleven papers in this special section focus on industrial communication systems and technologies (ICS). Major requirements of the ICS are real-time support and dependability under all considered load and fault assumptions. The ICS may need to meet stringent deadlines as a basis for the stability in control loops and alarm monitoring. At the same time, high reliability and availability is demanded to avoid down times. In particular, reliability with respect to critical failure modes is of utmost importance for safety-critical applications where failures may result in significant financial losses or risk to humans and the environment. From the point of view of the information and communication technology (ICT), current ICS solutions are based on wired systems to support distributed industrial controls. [ABSTRACT FROM PUBLISHER]

This paper considers the maximization of total throughput and device lifetime for point-to-point multiple-input multiple-output communication systems, where a transmission node has a battery which exhibits non-linear battery discharge behaviors. We adopt a battery model called Peukert’s law to render the non-linear battery characteristics and formulate the battery constraint from Peukert’s law. Then, we prove that the total throughput is a strictly concave function in terms of the battery lifetime under the battery constraint. Also, we derive the optimality conditions for the total throughput maximization and device lifetime maximization problems from the strict concavity, and compute the optimal solutions by a bi-section method. Furthermore, we provide a solution based on asymptotic analysis for the case, where the eigenvalue distribution of the channel matrix is not available. In the simulation section, we conduct accurate battery discharge simulations to validate this paper. We confirm that the analysis matches well with the battery simulations, and the derived optimal scheme outperforms the baseline schemes, which neglect the non-linear battery discharge properties. Also, the proposed solution based on asymptotic analysis is shown to have almost the same performance compared with the optimal solution. [ABSTRACT FROM PUBLISHER]

This paper investigates coverage analysis and performance optimization for a high-speed railway (HSR) communication system with carrier aggregation (CA). Initially, the system model is established. By using CA technology, the statistical characteristic of the received signal-to-noise ratio (SNR) is obtained. Then, for the systems with or without CA, unified theoretical expressions for the edge coverage probability (ECP) and the percentage of cell coverage area (CCA) are obtained, respectively. After that, a chance-constrained coverage optimization is proposed to improve the coverage performance, which is effectively solved by using a heuristic algorithm. Numerical results show that the derived expressions of the ECP and the percentage of CCA are accurate and can be directly used to evaluate the coverage performance without time-intensive simulations. Moreover, the impacts of CA, transmit power, cell radius, received SNR threshold, HSR scenario on coverage performance have been discussed. Furthermore, the effectiveness of the proposed coverage optimization scheme is also verified. [ABSTRACT FROM AUTHOR]

The demand for wireless connectivity has grown tremendously over the last few decades. A new perspective of wireless communication will soon be positioned worldwide. In the years beyond, the millimeter-wave spectrum for the mobile communication infrastructure has improved spectral efficiency. The problem of the technical challenges may be detected, identified, and resolved in different designs as traditional solutions involve the software tricks that prevent the modes of operation that trigger the trouble. In this research, we carry out the proposed Millimetre assisted wave technologies using 6G federated learning and high-performance computing for 6G assisted wireless communication systems for voice data optimization. Millimeter-wave technology is a specific part of the radio frequency spectrum, between 24 and 100 GHz. The outcome of this proposed technique will be voice data optimization in the 6G cellular networks for next-generation networks to improve the 6G assisted networks federated learning from existing cellular network technology generation, established on the tool of Flight stack or Autopilot. The observational results express to assess the performance of the device, we acquire theMillimetre assisted wave technologies and federated learning and high-performance computing at various data transmission rates and modulations, including real-time data transfer up to 96 Gbit/s at 420 GHz, with a low bit error rate and good signal-to-noise ratio. With 18 dBm emitted power at 0.4 THz, this method creates and transmits a signal with a 160 Gbits per second net rate across a 15 m distance. This monolithic dual-DFB PIC-based THz generating technique represents an important advancement toward completely integrated, economically viable, and energy-efficient THz transmitters. [ABSTRACT FROM AUTHOR]

Summary Millimeter Wave (mmWave) communication has emerged as a transformative technology at the forefront of wireless communication. One of the key challenges in harnessing the potential of mmWave technology is overcoming the increased susceptibility to propagation losses and environmental obstacles. To address these challenges, Three‐Dimensional Massive Multiple‐Input Multiple‐Output (3D Massive MIMO) systems have gained traction. The 3D aspect extends this concept by considering the elevation dimension, allowing for enhanced spatial resolution and coverage. Accurate estimation of the channel in 3D Massive MIMO scenarios is particularly challenging because of the complex propagation characteristics of mmWave signals. This paper introduces an efficient‐Aided Graph Neural Network Combining with Hierarchical Residual Learning (DPrGNN‐HrResNetL), designed specifically for beamspace Channel Estimation (CE)in mmWave‐Massive MIMO environments. The proposed model leverages deep priors and GNN mechanisms to enhance the extraction of spatial features, while hierarchical residual connections facilitate effective information flow through the network. DPrGNN enables the model to capture and understand complex spatial relationships among different antenna elements. The incorporation of deep priors provides a mechanism for leveraging prior knowledge about channel characteristics. This enhances the efficiency of the learning process, allowing the model to learn and adapt more effectively. The integration of hierarchical residual connections facilitates effective information flow through the network. This is particularly important for modeling complex dependencies within the beamspace channel data, enhancing the learning capacity of the network. The performance of the DPrGNN‐HrResNetL model is evaluated across a range of Signal‐to‐Noise Ratios (SNRs), utilizing metrics such as Normalized Mean Squared Error (NMSE) to measure the accuracy of the estimation. The outcomes underscore the resilience and efficacy of the DPrGNN‐HrResNetL approach in achieving precise CE within demanding mmWave scenarios. [ABSTRACT FROM AUTHOR]

The area of intelligent transport systems (ITS) is attracting growing attention because of the integration of the smart IoT with vehicles that improve user safety and overall travel experience. Vehicular ad hoc network (VANET) is the part of ITS; that deals with the routing protocols and security of smart vehicles. However, due to the rapid increase in the number of smart vehicles, the existing network technology’s resources unable to handle the traffic load. It expects that the 6G communication system has the ability to fulfill the requirements of VANETs. Only a few studies explore this area, but they also overlooked the security aspect of VANETs in 6G communications networks. In this paper, we present an approach to address authentication and security issues for vehicles in VANET. By authenticating cars in the VANET and identifying various cyber assaults such as DDoS, our method significantly contributes to the intelligent transport communication network. Our approach uses the concepts of identity-based encryption to provide access control to the vehicles and deep learning-based techniques for filtering malicious packets. Our identity-based encryption technique is IND-sID-CCA secure, and a state-of-the-art deep learning algorithm detects malicious packets with an accuracy of 99.72%. These results emphasize the validity of our proposed approach for VANETs in 6G communication systems. [ABSTRACT FROM AUTHOR]

To realize high energy-efficient, high spectrum-efficient, and high-throughput data transmission, a multi-carrier differential chaos shift keying communication system with hybrid index modulation, referred to as HIM-MC-DCSK system, is proposed in this paper. In the HIM-MC-DCSK system, we intelligently integrate the carrier-number-index technique and carrier-index technique into the MC-DCSK modulation in order to significantly boost the transmission efficiency in wireless communication systems. Especially, we use a pair of orthogonal signals to represent the active and inactive subcarriers in the HIM-MC-DCSK system so as to exploit all the subcarriers to transmit information bits. In addition, the theoretical bit-error-rate (BER) expressions of the HIM-MC-DCSK system are derived over additive white Gaussian noise (AWGN) and multipath Rayleigh fading channels. Also, the data rate, spectrum efficiency, energy efficiency, and complexity of the proposed system are carefully analyzed. Monte-Carlo simulations not only verify the accuracy of the theoretical analysis but also illustrate the superiority of the proposed system. [ABSTRACT FROM AUTHOR]

Reliability is of fundamental importance for the performance of secondary networks in cognitive radio networks (CRNs). To date, most studies have focused on predicting reliability parameters based on prior statistics of traffic patterns from user behavior. In this paper, we define a few reliability metrics for channel access in multichannel CRNs that are analogous to the concepts of reliability and availability in classical dependability theory. Continuous-time Markov chains are employed to model channel available and unavailable time intervals based on channel occupancy status. The impact on user access opportunities based on channel availability is investigated by analyzing the steady-state channel availability and several system times such as mean channel available time and mean time to first channel unavailability. Moreover, the complementary cumulative distribution function for channel availability is derived by applying the uniformization method, and it is evaluated as a measure of guaranteed availability for channel access by secondary users. The precision and the correctness of the derived analytical models are validated through discrete-event-based simulations. We believe that the reliability metric definitions and the analytical models proposed in this paper have their significance for reliability and availability analysis in CRNs. [ABSTRACT FROM PUBLISHER]

Antenna array synthesis is quite a popular topic in electromagnetics and finding suitable array parameter values is a challenging nonlinear engineering problem in this field. In recent years, it has been realized that optimization algorithms can be used to achieve desired solutions in this area. Besides, improvements in the optimization algorithms make it possible to perform synthesis processes on different array structures and to obtain better results. In this paper, a method based on a combination of ant lion optimizer (ALO) and sequential quadratic programming (SQP) is proposed for the concentric circular antenna array (CCAA) synthesis. Excitation amplitudes of array elements are optimized for CCAAs with low maximum sidelobe level (MSL), narrow first null beamwidth (FNBW), and low dynamic range ratio (DRR). The results of proposed ALO‐SQP method are compared with those of 11 different algorithms: ALO, moth flame optimizer (MFO), SQP, symbiotic organisms search (SOS), biogeography based optimization (BBO), opposition‐based gravitational search algorithm (OGSA), cat swarm optimization (CSO), firefly algorithm (FA), evolutionary programming (EP), backtracking search optimization algorithm (BSA), and seeker optimization algorithm (SOA). The comparisons indicate that the CCAAs synthesis using the proposed method presents good MSLs, FNBWs, and DRRs. [ABSTRACT FROM AUTHOR]

Electric cars have a lot of promise in future energy markets as a manageable load. A popular vehicle-to-grid control interface, which enables the aggregation of the charging mechanism for energy management in the distribution grid, is one of the most significant road blocks to realize this opportunity. Understanding the ecology of electric transportation and integrating it in local communities to alleviate the energy shortage at peak hours is very complicated. In this research paper, recent standardization initiatives aimed at overcoming obstacles such as the integration of electric cars into smart grids are discussed. A charge control scheme focused on vehicle-to-grid connectivity is implemented. It is observed that the rise of environmentally sustainable energy sources, such as photovoltaic (PV) and wind energy, is straining the power network and their infrequent power generation is causing problems in power system operation, regulation and planning. The introduction of electric vehicles (EVs) into the electricity grid has been proposed to overcome grid load variations. Finally, the article discusses the incorporation of renewable energy sources and latest potential solutions involving electric vehicles. [ABSTRACT FROM AUTHOR]

As an essential part of the emerging Internet of Things, connected and autonomous vehicles (CAVs) have the potential to reshape future transportation systems and change the commute style in people’s everyday life. Among many vehicular on-board devices, radar system and vehicle-to-vehicle (V2V) communication system are two important pillars for the realization of CAVs. In this paper, the concept of coordinated CRC (C-CRC) is proposed to serve as a cognitive mediator for bridging vehicular radar and communication systems. By establishing a mutual-assistance relationship, C-CRC provides a new safety mechanism that allows one system to learn from and react to the risks that the other system has encountered. Simulation results have shown that the proposed method has desirable performance in face of motion perturbation and/or jamming attack under various scenarios. [ABSTRACT FROM AUTHOR]

Exploiting short packets for communications is one of the key technologies for realizing emerging application scenarios such as massive machine type communications (mMTC) and ultra-reliable low-latency communications (uRLLC). In this paper, we investigate short-packet communications to provide both reliability and security guarantees simultaneously with an eavesdropper. In particular, an outage probability considering both reliability and secrecy is defined according to the characteristics of short-packet transmission, while the effective throughput in the sense of outage is established as the performance metric. Specifically, a general analytical framework is proposed to approximate the outage probability and effective throughput. Furthermore, closed-form expressions for these quantities are derived for the high signal-to-noise ratio (SNR) regime. Both effective throughput obtained via a general analytical framework and a high-SNR approximation are maximized under an outage-probability constraint by searching for the optimal blocklength. Numerical results verify the feasibility and accuracy of the proposed analytical framework, and illustrate the influence of the main system parameters on the blocklength and system performance under the outage-probability constraint. [ABSTRACT FROM AUTHOR]

One of the key challenges for future communication systems is to deal with fast changing channels due to the mobility of users. Having a robust protocol capable of handling transmission failures in unfavorable channel conditions is crucial, but the feedback capacity may be greatly limited due to strict latency requirements. This paper studies the hybrid automatic repeat request (HARQ) techniques involved in re-transmissions when decoding failures occur at the receiver and proposes a scheme that relies on codeword bundling and adaptive incremental redundancy (IR) to maximize the overall throughput in a limited feedback system. In addition to the traditional codeword extension IR bits, this paper introduces a new type of IR, bundle parity bits, obtained from an erasure code across all the codewords in a bundle. The type and number of IR bits to be sent as a response to a decoding failure is optimized through a Markov Decision Process. In addition to the single link analysis, the paper studies how the same techniques generalize to relay and multi-user broadcast systems. Simulation results show that the proposed schemes can provide a significant increase in throughput over traditional HARQ techniques. [ABSTRACT FROM AUTHOR]

This paper introduces a model to study both single tier and multitier wireless communication systems consisting of a multitude of wireless access points (AP), and operating according to the classical opportunistic beamforming framework. The AP locations in the proposed network model are determined by using planar Poisson point processes. The extreme value distribution of signal-to-interference-plus-noise-ratio (\rm SINR) on a beam is of fundamental importance for obtaining performance bounds for such an opportunistic communication system. Two tight distribution approximation results are provided for the distribution of maximum \rm SINR on a beam, which is hard to obtain due to correlation structure of the underlying inter-AP interference field, using key tools from stochastic geometry. These approximations hold for general path loss models that satisfy some mild conditions. Simulations and numerical evaluations are presented to validate the results, to provide further insights into the derived approximate maximum beam \rm SINR distributions, and to illustrate the utility of these approximations in obtaining performance bounds for opportunistic communication systems having multiple interfering APs. In particular, key performance measures such as beam outage probability and ergodic aggregate data rate of an AP are derived by utilizing the approximated distributions. [ABSTRACT FROM PUBLISHER]

Inverse, or negative binomial, sampling is often used when the observation of interest occurs extremely infrequently. As this is the case in bit error rate (BER) simulations, especially in high signal-to-noise ratio cases, negative binomial sampling can be advantageously employed in a computationally economic fashion to compare bit error rates between different systems. When the results of two negative binomial sampling tests are compared, point estimates and interval estimates quantify the performance relationship between the results of the tests. This paper derives a new, optimal, logarithmically symmetric confidence interval estimator for the ratio of BER estimates derived from two negative binomial tests. In addition, a three-sided hypothesis test with a single significance level is derived to quantify the confidence of the relationship between the two systems. Low-BER approximations for the confidence interval and decision thresholds are derived based on the F-distribution. The approximation is shown to work with BERs as high as 10^-2. An example inspired by bit interleaved coded modulation shows how the technique can be used to reduce simulation time by an order of magnitude and facilitate straightforward interpretation and comparison between different systems. Negative binomial sampling is recommended for comparison experiments where BER is the key metric. [ABSTRACT FROM PUBLISHER]

The coexistence between radar and communications systems has received considerable attention from the research community in the past years. In this paper, a radar waveform design method for target parameter estimation is proposed. Target time delay parameter is used as an example. The case where the two systems are not colocated is considered. Radar waveform optimization is performed using statistical criteria associated with estimation performance, namely Fisher Information (FI) and Cramér–Rao Bound (CRB). Expressions for FI and CRB are analytically derived. Optimization of waveforms is performed by imposing constraints on the total transmitted radar power, constraints on the interference caused to the communications system, as well as constraints on the subcarrier power ratio (SPR) of the radar waveform. The frequency-domain SPR is different than the peak-to-average power ratio, which is computed in time domain. It is shown, using simulation results, that the proposed optimization strategies outperform other strategies in terms of estimation error. It is also shown that the SPR constraint reduces the delay domain ambiguities. [ABSTRACT FROM AUTHOR]