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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]

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]

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]

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]

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]

Channel estimation is one of the main tasks in realizing practical intelligent reflecting surface-assisted multi-user communication (IRS-MUC) systems. However, different from traditional communication systems, an IRS-MUC system generally involves a cascaded channel with a sophisticated statistical distribution. In this case, the optimal minimum mean square error (MMSE) estimator requires the calculation of a multidimensional integration which is intractable to be implemented in practice. To further improve the channel estimation performance, in this paper, we model the channel estimation as a denoising problem and adopt a deep residual learning (DReL) approach to implicitly learn the residual noise for recovering the channel coefficients from the noisy pilot-based observations. To this end, we first develop a versatile DReL-based channel estimation framework where a deep residual network (DRN)-based MMSE estimator is derived in terms of Bayesian philosophy. As a realization of the developed DReL framework, a convolutional neural network (CNN)-based DRN (CDRN) is then proposed for channel estimation in IRS-MUC systems, in which a CNN denoising block equipped with an element-wise subtraction structure is specifically designed to exploit both the spatial features of the noisy channel matrices and the additive nature of the noise simultaneously. In particular, an explicit expression of the proposed CDRN is derived and analyzed in terms of Bayesian estimation to characterize its properties theoretically. Finally, simulation results demonstrate that the performance of the proposed method approaches that of the optimal MMSE estimator requiring the availability of the prior probability density function of channel. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surfaces (IRSs), which are capable of adjusting radio propagation conditions by controlling the phase shifts of the waves that impinge on the surface, have been widely analyzed for enhancing the performance of wireless systems. However, the reflective properties of widely studied IRSs restrict the service coverage to only one side of the surface. In this paper, to extend the wireless coverage of communication systems, we introduce the concept of intelligent omni-surface (IOS)-assisted communication. More precisely, an IOS is an important instance of a reconfigurable intelligent surface (RIS) that can provide service coverage to mobile users (MUs) in a reflective and a refractive manner. We consider a downlink IOS-assisted communication system, where a multi-antenna small base station (SBS) and an IOS jointly perform beamforming, for improving the received power of multiple MUs on both sides of the IOS, through different reflective/refractive channels. To maximize the sum-rate, we formulate a joint IOS phase shift design and SBS beamforming optimization problem, and propose an iterative algorithm to efficiently solve the resulting non-convex program. Both theoretical analysis and simulation results show that an IOS significantly extends the service coverage of the SBS when compared to an IRS. [ABSTRACT FROM AUTHOR]

A branch line coupler (BLC) with ultra-compact size and harmonic suppression ability using an LCL filter and meandered stubs is proposed in this paper. There are some important factors in microstrip coupler design, including size reduction, harmonic suppression, and low insertion loss. Thus, improving each of these factors will contribute to a more efficient design. In the proposed circuit, for the first time, LCL filters, including four T-shaped circuits and four meandered line open-ended stubs, were used together to reduce the circuit size and suppress unwanted harmonics. The proposed LCL filters, incorporated in the BLC branches, resulted in superior size reduction and harmonic suppression for the presented BLC. The proposed BLC correctly worked at 900 MHz with 300 MHz operating bandwidth, which showed 33% fractional bandwidth (FBW). Additionally, a wide suppression band from 1.4 GHz to 8.8 GHz, with more than 20 dB attenuation level was obtained, which suppressed the second to ninth unwanted harmonics. The overall size of the proposed 900 MHz coupler was only 11 mm × 10.4 mm (0.044 λ × 0.042 λ) while the size of the conventional 900 MHz coupler was 61.5 mm × 62.5 mm (0.25 λ × 25 λ). The proposed BLC had a very small size and only occupied 3% of the size of the conventional coupler, which corresponded to a 97% size reduction. To the best of the authors' knowledge, to date, the best size reduction has been obtained among the published couplers. Furthermore, the experimental results verified the simulated and analyzed results of the proposed technique and demonstrate its potential for improving the performance and miniaturizing the size of other similar BLCs. [ABSTRACT FROM AUTHOR]

Summary: The interactive risks of different devices, serving as clients or servers, have increasingly attracted huge attention in various communication systems, such as wireless sensor networks, wireless communication networks, and mobile crowd‐sensing. At present, lots of countermeasures had been proposed and deployed accordingly. Nevertheless, the investigation on the interaction risks between different devices is still very limited to date. In this paper, we propose a novel adverse effect inference mechanism TAEffect for malicious behaviors of devices emerged in various decentralized and open communication systems/networks through network‐percolation theory. At first, four typical malicious interactional behaviors are mapped into four topologies, then, upon which a network influence‐inspired approach is employed to quantify the adverse effect. Finally, multifacet experiments using five real‐world datasets and a synthetic testbed are performed to validate the efficiency and effectiveness. The experimental results show our proposed approach is significant and rational to quantitatively calculate and qualitatively mirror the four kinds of malicious interactional behaviors in diverse misbehavior‐emerged communication systems. [ABSTRACT FROM AUTHOR]

Multiple-input-multiple-output (MIMO) systems significantly increase the network throughput and coverage probability for short-packet communications. In this paper, a tight lower bound on the average achievable rate of MIMO short-packet communication systems is theoretically derived, considering flat uncorrelated Rayleigh fading MIMO channels. The derivations are based on the eigenvalue analysis of a Wishart matrix and Jensen's inequality. Given the actual transmission rate, a tight approximation on the average block error rate (BLER) of MIMO short-packet communication systems is also provided, which is based on the moment generating function and inverse Laplace transform. Numerical results demonstrate that both our theoretically derived analytical average achievable rate lower bound and approximate average BLER are almost the same as the simulation results. [ABSTRACT FROM AUTHOR]

Channel estimation is challenging for the reconfigurable intelligent surface (RIS)-aided wireless communications. Since the number of coefficients of the cascaded channel among the base station (BS), the RIS, and the user equipment (UE), is the product of the number of BS antennas, the number of RIS elements, and the number of UEs, the pilot overhead can be prohibitively high. In this paper, we propose a two-timescale channel estimation framework to exploit the property that the BS-RIS channel is high-dimensional but quasi-static, while the RIS-UE channel is mobile but low-dimensional. Specifically, to estimate the quasi-static BS-RIS channel, we propose a dual-link pilot transmission scheme, where the BS transmits downlink pilots and receives uplink pilots reflected by the RIS. Then, we propose a coordinate descent-based algorithm to recover the BS-RIS channel. Since the quasi-static BS-RIS channel is estimated less frequently than the mobile channel is, the average pilot overhead can be reduced from a long-term perspective. Although the mobile RIS-UE channel has to be frequently estimated in a small timescale, the associated pilot overhead is low thanks to its low dimension. Simulation results show that the proposed two-timescale channel estimation framework can achieve accurate channel estimation with low pilot overhead. [ABSTRACT FROM AUTHOR]

Adaptive radio techniques have been extensively studied over the past two decades. In this paper, we investigate an adaptive algorithm to attain additive white Gaussian noise (AWGN) performance in non-white channels, using multicarrier communications such as OFDM and filter-bank multicarrier (FBMC). The non-white channel can be from non-white noise, or more commonly, interference. Our proposed algorithm: spectral shaping (SS), follows a simple optimization problem to find what we term reliable subcarriers, and then per subcarrier power allocations are made in order to attain the AWGN channel bit error ratio (BER). Subcarriers that experience high interference are deactivated or chosen as guard subcarriers. After describing our analysis, we show some simulation results in two non-white interference signal examples: the Gaussian pulse shaped high-power distance measuring equipment (DME) and a classical rectangular-pulse signal. The DME example is pertinent for currently proposed aeronautical communication systems, where new multicarrier techniques, e.g., the L-band digital Aeronautical communication systems (LDACS) have been designed as an inlay approach between the high-power DME channels in L-band. Our results show that in high DME power interferences, using this adaptive technique can improve the overall reliability and attain AWGN BER performance in AWGN channel. We provide power spectral density (PSD) results to compare OFDM and FBMC based spectra when our SS algorithm is applied. Also, we provide BER vs. bit energy to noise density ratio (Eb/N0) results in the AWGN, as well as more realistic aeronautical channels to compare SS system performance. Our results show that in the presence of high power DME interference, using this adaptive technique can improve the overall reliability and enable us to attain non-interference BER performance in realistic aeronautical channels. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) swarm has emerged as a promising novel paradigm to achieve better coverage and higher capacity for future wireless network by exploiting the more favorable line-of-sight (LoS) propagation. To reap the potential gains of UAV swarm, the remote control signal sent by ground control unit (GCU) is essential, whereas the control signal quality are susceptible in practice due to the effect of the adjacent channel interference (ACI) and the external interference (EI) from radiation sources distributed across the region. To tackle these challenges, this paper considers priority-aware resource coordination in a multi-UAV communication system, where multiple UAVs are controlled by a GCU to perform certain tasks with a pre-defined trajectory. Specifically, we maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all the UAVs by jointly optimizing channel assignment and power allocation strategy under stringent resource availability constraints. According to the intensity of ACI, we consider the corresponding problem in two scenarios, i.e., Null-ACI and ACI systems. By virtue of the particular problem structure in Null-ACI case, we first recast the formulation into an equivalent yet more tractable form and obtain the global optimal solution via Hungarian algorithm. For general ACI systems, we develop an efficient iterative algorithm for its solution based on the alternating optimization methods. Extensive simulation results demonstrate that the proposed algorithms can significantly enhance the minimum SINR among all the UAVs and adapt the allocation of communication resources to diverse mission priority. [ABSTRACT FROM AUTHOR]

In this paper, new LC lumped components and composite lines are used to create a filtering branch line coupler (FBLC) with a small size and wide suppression band. New composite lines are proposed using applied LC lumped components, which are used as the coupler main branches. The proposed FBLC suppresses second to sixth harmonics with high attention level and provides a wide stopband from 1.6 GHz to 5 GHz with more than 20 dB of attention. The presented coupler is analyzed, designed, simulated, and implemented. The measured results show that the proposed FBLC correctly operates at 800 MHz with less than 0.25 dB of insertion loss. In addition, more than 29 dB of return loss and isolation is measured at operating frequency, which shows the correct performance of the proposed design. The size of the proposed FBLC is equal to 23.7 mm × 25.5 mm (0.086λ × 0.093λ), which shows an 87% size reduction. The proposed FBLC with the designed frequency can be used in the communication systems for narrow-band Internet of things (NB-IoT) and traffic control radar applications. [ABSTRACT FROM AUTHOR]

In this paper, we consider the precoder design for an underloaded or critically loaded downlink multi-user multiple-input multiple-output (MIMO) communication system. We propose novel precoding and decoding schemes which enhance system performance based on rate splitting at the transmitter and single-stage successive interference cancellation at the receivers. The proposed successive null-space (SNS) precoding utilizes linear combinations of the null-space basis vectors of the successively augmented MIMO channel matrices of the users as precoding vectors to adjust the inter-user-interference experienced by the receivers. We formulate a non-convex weighted sum rate optimization problem for the precoding vectors and the associated power allocation for the proposed SNS-based MIMO-rate-splitting multiple access (RSMA) scheme. We obtain a suboptimal solution for this problem via successive convex approximation. Moreover, we study the robustness of the proposed precoding scheme to imperfect channel state information (CSI) at the base station via derivative-based sensitivity analysis. Our analysis and simulation results reveal the enhanced performance and robustness of the proposed SNS-based MIMO-RSMA scheme over several baseline multi-user MIMO schemes, especially for imperfect CSI. [ABSTRACT FROM AUTHOR]

In this paper, we study to employ geographic information to address the blockage problem of air-to-ground links between UAV and terrestrial nodes. In particular, a UAV relay is deployed to establish communication links from a ground base station to multiple ground users. To improve communication capacity, we first model the blockage effect caused by buildings according to the three-dimensional (3-D) geographic information. Then, an optimization problem is formulated to maximize the minimum capacity among users by jointly optimizing the 3-D position and power allocation of the UAV relay, under the constraints of link capacity, maximum transmit power, and blockage. To solve this complex non-convex problem, a two-loop optimization framework is developed based on Lagrangian relaxation. The outer-loop aims to obtain proper Lagrangian multipliers to ensure the solution of the Lagrangian problem converge to the tightest upper bound on the original problem. The inner-loop solves the Lagrangian problem by applying the block coordinate descent (BCD) and successive convex approximation (SCA) techniques, where UAV 3-D positioning and power allocation are alternately optimized in each iteration. Simulation results confirm that the proposed solution significantly outperforms three benchmark schemes and achieves a performance close to the upper bound on the UAV relay system. [ABSTRACT FROM AUTHOR]

Quadrature amplitude modulation (QAM) is an exciting technology to accomplish a high communication data rate without increasing the system bandwidth, which can undoubtedly greatly improve the performance of free-space optical (FSO) communication systems. With the increase of experimental and theoretical results about atmospheric turbulence, scientists have confirmed that the anisotropy and non-Kolmogorov property cannot be ignored for atmospheric turbulence. In this paper, utilizing the new anisotropic non-Kolmogorov (ANK) turbulence spectrum models for the satellite links that are applicable to both vertical and slant links, we derive the average bit error rate (BER) expression of rectangular QAM for a Gaussian-beam transmission through the weak ANK ground-to-satellite uplink under the influences of both scintillation and beam wander. Numerical results display that, when the zenith angle is less than a certain angle, the average BER of the ground-to-satellite uplink decays with the enhancement of the anisotropic factor, but while the zenith angle is greater than the certain angle, the contrary trend will occur. Our work will benefit the design optimization of QAM/FSO communication systems in ANK satellite links. [ABSTRACT FROM AUTHOR]

To boost the performance of wireless communication networks, unmanned aerial vehicles (UAVs) aided communications have drawn dramatically attention due to their flexibility in establishing the line of sight (LoS) communications. However, with the blockage in the complex urban environment, and due to the movement of UAVs and mobile users, the directional paths can be occasionally blocked by trees and high-rise buildings. Intelligent reflection surfaces (IRSs) that can reflect signals to generate virtual LoS paths are capable of providing stable communications and serving wider coverage. This is the first paper that exploits a three-dimensional geometry dynamic channel model in IRS- assisted UAV-enabled communication system. Moreover, we develop a novel deep learning based channel tracking algorithm consisting of two modules: channel pre-estimation and channel tracking. A deep neural network with off-line training is designed for denoising in the pre-estimation module. Moreover, for channel tracking, a stacked bi-directional long short term memory (Stacked Bi-LSTM) is developed based on a framework that can trace back historical time sequence together with bidirectional structure over multiple stacked layers. Simulations have shown that the proposed channel tracking algorithm requires fewer epochs to convergence compared to benchmark algorithms. It also demonstrates that the proposed algorithm is superior to different benchmarks with small pilot overheads and comparable computation complexity. [ABSTRACT FROM AUTHOR]

Cloud operators require timely identification of Heavy Hitters (HH) and Hierarchical Heavy Hitters (HHH) for applications such as load balancing, traffic engineering, and attack mitigation. However, existing techniques are slow in detecting new heavy hitters. In this paper, we present the case for identifying heavy hitters through sliding windows. Sliding windows are quicker and more accurate to detect new heavy hitters than current interval-based methods, but to date had no practical algorithms. Accordingly, we introduce, design, and analyze the Memento family of sliding window algorithms for the HH and HHH problems in the single-device and network-wide settings. We use extensive evaluations to show that our single-device solutions are orders of magnitude faster than existing sliding window techniques and comparable in speed to state-of-the-art non-windowed sampling based technique. Furthermore, we exemplify our network-wide HHH detection capabilities on a realistic testbed. To that end, we implemented Memento as an open-source extension to the popular HAProxy cloud load-balancer. In our evaluations, using an HTTP flood by 50 subnets, our network-wide approach detected the new subnets faster and reduced the number of undetected flood requests by up to $37\times $ compared to the alternatives. [ABSTRACT FROM AUTHOR]

High penetration level of single-phase solar PVs in low voltage distribution systems has raised concern about the Voltage Unbalance (VU) level. Real-time phase reconfiguration using Dynamic Switching Devices (DSD) has been proposed as a cost-effective solution to mitigate the excessive level of Voltage Unbalance. DSDs are fast power electronics devices that could change the phase connection of a solar PV with minimum disturbances. Most of the proposed methodologies for controlling DSDs are based on a central controller and the assumption of availability of Advanced Metering Infrastructure (AMI) and a communication system. In this paper, we propose a distributed control methodology to control DSDs based on the measurement from the local node only and using the sensitivity of nodal voltage to power injection. Each DSD decides on phase swapping if it rectifies the unbalance index of the local node. We show that decreasing the Voltage Unbalance Factor (VUF) of individual nodes will improve the unbalance situation of the whole feeder. The proposed scheme does not require a communication system nor a central controller. The result of simulations confirms the efficacy of the proposed methodology. [ABSTRACT FROM AUTHOR]

In order to realize the reliable and safe operation of the smart railways, and provide high quality information transmission service for passengers, the railway system needs to develop innovative communication network and advanced communication technology to meet the gradually increasing service demand of multi-dimensional comprehensive information resources. The Space-Air-Ground Integrated Network (SAGIN) can provide seamless information services for land, sea, air and space users, and is an effective solution to the challenge posed by the future smart railways to the all-time, all-domain, all-air, high-reliability and high-throughput communication. This paper aims to comprehensively discuss the technical development and application examples of High-Speed Railways (HSRs) based on SAGIN. Firstly, we analysis the development of the SAGIN and the mobile communication network of the HSRs, and comprehensively discuss the single network architecture of the space-based, air-based and ground-based networks, as well as the integrated network, and discuss the application scenario and network structure of the combination of the integrated networks. At the same time, the communication services, existing problems and key technologies of the space-based, air-based and ground-based networks are discussed, and the application trend of the SAGIN in HSRs is presented. Furthermore, the application scenarios of Artificial Intelligence (AI) technologies in solving the efficient resource utilization of smart railways communication and the SAGIN are studied. Based on these technologies, we point out the research direction for the future development of AI technologies in SAGIN in HSRs communications. [ABSTRACT FROM AUTHOR]

In this paper, we study a quantized feedback scheme to maximize the goodput of a finite blocklength communication scenario over a quasi-static fading channel. It is assumed that the receiver has perfect channel state information (CSI) and sends back the CSI to the transmitter over a resolution-limited error-free feedback channel. With this partial CSI, the transmitter is supposed to select the optimum transmission rate, such that it maximizes the overall goodput of the communication system. This problem has been studied for the asymptotic blocklength regime, however, no solution has so far been presented for finite blocklength. Here, we study this problem in two cases: with and without constraint on reliability. We first formulate the optimization problems and analytically solve them. Iterative algorithms that successfully exploit the system parameters for both cases are presented. It is shown that although the achievable maximum goodput decreases with shorter blocklengths and higher reliability requirements, significant improvement can be achieved even with coarsely quantized feedback schemes. [ABSTRACT FROM AUTHOR]

Channel estimation is one of the main challenges for implementing reconfigurable intelligent surface (RIS)-aided communication system with a large number of antennas at the base station (BS) because RIS is equipped with many passive reflective elements without active transmitting/receiving and signal processing abilities. In this paper, we focus on the channel estimation in a general RIS-aided multi-user mmWave communication system. Specifically, a novel two-phase channel estimation scheme consisting of on-line and off-line phases is proposed to estimate the BS-RIS channel, RIS-user channel, and BS-user channel, respectively. Inspired by the characteristics of few scatterers in mmWave communication systems, the antenna domain channels are converted into the virtual angular domain channels by using the discrete Fourier transform (DFT) matrix. Thus, the estimation problem can be formulated as a compressed sensing (CS) problem, and solved by using efficient vector approximate message passing (VAMP) algorithm with expectation-maximization (EM) to learn unknown parameters and obtain the estimates simultaneously. Simulation results show that, the above algorithm with two choices of prior distributions in the proposed mmWave RIS-aided multi-user system has fast convergence speed and desirable estimation performance. [ABSTRACT FROM AUTHOR]

In this paper, we provide the performance analysis of free-space optical (FSO) communication systems over Fisher-Snedecor $\mathcal {F}$ fading channel with estimation errors in the presence of pointing errors. The probability density function (PDF) and cumulative distribution function (CDF) of signal-to-noise ratio (SNR) are derived in the presence of channel estimation errors and pointing errors. Subsequently, the novel expressions for the outage probability, average bit error rate (BER) and ergodic capacity are obtained. The asymptotic expressions for the outage probability and average BER are also presented to provide further insights into the FSO system performance in high SNR regime. Through numerical simulations, the impact of imprecise channel on the performance of the FSO communication systems is analyzed as compared with precise channel. Finally, Monte Carlo simulations are presented to validate the proposed analysis. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) has emerged as important transmission technology to improve the spectral/energy efficiency in the next-generation (beyond 5G (B5G) and 6G) wireless communication network and has numerous applications in the areas of Internet of Things (IoT) and vehicular communication systems. Thus, in this paper, we investigate the application of RIS in unmanned aerial vehicle (UAV) enabled vehicular communication system with infinite and finite block length codes, where UAV communicates with the single antenna ground vehicle in the presence of several interfering vehicles on the road. We have obtained the approximate closed-form statistics of received SINR at ground vehicle in the presence of multiple nonidentical interference links. Furthermore, we analyze the performance of the considered system in terms of the coverage probability, bit-error-rate, block error rate (BLER) and goodput. It has been shown through the numerical results that the deployment of RIS significantly improves the performance of UAV-enabled vehicular communication network, even in the presence of the direct link between the UAV and the ground vehicle. Additionally, we also investigate the impact of various channel and system parameters like practical reflection coefficients of RIS, number of RIS reflecting elements, and number of interfering vehicles on the system performance. The analytical results are corroborated with Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

The communication-based train control (CBTC) system is a typical cyber physical system in urban rail transit. The train-ground communication system is a very important subsystem of the CBTC system and uses the wireless communication protocols to transmit control commands. However, it faces some potential information security risks. To ensure information security of the train-ground communication system, an intrusion detection method based on machine learning and state observer is proposed to detect and recognize various attacks in this paper. The detection system not only detects the anomalies of the wireless network data, but also detects the anomalies of the train physical states. This method includes two layers. The first layer is used to detect and identify wireless network attacks based on machine learning algorithms, such as the random forest algorithm and the gradient boosted decision tree algorithm. The second layer is used to detect the abnormal physical state of train operation based on a state observer. By combining the results of the above two layers, a comprehensive intrusion detection result is given. The simulation results show that the proposed method is effective and practical. [ABSTRACT FROM AUTHOR]

In this paper, a deep learning (DL) framework for the optimization of the resource allocation in multi-channel cellular systems with device-to-device (D2D) communication is proposed. Thereby, the channel assignment and discrete transmit power levels of the D2D users, which are both integer variables, are optimized for maximization of the overall spectral efficiency whilst maintaining the quality-of-service (QoS) of the cellular users. Depending on the availability of channel state information (CSI), two different configurations are considered, namely 1) centralized operation with full CSI and 2) distributed operation with partial CSI, where in the latter case, the CSI is encoded according to the capacity of the feedback channel. Instead of solving the resulting resource allocation problem for each channel realization, a DL framework is proposed, where the optimal resource allocation strategy for arbitrary channel conditions is approximated by deep neural network (DNN) models. Furthermore, we propose a new training strategy that combines supervised and unsupervised learning methods and a local CSI sharing strategy to achieve near-optimal performance while enforcing the QoS constraints of the cellular users and efficiently handling the integer optimization variables based on a few ground-truth labels. Our simulation results confirm that near-optimal performance can be attained with low computation time, which underlines the real-time capability of the proposed scheme. Moreover, our results show that not only the resource allocation strategy but also the CSI encoding strategy can be efficiently determined using a DNN. Furthermore, we show that the proposed DL framework can be easily extended to communication systems with different design objectives. [ABSTRACT FROM AUTHOR]

The increased implementation of smart grid technologies in the power distribution grid presents unique opportunities that enable resiliency, but also brings challenges motivating needs for novel solutions and mitigation techniques. The bi-directional power and data flow allow for the grid to operate with increased resiliency, which is the ability to avoid discontinuity of service to end-use loads during extreme events. However, in applications where control of the distribution grid or microgrid relies on communication networks, the degradation of communication systems in the form of loss or high latency can cause maloperation and result in loss of end-use loads. This paper presents a novel framework to enable delay tolerance of centralized microgrid control schemes to mitigate communication system latency impacts and guarantee successful control action. We demonstrate the delay tolerance on a control scheme that operates a battery energy storage system (BESS) to offset the sudden loss of generation and maintain system frequency. During periods of severely degraded communication system performance, the proposed delay-tolerant algorithm compensates for the latency by utilizing a data-driven model generated at the device level using dynamic mode decomposition (DMD) to determine the performance of the communications. The DMD technique predicts the system’s frequency using device-level terminal measurements and provides updated control signals. The HELICS cosimulation platform evaluates the cyber-physical interaction of the power system model in GridLAB-D, the centralized control agent in Python, and the discrete network model in NS-3. The framework is tested and validated on the IEEE-123 node system modified to represent a networked remote microgrid model, and the results show an improvement in the dynamic performance of the control scheme when subject to communication delays. [ABSTRACT FROM AUTHOR]

From adapting to the propagation environment to reconstructing the propagation environment, reconfigurable intelligent surface (RIS) changes the design paradigm of wireless communications. To reconstruct the propagation environment, joint beamforming of RIS and multi-input multi-output (MIMO) is crucial. Unfortunately, due to the coupling effect of the active beamforming of MIMO and passive beamforming of RIS, it is difficult to find the optimal solution to the joint beamforming problem, so a serious performance loss will be caused. In this paper, inspired by the end-to-end (E2E) learning of communication system, we propose the E2E learning based RIS-aided communication system to mitigate the performance loss via deep learning techniques. The key idea is to simultaneously optimize the signal processing functions at base station (BS), RIS, and user, including active beamforming for BS and user, passive beamforming for RIS. This way is able to avoid the performance loss caused by alternately optimizing each function of the RIS-aided system. Specifically, we firstly utilize a deep neural network (DNN) to realize the modulation and beamforming for BS and utilize another DNN to realize the demodulation and combining for user. Then, the RIS passive beamforming is also represented by trainable parameters, which could be simultaneously optimized with the DNNs at BS and user. Simulation results show that the proposed E2E learning based RIS-aided communication system could achieve the better bit error rate (BER) performance than traditional RIS-aided communication systems. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel communication framework, Acoustic-WiFi, that utilizes the acoustic channel (i.e., microphone/speaker) as a control channel to develop more efficient Wi-Fi networks. We used the Acoustic-WiFi framework to develop a Power Saving Mode (PSM) scheme; A2PSM, and a smart contention resolution scheme; Harmony. In A2PSM, we propose an acoustic channel assisted power saving scheme for the Wi-Fi interface, which addresses the inefficiency of the existing power saving schemes in smart devices. In this scheme, we leverage the low power consumption of the acoustic interfaces to reduce the wakeup events of the Wi-Fi interface when it is in power saving mode. We develop a small-scale prototype testbed on real smartphones to evaluate the proposed A2PSM scheme. Experiments show that A2PSM could save up to more than 25% more power than the existing schemes. In Harmony, we address the overhead of the Wi-Fi backoff scheme (i.e., contention window countdown, DIFS) and reduce the overall collisions among the devices. Harmony uses the acoustic channel for contention resolution in Wi-Fi networks. We also evaluate Harmony using real testbed and simulation. Experiments using the testbed show a 40% gain in throughput over the 802.11 Wi-Fi backoff scheme, while simulation results show a gain of more than 27% for dense networks. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) has emerged as an important transmission technology for numerous applications in Internet of Things (IoT) systems. Thus, in this paper, we investigate the application of RIS in energy harvesting (EH) based unmanned aerial vehicle (UAV) communication network with finite block length (BL) codes, where a rotary wing type flying UAV communicates with the multiple single antenna IoT users with the aid of multiple RISs mounted on several skyscraper buildings. To transmit the signal to a particular IoT user, the UAV selects an RIS on the basis of either UAV-RIS (i.e., partial) or UAV-RIS-IoT (i.e., full) channel state information (CSI) and then transmits the signal through the selected RIS along with the direct link transmission. In particular, we derive (i) the expression for probability of RIS selection, (ii) the statistical distribution of instantaneously received information signal-to-noise ratio (SNR) at the IoT user. Based on the derived statistics, we analyze the performance of the considered system under finite BL codes in terms of the average outage probability, average block error rate (ABLER) and goodput averaged over entire flying duration. Moreover, the BLER performance with finite BL codes is also compared with the infinite BL codes scenario. Additionally, we also investigate the impact of various channel and system parameters like imperfect CSI, number of RISs and the number of reflecting elements at each RIS, location of IoT users, variable altitude of the UAV, and the severity of channel fading of UAV-RIS link on the system performance. Furthermore, we have obtained the optimum UAV location in each time slot which minimizes the ABLER per time slot over all the users in the network. The analytical results are corroborated with Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

In the field of communication, autoencoder (AE) refers to a system that replaces parts of the traditional transmitter and receiver with artificial neural networks (ANNs). To meet the system performance requirements, it is necessary for the AE to adapt to the changing wireless-channel conditions at runtime. Thus, online fine-tuning in the form of ANN-retraining is of great importance. Many algorithms on the ANN layer are developed to improve the AE's performance at the communication layer. Yet, the link of the system performance and the ANN topology to the hardware layer is not fully explored. In this paper, we analyze the relations between the design layers and present a hardware implementation of an AE-based demapper that enables fine-tuning to adapt to varying channel conditions. As a platform, we selected field-programmable gate arrays (FPGAs) which provide high flexibility and allow to satisfy the low-power and low-latency requirements of embedded communication systems. Furthermore, our cross-layer approach leverages the flexibility of FPGAs to dynamically adapt the degree of parallelism (DOP) to satisfy the system-level requirements and to ensure environmental adaptation. Our solution achieves 2000× higher throughput than a high-performance graphics processor unit (GPU), draws 5× less power than an embedded central processing unit (CPU) and is 5800× more energy efficient compared to an embedded GPU for small batch size. To the best of our knowledge, such a cross-layer design approach combined with FPGA implementation is unprecedented. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the performance of an RIS-aided wireless communication system subject to outdated channel state information that may operate in both the near- and far-field regions. In particular, we take two RIS deployment strategies into consideration: (i) the centralized deployment, where all the reflecting elements are installed on a single RIS and (ii) the distributed deployment, where the same number of reflecting elements are placed on multiple RISs. For both deployment strategies, we derive accurate closed-form approximations for the ergodic capacity, and we introduce tight upper and lower bounds for the ergodic capacity to obtain useful design insights. From this analysis, we unveil that an increase of the transmit power, the Rician- $K$ factor, the accuracy of the channel state information and the number of reflecting elements help improve the system performance. Moreover, we prove that the centralized RIS-aided deployment may achieve a higher ergodic capacity as compared with the distributed RIS-aided deployment when the RIS is located near the base station or near the user. In different setups, on the other hand, we prove that the distributed deployment outperforms the centralized deployment. Finally, the analytical results are verified by using Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

In this paper, we propose a covert wireless uplink transmission strategy in space-air-ground integrated vehicular networks, where the source vehicle transmits its own message over the channel that being used by the host communication system, to avoid being detected by the warden. It is obvious that the data transmission efficiency of the covert communication system is limited due to the co-channel interference. To improve the data transmission efficiency, we consider that the covert communication system adopts improper Gaussian signaling (IGS). We formulate a joint transmit power and IGS factor optimization problem to minimize the outage probability of the covert communication system. The minimum error detection probability of the warden is first analyzed with noise uncertainty, which is used to measure the system covertness. Under the constraints of the quality of service (QoS) of host communication system and the covertness requirement, the optimal transmit power is first derived with proper Gaussian signaling (PGS) scheme. Then, with the approximate outage probability derived under IGS scheme, the optimization problem is solved by jointly designing the transmit power and IGS factor. Finally, we provide extensive numerical results to validate the proposed covert transmission strategy, and demonstrate that the IGS scheme is beneficial in improving the data transmission efficiency in terms of outage probability compared to PGS scheme. [ABSTRACT FROM AUTHOR]