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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]

Due to their ability to create favorable line-of-sight (LoS) propagation environments, intelligent reflecting surfaces (IRSs) are regarded as promising enablers for future millimeter-wave (mm-wave) wireless communication. In this paper, we investigate channel estimation for IRS-assisted mm-wave multiple-input multiple-output (MIMO) wireless systems. By leveraging the sparsity of mm-wave channels in the angular domain, we formulate the channel estimation problem as an $\ell _{1}$ -norm regularized optimization problem with fixed-rank constraints. To tackle the non-convexity of the formulated problem, an efficient algorithm is proposed by capitalizing on alternating minimization and manifold optimization (MO), which yields a locally optimal solution. To further reduce the computational complexity of the estimation algorithm, we propose a compressive sensing- (CS-) based channel estimation approach. In particular, a three-stage estimation protocol is put forward where the subproblem in each stage can be solved via low-complexity CS methods. Furthermore, based on the acquired channel state information (CSI) of the cascaded channel, we design a passive beamforming algorithm for maximization of the spectral efficiency. Simulation results reveal that the proposed MO-based estimation (MO-EST) and beamforming algorithms significantly outperform two benchmark schemes while the CS-based estimation (CS-EST) algorithm strikes a balance between performance and complexity. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) has been regarded as a promising and revolutionary technology for future wireless communication systems owing to its capability of tailoring signal propagation environment in an energy/spectrum/ hardware-efficient manner. However, most existing studies on IRS optimizations are based on a simple and ideal reflection model that is impractical in hardware implementation, which thus leads to severe performance loss in realistic wideband/multi-band systems. To deal with this problem, in this paper we first propose a more practical and more tractable IRS reflection model that describes the difference of reflection responses for signals at different frequencies. Then, we investigate the joint transmit beamforming and IRS reflection beamforming design for an IRS-assisted multi-cell multi-band system. Both power minimization and sum-rate maximization problems are solved by exploiting popular second-order cone programming (SOCP), Riemannian manifold, minimization-majorization (MM), weighted minimum mean square error (WMMSE), and block coordinate descent (BCD) methods. Simulation results illustrate the significant performance improvement of our proposed joint transmit beamforming and reflection design algorithms based on the practical reflection model in terms of power saving and rate enhancement. [ABSTRACT FROM AUTHOR]

The incorporation of intelligent reflecting surface (IRS) into wireless communication systems can extend the coverage and enhance the data transmission rate. This paper studies the joint transceiver and IRS designs in IRS-assisted multi-input multi-output (MIMO) systems under both perfect channel state information (CSI) and imperfect CSI. Specifically, the transmit precoder, reflection coefficients at the IRS, and receive equalizer are jointly optimized to minimize the data detection mean square error (MSE), subject to the transmission power constraint and the modulus constraints for IRS reflection coefficients. The design problems, non-convex and challenging, are tackled under the framework of alternating optimization. For the design with perfect CSI, we successively optimize the IRS reflection coefficients given the precoder and present the closed-form optimal angle of one reflection coefficient given the others. For the robust design with imperfect CSI, we first average the detection MSE over channel uncertainties by using a generalized statistical CSI error model. Then, the averaged MSE is approximated by a more tractable upper bound. Subsequently, the robust design problem is elaborately transformed into a form similar to the problem with perfect CSI. Numerical results demonstrate the effectiveness of the proposed designs as compared to various benchmark schemes. [ABSTRACT FROM AUTHOR]

The joint transmission scheme of polar codes and sparse code multiple access (SCMA) has been regarded as a promising technology for future wireless communication systems. However, most of the existing polar-coded SCMA (PC-SCMA) systems suffer from high latency caused by the feedback iteration and list decoding. In addition, the error performance of PC-SCMA systems is unsatisfactory for ultra-reliable transmission. Inspired by the compelling benefits of non-binary polar codes, in this paper, we design a non-binary polar-coded SCMA (NB-PC-SCMA) system with a free order matching strategy to address the issues of delay and reliability. Specifically, we first formulate a joint factor graph for NB-PC-SCMA and propose a non-binary successive cancellation list (NB-SCL) and damping based joint iterative detection and decoding (NSD-JIDD) multiuser receiver to improve the BER and latency performance. Then, a lazy-search based NB-SCL (L-NB-SCL) decoding is proposed to reduce the computational complexity by simplifying the path search pattern of the list decoder. After that, we modify the update of user nodes for SCMA detection to improve the convergence error and finally propose the improved NSD-JIDD (ISD-JIDD) algorithm, which can avoid redundant operations by exploiting L-NB-SCL decoding. Simulation results show that the proposed NB-PC-SCMA system achieves better bit error rate (BER) performance and considerable latency gain when compared to its counterparts. In particular, the proposed ISD-JIDD can achieve similar BER performance of NSD-JIDD with less complexity. [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]

Mobile edge computing (MEC) has been recognized as a viable technology to satisfy low-delay computation requirements for resource-constrained Internet of things (IoT) devices. Nevertheless, the broadcast feature of wireless electromagnetic communications may lead to the security threats to IoT devices. In order to enhance the task offloading security, this paper proposes a reconfigurable intelligent surface (RIS)-assisted secure MEC network framework. Furthermore, we investigate the max-min computation efficiency problem under the secure computation rate requirements, by jointly optimizing the local computing frequencies and transmission power of IoT devices, time-slot assignment, and phase beamforming of the RIS. To solve the formulated non-convex problem, we further develop an iterative algorithm, in which the Dinkelbach-type method and block coordinate descent (BCD) technique are utilized to tackle the fractional objective function and coupled optimization variables, respectively. In particular, the successive convex approximation (SCA) and penalty function-based methods are exploited to solve the transmit power control and reflecting beamforming optimization subproblems, respectively, and the closed-form expression for local computing frequencies optimization subproblem is derived. Numerical results quantify the performance gain achieved by the proposed RIS-assisted secure MEC networks, when compared to existing benchmark methods. [ABSTRACT FROM AUTHOR]

In wireless communication networks, traffics of base stations (BS) can be effectively offloaded by sharing the cached content between mobile devices through wireless device-to-device (D2D) communication. To encourage the content caching of D2D users, a blockchain incentive scheme is proposed; the game theory is introduced to optimize the benefits for both the edge computing server (ECS) and D2D users in the blockchain network. In Stackelberg game, D2D users act as followers. After the successful implementation of the cache strategy, D2D users are rewarded by the system, which is a new block mining process. In this process, the required computing power is provided by the ECS. Therefore, D2D cache users need to pay the cost to purchase computing power from the ECS. As the game leader, the ECS prices the computing resources, and D2D users adjust their caching strategies based on the price. In this paper, we propose four algorithms based on different pricing methods and different cache reward methods. Simulations are performed to compare the cache quality and cache content dispersion of the four algorithms. The results show that the uniform pricing scheme with a linear reward relationship is more suitable for the scenarios that require higher cache quality, and the discriminatory pricing scheme with a nonlinear reward relationship is more suitable for the scenarios that require more evenly cache content distribution. [ABSTRACT FROM AUTHOR]

Underwater wireless optical communication (UWOC) has generated a much interest in the research community thanks to its wide range of applications, including submarine navigation, seafloor exploration, and military operation. Turbulence, scattering, and absorption phenomena generally can severely affect the performance of UWOC systems. The relaying methods have been shown as a promising technique to alleviate turbulence-induced and short-range fading. In this paper, under varying channel conditions, we present a unified framework to analyze the performance of multi-hop UWOC systems with both amplify-and-forward and decode-and-forward relaying techniques. In particular, for the end-to-end signal-to-noise ratio (SNR), we use the univariate and bivariate Fox-H functions to derive both the probability density function (PDF) and cumulative distribution function (CDF). Furthermore, for the performance of systems under both types of relaying techniques, we derive tight mathematical expressions for the outage probability and bit error probability. Besides, to highlight and provide some prominent engineering insights into the diversity order, an asymptotic analysis for the outage likelihood and average bit error rate (BER) is given. Consequently, results from Monte-Carlo simulations are used to verify the correctness of our derived mathematical expression. [ABSTRACT FROM AUTHOR]

We give an overview of recent developments in the modeling of radiowave propagation, based on machine learning (ML) algorithms. We identify the input and output specification and the architecture of the model as the main challenges associated with ML-driven propagation models. Relevant papers are discussed and categorized based on their approach to each of these challenges. Emphasis is given on presenting the prospects and open problems in this promising and rapidly evolving area. [ABSTRACT FROM AUTHOR]

Ambient backscatter (AB) communication enables the backscatter devices to modulate and transmit data by reflecting surrounding digital video broadcasting (DVB) signals, and has emerged as a promising solution to connect low-power and small-sized devices in Internet of Things (IoT). Unfortunately, the AB receiver may suffer from the strong direct-link interference (DLI) transmitted from the ambient DVB station, making it difficult to decode the backscattered signal (BS) directly. In this paper, to tackle this issue, we propose a DLI cancellation method for AB communications over ambient DVB signals. First, a general signal processing operation for DLI cancellation at the AB receiver is designed, where neither special design on waveform nor cooperation between the ambient DVB station and the backscatter transceiver is required. Then, by minimizing the residual reception power after DLI cancellation, a three-layer parameter searching algorithm is proposed to separate the BS from the ambient DVB signal, which is of low implementation complexity. Simulation results show that when the DLI-to-BS ratio (ISR) is greater than 30 dB, the proposed DLI cancellation method can effectively suppress the DLI at the AB receiver, and the demodulation and capacity performance of the ambient DVB transmission and the backscatter transmission are barely deteriorated, as compared with the scenarios of original DVB transmission without BS and pure backscatter transmission without DLI. In addition, there is a trade-off between the convergence time and the interference cancellation ratio (ICR) performance of the proposed DLI cancellation scheme. [ABSTRACT FROM AUTHOR]

Caching at the wireless edge nodes is a promising way to boost the spatial and spectral efficiency, for the sake of alleviating networks from content-related traffic. Coded caching originally introduced by Maddah-Ali and Niesen significantly speeds up communication efficiency by transmitting multicast messages simultaneously useful to multiple users. Most prior works on coded caching are based on the assumption that each user may request all content in the library. However, in many applications the users are interested only in a limited set of content that depends on their location. For example, assisted self-driving vehicles may access super High-Definition maps of the area through which they are travelling. Motivated by these considerations, this paper formulates the coded caching problem for location-based content with edge cache nodes. The considered problem includes a content server with access to ${\mathsf N}$ location-based files (e.g., High-Definition maps), ${\mathsf K}$ edge cache nodes located at different regions, and ${\mathsf K}$ users (i.e., vehicles) each of which is in the serving region of one cache node and can retrieve the cached content of this cache node with negligible cost. Depending on the location, each user only requests a file from a location-dependent subset of the library. The objective is to minimize the worst-case load (i.e., the worst-case number of broadcasted bits from the content server among all possible demands). For this novel coded caching problem, we propose a highly non-trivial converse bound under uncoded cache placement (i.e., each cache node directly copies some library bits in its cache), which shows that a simple achievable scheme is optimal under uncoded cache placement. In addition, this achievable scheme is also proved to be generally order optimal within a factor of 3. Finally, we extend the coded caching problem for location-based content to the multiaccess coded caching topology originally proposed by Hachem et al., where each user is connected to ${\mathsf L}$ nearest cache nodes. When ${\mathsf L}\geq 2$ , we characterize the exact optimality on the worst-case load. [ABSTRACT FROM AUTHOR]

This paper proposes a novel general sixth-generation (6G) irregular-shaped geometry-based stochastic model (IS-GBSM) for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) wireless communication systems. The proposed IS-GBSM can model spherical wavefront propagation, high delay resolution, and three-dimensional (3D) space-time-frequency non-stationarity of channels with time-space consistency, and thus can be applied to integrated communication and sensing systems. To capture the 3D non-stationarity with channel consistency, a new hybrid method combining geometric and parametric methods is developed. Based on the geometric method, i.e., visibility region (VR), the smooth and consistent appearance and disappearance of clusters during array-time evolution are modeled. With the help of the parametric method, a frequency-dependent path gain is introduced, and a visible factor based on uniform continuity theorem and bounded variation function is further proposed to capture the soft cluster power handover during cluster array-time evolution. Key channel statistics of the proposed IS-GBSM are derived and investigated. Simulation results demonstrate that the space-time-frequency non-stationarity is modeled and time-space consistency is captured. Finally, the utility of the proposed IS-GBSM is verified by the excellent agreement between simulation results and measurement. [ABSTRACT FROM AUTHOR]

Edge federated learning (FL) is an emerging paradigm that trains a global parametric model from distributed datasets based on wireless communications. This paper proposes a unit-modulus over-the-air computation (UMAirComp) framework to facilitate efficient edge federated learning, which simultaneously uploads local model parameters and updates global model parameters via analog beamforming. The proposed framework avoids sophisticated baseband signal processing, leading to low communication delays and implementation costs. Training loss bounds of UMAirComp FL systems are derived and two low-complexity large-scale optimization algorithms, termed penalty alternating minimization (PAM) and accelerated gradient projection (AGP), are proposed to minimize the nonconvex nonsmooth loss bound. Simulation results show that the proposed UMAirComp framework with PAM algorithm achieves a smaller mean square error of model parameters’ estimation, training loss, and test error compared with other benchmark schemes. Moreover, the proposed UMAirComp framework with AGP algorithm achieves satisfactory performance while reduces the computational complexity by orders of magnitude compared with existing optimization algorithms. Finally, we demonstrate the implementation of UMAirComp in a vehicle-to-everything autonomous driving simulation platform. It is found that autonomous driving tasks are more sensitive to model parameter errors than other tasks since the neural networks for autonomous driving contain sparser model parameters. [ABSTRACT FROM AUTHOR]

Massive trajectory data stem from the prevalence of equipment-supporting GPS and wireless communication technology. Especially, activity trajectory from Location-based Social Network (LBSN) endows traditional trajectory data with additional user semantic activities, e.g., visiting work/home/entertainment places. Measuring the similarity between activity trajectories is to compare their proximity in multiple dimensions such as time, location, and semantics. In this way, we can mine implicit user preference and apply it to route planning, POI recommendation or any other online tasks. The key challenge of comparing activity trajectories (i.e., computing their similarity) lies in two aspects. One is the uneven sampling rate in both time and space. The other is the discrepancy of individual activities. Previous effort alleviates the issue of uneven sampling rate via trajectory complements, which is limited to spatial-temporal information. In this paper, we propose to learn a representation for one activity trajectory by jointly considering the spatio-temporal characteristics and the activity semantics. The similarity of two trajectories is computed by weighting individual trajectory points and contextual features with multi-level attention mechanisms. In specific, we propose a point-level and feature-level attention mechanism to adaptively select critical elements and contextual factors for learning trajectory representation. Our proposed approach, called At2vec, demonstrates better performance than existing baselines in extensive experimental evaluation on real trajectory databases. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of vertical underwater wireless optical communication (UWOC) systems in the presence of air bubbles and temperature gradients. We consider a generalized UWOC channel model that contains $N$ layers, each having the same distribution but with different parameters to consider the vertically inhomogeneous nature of the underwater environment. To capture the effects of air bubbles and temperature gradients on channel statistics, we model each layer with a mixture exponential-generalized Gamma distribution. A closed-form probability density function (PDF) is presented for the end-to-end signal-to-noise ratio. Based on the PDF, we derive the average bit-error-rate (BER) and the ergodic capacity. Moreover, we provide asymptotic BER and ergodic capacity results in simple forms. The performance and behavior of vertical UWOC systems are thoroughly analyzed, and all the derived expressions are verified via Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

This study focuses on the downlink (DL) transmission of massive multiple-input-multiple-output (MIMO) with the support of simultaneous wireless information and power transfer (SWIPT) system based on a power-splitting (PS) scheme. To reach the green design target in the concept of wireless communication networks, this paper maximizes the system’s energy efficiency (EE) using joint system-level optimization. First, the closed-form expressions of the harvested energy and achievable data rate are first derived. Then, we formulated the EE maximization problem by jointly optimizing user equipment (UE) pilot transmission time allocation, the received PS ratios, a base station (BS) transmit power allocation, and a number of BS antennas while considering the minimum data rate as user’s quality-of-service (QoS) requirement and the maximum BS power transmission constraint. However, the formulated EE maximization problem is non-convex and non-linear which is difficult to solve. Hence, we propose an efficient low-complex alternative optimization (LCAO) algorithm to solve the non-convex problem iteratively with an acceptable computational complexity level. Simulation results are provided to evaluate the derived closed-form expressions and validate the proposed LCAO algorithm’s efficiency. The optimality, convergence, and complexity of the LCAO algorithm are analytically investigated. Results indicate that the proposed LCAO algorithm outperforms the equal power allocation (EPA) scheme by 15% better EE gain and 4% better than the max-min fairness scheme when the BS power transmission capacity is higher than 3 (Watt). [ABSTRACT FROM AUTHOR]

Nowadays, Wi-Fi or Bluetooth based indoor localization algorithms are designed to improve the effects from variation of receive signal strength (RSS) at different locations. However, the positioning accuracy of targeting device is highly related to the indoor topological layout, e.g., the blocking effects from partitioning walls. In this paper we propose a novel skeleton-based positioning and reference point deployment for hybrid wireless localization (S-PDH) that utilizes the spatial skeleton of indoor layout to improve location estimation accuracy. Firstly, we adopt an automatic map analysis algorithm to generate appropriate reference points (RPs) from the spatial skeleton of indoor layout. Secondly, we propose a novel clustering algorithm to consider both spatial layout and RSS information in order to enhance the accuracy of RP matching. Those outlier RPs resulting poor performance will be replaced by appropriate deployment of BLE APs. Finally, the cluster-based positioning algorithm is presented to estimate the target’s location. Compared to existing methods, both the simulation and experimental results show that the proposed S-PDH system can achieve better positioning accuracy along with the benefits of automatically generating the spatial layout information for wireless indoor localization. [ABSTRACT FROM AUTHOR]

Recently, the reconfigurable intelligent surface (RIS) aided communication system has emerged as a promising candidate for future wireless communications. The existing channel estimation methods for RIS-aided millimeter-wave systems assume that the RIS is ideal without blockage. However, in practice, RIS may be blocked by the rain, snow, or dust, which will cause absorption and scattering of the incident/reflected signals and change the channel characteristics. In this paper, we formulate the system model of RIS-aided multi-user communications considering the blocked RIS. Then, we propose a variational Bayesian based channel estimation algorithm that is robust to RIS blockage, where we can simultaneously estimate the channel information and the RIS blockage. Simulation results demonstrate the superior performance of the proposed algorithm to existing ones. [ABSTRACT FROM AUTHOR]

In this paper, we propose a cost-efficient beamforming optimization algorithm for multi-user wireless communication systems associated with the intelligent reflecting surface (IRS). From the baseline successive refinement algorithm, which gives a sub-optimal solution for the power minimization problem under the signal-to-interference-plus-noise-ratio (SINR) constraint at each user, several optimization techniques are proposed to reduce the computation complexity while maintaining the algorithm-level performance. To reduce the number of required multiply-accumulate (MAC) operations, we first simplify the complicated matrix inversion by utilizing the channel hardening effect. We also present the two-phase refinement process for the group-level optimization of phase-shift elements, further relaxing the computation complexity as well as the processing latency. Applying the proposed optimization techniques, as a result, numerical results show that the fully-optimized algorithm can reduce the computational costs by up to 89.4% while showing less than 1 dB power loss, leading to the practical solution for the next-generation IRS-aided communication. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) and coordinated multi-point (CoMP) are two fundamental techniques considered for the fifth generation (5 G) of wireless communications. In this paper, a hybrid satellite-unmanned aerial vehicle (UAV) relay network (HSURN) is proposed where the UAV relays (URs) employ CoMP transmission to serve the terrestrial users (UEs). Furthermore, all UEs associated with the CoMP-URs form a single NOMA cluster. For this model, an optimization problem is formulated subject to the minimum quality of services (QoSs) requirements of the UEs, transmission power budgets and, successive interference cancellation (SIC), to select URs and allocate their transmission powers for the energy efficiency (EE) maximization. With this insight, first, a computationally efficient sub-optimal UR selection scheme is proposed. Then, the powers are allocated to the selected URs via the Lagrange multipliers optimization (LMO) method. Due to the non-convex nature of the considered problem, it is relatively difficult to be solved. Hence, a metaheuristic teaching-learning-based optimization (TLBO) algorithm is employed to achieve an efficient solution. Simulation results are provided to verify the effectiveness of the proposed sub-optimal relay selection scheme and the TLBO-based power allocation method compared to the LMO conventional method. Besides, the obtained results also reveal that the CoMP-NOMA transmission in the proposed scenario significantly improves the spectral efficiency (SE) and outage probability (OP) of the system compared to non-comp NOMA transmission case. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) and Terahertz (THz) technology are envisioned to play paramount roles in next-generation wireless communications. In this paper, we present a novel secure UAV-assisted mobile relaying system operating at THz bands for data acquisition from multiple ground user equipments (UEs) towards a destination. We assume that the UAV-mounted relay may act, besides providing relaying services, as a potential eavesdropper called the untrusted UAV-relay (UUR). To safeguard end-to-end communications, we present a secure two-phase transmission strategy with cooperative jamming. Then, we devise an optimization framework in terms of a new measure $-$ secrecy energy efficiency (SEE), defined as the ratio of achievable average secrecy rate to average system power consumption, which enables us to obtain the best possible security level while taking UUR’s inherent flight power limitation into account. For the sake of quality of service fairness amongst all the UEs, we aim to maximize the minimum SEE (MSEE) performance via the joint design of key system parameters, including UUR’s trajectory and velocity, communication scheduling, and network power allocation. Since the formulated problem is a mixed-integer nonconvex optimization and computationally intractable, we decouple it into four subproblems and propose alternative algorithms to solve it efficiently via greedy/sequential block successive convex approximation and non-linear fractional programming techniques. Numerical results demonstrate significant MSEE performance improvement of our designs compared to other known benchmarks. [ABSTRACT FROM AUTHOR]

The new generation of communication technologies are constantly being pushed to meet a diverse range of user requirements such as high data rate, low power consumption, very low latency, very high reliability and broad availability. To address all these demands, fifth generation (5G) radio access technologies have been extended into a wide range of new services. However, there are still only a limited number of applications for radio frequency (RF) based wireless communications inside aircraft cabins that comply with the 5G vision. Potential interference and safety issues in on-board wireless communications pose significant deployment challenges. By transforming each reading light into an optical wireless access point (AP), light-fidelity (LiFi), could provide seamless on-board connectivity in dense cabin environments without RF interference. Furthermore, the utilization of available reading lights allows for a relatively simple, cost-effective deployment with the high energy and spectral efficiency. To successfully implement the aeronautical cabin LiFi applications, comprehensive optical channel characterization is required. In this paper, we propose a novel Monte Carlo ray-tracing (MCRT) channel modelling technique to capture the details of in-flight LiFi links. Accordingly, a realistic channel simulator, which takes the cabin models, interior elements and measurement based optical source, receiver, surface material characteristics into account is developed. The effect of the operation wavelength, cabin model accuracy and user terminal mobility on the optical channel conditions is also investigated. As a final step, the on-board direct-current biased optical orthogonal frequency division multiplexing (DCO-OFDM) performance is evaluated by using obtained in-flight LiFi channels. Numerical results show that the location of a mobile terminal and accurate aircraft cabin modelling yield as much as 12 and 2 dB performance difference, respectively. [ABSTRACT FROM AUTHOR]

Cybertwin is a promising networking paradigm that supports complex wireless interconnectivity and enhances reliable communications by using edge capability in different applications. However, the computation-heavy phased-array weight vector generation is the significant barrier for the implementation of physical layer security (PLS) for millimeter-wave (MMWV) communications in cybertwin-driven scenarios, like vehicle-to-everything (V2X) applications. In this paper, we propose a low-complexity phased-array PLS scheme in MMWV wireless communications for cybertwin-driven V2X applications. The computational complexity is much reduced in a cost-effective way, where the transmitting phased-array weight vectors are updated at the symbol rate by randomly swapping the pre-calculated initial weight vector elements instead of directly calculating a new weight vector. Performance evaluation shows the proposed scheme possesses a lower computational complexity than the compared PLS schemes. The simulation demonstrates that the constellations are scrambled at undesired directions, while the constellation at target direction remains clear and constant. Thus, the communication secrecy is preserved for the receiver at the target direction in cybertwin-driven vehicular communications. Finally, we demonstrate that the proposed scheme achieves good security performances in terms of symbol error rate (SER) and secrecy outage probability. [ABSTRACT FROM AUTHOR]

With the development of 6G wireless communication technologies, various resource-intensive and delay-sensitive vehicle application tasks are generated. These application tasks can be offloaded to Mobile Edge Computing (MEC) which deploys computing resources at the edge of networks. Besides, the recent proposed Cybertwin, as the digital representation of the complicated physical end-systems, can help the terminals obtain the required services from networks. Vehicles enabled by Cybertwin can offload their tasks to MEC and achieve better performance. In this paper, we focus on the study of a hybrid energy-powered multi-server MEC system with Cybertwin. Vehicles enabled by Cybertwin and edge servers send the current network status and unprocessed vehicle application tasks to the macro base station (MBS) to achieve the better allocation of resources. Energy harvesting (EH) devices are deployed on edge servers to form a “green energy-grid” hybrid energy supply model. We formulate a stochastic offloading optimization problem, and the goal is to minimize the system cost. The stochastic optimization problem is decomposed into three sub-problems. Then, we design an efficient multi-vehicle task offloading (EMT) algorithm to achieve the trade-off between system cost and task queue length. Theoretical analysis shows that EMT algorithm can optimize the total cost of the MEC system and guarantee the system performance. According to experimental evaluation, we verify the performance of the EMT algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a novel intelligent reflecting surface (IRS)-assisted wireless powered communication network (WPCN) architecture is proposed for power-constrained Internet-of-Things (IoT) smart devices, where IRS is exploited to improve the performance of WPCN under imperfect channel state information (CSI). We formulate a hybrid access point (HAP) transmit energy minimization problem by jointly optimizing time allocation, HAP energy beamforming, receiving beamforming, user transmit power allocation, IRS energy reflection coefficient and information reflection coefficient under the imperfect CSI and non-linear energy harvesting model. On account of the high coupling of optimization variables, the formulated problem is a non-convex optimization problem that is difficult to solve directly. To address the above-mentioned challenging problem, alternating optimization (AO) technique is applied to decouple the optimization variables to solve the problem. Specifically, through AO, time allocation, HAP energy beamforming, receiving beamforming, user transmit power allocation, IRS energy reflection coefficient and information reflection coefficient are divided into three sub-problems to be solved alternately. The difference-of-convex (DC) programming is used to solve the non-convex rank-one constraint in solving IRS energy reflection coefficient and information reflection coefficient. Numerical simulations verify the superiority of the proposed optimization algorithm in decreasing HAP transmit energy compared with other benchmark schemes. [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]

Source localization plays a key role in many applications including radar, wireless and underwater communications. Among various localization methods, the most popular ones are Time-Of-Arrival (TOA), Time-Difference-Of-Arrival (TDOA), Angle-Of-Arrival (AOA) and Received Signal Strength (RSS) based. Since the Cramér-Rao lower bounds (CRLB) of these methods depend on the sensor geometry explicitly, sensor placement becomes a crucial issue in source localization applications. In this paper, we consider finding the optimal sensor placements for the TOA, TDOA, AOA and RSS based localization scenarios. We first unify the three localization models by a generalized problem formulation based on the CRLB-related metric. Then a unified optimization framework for optimal sensor placement (UTMOST) is developed through the combination of the alternating direction method of multipliers (ADMM) and majorization-minimization (MM) techniques. Unlike the majority of the state-of-the-art works, the proposed UTMOST neither approximates the design criterion nor considers only uncorrelated noise in the measurements. It can readily adapt to to different design criteria (i.e. A, D and E-optimality) with slight modifications within the framework and yield the optimal sensor placements correspondingly. Extensive numerical experiments are performed to exhibit the efficacy and flexibility of the proposed framework. [ABSTRACT FROM AUTHOR]

In this paper, we study an uplink non-orthogonal multiple access (NOMA) wireless system with short-packet communications (SPC) in the presence of channel estimation errors and residual transceiver hardware impairments. Two NOMA decoding schemes are considered, namely fixed-order decoding (i.e., mean-power based decoding) and dynamic-order decoding (i.e., instantaneous-power based decoding). We first derive the closed-form exact and asymptotic expressions of the cumulative distribution functions (CDF) of the signal-to-interference-plus-noise ratio (SINR) associated with the two decoding schemes. Based on this, we analyze the average block error-rate (BLER) achieved in the finite block-length regime. Next, we formulate maximum throughput optimization problems with respect to the length of the packet. This problem is shown to be quasi-concave, which facilitates the finding of the optimal block-length via a search algorithm. Analytical comparisons between the NOMA-SPC system and its OMA-SPC counterpart are also performed. Simulation results are provided to validate the analysis. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc network (VANET) underpins most intelligent transportation systems and given the open nature of the wireless communication environment, edge nodes in the VANET are subject to varying levels of attacks. While there are a large number of conditional privacy-preserving authentication protocols in the literature, most of these protocols (whose security is guaranteed by classical cryptographic primitives) are not designed to resist quantum attacks. In this paper, we propose a lattice-based conditional privacy-preserving authentication protocol for VANETs, designed to simultaneously achieve mutual authentication and privacy protection. We also show that our protocol is secure in the random oracle model, relying on the small integer solution problem. Additionally, we demonstrate the potential utility of our protocol by evaluating its performance. [ABSTRACT FROM AUTHOR]

Satellite and unmanned aerial vehicle (UAV) networks have been introduced as enhanced approaches to provide dynamic control, massive connections and global coverage for future wireless communication systems. This paper considers a coordinated satellite-UAV communication system, where the UAV performs the environmental reconnaissance task with the assistance of satellites in a hostile jamming environment. To fulfill this task, the UAV needs to realize autonomous trajectory control and upload the collected data to the satellite. With the aid of the uploading data, the satellite builds the environment situation map integrating the beam quality, jamming status, and traffic distribution. Accordingly, we propose a closed-loop anti-jamming dynamic trajectory optimization approach, which is divided into three stages. Firstly, a coarse trajectory planning is made according to the limited prior information and preset points. Secondly, the flight control between two adjacent preset points is formulated as a Markov decision process, and reinforcement learning (RL) based automatic flying control algorithms are proposed to explore the unknown hostile environment and realize autonomous and precise trajectory control. Thirdly, based on the collected data during the UAV’s flight, the satellite utilizes an environment situation estimating algorithm to build an environment situation map, which is used to reselect the preset points for the first stage and provide better initialization for the RL process in the second stage. Simulation results verify the validity and superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

Optical wireless communication (OWC) over atmospheric turbulence and pointing errors is a well-studied topic. Still, there is limited research on signal fading due to random fog in an outdoor environment for terrestrial wireless communications. In this paper, we analyze the performance of a decode-and-forward (DF) relaying under the combined effect of random fog, pointing errors, and atmospheric turbulence with a negligible line-of-sight (LOS) direct link. We consider a generalized model for the end-to-end channel with independent and not identically distributed (i.ni.d.) pointing errors, random fog with Gamma distributed attenuation coefficient, double generalized gamma (DGG) atmospheric turbulence, and asymmetrical distance between the source and destination. We develop density and distribution functions of signal-to-noise ratio (SNR) under the combined effect of random fog, pointing errors, and atmospheric turbulence (FPT) channel and distribution function for the combined channel with random fog and pointing errors (FP). Using the derived statistical results, we present analytical expressions of the outage probability, average SNR, ergodic rate, and average bit error rate (BER) for both FP and FPT channels in terms of OWC system parameters. We also develop simplified and asymptotic performance analysis to provide insight on the system behavior analytically under various practically relevant scenarios. We demonstrate the mutual effects of channel impairments and pointing errors on the OWC performance, and show that the relaying system provides significant performance improvement compared with the direct transmissions, especially when pointing errors and fog becomes more pronounced. [ABSTRACT FROM AUTHOR]

Recent research on joint source channel coding (JSCC) for wireless communications has achieved great success owing to the employment of deep learning (DL). However, the existing work on DL based JSCC usually trains the designed network to operate under a specific signal-to-noise ratio (SNR) regime, without taking into account that the SNR level during the deployment stage may differ from that during the training stage. A number of networks are required to cover the scenario with a broad range of SNRs, which is computational inefficiency (in the training stage) and requires large storage. To overcome these drawbacks our paper proposes a novel method called Attention DL based JSCC (ADJSCC) that can successfully operate with different SNR levels during transmission. This design is inspired by the resource assignment strategy in traditional JSCC, which dynamically adjusts the compression ratio in source coding and the channel coding rate according to the channel SNR. This is achieved by resorting to attention mechanisms because these are able to allocate computing resources to more critical tasks. Instead of applying the resource allocation strategy in traditional JSCC, the ADJSCC uses the channel-wise soft attention to scaling features according to SNR conditions. We compare the ADJSCC method with the state-of-the-art DL based JSCC method through extensive experiments to demonstrate its adaptability, robustness and versatility. Compared with the existing methods, the proposed method takes less storage and is more robust in the presence of channel mismatch. [ABSTRACT FROM AUTHOR]

This paper investigates an intelligent reflecting surface (IRS) aided secure wireless powered communication network (WPCN), where a transmitter first harvests energy from a power station (PS), and then uses the collected energy to transmit information to multiple internet of things (IoT) devices in the form of multicast in the presence of multiple eavesdroppers. An IRS is deployed to enhance the efficiency of wireless energy transfer (WET) and secure wireless information transfer (WIT). Considering that the PS and transmitter belong to different service providers, we model this energy interaction through a Stackelberg game and propose an IRS-aided energy trading and secure communication (IRS-ETSC) scheme, in which the transmitter needs to pay an energy price as an incentive for the PS energy service. Specifically, the transmitter as the leader can control the energy price, WET time, two-stage phase shifts, and beamforming vector, while the PS as the follower can adjust the transmit power. To solve the non-convex leader game problem, we propose a two-step approach to decompose the original problem into two subproblems. The first subproblem can be solved independently by an efficient alternating optimization (AO) based algorithm, in which the closed-form optimal beamforming vector and energy phase shifts are alternately optimized. Then, the second subproblem is relaxed by semidefinite relaxation (SDR) and solved by an iterative algorithm based on block coordinate descent (BCD), where the optimal energy price, optimal WET time, and suboptimal information phase shifts can be obtained by golden section method and successive convex approximation (SCA) respectively. Both subproblems can converge to a stationary point. Numerical results show that compared with the traditional non-IRS scheme, the proposed IRS-ETSC scheme achieves utility improvement for both the PS and transmitter. [ABSTRACT FROM AUTHOR]

Fifth-generation (5G) wireless communications confront explosive growth in mobile data demand and massive intensive user equipment (UE) connections. The optimization of base station (BS) array orientations in large-scale networks can provide high potential performance gain to meet these requirements. However, traditional schemes are highly dependent on experience and difficult in implementation due to their demand on repeated drive tests and large amounts of data samples. In this paper, via exploiting UE locations and channel direction information, we propose an intelligent network optimization framework to maximize the long-term network rate performance. For the augmentation of limited drive test data, a deep Gaussian process regression (DGPR) model is designed to construct a scenario-specific channel modeling. In addition, via a domain-knowledge driven fusion of convolutional neural network (CNN) and multi-layer perceptron (MLP), we propose a multi-branch deep neural network (DNN) to accurately map BS array orientations and concise channel measurements to the network performance. Finally, based on the scenario-specific modeling, an efficient gradient search approach is proposed to optimize BS array orientations via neural network (NN) backpropagation. Both simulations and field tests in 5G experimental networks validate the effectiveness and efficiency of our proposed framework, especially with limited drive test data. [ABSTRACT FROM AUTHOR]

The so-called Internet of Vehicle (IoV) systems will interconnect numerous vehicles to communicate significant information through an Internet of Things (IoT) enabled network. It has emerged as a promising system wherein various data authentication techniques have been introduced using clumsy certificate management and Diffie-Hellman (DH) assumption. However, in the presence of quantum cryptanalysis, DH-type problems could be solved in polynomial-time. In this paper, a novel certificateless data authentication protocol is designed, enabling security features in open wireless communication in the IoV. The proposed protocol resists a quantum attack using lattice cryptography. Further, a reliable blockchain mechanism is shown to provide vehicles’ trustworthiness in batch data verification. Rigorous formal analysis shows the ability of the proposed algorithm to resist existential unforgeability against the chosen-message attack. Nonetheless, the developed protocol supports other essential security functionalities, including unlikability, conditional-traceability, anti-replay, and data authenticity. Performance analysis exhibits the simulation orchestration and shows the way the proposed protocol outperforms other related techniques in energy consumption, data computation, communication, and cryptographic key storage overheads. [ABSTRACT FROM AUTHOR]

To mitigate the impact of the dramatic growth of wireless video traffic and provide users with heterogeneous video services, a collaborative caching-transmission method in cache-enabled terahertz (THz) heterogeneous networks is studied in this paper. We propose a novel scalable video coding (SVC)-based two-tier cooperative caching strategy that can provide video services with different perceptual qualities. In addition, we propose a hierarchical hybrid transmission scheme where users obtain the requested layer files through the cooperative cellular multicast-D2D and THz transmissions. A base layer-based multicast group aggregation process is presented to improve resource utilization, while an efficient multicast transmission resource scheduling scheme can make better use of the spatial reusability. We derive the expressions for the average offloading capacity gain and average energy consumption, and formulate an efficiency function optimization problem. The problem is efficiently solved by the greedy strategy-gradient descent based caching solution and long tail distribution based heuristic caching solution. Simulation results validate the two solutions and demonstrate that the proposed caching-transmission method can achieve quasi-optimal performance compared with other contrasting schemes. [ABSTRACT FROM AUTHOR]

In this paper, an intelligent reflecting surface (IRS) is deployed to assist the terahertz (THz) communications. The sum-rate of user equipments (UEs) is maximized while guaranteeing the rate requirement of each UE. A block coordinate searching (BCS) algorithm is proposed to jointly optimize the IRS’s coordinates, phase shifts, THz sub-bands allocation and power control. Specifically, the relaxation with penalties based (RPB) algorithm is developed to obtain feasible coordinates of the IRS and guarantee the monotonicity of objective value. In addition, the IRS phase shifts are formulated as closed-form expressions with introduced pricing factors. Simulation results show that the proposed scheme can significantly enhance system performance. [ABSTRACT FROM AUTHOR]

In this paper, we consider a computation offloading problem in a three-tier network consisting of multiple mobile users (MUs), multiple edge clouds, and a central cloud. On this basis, we formulate a system Energy Time Cost ($ETC$) minimization problem by jointly optimizing the resource allocation, mobile-edge matching decision, and offloading decision. Due to the difficulty of directly solving the formulated problem, we decompose it and propose two offloading algorithms, namely Gale-Shapley based Minimum/Sequential Offloading Algorithm (GS-MOA/GS-SOA), to optimize offloading decisions by minimizing the system $ETC$ , in which the mobile-edge matching strategies based on the proposed Gale-Shapley based Mobile-Edge mAtching aLgorithm (GS-MEAL) and resource allocations are optimized iteratively. The simulations show the effectiveness of our proposed algorithm. [ABSTRACT FROM AUTHOR]

A hybrid satellite-terrestrial relay network (HSTRN) has been envisioned as a promising communication architecture for future wireless communications, which can prominently reduce the impact of shadow fading and expand service coverage. In this paper, we conduct theoretical performance analysis for a dual-hop communication system, consisting of multiple relays and multiple users in the HSTRN with the amplify-and-forward protocol. For the shadowing effect and the multipath effect, we introduce the shadowed-Rician distribution and Nakagami- $m$ fading to characterize the channel models for the satellite-relay and the relay-user links, respectively. Additionally, the opportunistic scheduling scheme is employed, where the optimal relay and the optimal user are selected from the alternative nodes with the maximum instantaneous signal-to-noise ratio. Thereafter, the analytical expressions including ergodic capacity and average symbol error rate (SER) are derived. More specifically, we also present the asymptotic behavior of the average SER at the high signal-to-noise ratio (SRN) regime. The theoretical analysis is validated by the numerical results. Moreover, the results also reveal that the performance of the system can be improved by increasing the number of relays and users under different channel parameters and various modulation schemes. [ABSTRACT FROM AUTHOR]

This paper investigates power allocation for minimizing the average bit error probability (BEP) in hybrid communication systems, more specifically hybrid power line/wireless systems (HPWSs). In this regard, a brief discussion of HPWSs is presented in order to point out the complementary characteristics of power line and wireless media. By considering orthogonal frequency-division multiplexing and maximal-ratio combining, optimization problems associated with the minimization of the average BEP in hybrid communication systems are formulated under two distinct transmission power constraints, called sum power and sum power-channel constraints. For both constraints, it is demonstrated that at most one medium should be used for data transmission over subchannels with the same index in order to minimize the average BEP in hybrid communication systems. Based on this finding, power allocation algorithms are derived for this purpose in HPWSs. Numerical analyses are then used to validate the proposed algorithms by comparing them with interior-point-based power allocation algorithms. In addition, performance comparisons show the advantage of the proposals over alternative algorithms from the literature, including previous algorithms proposed for maximizing the achievable data rate. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) consisting of a large number of programmable near-passive units has been a hot topic in wireless communications due to its capability in providing smart radio environments to enhance the communication performance. However, the existing research are mainly based on simplistic channel models, which will, in principle, lead to inaccurate analysis of the system performance. In this paper, we propose a general three-dimensional (3D) wideband non-stationary end-to-end channel model for RIS assisted multiple-input multiple-output (MIMO) communications, which takes into account the physical properties of RIS, such as unit numbers, unit sizes, array orientations and array configurations. By modeling the RIS by a virtual cluster, we describe the end-to-end channel by a superposition of virtual line-of-sight (V-LoS), single-bounced non-LoS (SB-NLoS), and double-bounced NLoS (DB-NLoS) components. We also derive an equivalent cascaded channel model and show the equivalence between end-to-end and cascaded modeling of RIS channels. Then, a sub-optimal solution with low complexity is used to derive the RIS reflection phases. The impact of physical properties of RIS, such as unit numbers, unit sizes, array orientations, array configurations and array relative locations, on channel statistical characteristics has been investigated and analyzed, the results demonstrate that the proposed model is helpful for characterizing the RIS-assisted communication channels. [ABSTRACT FROM AUTHOR]

Cloud-based cell-free massive multiple-input multiple-output (CFmMIMO) technology, which exploits a large number of distributed antennas to cooperatively serve multiple users, constitutes an appealing technique for B5G/6G wireless communications. However, the distributed nature of cloud-based CFmMIMO imposes great challenges in analyzing the error probability bounds, and very few efforts have so far been paid to optimize the detector design. In this paper, we try to add a stroke to this blank by analyzing the symbol error rate (SER) and design near-optimal detection algorithms. Specifically, considering non-identical large-scale coefficients and channel estimation errors, we first leverage the pairwise error probability to derive an asymptotic SER bound for uplink cloud-based CFmMIMO systems, which is verified by simulation results. Furthermore, motivated by the concepts of successive interference cancellation (SIC) and error correction mechanism (ECM), we design two distinct types of near-optimal detectors for cloud-based CFmMIMO systems and analyze their complexity and convergence performance. Finally, extensive simulation results show that our proposed SIC and ECM based detectors outperform conventional matched filtering (MF) and minimum mean squred error (MMSE) counterparts. In particular, the MMSE-SIC and MMSE-ECM detectors approach the derived asymptotic bound, and the MF-ECM detector strikes a balance between the SER and complexity in ultra CFmMIMO scenarios. [ABSTRACT FROM AUTHOR]

The use of massive multiple-input multiple-output (MIMO) technology has enabled increased efficiency and capacity of wireless communication systems. When the downlink channel to user terminals (UTs) is known at the base station (BS), the BS can precode transmission to simplify detection at the UTs. In frequency division duplexed (FDD) systems, obtaining CSI at the transmitter to fully exploit the advantages of massive MIMO is complicated, since the number of channel coefficients to be trained and fed back from the UT is very large. This is exacerbated in the case where the channel is time varying, where frequent retraining and feedback is required. However, if the channel coefficients are spatially correlated, the amount of feedback required can be reduced significantly. In this paper, we consider the case where the channel coefficients from the BS to the UTs are spatially correlated, and show that efficient allocation of training power based on eigenvalues of the channel correlation matrix significantly boosts achievable rates. Further, we show that using Kalman filters to track the evolving channel coefficients reduces the need to retrain channels, and the reduced training requirement translates to higher data rates. Simulations confirm that optimal training and tracking channel modes enhances rates significantly. [ABSTRACT FROM AUTHOR]

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

Highmobility is an important scenario for the 5G wireless communications. However, the high mobility results in serious time-varying fading, bringing challenges to the reliabilities of wireless transmission. Diversity technique is an essential method to overcome the effect of fading. It is noted that an LDPC (low-density parity-check) code is a concatenation of single parity-check codes (SPC) and repetition codes (REP) by a random interleaver, and a REP can be viewed as a typical example for achieving time/frequency diversity. Thus, it is appealing to jointly consider LDPC codes and diversity techniques. This paper presents parity-check coding transmit diversity (PCTD) schemes based on half-rate invertible LDPC codes, which are capable of achieving both diversity and coding gains from the time-varying fading channels. Besides, we investigate two types of PCTD schemes, and take into consideration two transmission modes, which are single-carrier (SC) and orthogonal frequency division multiplexing (OFDM) transmission modes. A chase combining based message passing algorithm (CC-MPA) is proposed for the proposed schemes, which can take full advantages of signal processing and coding gains. Furthermore, the Shannon limits of time-varying fading channels with BPSK signaling are deduced. Simulation results show that the proposed PCTD with CC-MPA algorithm can provide better BER performances than the classical time-domain diversity scheme in time-varying fading channels, which is appealing for the high mobility communications. [ABSTRACT FROM AUTHOR]

As a crucial part of the communication-based train control (CBTC) system, the wireless communication subsystem is responsible for transmitting safety-critical information, which directly affects the safe and efficient operation of the urban rail transit. The dependability analysis method for wireless communication in CBTC is critical and necessary to ensure the stable transmission of safety-critical information. However, most of the existing researches in CBTC lack a unified evaluation index to analyze the wireless communication dependability. In this paper, the dependability of wireless communication system in CBTC is established. To be specific, timeliness, integrity, security, and safety are used to describe the dependability of CBTC wireless communication system in our definition. The age of information (AoI) is used to quantitatively analyze the dependability in the CBTC wireless communication system. To verify the effectiveness of the proposed dependability analysis method, we quantitatively introduce the dependability of wireless communication system in CBTC with different scenarios, including packet-drop, re-transmission, handoff, and cyber-attack. Simulation results show that the designed dependability analysis method with quantized AoI can efficiently describe the dependability of CBTC wireless communication system in different scenarios. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) composed of unit cells with tunable reflection coefficients, amplitude, phase, and frequency has received great interest from wireless communication communities. In this paper, a non-stationary three-dimensional (3-D) wideband IRS-assisted channel model, including spatial and temporal non-stationarity, is proposed for high altitude platform (HAP) multiple-input multiple-output (MIMO) communication systems. The proposed channel model considers the propagation environment around the receiver (Rx) and that around both the IRS and Rx, which are modeled using multiple 3-D cylinders and confocal elliptic-cylinders, respectively. In the proposed model, the optimal reflection phases of the IRS reflection units are proposed, and the number and the size of the IRS reflection units are also considered, which are used to investigate received signal strength, multipath fading effect, and spatial non-stationarity of IRS-aided communication systems. The numerical results show that the IRS reflection units can improve the received signal strength by adjusting the reflection phases and multipath fading effect can be effectively reduced by increasing the number and the size of IRS reflection units. Also, it is found that the spatial correlation significantly decreases with the increase of the number and size of the IRS reflection units, and the proposed IRS-aided channel model presents spatial non-stationarity. These observations can provide guidelines and references for systems design and performance analysis of IRS-aided HAP-MIMO communication systems. [ABSTRACT FROM AUTHOR]