Existing differentially private graph neural network (GNN) methods predominantly rely on a uniform privacy budget and coarse-grained perturbation mechanisms, implicitly assuming homogeneous privacy sensitivity across graph nodes. However, real-world graphs exhibit pronounced community structures and topological heterogeneity, causing uniform privacy protection to simultaneously under-protect structurally critical regions and over-perturb peripheral ones, thereby degrading both privacy guarantees and model utility. To address this limitation, we propose a community-aware personalized differential privacy (DP) framework for GNNs, which enables fine-grained node-level privacy protection by explicitly leveraging community structure. Specifically, we apply the Louvain algorithm to detect community structures and introduce a community importance evaluation method that integrates structural connectivity with node attribute similarity. Based on these importance scores, we design a personalized privacy budget allocation scheme that adaptively assigns differentiated privacy budgets to nodes under a global (ϵ, δ)-DP constraint. We theoretically prove that the proposed mechanism satisfies node-level (ϵ, δ)-DP, and further demonstrate that it significantly enhances robustness against practical privacy threats such as attribute inference and link reconstruction attacks. Extensive experiments on five real-world graph datasets show that our approach consistently outperforms state-of-the-art DP-GNN baselines across multiple architectures, including GCN, GIN, and GraphSAGE, achieving a superior privacy–utility trade-off.

Energy efficiency is of paramount importance for covert integrated sensing and communication (ISAC) networks to ensure sustained operation. In light of the imperfect channel state information (CSI) encountered in practical scenarios, we investigate the energy efficiency of these networks. Taking into account a variety of CSI estimation errors, our algorithm optimizes both sensing and information beamforming design while ensuring a low detection probability by multiple untrusted wardens. The energy-efficient beamforming design is formulated as a non-convex fractional programming problem. First, we establish that the covariance matrices of communication beamforming vectors are rank-one. Subsequently, we exploit this property to transform the original problem into a semi-definite relaxed version. For Gaussian CSI estimation errors, we adopt Bernstein-type inequalities to handle the probability constraints of interception and exploit Dinkelbach’s algorithm to address the nonlinear fractional objective function. For bounded CSI estimation errors, we employ an S-procedure to tackle the non-convex constraints associated with covert communications, followed by a successive convex optimization algorithm to provide an effective solution to the original problem. Extensive simulations confirm the superiority of our proposed algorithms, demonstrating a remarkable performance gain compared with baseline schemes adopting existing approaches. Specifically, deploying a larger number of antenna elements can enhance the energy efficiency of covert ISAC networks, while simultaneously reducing the system’s total power consumption. Furthermore, the sensing beam power threshold and the outage probability of covertness serve as important trade-off parameters in covert ISAC networks.

Considering a multi-user Downlink scenario, this paper studies transmit antenna selection (AS) for uniform rectangular arrays (URA). A key contribution of this work is the investigation of the trade-off between AS and the array directivity (or gain), and its impact on spectral efficiency (SE) and energy efficiency (EE). In this context, to improve EE of the system, a directivity-aware transmit AS and power allocation problem is formulated and solved using a sequential approach. For AS, two schemes are proposed: Block selection (BS) and L´evy flight based binary particle swarm optimization (LFBPSO), while for power allocation, an iterative quadratic transform method is employed in combination with Dinkelbach algorithm for EE maximization. The performance of the AS schemes is evaluated with respect to SE, EE and computational complexity. The results show that the array directivity varies with the subset of selected antennas and has a direct impact on the performance of the AS schemes in terms of achievable SE and EE. Furthermore, AS without considering the directivity can incur a loss in the array gain of up to 2 dB in a λ/2 spaced array. The results also show that LFBPSO outperforms other AS schemes with respect to SE and EE. Finally, another important contribution of this work is the experimental validation of the impact of array directivity on the AS process. This is demonstrated by replacing the isotropic antenna array with a measured 4×8 uniform rectangular array of patch antennas in the simulation framework.

This paper investigates the block error rate (BLER) of three encoding schemes: joint encoding (JE), separate encoding (SE), and rate splitting (RS) in low-throughput massive multiple input and multiple output (mMIMO) networks with finite blocklength transmission. To reduce pilot overhead, we first partition users via k-means clustering based on their spatial coordinates, by which the pilot sequences can be reused among different groups. Then, we apply a minimum mean square error (MMSE) estimator to examine their instantaneous signal-to-interference-plus-noise ratios (SINRs). In particular, a local successful interference cancellation is proposed to mitigate co-channel interference for the RS scheme. Then, we formulate min-max problems for optimizing the error performance, with power control coefficients, combining vectors, and blocklength being their variables. Additionally, we employ the iterative MMSE combiner as the combining vector and then transform the problem for minimizing the BLER to a max-min problem maximizing the SINR at each iteration based on the monotonicity of the BLER expression with respect to the SINR. Power control coefficients are then updated by solving the reformulated geographic programming (GP) problem. Furthermore, the golden-section and bisection methods are applied to optimize the blocklength allocation strategy for the SE scheme and to search for the achievable BLER for the RS scheme. Simulation results, using network availability as the performance metric, demonstrate the comparable performance of the JE and RS schemes when combined with the proposed solving methods, whereas the SE scheme exhibits moderate performance.

As intelligent transportation systems (ITS) become more integrated into modern infrastructure, vehicular communication systems face increasing cybersecurity threats. Traditional centralized intrusion detection system (IDS) has significant limitations in terms of the scalability, privacy preservation, and trust establishment, which are critical challenges in ITS environments. To address these issues, this paper proposes an intrusion detection method tailored specifically for ITS, integrating federated learning (FL) and blockchain technology to create a secure, scalable, and privacy-preserving threat detection system for the Internet of Vehicle (IoV). The proposed method utilizes FL for distributed model training, avoiding the sharing of raw data and employing encryption techniques to protect user privacy at the edge devices. Blockchain technology ensures the integrity and tamper-proof nature of model updates and fosters trust between entities. Moreover, to accommodate the heterogeneous and dynamic data environment in IoV, the method supports both independent and identically distributed (IID) and non-IID data scenarios, enhancing the system’s adaptability and robustness. Given the computational limitations of vehicular devices, this work incorporates knowledge distillation and lightweight model designs, effectively reducing the local computational burden. Experimental results demonstrate the significant effectiveness of the proposed approach: on the CICIDS2017 dataset, the model achieves an accuracy of 97.10% with 11,904 parameters and a memory consumption of 0.045MB; on the Car-Hacking dataset, it achieves an accuracy of 99.10% with 9,989 parameters and a memory consumption of 0.038MB; on the CICIoV2024 dataset, it achieves an accuracy of 99.65% with 9,861 parameters and a memory consumption of 0.038MB. Compared to traditional baseline models, the proposed approach significantly reduces both the number of training parameters and memory consumption, while maintaining high accuracy, making it highly suitable for deployment in resource-constrained vehicular environments within ITS.

This paper studies movable pinching-antenna (PA) assisted communications, where a single PA can be dynamically placed along a dielectric waveguide to serve multiple users via orthogonal or non-orthogonal multiple access (OMA/NOMA). In contrast to the ideal-lossless waveguide model commonly assumed, our work explicitly incorporates waveguide-power loss, thereby introducing a fundamental trade-off with free-space path loss that jointly determines system performance. By deriving the lower-bound-based suboptimal PA position for two- and three-user scenarios under both with and without waveguide-power loss, we establish a general positioning guideline applicable to multi-user scenarios. Furthermore, we perform a comparative analysis between the proposed single-PA scheme and a static multi-PA scheme. Analytical and simulation results derive the conditions determining which scheme is superior, revealing that the trade-off is fundamentally governed by the relative dominance between the waveguide-power loss and the free-space path loss. Simulation results validating our analytical derivations also confirm that the proposed PA positioning rule in NOMA-based PASS holds for both successive interference cancellation (SIC) decoding orders, demonstrating its robustness regardless of whether the near user decodes the far user’s signal first or vice versa.

This paper investigates the covert communication (CC) performance of a simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) assisted non-orthogonal multiple access (NOMA) and rate splitting (RS) systems operating over Rician fading channels. Alice applies RS and NOMA to the downlink transmission of two legitimate users aided by the STAR-RIS in the presence of two non-colluding illegal users. Specifically, closed-form expressions for detection error probability, optimal detection threshold, minimum detection error probability (MDEP) of the warden, and the covert rate of the NOMA user pair are derived. The accuracy of the derived results is verified through Monte Carlo simulations. The results demonstrate that the MDEP depends only on the power allocation factor of the covert users and is independent of the transmit power or STAR-RIS deployment distance. Furthermore, the RS-NOMA system exhibits superior CC performance compared to the conventional NOMA system.

In this paper, we investigate the information piggyback capability of the unmanned aerial vehicle (UAV) by encoding observed motion states. Specifically, at specific moments throughout the holistic navigation process, the distance between the UAV and the starting point, the position expressed by the three-dimensional (3D) Cartesian coordinates, the linear velocities, and the attitude angles are encoded into 16-bit digital symbols through a proposed codebook. In this way, covert data communications can be enabled, complementing conventional radio frequency (RF) communications in harsh electromagnetic environments. To achieve a well-designed flight controller that is necessary to enable fluent movement against external disturbances and accurate motion state encoding, we introduce the whole flight control structure and perform system dynamic analysis. The proposed motion control mechanism can mitigate the jitter and oscillation during the journey while ensuring the required motion status for information encoding purposes.

Existing end-to-end task-oriented semantic communication (ToSC) systems rely on deep neural network-based joint source and channel coding (DeepJSCC) architecture, which can extract and transmit task-relevant information for remote task inference. Although this architecture provides an efficient communication method, it poses specific security issues. Since DeepJSCC delivers task-related data without encryption, the risks of signal interception and reverse inference increase. This paper provides a physical layer solution for security protection via precoding optimization. We compare the proposed method with the traditional physical layer security strategy, which aims to maximize secrecy capacity under different task classifiers. Simulation results show the superiority of the proposed method in securing the task-inference performance.

This paper investigates point-to-point uplink rate-splitting (RS) transmission in the finite blocklength (FBL) regime. The system is configured with a dual-polarized (DP) multiple-input and multiple-output (MIMO) antenna array at the receiver and a pair of DP antennas at the transmitter. During transmission, vertically and horizontally polarized antennas at the transmitter are employed to transmit two independent data streams partitioned via RS. At the receiver, a successive interference cancellation (SIC)-free detection scheme is adopted to exploit the polarization-domain degree of freedom (DoF), thereby eliminating the need for SIC. To validate the efficacy of the proposed RS-DP scheme, we optimize the combining vectors for both vertical and horizontal data streams to maximize the sum spectral efficiency under FBL constraints. Simulation results demonstrate that the RS-DP scheme achieves performance comparable to the conventional RS-SIC approach, thereby corroborating its effectiveness in balancing system performance and decoding complexity.

Unmanned aerial vehicle base station (UAV-BS) communication networks are considered as a promising solution for temporarily recovering urban telecommunication services interrupted by natural disasters. However, the deployment of UAV-BSs remains a challenge in disaster scenarios where terrestrial base stations may be unavailable, and users' locations (mobility in 3D, both on the ground and in the building) and requirements are constantly changing over time. In this paper, we propose a new digital twin (DT) framework to dynamically optimize UAV-BSs deployment in terms of both quantity and location while ensuring guaranteed network quality of service (QoS) and satisfied user requirements. It leverages a graph neural network (GNN) with new random walk for network modeling, a convolutional neural network (CNN) with online learning for QoS prediction, and deep reinforcement learning (DRL) models for optimizing UAV-BSs quantity and location. These three computing paradigms work collaboratively to respond to the evolving disaster context in an adaptive manner, improving UAV-BSs deployment subject to dynamic user requirements and mobility. Simulation results confirm the DT framework's effectiveness in optimizing UAV-BSs deployment in disaster scenarios.

This article studies the secrecy performance for a dual-hop nonorthogonal multiple access (NOMA)-aided unmanned aerial vehicle (UAV) network in the face of a passive untrusted far user (UFU). A new artificial noise (AN) scheme is proposed, where AN generated in the first hop can be used to encrypt confidential signals by XOR operation in the second hop. Based on the proposed AN (PAN) scheme, we analyze the exact and asymptotic outage probabilities (OPs) for both NOMA users and intercept probability (IP) for trusted near user (TNU). Simulation results verify the correctness of our theoretical analysis and demonstrate that the PAN scheme significantly improves the security for TNU at the cost of negligible reliability of UFU compared to the benchmark schemes.

With the flourishing development of sixth-generation wireless networks, the demand of spectrum efficiency rapidly increases, in order to support the demands of high-quality data transmission and connections of massive users. Among various promising technologies, the technology of near-field communications provides a great potential to address such an issue due to the unique spherical-wave channels for electromagnetic (EM) propagation. In this article, multiple-input-single-output (MISO) near-field communications is comprehensively studied to clarify the influences of the shape and structure of uniform planar array (UPA) on the system performance, considering three cases with uniform linear array (ULA), rectangular UPA, and circular UPA. In particular, we reveal the properties of rapid deterioration for signal-to-noise ratio (SNR) from the reduced projection aperture in near-field communications and investigate the single spherical crown antenna design and spherical crown antenna array design in order to address this issue. Moreover, we also characterize the role of antenna projection aperture in detail, and theoretically analyze the shape of UPA, yielding the corresponding exact closed-form expressions for SNR and outage probability (OP). Based on these above analytical results, we find out that adjusting the spacing between adjacent antennas to control the relative angle between user and selected antennas is an efficient way to improve the projection aperture of antenna and SNR. Simulation results are shown to well match analytical results, which validate the correctness of our analysis, clarifying that our proposed designs outperform the conventional works and illustrating a better stability for angle variations.

The imminent deployment of sixth-generation (6G) wireless communication systems promises new opportunities and challenges for model training using data from the edge devices in the Internet of Things (IoT). However, current research has yet to fully address the efficiency and scalability challenges arising from the extensive connectivity of edge devices across various scenarios. The presence of malicious devices further intensifies the system uncertainty during large-scale data interactions and model training, making it difficult for a single model to effectively manage the complexities introduced by heterogeneous devices and dynamic network conditions. To overcome these challenges, we propose FedSC, an innovative edge computing framework that leverages side-chain technology for efficient edge node management and employs federated learning to enable robust cross-device and cross-scenario model interactions. To accelerate the multimodel aggregation process, we introduce an asynchronous cross-domain iterative algorithm (ACDI) based on smart contracts. Additionally, to mitigate the impact of malicious and inactive nodes, we propose a robust consensus algorithm and a committee mechanism for leader node election based on contribution value. Experimental results demonstrate that the proposed FedSC achieves a 3.2% and 44.23% accuracy improvement on i.i.d. and non-i.i.d. dataset, respectively, along with a remarkable latency reduction of 256.51%, compared to FedAvg. Our work is conducive to the training of multiple models in different IoT scenarios, utilizing substantial amounts of IoT device data and facilitating collaboration between models. Furthermore, it enables the provision of fundamental services to diverse applications in 6G.

The one-time pad secure transmission based on wireless channel reciprocity (CR-OTP) has drawn great attention recently due to its capability of providing perfect secrecy of data, as well as the modulation information. However, existing CR-OTP schemes encounter both reliability and security challenges as their assumptions of channel reciprocity and randomness are not always well satisfied in practical application scenarios. To tackle these issues, we propose a dynamic constellation obfuscation (DCO) method that obfuscates the plaintext by rotating its constellation dynamically. This kind of analog encryption method is proven to be more robust than the existing digital exclusive OR (XOR) encryption method as the former achieves a lower symbol error rate (SER) by reducing the double quantization loss to one. The rotation pattern is jointly dependent on the channel state information (CSI) and the previous message, which guarantees the randomness of the rotation pattern subjected to environmental drifts. Only the legitimate receiver that correctly recovers the previous message correctly and observes a similar CSI is able to decode the newly transmitted message. We proved that the secrecy capacity of the proposed DCO method is higher than that of the state-of-the-art. Simulation results confirm that the proposed method delivers superior performance regarding secrecy capacity and SER, achieving a 4.5 dB signal-to-noise ratio (SNR) gain at a SER of 0.1; moreover, when the secrecy capacity is 0.1, the main channel SNR gain reaches 6.5 dB when the wiretap channel SNR is 20 dB.

Polarization-spatial modulation (PSM) has been recently proposed to improve the system performance and energy efficiency of the conventional polarization modulation (PM) by activating only a single dual-polarized (DP) antenna for signal transmission. However, the spectral efficiency (SE) of PSM is significantly degraded due to the single DP antenna transmission. To tackle this problem, we propose a generalized PSM (GPSM) scheme to enhance the SE of PSM through transmission using multiple DP antennas. In the GPSM scheme, the information bits are mapped to antenna activation patterns (AAPs), polarization matrix activation patterns (PAPs), and modulated symbols. To further enhance the SE of GPSM, we propose a more practical scheme termed multi-mode GPSM (MM-GPSM), which transmits information bits not only through the AAPs, PAPs, and modulated symbols but also via the constellation activation patterns (CAPs). A low-complexity maximum likelihood (ML) detector and a log-likelihood ratio detector for both GPSM and MM-GPSM are proposed to relieve the high computational complexity of the optimal ML detector at the cost of a negligible performance loss. An upper bound on the bit error rate (BER) is derived in closed-form to evaluate the performance of both GPSM and MM-GPSM. Simulation results show that GPSM and MM-GPSM outperform the conventional PM and PSM schemes, particularly in the high signal-to-noise ratio (SNR) region, and verify the accuracy of the theoretical analysis of the upper-bounded BER.

In the online social networks (OSNs) and social Internet of Things (SIOT), communities of interest (CoI) are often used to facilitate information sharing among user devices. However, the risk of information leaks across communities persists due to inadequate control over users’ sharing behavior. In this paper, we propose a novel trust-based community sharing mechanism to control users who are contributing to high privacy leakage across communities of an OSN. In detail, we firstly formulate privacy loss in community based on the sensitivity and willingness of users in sharing. Secondly, we use this loss as the key determinant when updating trust to dynamically hold users accountable for privacy leakage. Thirdly, we use an adjustable threshold to enable or disable sharing users and evaluate the amount of information shared before and after control, as well as the changes in community user trust. Finally, we propose an optimization method based on the upper confidence bound to make a trade-off between information sharing and leakage through a payoff function over discretized thresholds. Simulations on three real OSNs datasets — BlogCatalog, Flickr, and YouTube — demonstrated that our proposed mechanism can effectively reduce community privacy loss by achieving the best payoff score of 1076.53, while the state-of-the-art baselines PDC-InfoSharing and UTV scored 506.33 and 957.98, respectively.

Reconfigurable intelligent surface (RIS)-aided cell-free massive multiple-input multiple-output (CF-mMIMO) technology has tremendous potential to revolutionize wireless communications by dynamically adapting wireless channels to boost average rate and energy efficiency (EE) of Internet of Things (IoT) networks for meeting the specifications of ultra-reliable and low-latency communications (URLLC). In this paper, we study the downlink harvested energy (HE), uplink rate, and total EE of the wireless-powered RIS-aided CF-mMIMO communication system with hardware impairments under finite blocklength. IoT devices harvest energy from the energy signals transmitted from access points (APs) during the downlink and use it for the uplink pilot and data transmission. Specifically, based on the unique characteristics of the channel fading model and the RIS deployment location, we propose a novel RIS phase shift design according to the line-of-sight (LoS) components of channels. Furthermore, we derive the average HE and uplink rate in closed form with a two-layer decoding method. We also validate the effectiveness of the proposed RIS phase shift design and the derived closed-form expressions by Monte Carlo simulations. Moreover, it is interesting to find that local minimum mean squared error (L-MMSE) combining is recommended to meet the requirements of URLLC, including communication reliability and delay. More notably, the numerical results show that the RIS-aided system with impaired hardware exhibits even superior performance, compared to the system with ideal hardware and more APs but lacking the assistance of RISs.

Unmanned aerial vehicles (UAVs) serve as flexible aerial platforms, enriching air-ground communication networks in various ways. To support massive connectivity within limited time-frequency blocks, non-orthogonal multiple access (NOMA) is proposed to be integrated into UAV networks. However, a common issue associated with almost all NOMA schemes is the susceptibility to inter-user interference (IUI). Therefore, in this paper, we propose a multi-UAV cooperative system aided by NOMA with index modulation (IM), termed MCU-NOMA-IM, to improve the performance of air-ground networks by mitigating IUI and also avoiding the successive interference cancellation (SIC) decoding method that is prone to error floors. With MCU-NOMA-IM, the information bits pertaining to multiple UAVs are mapped into multiple dimensions, including the modulated symbols, subcarrier indices, and energy allocation patterns. To fully investigate the performance of MCU-NOMA-IM on air-ground networks, we consider scenarios in the presence of three and four UAVs and derive upper-bounds for the bit error rates (BERs). In addition, we propose a multi-clustered-UAV cooperative system aided by NOMA with IM (MCCU-NOMA-IM), which groups closely located UAVs into several clusters to reduce the requirement for time resources. Simulation results demonstrate that both MCU-NOMA-IM and MCCU-NOMA-IM greatly outperform cooperative NOMA and non-cooperative NOMA-IM schemes, especially for distant UAVs when the signal-to-noise ratio is sufficiently high. Also, we show that the derived BER upper bounds are asymptotically tight.

Channel model substitution (CMS) is a technique that aims to replace a computationally challenging channel model with a simpler substitute. This technique is powerful for rapid adaptive signal processing and closed-form performance analytics. The parametric mapping between an original channel model and its substitute determines the utility of CMS. In the past decades, the moment matching criterion has dominated for conducting parametric mapping, which, however, is heuristic and has been proven non-optimal. In this paper, we propose to utilize particle swarm optimization (PSO) to obtain optimal parametric mapping relations for a general CMS problem, regardless of the distributional forms of the original channel model and the substitute. Taking the CMS techniques for the lognormal shadowed channel model as examples, simulation results show that the PSO enabled parametric mapping approach is capable of converging to the global optima under diverse system configurations, making CMS computationally feasible.

Cell-free symbiotic radio (CF-SR) is a promising technology to meet the demands of good quality-of-service and spectrum-efficient communications. However, the introduction of SR brings additional interference terms, which can seriously degrade the performance of the CF-SR systems. To suppress the interference, we adopt a rate-splitting (RS) transmission scheme to CF-SR. In this paper, we derive downlink spectral efficiency (SE) expressions of the CF-SR system with RS. Furthermore, in a conventional two-phase (TP) channel estimation scheme, the direct link causes heavy interference to the backscatter link, consequently diminishing the accuracy of the backscatter-link channel estimation. To this end, we propose a collaborative cancellation (CC) channel estimation scheme, which can eliminate the interference from the direct link and thus improve the accuracy of the backscatter-link channel estimation. Moreover, we derive the novel closed-form SE expressions under the CC channel estimation scheme using maximum ratio (MR) precoding. Simulation results show that the normalized mean square error (NMSE) of the CC channel estimation is consistently better than the one of the TP channel estimation, both on the direct and backscatter links. Furthermore, the advantages of the CC channel estimation scheme on the backscatter link can be further amplified in scenarios with a sufficient number of pilots. In addition, simulation results demonstrate that both the CC channel estimation scheme and the RS transmission scheme can provide significant improvements.

In this paper, we focus on the energy efficiency (EE) optimization and analysis of reconfigurable intelligent surface (RIS)-assisted multiuser downlink near-field communications. Specifically, we conduct a comprehensive study on several key factors affecting EE performance, including the number of RIS elements, the types of reconfigurable elements, reconfiguration resolutions, and the maximum transmit power. To accurately capture the power characteristics of RISs, we adopt more practical power consumption models for three commonly used reconfigurable elements in RISs: PIN diodes, varactor diodes, and radio frequency (RF) switches. These different elements may result in RIS systems exhibiting significantly different energy efficiencies (EEs), even when their spectral efficiencies (SEs) are similar. Considering discrete phases implemented at most RISs in practice, which makes their optimization NP-hard, we develop a nested alternating optimization framework to maximize EE, consisting of an outer integer-based optimization for discrete RIS phase reconfigurations and a nested non-convex optimization for continuous transmit power allocation within each iteration. Extensive comparisons with multiple benchmark schemes validate the effectiveness and efficiency of the proposed framework. Furthermore, based on the proposed optimization method, we analyze the EE performance of RISs across different key factors and identify the optimal RIS architecture yielding the highest EE.

Irregularly spaced arrays are appearing in diverse ares, such as wearable devices, stealth aircrafts. This paper studies the two-dimensional (2D) direction-of-arrival (DOA) estimation issue for an irregularly spaced electromagnetic vector sensor (EMVS) array. An estimation method of signal parameters via rotational invariance technique (ESPRIT) approach is developed. Unlike existing ESPRIT-like algorithms, the proposed approach in this paper not only estimates the rough directional cosine waveform via the rotational invariance of the polarized response matrix, but also finds the refined directional cosine waveform via the rotational invariance of the spatial response matrix. This proposed algorithm is capable of offering closed-form analytics, thus greatly facilitating 2D-DOA estimation. Numerical results shown in this paper verify that the proposed approach outperforms existing ESPRIT-like algorithms at a sightly increased costs of computation. In addition, numerical results presented in this paper for the proposed 2D-DOA estimation approach also corroborate the theoretical derivations.

As a promising technology, simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is proposed to improve the transmission quality and coverage, which is considered to be widely applied to intelligent automated transportation (IAT) systems. However, due to the open environment and 360° omnidirectional transmission, security issues have always been a major difficulty hindering the implementation of STAR-RIS aided IAT systems. To tackle this challenge, we propose an enhanced security index modulation (ESIM) scheme in this paper, which combines well-known IM and higher-order linear decoding quasi-orthogonal space-time block coding (LD-QO-STBC) techniques to improve the system security performance. Specifically, the information bits and transmit antennas are organized into four distinct sets, with each subset of antennas activated by individual IM. Subsequently, the STAR-RIS is employed to meticulously craft the LD-QO-STBC scheme, which involves determining the phase of the unmodulated carrier. In a strategic move to thwart eavesdropping attempts, the eavesdroppers are subjected to continuously varying and disruptive continuous artificial noise (CAN) and is thus unable to reliably decode the transmitted information, which collectively achieves secure communications amidst potential eavesdropping threats. In addition, the theoretical analysis of both the bit error rate (BER) and secrecy capacity are derived to explore the potential of ESIM-LD-QO-STBC. Numerical results reveal that our proposed method excels in achieving a substantial diversity gain while maintaining low computational complexity. Furthermore, our proposed scheme enhances secrecy capacity by at least 50%, offering a marked improvement over conventional physical layer security (PLS) schemes.

Direct acyclic graph (DAG)-based ledgers and distributed consensus algorithms have been proposed for use in the Internet of Things (IoT). The DAG-based ledgers have many advantages over single-chain blockchains, such as low resource consumption, low transaction fee, high transaction throughput, and short confirmation delay. However, the scalability of the DAG consensus has not been comprehensively verified on a large scale. This paper explores the scalability of DAG consensus within the low-to-high load regime (L2HR) using the tangle model, where L2HR characterizes the transition from a phase of low network load to another phase of high network load. In particular, we determine the average number of tips in the tangle in L2HR when adopting the uniform random tip selection (URTS) and rigorously prove that using the tangle model, the average number of tips at the end of L2HR converges to a constant. We also analyze the probability that a transaction in L2HR becomes an abandoned tip, the approximate average time required for the network load to transition from low load regime (LR) to high load regime (HR), and the average time required for a tip being approved for the first time in L2HR. All analytics are verified by numerical simulations.

Blockchain plays a key role in establishing secure and decentralized Industrial Internet of Things (IIoT) systems. Currently, the dependent transactions generated by IIoT devices require a packing process to select a set of non-conflicted transactions, which results in significant delay and deviation of the transaction response time. In this paper, we propose a novel transaction packing algorithm named Priority-Pack to address the above issue. Firstly, we use directed acyclic graphs to model the dependent transactions in IIoT systems to establish the mathematical relationships between transaction priority and waiting time as well as dependencies. Secondly, we propose an algorithm to specify a higher priority to a transaction with longer waiting time without violating transaction dependencies. It eliminates the time required to traverse the subsets of transactions in other algorithms. Thirdly, to further reduce the response delay for transactions with the same priority level, we choose to first pack transactions with smaller sizes. We prove that this selection can achieve the lowest average response time. Finally, simulations are conducted to benchmark the Priority-Pack against the state-of-the-art algorithms including Fair-Pack and Random-Pack. The results demonstrate that Priority-Pack outperforms the others in terms of average response time and deviations.

Reconfigurable intelligent surface (RIS) and ambient backscatter communication (AmBC) technologies are recognized for their programmability and high energy efficiency respectively, which will be the key parts of the future sixth generation (6G) mobile communication technology. The combination of the two technologies can improve communication security by reducing the probability of detection and decoding through enhanced transmission and backscatter transmission in different communication slots. In this paper, a dual-function RIS that supports cooperative relaying for covert communications is proposed. It operates in different communication slots (enhanced transmission slot and backscatter slot), but the performance is affected by phase errors due to function switching. A source covertly communicates with an intended destination via the help of RIS and cooperative relay. There is an illegal monitor aims to detect and eavesdrop the covert message. For this system, the outage probability (OP), intercept probability (IP), and detection error probability (DEP) in different communication slots are derived to examine the system reliability and security. Moreover, the system security probability (SSP) is proposed, and a block coordinated ascent (BCA)-based iterative algorithm is used to jointly optimize the power allocation coefficients to maximize the SSP. Simulation results show that increasing the number of elements can improve the security performance and mitigate the negative impact of RIS phase errors.

This paper investigates the uplink power control scheme for massive multiple-input and multiple-output non-orthogonal multiple access (mMIMO-NOMA) in massive ultra-reliable and low-latency communications (mURLLC) for Industrial Internet of Things (IIoT) applications. By the proposed NOMA scheme, the connected sensors are divided into several groups, and only group-level successive interference cancellation (GL-SIC) is considered at the receiver to reduce the decoding complexity and processing delay. Two schemes, i.e., mMIMO-NOMA with and without pilot sharing, are developed to fully explore the superiority of mMIMO-NOMA in mURLLC with a finite blocklength. For both schemes, the closed-form expressions of the achievable rate are obtained for the minimum mean square error (MMSE) estimator and zero-forcing (ZF) detector. Next, to address the formulated sum rate maximization problem, we develop a successive condensation approach (SCA)-based algorithm to jointly optimize pilot and data power. Besides, the max-min fairness (MMF) rate is also analyzed by verifying the feasibility of the problem. Finally, the simulation results demonstrate the effectiveness of the proposed SCA power control scheme. In addition, the advantages of the proposed two NOMA schemes in both sum rate and sum MMF rate are verified compared to traditional multi-user MIMO systems in mURLLC scenarios.

Millimeter wave (mmWave) communications are highly affected by blockage, whereas the emerging reconfigurable intelligent surface (RIS) has the potential to overcome this issue. This paper proposes a Neyman-Pearson (N-P) criterion-based blockage-aware algorithm to improve resilience to blockage in mobile mmWave multiple input multiple output (MIMO) systems. By virtue of this pragmatic blockage-aware technique, we further propose an outage-constrained beamforming design for RIS-assisted mmWave MIMO transmission to achieve outage probability minimization and achievable rate maximization. Specifically, we propose an accelerated projected gradient descent (PGD) algorithm to solve the computational challenge of high-dimensional RIS phase-shift matrix (PSM) optimization. Particularly, we formulate a new Nesterov momentum acceleration scheme to speed up the convergence rate. Extensive experiments confirm the effectiveness of the proposed blockage-aware approach and the proposed accelerated PGD algorithm outperforms a number of representative baseline algorithms in terms of the achievable rate performance.

The integrated sensing and communication (ISAC), which will power the future sixth generation (6G) of mobile communication, is anticipated to offer smarter and varied services. The functions of communication and sensing can be integrated because they contain some similarities, but conflicts and differences need to be resolved. The terahertz (THz) band, due to its special characteristics, can combine both communication and sensing capabilities. This article delves into the current trends, prospects, and challenges of ISAC utilizing THz radios. We outline the functions and advantages of THz radios for communication and sensing processes in the context of ISAC. Then, a generic system framework of ISAC in the THz band is given, and the integrated evolution of the communication and sensing functions of ISAC is described. Finally, an outlook on ISAC is given with the hope that THz technology will help realize a ubiquitous, smarter, and more reliable ISAC system in the near future.

Web 3.0 or Web3 is the next-generation Internet paradigm, which aims to realize the vision of a decentralized and open Web that provides greater utility to users. Due to the rapid development of technologies, such as augmented reality (AR) and extended reality (XR), the industrial metaverse, which is an immersive and interactive simulation mirroring real-world processes, systems, and objects, is gradually becoming a reality. Simultaneously, the industrial metaverse represents a new paradigm for the next generation of industrial transformation. However, data communications supporting the industrial meta-verse require extremely high reliability and low latency to ensure a high level of quality of service (QoS). Therefore, achieving an immersive and interactive industrial metaverse poses significant challenges to data communications. In this article, we first review the big picture of the industry and high-tech companies of industrial metaverse. Subsequently, we explore the application potential of sixth-generation (6G) communication technology and various enabling technologies, such as ultra-reliable and low-latency communication (URLLC) and reconfigurable intelligent surfaces (RIS) in the industrial metaverse. Then, we propose an RIS-enhanced communication paradigm to solve the challenges of privacy, security, reliability, and latency for industrial meta-verse applications. Finally, we discuss potential communication research directions for the industrial metaverse.

This paper presents a novel deep learning model for achieving millimeter accuracy for indoor localization. The model comprises a multi-head self-attention model and a convolutional neural network (CNN), allowing for robust feature extraction from the captured wireless signals. To further enhance the localization accuracy, we also introduced a data augmentation method to increase the size and diversity of the dataset by creating synthetic variants. The performance of the proposed model is tested on an open dataset containing measured channel state information (CSI) signals from a massive multiple-input multiple-output (MIMO) system. The validation accuracies for all three cases are more than seven times higher than the state of the art. The model has also been further evaluated in the COST INTERACT CA20120 Machine Learning Challenge. The performance of our model is competitive with the measured position and significantly outperforms other teams. The proposed model and the associated approaches contribute to the development of practical millimeter-level indoor localization systems using deep learning architectures.

Cybertwin technology introduces a novel paradigm employing digital twins to model complex physical systems within a cyber environment, thus enhancing communication, collaboration, and decision-making capabilities. By harnessing advanced technologies, such as reconfigurable intelligent surfaces (RISs) and multiaccess edge computing (MEC), seamless interaction between physical and virtual entities is facilitated. In this article, we propose a cybertwin-driven edge computing framework that leverages RIS technology, complemented by an efficient computing offloading strategy to support large-scale Internet of Everything (IoE) applications. Specifically, the proposed strategy focuses on a multicell system where numerous randomly distributed end users have the option to offload delay-sensitive and computing-intensive tasks to edge computing nodes. The offloading channels are enhanced by RISs through passive beamforming, while cybertwin technology directs resource cooperation among multicells and allocates computing and communication resources. Our main objective is to optimize the system’s utility with respect to task completion latency and energy consumption reduction. To achieve this goal, we conduct the joint optimization of task offloading and resource allocation. Furthermore, we develop a joint task offloading and resource allocation (JTORA) algorithm to derive optimal solutions for passive beamforming design, computing offloading decisions, communication resource scheduling, and computing capacity allocation. The simulation results demonstrate the superiority of the proposed algorithm over benchmark schemes in terms of edge computing efficiency. Furthermore, the system utility can be further enhanced by increasing the number of embedded RIS elements.

Because of the equilibrium between mathematical tractability and approximation accuracy maintained by the inverse Gaussian (IG) distributional model, it has been regarded as the most appropriate approximation substitute for the lognormal distributional model for shadowed and atmospheric turbulence induced (ATI) fading in the past decades. In this paper, we conduct an in-depth information-theoretic analysis for the lognormal-to-IG channel model substitution (CMS) technique and study its parametric mapping optimality achieved by minimizing the Kullback-Leibler (K-L) divergence between the two distributional models. In this way, we rigorously prove that the moment matching criterion produces the optimal IG substitute for lognormal reference distributions, which has never been observed in other CMS techniques. In addition, we clarify a myth in the realm of CMS that the IG substitute outperforms the gamma substitute for approximating lognormal reference distributions; instead, the substitution superiority shall depend on the parametric mapping criterion and the scale parameter of the lognormal reference distribution. All analytical insights presented in this paper are validated by simulation results.

The upcoming sixth generation (6G) driven Internetof- Things (IoT) will face the great challenges of extremely low power demand, high transmission reliability and massive connectivities. To meet these requirements, we propose a novel hybrid ambient backscatter communication (ABCom) or relay communication (RCom) assisted non-orthogonal multiple access (NOMA) network, which simultaneously enables traditional relay networks and ABCom-assisted IoT networks. Specifically, we investigate the reliability and the finite signal-to-noise ratio (SNR) diversity-multiplexing trade-off (f-DMT) of the proposed system to characterize the outage performance of the proposed system in the non-asymptotic SNR region. We derive the outage probability (OP) and the finite SNR diversity gain when two sources aim to communicate through either ABCom or RCom. On the basis that the results of Monte Carlo simulation and analysis are in perfect agreement, we discover that in the high SNR regime, the OP for ABCom tends to be a constant, leading to a zero diversity gain and an error floor, while the OP for RCom is monotone decreasing with respect to the SNR. Also, compared with the imperfect successive interference cancellation (ipSIC) mode, the reliability of the system under the ideal condition is significantly improved; Moreover, in the lower multiplexing gain regime, for both ABCom and RCom, the higher finite SNR diversity gain results in better system reliability, which provides good opportunities for ABCom to adapt f-DMT and improve relevant performance metrics by adapting the reflection parameter.

Direct acyclic graph (DAG) based ledgers with multi-chain structures aim to solve the technical bottlenecks associated with classical blockchain technologies in the Internet of Things (IoT). The basic working principle of DAG-based ledgers is to validate new transactions by previous transactions in order to be added to the system. During the tip selection process in the unsteady regime, the state transition probability refers to the probability of a transaction changing from the initial state to an arbitrary state. The state transition probability plays an indispensable role in the performance and security analysis of the IoT relying on DAG-based ledgers. In this paper, we derive the exact expression and an approximate expression of the state transition probability, which both are in closed form. In addition, we propose and analyze three performance metrics, i.e., the expected cumulative weight, the expected number of steps, and the confirmation failure probability, which are derived from the state transition probability and greatly enrich the performance analysis and evaluation of the IoT. Markov chain Monte Carlo (MCMC) simulations are carried out to verify the derived analytical results and provide insight into the IoT using DAG-based ledgers.

Federated learning (FL) is a distributed privacy-preserving paradigm of machine learning that enables efficient and secure model training through the collaboration of multiple clients. However, imperfect channel estimation and resource constraints of edge devices severely hinder the convergence of typical wireless FL, while the trade-off between communications and computation still lacks in-depth exploration. These factors lead to inefficient communications and hinder the full potential of FL from being unleashed. In this regard, we formulate a joint optimization problem of communications and learning in wireless networks subject to dynamic channel variations. For addressing the formulated problem, we propose an integrated adaptive$n$-ary compression and resource management framework (ANC) that is capable of adjusting the selection of edge devices and compression schemes, and allocates the optimal resource blocks and transmit power to each participating device, which effectively improves the energy efficiency and scalability of FL in resource-constrained environments. Furthermore, an upper bound on the expected global convergence rate is derived in this paper to quantify the impacts of transmitted data volume and wireless propagation on the convergence of FL. Simulation results demonstrate that the proposed adaptive framework achieves much faster convergence while maintaining considerably low communication overhead.

Millimeter wave (mmWave) communications are sensitive to blockage over radio propagation paths. The emerging paradigm of reconfigurable intelligent surface (RIS) has the potential to overcome this issue by its ability to arbitrarily reflect the incident signals toward desired directions. This paper proposes a Neyman-Pearson (NP) criterion-based blockage-aware algorithm to improve communication resilience against blockage in mobile mmWave multiple input multiple output (MIMO) systems. By virtue of this pragmatic blockage-aware technique, we further propose an outage-constrained beamforming design for downlink mmWave MIMO transmission to achieve outage probability minimization and achievable rate maximization. To minimize the outage probability, a robust RIS beamformer with variant beamwidth is designed to combat uncertain channel state information (CSI). For the rate maximization problem, an accelerated projected gradient descent (PGD) algorithm is developed to solve the computational challenge of high-dimensional RIS phase-shift matrix (PSM) optimization. Particularly, we leverage a subspace constraint to reduce the scope of the projection operation and formulate a new Nesterov momentum acceleration scheme to speed up the convergence process of PGD. Extensive experiments confirm the effectiveness of the proposed blockage-aware approach, and the proposed accelerated PGD algorithm outperforms a number of representative baseline algorithms in terms of the achievable rate.

The earliest direct observation of gravitational waves was by LIGO in 2016, which is critical and leads to the confirmation of the preliminary speculations and theories related to gravitational waves. Ever since this detection and even before, physicists and engineers have advocated numerous methods to sense and generate gravitational waves. Moreover, such a discovery proposes the formation of an innovative means of information propagation owing to some similar characteristics of gravitational and electromagnetic waves. Therefore, gravitational wave communications offer a potential solution to the rise of congestion in the electromagnetic spectrum by expanding the capacity of communication systems. This survey paper explores the history of gravitational wave generation and reception from existing research such as papers, patents, and other publications following the postulation of general relativity. An overview of the documentation is presented, and various applications of gravitational wave communications are appraised. The practicality of such a futuristic communication system is analyzed by comparing the key features of both gravitational and electromagnetic waves and discussing their pros and cons.

Secrecy performance is one of the most crucial and interesting aspects of physical layer security (PLS) that has been analyzed in the past decades. Reconfigurable intelligent surface (RIS) is a cost-effective component to enhance the secrecy performance of wireless communication systems. In this letter, we assume that an RIS is located between the legitimate source and the destination, existing an eavesdropper overhearing the radios transmitted from the source and the RIS. Based on the assumption, we derive the probability density function (PDF) and cumulative distribution function (CDF) of the received signal-to-noise ratio (SNR). In addition, we deduce a converged approximation of the secrecy outage probability (SOP). Finally, simulation results verify our performance analysis. These results also demonstrate the potential of introducing RIS for improving the secrecy performance of wireless communication systems.

The channel estimation techniques for intelligent reflecting surface (IRS)-assisted communication are investigated in this paper. IRS-aided communications rely on the accurate and efficient channel estimation of IRS component-related channels. But conventional channel estimation methods are inefficient due to the sequential estimations of pilot signals. This paper proposes a hybrid coding method, which changes the amplitude and phase of the signal over time and generates two pilot signals at the required frequencies to estimate the channel using interpolation. By using this method, a new send-and-reflect estimation scheme is proposed for separated channel estimation. The method is evaluated based on discrete and continuous coding, respectively. Numerical results show that the proposed method significantly reduces the channel estimation time with a trivial penalty on transmitting accuracy. Besides, the feasibility of discrete hybrid coding is also validated through simulations.

In this paper, we consider a multi-carrier railway communication scenario, where each train wagon is equipped with a single cooperative relay for quality of service (QoS) enhancement. For a classic Long-Term Evolution for Railways (LTE-R) resource allocation problem in the context of railway communications, we propose a low-complexity resource allocation scheme by the dimension reduction of cost matrix, aiming to maximize the number of clients properly served in the cabin by a minimum amount of energy consumption. The proposed scheme can produce near-optimal solutions compared to the optima returned by the extensive search over the entire solution space. As a key advantage of the proposed scheme, the computational complexity is greatly reduced, leading to a much faster LTE-R resource allocation process than that of the optimal benchmark. Such an advantage well satisfies the real-time service demands of users traveling through high-speed railway (HSR) when the network topology and wireless environment could drastically vary over a short period of time (100-500 ms). We carry out extensive simulations to verify the effectiveness and efficiency of the proposed LTE resource allocation scheme for relay-assisted railway communications.

Ambient backscatter communication (AmBC) and non-orthogonal multiple access (NOMA) are two promising technologies for the future wireless communication networks owing to their high energy and spectral efficiencies. The AmBC-aided NOMA symbiotic radio is a promising technology because of possessing advantages of AmBC and NOMA. Nonetheless, when a number of devices with limited power and computation capability access to the AmBC-based NOMA symbiotic networks, communication security becomes a critical issue. In this paper, we investigate physical-layer authentication (PLA) to identify the users and prevent illegal access and malicious activities for AmBC-based NOMA symbiotic networks. Moreover, channel estimation errors are considered when calculating the probability of false alarm (PFA) and probability of detection (PD) of the far user and near user. To enhance the authentication performance, three PLA schemes for the considered networks are designed according to the multiplexing form of the authentication tags: i) PLA with shared authentication tag (PLA-SAT); ii) PLA with space division multiplexing authentication tags; iii) PLA with time-division multiplexing authentication tags. To characterize the proposed PLA schemes, we first derive the PFA and the PD of the considered AmBC-based NOMA symbiotic networks. Then, the covertness is studied in terms of outage probability and asymptotic behavior in the high signal-to-noise ratio regime. Extensive analytical and computer simulated results show that: i) The PLA-SAT scheme has better performance than the other two authentication schemes with the same threshold; ii) The outage performance of systems employing authentication schemes is worse than those without authentication; iii) There exists a trade-off between robustness and covertness.

Communication and sensing are two important features of connected and autonomous vehicles (CAVs). In traditional vehicle-mounted devices, communication and sensing modules exist but in an isolated way, resulting in a waste of hardware resources and wireless spectrum. In this paper, to cope with the above inefficiency, we propose a vehicular behavior-aware integrated sensing and communication (VBA-ISAC) beamforming design for the vehicle-mounted transmitter with multiple antennas. In this work, beams are steered based on vehicular behaviors to assist driving and meanwhile provide spectral-efficient uplink data services with the help of a roadside unit (RSU). Specifically, we first predict the area of interest (AoI) to be sensed based on the vehicles’ trajectories. Then, we formulate a VBA-ISAC beamforming design problem to sense the AoI while maximizing the spectral efficiency of uplink communications, where a trade-off factor is introduced to balance the communication and sensing performance. A semi-definite relaxation-based beampattern mismatch minimization (SDR-BMM) algorithm is proposed to solve the formulated problem. To reduce the hardware cost and power consumption, we further improve the proposed VBA-ISAC beamforming design by introducing the hybrid analog-digital (HAD) structure. Numerical results verify the effectiveness of VBA-ISAC scheme and show that the proposed beamforming design outperforms the benchmarks in both spectral efficiency and radar beampattern.

Intelligent Transportation Systems (ITS) play an increasingly significant role in our life, where safe and effective vehicular networks supported by sixth-generation (6G) communication technologies are the essence of ITS. Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications need to be studied to implement ITS in a secure, robust, and efficient manner, allowing massive connectivity in vehicular communications networks. Besides, with the rapid growth of different types of autonomous vehicles, it becomes challenging to facilitate the heterogeneous requirements of ITS. To meet the above needs, intelligent reflecting surfaces (IRS) are introduced to vehicular communications and ITS, containing the reflecting elements that can intelligently configure incident signals from and to vehicles. As a novel vehicular communication paradigm at its infancy, it is key to understand the latest research efforts on applying IRS to 6G ITS as well as the fundamental differences with other existing alternatives and the new challenges brought by implementing IRS in 6G ITS. In this paper, we provide a big picture of deep learning enabled IRS for 6G ITS and appraise most of the important literature in this field. By appraising and summarizing the existing literature, we also point out the challenges and worthwhile research directions related to IRS aided 6G ITS.

The upcoming Intelligent Transportation System (ITS) supported by sixth generation (6G) communication technologies is expected to face the great challenges of spectrum scarcity, large-scale connectivity, ultra-low latency, and various security threats. To mitigate these challenges and implement the ITS in practice, we propose an overlay cognitive ambient backscatter communication non-orthogonal multiple access (ABCom-NOMA) network for the ITS. Specifically, we elaborate on the secrecy performance the overlay cognitive ABCom-NOMA based on ITS in the presence of an eavesdropping vehicle by deriving the secrecy outage probability (SOP) between the primary network, overlay secondary network, and the eavesdropping vehicle of the considered networks, respectively. For comparison, the secrecy performance of secondary receiving vehicles is taken into account, and a series of numerical simulations by Monte-Carlo methods are carried out to investigate the secrecy performance. From the numerical results yielded by the simulations, we can conclude: 1) The secrecy performance of the proposed the overlay secondary network is superior to the one of the primary network; 2) The increasing of the power allocation factor yields a positive effect on the secrecy performance of the primary receiving vehicles but a negative effect on that of the secondary receiving vehicles.

Multi-carrier relay selection is of particular interest and challenge due to the spatio-frequency coupling and the dynamics of available spectral and relay resources. Among a number of promising relay selection schemes, combined relay selection stands out as an equilibrium between system complexity and reliability. Recent research progress has witnessed the capability of neural computing as a powerful tool to efficiently realize combined relay selection for a given network topology where the number of relays and their locations are fixed and known. However, for contemporary wireless networks that are highly dynamic, the classic neural computing methods can hardly help out because of the scale drifts of input and output matrices. To enable multi-carrier combined relay selection in stochastic wireless networks (SWNs) where the number of available relays for selection could vary, we propose a recurrent neural network (RNN) based framework and devise several training methods suited for various application scenarios. In addition, we conduct a set of computer experiments to verify the effectiveness and efficiency of the proposed RNN-based framework compared with several baselines. With the obtained experimental results, we also evaluate and discuss the proposed framework's reliability, generalization ability, and the robustness against imperfect channel state information (CSI).

In this paper, we propose a composite multiple-mode orthogonal frequency division multiplexing with index modulation (C-MM-OFDM-IM) scheme to increase the spectral efficiency (SE) of OFDM-IM systems by extending the indexing to the energy and constellation domains. In C-MM-OFDM-IM, the information bits are mapped to not only the subcarrier activation patterns (SAPs) and modulation symbols, but also the energy allocation patterns (EAPs) and constellation activation patterns (CAPs). To cope with the practical situations, we propose a variant IM scheme named C-MM-OFDM-IM-II to build a new mapping rule between information bits and the increased CAPs, capable of further increasing the SE of C-MM-OFDM-IM. Upper-bounded bit error rate (BER) and lower-bounded achievable rate are both derived in closed-form to evaluate the performance of C-MM-OFDM-IM(-II). Moreover, we further propose two enhanced schemes, named generalized C-MM-OFDM-IM(-II) and C-MM-OFDM with in-phase/quadrature IM(-II), where the former jointly considers all SAPs, EAPs, CAPs and modulated symbols, while the latter expands the index implementation to the in-phase and quadrature constellation domains. Simulation results show that C-MM-OFDM-IM(-II) outperforms the conventional OFDM-IM related schemes, especially in the high signal-to-noise ratio (SNR) region, and verify the accuracy of the theoretical analysis for the upper-bounded BER and achievable rate.

Physical layer encryption (PLE) has emerged as a promising technique to secure wireless communications. Different from conventional cryptography implemented at higher layers, PLE exploits the randomness of wireless channels to adjust symbol patterns at the physical layer, by which both data and modulation information can be protected. However, existing PLE schemes face challenges of security and robustness in practical usage. In a slowly varying environment, the constellation variation is negligible, which results in the vulnerability of PLE to the differential attack. Moreover, the decryption error rate of PLE is high when the channel reciprocity is not ideal. To tackle these problems, we exploit data randomness to enhance the dynamics of constellation variations between adjacent frames. Then we utilize analog-based encryption instead of digital-based encryption to dynamically rotate constellation, which reduces quantization loss and improves robustness to channel phase errors. Simulation results verify that the proposed scheme can effectively resist the differential attack and provide approximately a 4.5 dB gain when the bit error ratio (BER) is 0.001.

To expedite research progress on terahertz (THz) communications, we analyze the outage performance of THz communication systems by a compound channel model in this paper. Different from existing models, the compound channel model incorporates the effects of spreading loss, molecular absorption loss, shadowing, and multi-path fading via a composite distribution. By using this model, we maintain an equilibrium of the outage performance analysis between mathematical tractability and the fidelity of realistic THz channels. Specifically, by utilizing the compound channel model, outage performance analysis can get rid of sophisticated case-specific channel modeling relying on field measurement and the ray-tracing assessment. To facilitate the application of the proposed channel model, we also design a maximum likelihood estimation (MLE) based channel parameter estimation approach for the compound channel model. The analytical results of outage performance by using the compound channel model are given in closed form and verified by numerical results.

This article proposes an optimal-distributed control protocol for multivehicle systems with an unknown switching communication graph. The optimal-distributed control problem is formulated to differential graphical games, and the Pareto optimum to multiplayer games is sought based on the viability theory and reinforcement learning techniques. The viability theory characterizes the controllability of a wide range of constrained nonlinear systems; and the viability kernel and the capture basin are the pillars of the viability theory. The capture basin is the set of all initial states, in which there exist control strategies that enable the states to reach the target in finite time while remaining inside a set before reaching the target. In this regard, the feasible learning region is characterized by the reinforcement learner. In addition, the approximation of the capture basin provides the learner with prior knowledge. Unlike the existing works that employ the viability theory to solve control problems with only one agent and differential games with only two players, the viability theory, in this article, is utilized to solve multiagent control problems and multiplayer differential games. The distributed control law is composed of two parts: 1) the approximation of the capture basin and 2) reinforcement learning, which are computed offline and online, respectively. The convergence properties of the parameters' estimation errors in reinforcement learning are proved, and the convergence of the control policy to the Pareto optimum of the differential graphical game is discussed. The guaranteed approximation results of the capture basin are provided and the simulation results of the differential graphical game are provided for multivehicle systems with the proposed distributed control policy.

In this paper, we propose and study an energy-efficient trajectory optimization scheme for unmanned aerial vehicle (UAV) assisted Internet of Things (IoT) networks. In such networks, a single UAV is powered by both solar energy and charging stations (CSs), resulting in sustainable communication services, while avoiding energy outage. In particular, we optimize the trajectory design of UAV by jointly considering the average data rate, the total energy consumption, and the fairness of coverage for the IoT terminals. A dynamic spatial-temporal configuration scheme is operated for terminals working in the discontinuous reception (DRX) mode. The module-free, action-confined on-policy and off-policy reinforcement learning (RL) approaches are proposed and jointly applied to solve the formulated optimization problem in this paper. We evaluate the effectiveness of the proposed strategy by comparing it with other dynamic benchmark algorithms. The extensive simulation results provided in this paper reveal that the proposed scheme outperforms the benchmarks in terms of data transmission, energy efficiency and adaptivity of avoiding battery depletion. By deploying the proposed trajectory scheme, the UAV is able to adapt itself according to the temporal and dynamic conditions of communication networks.

Reconfigurable intelligent surfaces (RISs) appear as one of the most promising paradigms for future wireless communications, because of their high adjustability for diverse communication demands and the additional information-carrying capability by reflecting patterns. This paper investigates the capacity of RIS-assisted multiple-input multiple-output (MIMO) symbiotic communications utilizing multiple reflecting patterns, where each reflecting pattern is non-uniformly activated to carry additional information. To enhance transmission performance, the reflecting patterns, reflecting activation probability, and the transmit covariance matrix are jointly designed. Since the exact expression of the system capacity is intractable, the lower and upper bounds on the capacity are derived and used for optimization in this paper. Based on the lower bound on the capacity, a gradient ascent algorithm is developed to find the optimal reflecting patterns, reflecting activation probability, and the transmit covariance matrix. By taking advantage of the concise-form upper bound on the capacity, closed-form solutions of the reflecting activation probability and transmit covariance matrix can be derived after optimizing the reflecting patterns. The superiority of the proposed design is investigated and verified by computer simulations. Some selected numerical results demonstrate that the proposed design can achieve a higher capacity than the benchmark adopting only one reflecting pattern.

In this paper, we propose a hybrid OFDM-SNM scheme, named joint-mapping OFDM-SNM (JM-OFDM-SNM), to avoid transmitting variable lengths of information bits. In JM-OFDM-SNM, the signal vectors are generated by jointly considering subcarrier activation patterns and constellation symbols. To relieve the high computational complexity of the optimal maximum-likelihood (ML) detection, we design a low-complexity detection method via resorting to the log-likelihood ratio criterion. We also analyze the upper bound on the bit error rate of JM-OFDM-SNM. To further enhance the utilization of frequency resource, we propose a more general scheme, named adaptive JM-OFDM-SNM (AJM-OFDM-SNM), to accommodate the constellation orders for different numbers of activated subcarriers. Simulation results show that AJM-OFDM-SNM achieves better performance than both JM-OFDM-SNM and OFDM-SNM at the same spectral efficiency. The low-complexity detection method of JM-OFDM-SNM achieves very close performance to the optimal ML detection, and the theoretical curves well match the simulation curves in the high signal-to-noise ratio region.

Wireless traffic prediction is essential for cellular networks to realize intelligent network operations, such as load-aware resource management and predictive control. Existing prediction approaches usually adopt centralized training architectures and require the transferring of huge amounts of traffic data, which may raise delay and privacy concerns for certain scenarios. In this work, we propose a novel wireless traffic prediction framework named Dual Attention-Based Federated Learning (FedDA), by which a high-quality prediction model is trained collaboratively by multiple edge clients. To simultaneously capture the various wireless traffic patterns and keep raw data locally, FedDA first groups the clients into different clusters by using a small augmentation dataset. Then, a quasi-global model is trained and shared among clients as prior knowledge, aiming to solve the statistical heterogeneity challenge confronted with federated learning. To construct the global model, a dual attention scheme is further proposed by aggregating the intra-and inter-cluster models, instead of simply averaging the weights of local models. We conduct extensive experiments on two real-world wireless traffic datasets and results show that FedDA outperforms state-of-the-art methods. The average mean squared error performance gains on the two datasets are up to 10% and 30%, respectively.

With the explosive growth of ubiquitous mobile services and the advent of the 5G era, ultra-dense wireless network (UDN) architectures have entered daily production and life. However, the massive access capacity provided by 5G networks and the dense deployment of micro base stations also bring challenges such as high energy consumption, high maintenance costs, and inflexibility. Fiber-based visible light communication (FVLC) has the advantages of large bandwidth and high speed, which provides an efficient connection option for UDN. Thus, in order to make up for the poor flexibility of UDN, we propose a new FVLC-UDN architecture based on software-defined networks (SDNs). Specifically, SDN decouples the data plane and the control plane of the device and centralizes the control of the LED in the cell through a unified control plane, which can not only improve the resource allocation ability of the network but also transmit the data only as the data plane, reducing the manufacturing and implementation costs of the LED. In order to get a better resource allocation scheme, this paper proposes a model for predicting cell traffic based on convolutional neural networks. By predicting the traffic of each cell in the control domain, the traffic trend and cells’ status in the future period of time in the control domain can be obtained, so that a much more efficient resource allocation scheme can be formulated proactively to reduce energy consumption and balance communication loads. The experimental results show that on the real cell traffic dataset, this method is better than the existing prediction methods when the size of training dataset is limited.

Orthogonal frequency division multiplexing with subcarrier number modulation (OFDM-SNM) has been recently proposed to improve the spectral efficiency (SE) of the traditional OFDM system. In this paper, we propose a joint-mapping OFDM-SNM (JM-OFDM-SNM) scheme to transmit the signal vector with a constant length of information bits by jointly considering the subcarrier activation patterns and constellation symbols. A low-complexity detection scheme based on log-likelihood ratio criterion is proposed to relieve the high computational complexity of the maximum-likelihood detection at the cost of a negligible performance loss. Upper-bounded bit error rate (BER) and lower-bounded achievable rate are both derived in closed-form to evaluate the performance of JM-OFDM-SNM. To suit different application scenarios, we further propose two enhanced schemes, named adaptive JM-OFDM-SNM (AJM-OFDM-SNM) and JM-OFDM with in-phase/quadrature SNM (JM-OFDM-IQ-SNM), where the former adjusts the constellation orders for different numbers of active subcarriers, and the latter extends the indexing to in-phase and quadrature domains. Simulation results corroborate the tightness of the derived BER expression in the high signal-to-noise ratio region and show that (A)JM-OFDM-SNM improves the performance of OFDM-SNM, while both AJM-OFDM-SNM and JM-OFDM-IQ-SNM schemes perform better than JM-OFDM-SNM at the same SE.

With the prevalence of orthogonal frequency-division multiplexing (OFDM) in many standards, e.g., IEEE 802.11, IEEE 802.16, DVB-T, and DVB-T2, a number of variant modulation schemes based on OFDM have been proposed, which resort to signal sparsity to further enhance spectral efficiency and mitigate the high peak-to-average ratio (PAPR) problem. Among these variants, OFDM with subcarrier number modulation (OFDM-SNM) has been proven to be efficient for simple communication systems with low constellation modulation orders and limited decoding capability. To rigorously verify the performance advantages of OFDM-SNM, we present the study of OFDM-SNM in this paper from the information-theoretic perspective. In particular, we determine an upper bound on the mutual information of OFDM-SNM in closed form by using the log sum inequality. Also, we analyze the optimal pattern utilization probabilities (PUPs) for OFDM-SNM by channel-dependent coding and propose an easy-to-implement iterative algorithm to approach the optimal PUPs. Moreover, considering the practical achievability, we propose a Huffman coding based achievable PUP vector construction scheme to obtain the achievable PUPs and the corresponding achievable rate. We carry out numerical simulations to verify the effectiveness of this study and illustrate the efficiency of the obtained PUPs in comparison with several benchmarks.

Quadrature spatial modulation (QSM) is recently proposed to increase the spectral efficiency (SE) of SM, which extends the transmitted symbols into in-phase and quadrature domains. In this paper, we propose a generalized QSM (GQSM) scheme to further increase the SE of QSM by activating more than one transmit antenna in in-phase or quadrature domain. A low-complexity detection scheme for GQSM is provided to mitigate the detection burden of the optimal maximum-likelihood (ML) detection method. An upper bounded bit error rate is analyzed to discover the system performance of GQSM. Moreover, by collaborating with the non-orthogonal multiple access (NOMA) technique, we investigate the practical application of GQSM to cooperative vehicular networks and propose the cooperative GQSM with OMA (C-OMA-GQSM) and cooperative GQSM with NOMA (C-NOMA-GQSM) schemes. Computer simulation results verify the reliability of the proposed low-complexity detection as well as the theoretical analysis, and show that GQSM outperforms QSM in the entire SNR region. The superior BER performance of the proposed C-NOMA-GQSM scheme make it a promising modulation candidate for next generation vehicular networks.

Publishing datasets plays an essential role in open data research and promoting transparency of government agencies. However, such data publication might reveal users' private information. One of the most sensitive sources of data is spatiotemporal trajectory datasets. Unfortunately, merely removing unique identifiers cannot preserve the privacy of users. Adversaries may know parts of the trajectories or be able to link the published dataset to other sources for the purpose of user identification. Therefore, it is crucial to apply privacy preserving techniques before the publication of spatiotemporal trajectory datasets. In this paper, we propose a robust framework for the anonymization of spatiotemporal trajectory datasets termed as machine learning based anonymization (MLA). By introducing a new formulation of the problem, we are able to apply machine learning algorithms for clustering the trajectories and propose to use k-means algorithm for this purpose. A variation of k-means algorithm is also proposed to preserve the privacy in overly sensitive datasets. Moreover, we improve the alignment process by considering multiple sequence alignment as part of the MLA. The framework and all the proposed algorithms are applied to T-Drive, Geolife, and Gowalla location datasets. The experimental results indicate a significantly higher utility of datasets by anonymization based on MLA framework.

Recent years have seen rising needs for location-based services in our everyday life. Aside from the many advantages provided by these services, they have caused serious concerns regarding the location privacy of users. Adversaries can monitor the queried locations by users to infer sensitive information, such as home addresses and shopping habits. To address this issue, dummy-based algorithms have been developed to increase the anonymity of users, and thus, protecting their privacy. Unfortunately, the existing algorithms only assume a limited amount of side information known by adversaries, which may face more severe challenges in practice. In this paper, we develop an attack model termed as Viterbi attack, which represents a realistic privacy threat on user trajectories. Moreover, we propose a metric called transition entropy that enables the evaluation of dummy-based algorithms, followed by developing a robust algorithm that can defend users against the Viterbi attack while maintaining significantly high performance in terms of the traditional metrics. We compare and evaluate our proposed algorithm and metric on a publicly available dataset published by Microsoft, i.e., Geolife dataset.

Multiple-input multiple-output orthogonal frequency division multiplexing with index modulation (MIMO-OFDM-IM) has recently received increased attention, due to the potential advantage to balance the trade-off between spectral efficiency (SE) and energy efficiency (EE). In this paper, we investigate the application of MIMO-OFDM-IM to millimeter wave (mmWave) communication systems, where a hybrid analogy-digital (HAD) beamforming architecture is employed. Taking advantage of the Pareto-optimal beam design, we propose a feasible solution to approximately achieve a globally Pareto-optimal trade-off between SE and EE, and the collision constraints of the multi-objective optimization problem (MOP) can be solved efficiently. Correspondingly, the MOP of SE-EE trade-off can be converted into a feasible solution for energy-efficient resource usage, by finding the Pareto-optimal set (POS) towards the Pareto front. This combinatorial-oriented resource allocation approach on the SE-EE relation considers the optimal beam design and power control strategies for downlink multi-user mmWave transmission. To ease the system performance evaluation, we adopt the Poisson point process (PPP) to model the mobile data traffic, and the evolutionary algorithm is applied to speed up the search efficiency of the Pareto front. Compared with benchmarks, the experimental results collected from extensive simulations demonstrate that the proposed optimization approach is vastly superior to existing algorithms.

Multiple-input multiple-output orthogonal frequency division multiplexing with index modulation (MIMO-OFDM-IM) has the potential advantage to balance the trade-off between spectral efficiency (SE) and energy efficiency (EE). This paper investigates the application of MIMO-OFDM-IM to millimeter wave (mmWave) communication systems. Taking advantage of the properties of Pareto optimality, we propose a feasible solution to achieve a globally Pareto-optimal trade-off between SE and EE, and the collision constraints of multi-objective optimization problem (MOP) can be solved efficiently. The MOP of SE-EE trade-off can then be converted into a Pareto-optimal set (POS) solution problem. This combinatorial-oriented resource allocation approach on SE-EE relation considers the optimal beam design and power reallocation for downlink multi-user mmWave transmission. We adopt the Poisson point process (PPP) to model the mobile data traffic, and the evolutionary algorithm is applied to speed up the search efficiency of the Pareto front. Compared with benchmarks, the experimental results collected from extensive simulations reveal that the proposed optimization approach is vastly superior to existing algorithms.

The controversy and argument on the usefulness of the physical layer (PHY) academic research for wireless communications are long-standing since the cellular communication paradigm gets to its maturity. In particular, researchers suspect that the performance improvement in cellular communications is primarily attributable to the increases in telecommunication infrastructure and radio spectrum instead of the PHY academic research, whereas concrete evidence is lacking. To respond to this controversy from an objective perspective, we employ econometric approaches to quantify the contributions of the PHY academic research and other performance determinants. Through empirical analysis and the quantitative evidence obtained, albeit preliminary, we shed light on the following issues: 1) what determines the cross-national differences in cellular network performance; 2) to what extent the PHY academic research and other factors affect cellular network performance; 3) what suggestions we can obtain from the data analysis for the stakeholders of the PHY research.

This paper proposes a Bayesian approach for angle-based hybrid beamforming and tracking that is robust to uncertain or erroneous direction-of-arrival (DOA) estimation in millimeter wave (mmWave) multiple input multiple output (MIMO) systems. Because the resolution of the phase shifters is finite and typically adjustable through a digital control, the DOA can be modeled as a discrete random variable with a prior distribution defined over a discrete set of candidate DOAs, and the variance of this distribution can be introduced to describe the level of uncertainty. The estimation problem of DOA is thereby formulated as a weighted sum of previously observed DOA values, where the weights are chosen according to a posteriori probability density function (pdf) of the DOA. To alleviate the computational complexity and cost, we present a motion trajectory-constrained a priori probability approximation method. It suggests that within a specific spatial region, a directional estimate can be close to true DOA with a high probability and sufficient to ensure trustworthiness. We show that the proposed approach has the advantage of robustness to uncertain DOA, and the beam tracking problem can be solved by incorporating the Bayesian approach with an expectation-maximization (EM) algorithm. Simulation results validate the theoretical analysis and demonstrate that the proposed solution outperforms a number of state-of-the-art benchmarks.

In this paper, we propose an enhanced Huffman coded orthogonal frequency-division multiplexing with index modulation (EHC-OFDM-IM) scheme. The proposed scheme is capable of utilizing all legitimate subcarrier activation patterns (SAPs) and adapting the bijective mapping relation between SAPs and leaves on a given Huffman tree according to channel state information (CSI). As a result, a dynamic codebook update mechanism is obtained, which can provide more reliable transmissions. We take the average block error rate (BLER) as the performance evaluation metric and approximate it in closed form when the transmit power allocated to each subcarrier is independent of channel states. Also, we propose two CSI-based power allocation schemes with different requirements for computational complexity to further improve the error performance. Subsequently, we carry out numerical simulations to corroborate the error performance analysis and the proposed dynamic power allocation schemes. By studying the numerical results, we find that the depth of the Huffman tree has a significant impact on the error performance when the SAP-to-leaf mapping relation is optimized based on CSI. Meanwhile, through numerical results, we also discuss the trade-off between error performance and data transmission rate and investigate the impacts of imperfect CSI on the error performance of EHC-OFDM-IM.

This paper investigates adaptive signal shaping methods for millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications based on the maximizing the minimum Euclidean distance (MMED) criterion. In this work, we utilize the indices of analog precoders to carry information and optimize the symbol vector sets used for each analog precoder activation state. Specifically, we firstly propose a joint optimization based signal shaping (JOSS) approach, in which the symbol vector sets used for all analog precoder activation states are jointly optimized by solving a series of quadratically constrained quadratic programming (QCQP) problems. JOSS exhibits good performance, however, with a high computational complexity. To reduce the computational complexity, we then propose a full precoding based signal shaping (FPSS) method and a diagonal precoding based signal shaping (DPSS) method, where the full or diagonal digital precoders for all analog precoder activation states are optimized by solving two small-scale QCQP problems. Simulation results show that the proposed signal shaping methods can provide considerable performance gain in reliability in comparison with existing mmWave transmission solutions.

In this paper, a novel opportunistic spectrum sharing scheme, based on orthogonal frequency division multiplexing with index modulation (OFDM-IM), is proposed for cognitive radio (CR) networks. In the considered OFDM-IM based CR (OFDM-IM-CR) model, the primary transmitter (PT) communicates with the primary receiver with the aid of an amplify-and-forward (AF) relay by transmitting OFDM-IM signals. Meanwhile, the secondary transmitter (ST) passively senses the spectrum and transmits its own information over those inactive subcarriers of the primary network to the secondary receiver if the signal-to-noise ratio of the PT$\to $ST link is above a predefined threshold; otherwise, the ST stays in silent mode. Two different types of maximum-likelihood (ML) detectors are designed for the primary network, based on the knowledge of either the estimated channel state information or the statistical channel information of the secondary network. A complexity-reducing method, which is applicable to both types and achieves near optimal performance, is further proposed. To evaluate the performance, a tight upper bound on the bit error rate (BER) is derived, assuming the first type of ML detection. Simulation results corroborate the analysis and show that OFDM-IM-CR has the potential of outperforming OFDM-CR and OFDM-IM-AF in terms of BER with higher spectral efficiency.

By incorporating the merits of satellite, aerial, and terrestrial communications, the space-air-ground integrated network (SAGIN) emerges in recent years as a promising solution to support seamless, high-rate, and reliable transmission with an extremely larger coverage than a classic terrestrial network. In essence, SAGIN is a cooperative relay network, in which high-altitude platforms (HAPs) and terrestrial base stations (BSs) serve as intermediates relaying signals between end device and satellite. In this article, we thereby view the SAGIN from the perspective of cooperative communications and introduce relay networking technologies to model and construct the framework of SAGIN. Meanwhile, we take the realistic propagation environment, HAP mobility and mathematical tractability into account and reconstruct the cooperative channel models for SAGIN, including the space-air, space-ground and air-ground links. Based on the constructed framework of SAGIN, we analyze the outage performance and approximate the outage probability as well as asymptotic outage probability in closed form. Numerical results generated by computer simulations verify our analysis and provide insight into the applicability of SAGIN. Although the relaying scenarios considered in this work are simplistic, the good tractability and expandability of the constructed framework provide a solid foundation for further research of advanced systems with complex configurations.

A Bayesian approach for joint beamforming and tracking is presented, which is robust to uncertain direction-of-arrival (DOA) estimation in millimeter wave (mmWave) multiple input multiple output (MIMO) systems. The uncertain or completely unknown DOA is modeled as a discrete random variable with a priori distribution defined over a set of candidate DOAs, which describes the level of uncertainty. The estimation problem of DOA is formulated as a weighted sum of previously observed DOA values, where the weights are chosen according to a posteriori probability density function (pdf) of the DOA. In particular, we present a motion trajectory-based a priori probability approximation method, which implies a high probability to perform a directional estimate within a specific spatial region. We demonstrate that the proposed approach is robust to DOA uncertainty, and the beam tracking problem can be addressed by incorporating the Bayesian approach with an expectation-maximization (EM) algorithm. Simulation results validate the theoretical analysis and demonstrate the effectiveness of the proposed solution.

A novel modulation scheme termed orthogonal frequency-division multiplexing with subcarrier number modulation (OFDM-SNM) has been proposed and regarded as one of the promising candidate modulation schemes for next generation networks. Although OFDM-SNM is capable of having a higher spectral efficiency (SE) than OFDM with index modulation (OFDM-IM) and plain OFDM under certain conditions, its reliability is relatively inferior to these existing schemes, because the number of active subcarriers varies. In this regard, we propose an enhanced OFDM-SNM scheme in this paper, which utilizes the flexibility of placing subcarriers to harvest a coding gain in the high signal-to-noise ratio (SNR) region. In particular, we stipulate a methodology that optimizes the subcarrier activation pattern (SAP) by subcarrier assignment using instantaneous channel state information (CSI) and therefore the subcarriers with higher channel power gains will be granted the priority to be activated, given the number of subcarriers is fixed. We also analyze the proposed enhanced OFDM-SNM system in terms of outage and error performance. The average outage probability and block error rate (BLER) are derived and approximated in closed-form expressions, which are further verified by numerical results generated by Monte Carlo simulations. The high-reliability nature of the enhanced OFDM-SNM makes it a promising candidate for implementing in the Internet of Things (IoT) with stationary machine-type devices (MTDs), which are subject to slow fading and supported by proper power supply.

This paper proposes a Markov decision process (MDP)-based pilot placement optimization approach for the radio access in 5G vehicle to everything communications to support Internet of Vehicles applications. The optimal placement problem of pilot symbols is based on a typical pilot-assisted frequency-division multiplexing transmission and simplified to a finite state-space representation. We propose and formulate a finite MDP so as to determine an appropriate pilot pattern from a set of candidate pilot configurations. Also, an enhanced pilot placement scheme is developed to reduce the complexity for solving the formulated MDP problems. Furthermore, we derive analytical expressions of the mutual information, which to some extent allow us to jointly evaluate the dynamics of the channel state in time and frequency domains. Numerical results generated by Monte Carlo simulations show that the proposed pilot optimization policy is capable of improving the channel estimation in fast time-varying vehicular channels, and the mutual information-based measurement criteria can yield more accurate evaluations in fast time-varying vehicular channels than other conventional schemes.

Orthogonal frequency-division multiplexing with index modulation (OFDM-IM) has become a high-profile modulation scheme for the fifth-generation (5G) wireless communications and has thus been extended to multi-hop scenarios in order to improve the network coverage and energy efficiency. However, the extension of OFDM-IM to multi-relay cooperative networks is not trivial, since it is required that a complete OFDM block should be received and decoded as an entity in one node. This requirement prevents the employment of multiple relays to forward fragmented OFDM blocks on individual subcarriers. In this regard, we propose a distributed processing scheme for multi-relay assisted OFDM-IM, by which multiple relays are selected to forward signals in a per-subcarrier manner to provide optimal error performance for two-hop decode-and-forward (DF) OFDM-IM systems. Specifically, a single selected relay only needs to decode partial information carried on certain active subcarriers and forward just as for traditional OFDM systems without IM. After receiving all signals on active subcarriers forwarded by different relays, the destination can reconstruct the complete OFDM block and retrieve the full information. We analyze the average block error rate and modulation capacity of the two-hop OFDM-IM system employing the proposed distributed DF protocol and verify the analysis by numerical simulations.

Orthogonal frequency-division multiplexing with index modulation (OFDM-IM) has recently attracted many researchers' attention due to its superior spectrum efficiency and reliability compared to the traditional OFDM. Cooperative decode-and-forward relaying has been incorporated with OFDM-IM, which provides a higher energy efficiency and better network coverage. However, it might not be feasible in realistic applications, owing to the high system complexity and transmission delay rendered by complex decoding and channel estimation procedures. Therefore, in this paper, we propose a fixed-gain (FG) amplify-and-forward (AF) relay-assisted OFDM-IM system, which does not need to perform complex decoding and channel estimation at the relay, but only requires a pre-processing capability at the relay, e.g., cyclic prefix removal and re-insertion. Therefore, the system complexity can be reduced and the forwarding delay as well as power consumption caused by processing at the relay also decline. We analyze the average outage probability, block error rate, and achievable rate of the proposed system and verify all analysis by numerical results. The proposed FG AF relay-assisted OFDM-IM provides a simple solution to the implementation of OFDM-IM in new network paradigms, where nodes are simple, power limited, and/or complexity limited.

This paper investigates the generic signal shaping methods for the multiple-data-stream generalized spatial modulation (GenSM) and the generalized quadrature spatial modulation (GenQSM). Three cases with different channel state information at the transmitter (CSIT) are considered, including no CSIT, statistical CSIT, and perfect CSIT. A unified optimization problem is formulated to find the optimal transmit vector set under size, power, and sparsity constraints. We propose an optimization-based signal shaping (OBSS) approach by solving the formulated problem directly and a codebook-based signal shaping (CBSS) approach by finding the sub-optimal solutions in discrete space. In the OBSS approach, we reformulate the original problem to optimize the signal constellations used for each transmit antenna combination (TAC). Both the size and the entry of all signal constellations are optimized. Specifically, we suggest the use of a recursive design for the size optimization. The entry optimization is formulated as a non-convex large-scale quadratically constrained quadratic programming (QCQP) problem and can be solved by the existing optimization techniques with rather high complexity. To reduce the complexity, we propose the CBSS approach using a codebook generated by the quadrature amplitude modulation (QAM) symbols and a low-complexity selection algorithm to choose the optimal transmit vector set. The simulation results show that the OBSS approach exhibits the optimal performance in comparison with existing benchmarks. However, the OBSS approach is impractical for large-size signal shaping and adaptive signal shaping with instantaneous CSIT due to the demand of high computational complexity. As a low-complexity approach, the CBSS shows comparable performance and can be easily implemented in large-size systems.

This paper proposes an Markov decision process (MDP) based pilot placement optimization approach for the radio access in 5G vehicle to everything (V2X) communications. The optimal placement problem of pilot symbols is based on a typical pilot-assisted OFDM transmission and simplified to a finite state-space representation. We propose and formulate a finite MDP so as to determine an appropriate pilot pattern from a set of candidate pilot configurations. Additionally, an enhanced pilot placement scheme is developed to reduce the complexity of solving MDP problems. We derive analytical expressions of the mutual information, which to some extent allow us to jointly evaluate the dynamics of the channel state in time and frequency domain. Numerical results show that the proposed pilot optimization policy is capable of improving the channel estimation, and the mutual information based measurement criteria can yield more accurate evaluations in fast time-varying vehicular channels.

In this paper, we propose an adaptive orthogonal frequency-division multiplexing with index modulation (OFDM-IM) for two-hop relay networks. In contrast to the traditional OFDM-IM with a deterministic and fixed mapping scheme, in this proposed adaptive OFDM-IM, the mapping schemes between a bit stream and indices of active subcarriers for the first and second hops are adaptively selected by a certain criterion. As a result, the active subcarriers for the same bit stream in the first and second hops can be varied in order to combat slow frequency-selective fading. In this way, the system reliability can be enhanced. In addition, considering the fact that a relay device is normally a simple node, which may not always be able to perform mapping scheme selection due to limited processing capability, we also propose an alternative adaptive methodology in which the mapping scheme selection is only performed at the source and the relay will simply utilize the selected mapping scheme without changing it. The analyses of average outage probability, network capacity, and symbol error rate are given in closed form for decode-and-forward relaying networks and are substantiated by numerical results generated by Monte Carlo simulations.

In this paper, we propose a novel codebook design scheme for orthogonal frequency-division multiplexing with index modulation (OFDM-IM) to improve system performance. The optimization process can be implemented efficiently by the lexicographic ordering principle. By applying the proposed codebook design, all subcarrier activation patterns with a fixed number of active subcarriers will be explored. Furthermore, as the number of active subcarriers is fixed, the computational complexity for estimation at the receiver is reduced and the zero-active subcarrier dilemma is solved without involving complex higher layer transmission protocols. It is found that the codebook design can potentially provide a tradeoff between diversity and transmission rate. We investigate the diversity mechanism and formulate three diversity-rate optimization problems for the proposed OFDM-IM system. Based on the genetic algorithm, the method of solving these formulated optimization problems is provided and verified to be effective. Then, we analyze the average block error rate and bit error rate of the OFDM-IM systems applying the codebook design. Finally, all analyses are numerically verified by the Monte Carlo simulations. In addition, a series of comparisons are provided, by which the superiority of the codebook design is confirmed.

In this paper, we analyze the outage performance of two multicarrier relay selection schemes, i.e. bulk and per-subcarrier selections, for two-hop orthogonal frequency-division multiplexing (OFDM) systems. To provide a comprehensive analysis, three forwarding protocols: decode-and-forward (DF), fixed-gain (FG) amplify-and-forward (AF) and variable-gain (VG) AF relay systems are considered. We obtain closed-form approximations for the outage probability and closed-form expressions for the asymptotic outage probability in the high signal-to-noise ratio (SNR) region for all cases. Our analysis is verified by Monte Carlo simulations, and provides an analytical framework for multicarrier systems with relay selection.

In this paper, we apply the concept of combined bulk/per-subcarrier selection to two-hop relay selection systems employing OFDM. The outage probability of the proposed strategy is analyzed in the high SNR regime when decode-and-forward, fixed-gain amplify-and-forward and variable-gain amplify-and-forward are employed at the relays. Meanwhile, a generalized situation without specifying the relaying protocol is also analyzed. We mathematically prove that the combined selection strategy is able to achieve an optimal outage probability equivalent to conventional per- subcarrier selection in the high SNR regime without using the full set of available relays for selection. Moreover, we demonstrate through numerical simulations that this performance advantage holds when channels are spatially correlated.

The intra-tier interference in heterogeneous networks becomes more serious when the number of macro-femto cells dramatically increases. Based on the singular value decomposition (SVD) algorithm, this paper proposes an optimized interference alignment (IA) algorithm to improve the macro-femto cell downlink rate. To achieve the largest degrees of freedom (DOF), we adopt the precoding matrix as a powerful tool to mitigate the effect of inter-channel interference (ICI). In comparison with the conventional interference coordination methods, the SVD algorithm is capable of adjusting the coefficients of the precoding matrix with lower complexity. Furthermore, zero-forcing algorithm is also used to combat ICI in this proposed IA algorithm. The numerical results show that ICI can be suppressed effectively and the system performance in terms of throughput and signal-to-interference- plus-noise ratio (SINR) can be improved considerably.