Wireless sensing has become a fundamental enabler for intelligent environments, supporting applications such as human detection, activity recognition, localization, and vital sign monitoring. Despite rapid advances, existing datasets and pipelines remain fragmented across sensing modalities, hindering fair comparison, transfer, and reproducibility. We propose the Sensing Dataset Protocol (SDP), a protocol-level specification and benchmark framework for large-scale wireless sensing. SDP defines how heterogeneous wireless signals are mapped into a unified perception data-block schema through lightweight synchronization, frequency-time alignment, and resampling, while a Canonical Polyadic-Alternating Least Squares (CP-ALS) pooling stage provides a task-agnostic representation that preserves multipath, spectral, and temporal structures. Built upon this protocol, a unified benchmark is established for detection, recognition, and vital-sign estimation with consistent preprocessing, training, and evaluation. Experiments under the cross-user split demonstrate that SDP significantly reduces variance (approximately 88%) across seeds while maintaining competitive accuracy and latency, confirming its value as a reproducible foundation for multi-modal and multitask sensing research.

With the rapid growth of the low-altitude economy, emerging applications such as urban air mobility and smart logistics demand reliable and low-latency beamforming for unmanned aerial vehicle-to-vehicle (UAV-to-UAV, U2U) communications in millimeter-wave (mmWave) bands under highly dynamic and non-line-of-sight (NLOS) conditions. Traditional beam alignment methods relying on exhaustive search or channel feedback incur heavy training overhead and degraded accuracy in rapidly varying environments. To address these challenges, we propose multi-modal perception-assisted fusion network (MPFusionNet), a multi-modal perception-enhanced Transformer framework for predictive beamforming. Our approach leverages heterogeneous onboard sensing data including global positioning system (GPS), red-green-blue (RGB) cameras, LiDAR, and radar altimeters, incorporates a dynamic time warping (DTW)-based alignment mechanism, and embeds geometry-aware priors within a perceiver input-output (PerceiverIO)-based fusion architecture to achieve robust spatiotemporal representation. Experiments on a simulated U2U dataset show that MPFusionNet attains a top-3 beam prediction accuracy of 97.59%, substantially surpassing conventional models. These results demonstrate the effectiveness of multi-modal learning in improving robustness and generalization of predictive beamforming for future autonomous aerial communication systems.

The affine frequency division multiplexing (AFDM) waveform, with its superior capability in separating delay and Doppler shifts, emerges as a promising solution to achieve reliable high-mobility satellite-air integrated communications. However, the secure AFDM waveform design remains a critical challenge when deployed in satellite-air integrated communications. To tackle this issue, in this article, we first analyze the roles of the key parameters in the AFDM waveform, revealing the admissible ranges of these parameters. Afterwards, we propose a time-varying parameter-hopping (PH) AFDM scheme, where the parameters are dynamically adjusted during transmission, which enhances the flexibility of the AFDM waveform. In addition, the scheme can support both basic encryption strategy and advanced encryption strategy for different security requirements in high-mobility satellite-air integrated communications. Specifically, the basic strategy employs a single dynamically hopping parameter, while the advanced encryption strategy, trading the spectral efficiency for a larger parameter space, employs two dynamically hopping parameters. Besides, the anti-eavesdropping performance, quantified by waveform entropy, can be significantly improved through the proposed PH mechanism. Numerical simulations demonstrate the validity of the analysis and the effectiveness of the proposed scheme.

Long-range (LoRa) technology has emerged as a promising solution for Internet of Things applications due to its low power consumption and long communication range. However, its pure ALOHA-based MAC layer leads to severe packet collisions as the network scale expands, significantly degrading the system energy efficiency (EE). While careful allocation of transmission parameters such as channel (CH), transmission power (TP), and spreading factor (SF) could mitigate this issue, the complex interference patterns in multi-gateway scenarios and the time-consuming nature of EE evaluation pose significant challenges. Therefore, we propose an analytical model to calculate the system EE while fully considering the impacts of multiple gateways, duty cycling, quasi-orthogonal SFs and capture effects. Based on this model, we formulate a joint CH, SF, and TP allocation problem to optimize the system EE. To solve this NP-hard optimization problem, we decompose it into CH assignment and SF/TP assignment subproblems. A two-phase optimization framework is then designed. In the first phase, a matching-based algorithm is designed for CH assignment. In the second phase, a multi-agent reinforcement learning approach that incorporates a two-stage attention mechanism and graph convolutional networks is proposed for SF/TP assignment, which effectively captures and weights inter-ED interactions in multi-gateway scenarios. Simulation results indicate that the proposed approach well-suited for complex multi-GW LoRa network topologies and outperforms state-of-the-art algorithms.

Symbiotic radio (SR) has emerged as a spectrum and energy-efficient paradigm to support massive Internet of Things connections. This paper investigates secure transmission in a multi-antenna artificial noise (AN)-assisted SR network under both parasitic SR (PSR) and commensal SR (CSR) setups, with a particular focus on the challenges posed by the presence of a passive eavesdropper. Specifically, the transmitter allocates part of its power for AN generation to disrupt the eavesdropper deliberately without affecting the legitimate receiver. To evaluate the secrecy performance in both setups, new approximate closed-form expressions for the secrecy outage probability in primary and backscatter links are derived using the Gauss-Chebyshev quadrature method. The secrecy diversity orders of the system are studied by analyzing the asymptotic behaviours in the high signal-to-noise ratio regime. Furthermore, the secrecy performance under imperfect channel state information is investigated to evaluate the robustness of the proposed scheme in practical scenarios. Monte Carlo simulations are performed to validate the correctness and effectiveness of the analytical results, which demonstrate that the CSR setup provides stronger secrecy performance than the PSR setup in primary signal decoding.

Affine frequency division multiplexing (AFDM) and orthogonal time frequency space (OTFS) are two promising advanced waveforms proposed for reliable communications in high-mobility scenarios. In this paper, we introduce a simple transmit diversity technique, termed cyclic delay-Doppler shift (CDDS), for these two advanced waveforms to achieve ultra-reliable communications in doubly selective channels (DSCs). Two simple CDDS schemes, named modulation-domain CDDS (MD-CDDS) and time-domain CDDS (TD-CDDS), are proposed, which perform CDDS in advance at the transmitter before and after the modulation, respectively. We demonstrate that both of the two proposed CDDS schemes can be implemented efficiently and flexibly by multiplying the transmit vector with a well-designed precoding matrix, which is nothing but a sparse phase-compensated permutation matrix. Moreover, we theoretically and numerically prove that CDDS can provide MIMO-AFDM and MIMO-OTFS with optimal transmit diversity gain when a proper CDDS step is adopted. Compared to the conventional transmit diversity techniques, the proposed CDDS scheme enjoys the advantages of lower channel estimation overhead, implementation complexity, and signal processing latency, making it particularly suitable for ultra-reliable communications in high-mobility scenarios.

By harnessing compressed-sensing principles, sparse vector coding (SVC) compresses and delivers data with ultra-low latency and near-instantaneous reliability, making it a vital enabler of next-generation ultra-reliable and low-latency communications (xURLLC) services in sixth generation (6G) wireless communication systems. A fundamental challenge in SVC systems is developing a generalized sparse mapping mechanism that does not rely on either an index table or constellation labels. To meet such a requirement, this work proposes a base conversion index modulation (IM) that uses a novel mapping strategy for sparse vector construction. The proposed design achieves higher resource efficiency, requiring fewer positional resources for bit representation than conventional combination-based IM. Building on this foundation, a generalized SVC (GSVC) scheme is developed to enable fully pipelined bit-stream mapping and demapping. A further extension, termed enhanced GSVC (EGSVC), adopts a pairwise-grouped constellation assignment to improve the transmission performance of GSVC. Simulation results confirm that GSVC achieves block error rate (BLER) performance comparable to conventional SVC in single-block coding modes, yet significantly improves BLER under high-coding-rate multi-block coding modes. By balancing the constellation label count and the sparse vector length, EGSVC delivers superior BLER compared to conventional SVC schemes while maintaining lower latency.

Rate-splitting multiple access (RSMA) has been studied for multiuser multiple-input multiple-output (MU-MIMO) systems especially in the presence of imperfect channel state information (CSI) at the transmitter. However, its precoding designs that maximize the sum rate normally have high computational complexity. To implement an efficient RSMA scheme for the MU-MIMO system, in this work, we propose a novel robust precoding design, which can handle imperfect CSI. Specifically, we first adopt the generalized mutual information to construct a lower bound of the objective function in the sum rate maximization problem. Then, we apply a smooth lower bound of the non-smooth sum rate objective function to construct a new optimization problem. By revealing the relationship between the generalized signal-to-interference-plus-noise ratio and the minimum mean square error matrices, we transform the constructed problem into a tractable one. After decomposing the transformed problem into three subproblems, we investigate a new alternating precoding design based on sequential solutions. Simulation results demonstrate that the proposed precoding scheme achieves comparable performance to conventional methods, while significantly reducing the computational complexity.

In this paper, we explore the utilization of double-irregular intelligent reflecting surfaces (IRS) for secure wireless transmissions, where "irregular" refers to the non-uniform arrangement of IRS elements on an expanded grid surface. The main objective is to maximize the secrecy rate by optimizing the arrangement of IRS micro-units and their phase shift matrices, subject to the transmission power constraint. Unlike previous research, this paper introduces the deployment of two IRSs with irregular distributions in a secure communication system, taking into account their reflective interaction. Additionally, the paper addresses both discrete and continuous phase considerations for IRS elements. Toward this end, we first propose a tabu search algorithm to simultaneously optimize the distribution matrices of both IRSs. Specifically, it employs alternating optimization techniques: for discrete phase adjustments, a cross-entropy method is applied, while for continuous phase shifts, a simulated annealing algorithm is used. Numerical results show that the proposed scheme significantly outperforms the regular scheme in enhancing the secrecy performance of the proposed system. Moreover, the suboptimal solutions found are closer to the optimal solution when leveraging irregular scheme.

Integrated sensing and communication (ISAC) sys-tems have attracted considerable attention in recent years. This paper proposes an ISAC system based on orthogonal time frequency space (OTFS) modulation, designed to enhance performance in high-mobility environments. We introduce a novel passive sensing method that enables high-resolution target parameter estimation through fine-grained grid search, two-stage parameter refinement. Additionally, we develop a channel estimation method that leverages sensing parameters, utilizing delay-Doppler domain information in OTFS systems to enhance accuracy in high-mobility scenarios. Simulation results across various Signal-to-Noise ratio (SNR) conditions demonstrate the effectiveness of the proposed methods, showing a significant reduction in the root mean square error (RMSE) of distance and velocity estimations as SNR increases. These findings highlight the accuracy of the proposed algorithms in high-noise environments.

Internet of things (IoT) is crucial for the hierarchical medical system, which enables the real-time monitoring and collection of data, thereby improving patient treatment outcomes. However, achieving secure, efficient, timely, and controllable medical IoT data sharing between higher-lever hospital (HLH) and lower-level hospital (LLH) is a challenging task for the hierarchical medical system. Consortium blockchain, which is an effective way to achieve secure and trustworthy data sharing, has the potential to address these issues. In this article, we propose a novel cloud-chain sharing scheme for medical IoT data based on consortium blockchain. In this scenario, HLH and LLH establish a consortium blockchain, where medical IoT data is stored both on-chain and off-chain. On-chain data adopt a proxy re-encryption based on elliptic curve cryptography (ECC-PRE) strategy and attribute-based strategy to facilitate secure access and controlled sharing of data. Off-chain data sharing provides three different modes, namely, private data collection (PDC), direct channel (DC), and cloud storage (CS), according to the urgency of patient and the sensitivity of the data. Furthermore, a file security breakpoint resume scheme, rooted in the consortium blockchain, and a file weighting strategy are employed to enhance the efficiency and timeliness of data sharing. Finally, the security and performance of our proposed scheme are verified, and the results demonstrate that our scheme is secure, feasible, and efficient.

Mission-critical applications are of significant importance to sixth generation (6G)’s massive and ubiquitous Internet of things (IoT) communications. The mission-critical applications mostly fall within the scope of finite blocklength (FBL), and in order to assess the information freshness, age of information (AoI) has been introduced. However, packet error is inevitable in the FBL regime, which exerts impacts on the time for successful packet transmission, and thus increases the AoI. To optimize the performance of AoI, the management of queue packets is an effective way. Motivated by optimizing the AoI performance in the FBL regime, we consider a system equipped with a single buffer, and propose two schemes of packet management in this article. We subsequently derive the closed-form expressions for the average AoI and the average peak AoI and we discuss the relationship between AoI and the factors, i.e. the blocklength, data generation rate and signal-to-noise ratio. Afterwards, we give the optimal blocklength expression associated with the optimal AoI. In order to examine the information timeliness in the network under the proposed schemes, the closed-form expressions of the average delay are deduced. The simulation results validate the theoretical analysis and demonstrate the advantage of the proposed scheme in terms of the performance of AoI, delay, and their trade-off.

Spectral and spatial fidelity remains a longstanding challenge in the field of pansharpening, which aims to generate high-resolution multispectral (HRMS) images by integrating high-resolution panchromatic (PAN) images with low-resolution multispectral (LRMS) images. This study proposes a high-fidelity pansharpening network that utilizes bidirectional trigeminal pyramid decoding of features encoded by a CNN-Transformer architecture. Specifically, local and global features at multiple scales are initially extracted using a CNN-Transformer encoder to facilitate multi-scale feature fusion. Subsequently, we design a decoder based on bidirectional trigeminal pyramids to achieve a high-fidelity fusion output. One reverse decoding pyramid decodes the fused features of LRMS and PAN images from the encoder. One spectral feature pyramid is employed to enhance the spectral information of the reverse decoding pyramid, while the last spatial feature pyramid is utilized to enrich the spatial information, thereby improving the overall spectral and spatial fidelity of the fused output. Furthermore, content-guided attention (CGA) is incorporated to adaptively integrate the spectral and spatial feature pyramids into the reverse decoding pyramid. Extensive experiments demonstrate that our network surpasses the comparative state-of-the-art (SOTA) methods in both qualitative and quantitative evaluations. The code is available at https://github.com/songvvvv/pansharpening.

In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC system. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU’s utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO algorithm, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 86.88%, increasing the EU utility function value by 0.13%, and decreasing the variance of results by 96.78%.

Internet of Things (IoT) is crucial for the hierarchical medical system, and enables the real-time monitoring and collection of data, thereby improving patient treatment outcomes. However, achieving secure, efficient, timely, and controllable medical IoT data sharing between higher-lever hospital (HLH) and lower-level hospital (LLH) is a challenging task for the hierarchical medical system. Consortium blockchain, which is an effective way to achieve secure and trustworthy data sharing, has the potential to address these issues. In this article, we propose a novel cloud-chain sharing scheme for medical IoT data based on consortium blockchain. HLH and LLH establish a consortium blockchain, where medical IoT data is stored both on-chain and off-chain. On-chain data adopt a proxy re-encryption based on elliptic curve cryptography (ECC-PRE) strategy and attribute-based strategy to facilitate secure access and controlled sharing of data. Off-chain data sharing provides three different modes: private data collection (PDC), direct channel, and cloud storage (CS), according to the urgency of patient and the sensitivity of the data. Furthermore, a file security breakpoint resume scheme, rooted in the consortium blockchain, and a file weighting strategy are employed to enhance the efficiency and timeliness of data sharing. Finally, the performance of our proposed solution is verified by experimental results, and the results demonstrate our solution is feasible and efficient. In future work, we plan to use searchable encryption technology to make this scheme more versatile and gradually implement dynamic adjustment of permissions.

Limited feedback is a key technique for conveying channel state information (CSI) back to the base station (BS). However, its reliance on quantization to select the optimum code-word from a predefined codebook results in severe degradation in achievable rate due to quantization error. To address this issue, robust techniques should be developed. In this paper, we first examine an approximation for the second-order statistics of quantized CSI. Based on the proposed approximation, we then propose a novel robust precoding design that minimizes the conditional expectation based mean square error (MSE). Numerical results show that the proposed design significantly improves the achievable rate compared to conventional precoding schemes.

Employing ambient backscatter communication (AmBC) technology in Intelligent Transportation Systems (ITS) has emerged as an appealing solution to boost the awareness of crosswalks. However, the AmBC-based ITS is expected to face serious security threats due to the presence of malicious eavesdroppers. In this paper, we investigate the secure multi-antenna transmission in an AmBC-based ITS coexisting with a passive eavesdropper with jamming. Specifically, a cooperative jammer is placed in the system to deliberately disrupt the eavesdropper without affecting the legitimate receiver. In order to characterize the performance of the proposed scheme, new approximate closed-form expressions of secrecy outage probability (SOP) are derived by adopting the Gauss-Chebyshev quadrature. Additionally, the asymptotic behavior of SOP at the high signal-to-noise ratio (SNR) regime is also studied to provide more insights into the system design. We also derive the asymptotic SOP, when the number of transmit antennas tends to infinity. Monte Carlo simulations are provided to demonstrate the validity of our analytical results and to show that 1) the secrecy performance can be significantly improved by allocating part of the transmit power to perform cooperative jamming and 2) the optimal power allocation factor is related to the total transmit power.

With the emergence of communication services with stringent requirements such as autonomous driving or on-flight Internet, the sixth-generation (6G) wireless network is envisaged to become an enabling technology for future transportation systems. In this paper, two ways of interactions between 6G networks and transportation are extensively investigated. On one hand, the new usage scenarios and capabilities of 6G over existing cellular networks are firstly highlighted. Then, its potential in seamless and ubiquitous connectivity across the heterogeneous space-air-ground transportation systems is demonstrated, where railways, airplanes, high-altitude platforms and satellites are investigated. On the other hand, we reveal that the introduction of 6G guarantees a more intelligent, efficient and secure transportation system. Specifically, technical analysis on how 6G can empower future transportation is provided, based on the latest research and standardization progresses in localization, integrated sensing and communications, and security. The technical challenges and insights for a road ahead are also summarized for possible inspirations on 6G enabled advanced transportation.

Ambient backscatter communication (AmBC) has emerged as a paradigm distinguished by its energy-efficient attributes and low-power dynamics, ideally suited to address the vast expanse of the Internet of Things (IoT). Unmanned aerial vehicles (UAVs) deployed with flexibility can effectively establish wireless connections for isolated IoT devices through AmBC. This paper delves into the exploration of secure transmission within a UAV-assisted AmBC network, particularly addressing the challenges posed by the presence of a passive eavesdropper. Specifically, a UAV is utilized as an aerial base station to offer services to an isolated ground user, an AmBC tag transmits its information to its associated receivers by leveraging the UAV’s radio frequency (RF) signals. Furthermore, a multi-antenna cooperative jammer is integrated within the system to intentionally interfere with the eavesdropper without affecting legitimate receivers. To characterize the secrecy performance, the expressions of secrecy outage probability of the air-ground link and backscatter link are both deduced leveraging a two-layer Gaussian-Chebyshev quadrature. Moreover, the asymptotic behaviors under the high signal-to-noise ratio (SNR) regime are also analyzed. Monte Carlo simulations are performed to validate the correctness and effectiveness of the analytical results.

It has been well known that the achievable rate of multiuser multiple-input multiple-output systems with limited feedback is severely degraded by quantization errors when the number of feedback bits is not sufficient. To overcome such a rate degradation, we propose new robust precoding designs which can compensate for the quantization errors. In this paper, we first analyze the achievable rate of traditional precoding designs for limited feedback systems. Then, we obtain an approximation of the second-order statistics of quantized channel state information. With the aid of the derived approximation, we propose robust precoding designs in terms of the mean square error (MSE) with conditional expectation in non-iterative and iterative fashions. For the non-iterative precoding design, we study a robust minimum MSE (MMSE) precoding algorithm by extending a new channel decomposition. Also, in the case of iterative precoding, we investigate a robust weighted MMSE (WMMSE) precoding to further improve the achievable rate. Simulation results show that the proposed precoding schemes yield significant improvements over traditional precoding designs.

Orthogonal Time Frequency Space (OTFS) modulation has been proposed to provide seamless and reliable communication in high-mobility environments. In particular, OTFS modulation enables the delay-Doppler (DD) domain model to be represented sparsely so that the compressed sensing (CS) algorithms can be applied in channel estimation to get more accurate channel state information (CSI). However, conventional CS algorithms in OTFS channel estimation assume that channel sparsity$K$is known, which is not available in some practical applications. In this paper, we propose the sparsity adaptive matching pursuit (SAMP) algorithm for OTFS channel estimation without prior information of the channel sparsity$K$, and we further propose a variable step-size backtracking sparsity adaptive matching pursuit (VSB-SAMP) to improve both accuracy and reconstruction speed. We first formulate the channel estimation problem as a sparse signal recovery problem. Then, the SAMP algorithm is introduced to solve the problem that channel sparsity is unknown. Furthermore, a VSB-SAMP is proposed to accelerate reconstruction speed. Simulation results show that the proposed algorithm can achieve accurate channel state information with less iterations.

In non-terrestrial networks (NTN), optimal planning of the optimization problem of 3-dimensional (3D) trajectory is a key research topic. In this article, the optimization problem of the unmanned aerial vehicle (UAV) aided wireless sensor networks is addressed. To maximize the energy efficiency (EE) performance, we formulate the 3D trajectory optimization problem as a non-convex optimization and divide it into two sub-problems, the UAV's horizontal trajectory optimization problem with given altitude and the UAV's altitude optimization problem with given horizontal location. By combining with the discrete linear state-space approximation method, the energy-efficient algorithm with given transmit power of each sensor is proposed. Numerical results show that the proposed methods achieve significant improvements compared to the existing; EE schemes.

With the advent of edge computing, more and more tasks are offloaded to edge servers, but the computing and storage capabilities of edge servers are limited. Although some works propose efficient schemes for task allocation and pricing, they may ignore users' preferences for continuous tasks. However, the combinatorial preference causes high computational complexity. In this paper, we propose a dominant-strategy incentive compatibility (DSIC) and computationally efficient mechanism for green continuous task allocation based on the combinatorial auction. Besides, the activity on edge (AOE) network is introduced to describe the continuity of tasks. The proposed mechanism gives an approximate solution to the winner determination problem (WDP) in polynomial time and a pricing strategy that can guarantee the truthfulness and individual rationality of auction participants. We demonstrate the approximate ratio of the proposed algorithm through theoretical analysis. Experimental results show that the proposed mechanism achieves truthfulness, individual rationality, and high computational efficiency while considering green continuous task allocation.

In this article, the watermarking framework for electromagnetic systems is established with nonblind, semiblind, and blind watermarking demodulation processes mitigated to applications like radar detection, synchronization, integrated dual function, etc. Desired for similar advantages and trade-offs of watermarking technology, the dual function radar and communication (DFRC) system is specifically concerned for covert communication and low possibility of interception (LPI) radar sensing. In this respect, we propose a novel DFRC waveform design method where peak sidelobe level (PSL) of autocorrelation function (ACF) is considered as the figure of merit with information embedded via discrete Fourier transform (DFT) watermarking strategy. Meanwhile, the peak-to-average ratio (PAR) and energy constraints are forced to ensure compatibility with current hardware technique. To handle the resulting NP-hard design problems, the proximal method of multipliers (PMM) is employed with the overall computational burden linear with the amount of information per pulse and quadratic with respect to the code length. Finally, numerical and experimental results are provided to evaluate the effectiveness of the proposed DFRC waveform design scheme with application in joint synthetic aperture radar (SAR) imaging and communication.

In this paper, we investigate the collaborative computation offloading method in satellite edge computing by allowing computation tasks to be executed by multiple satellites with computing capacity. The main purpose is to optimize the resource allocation to minimize the energy consumption of the network, which is formulated as a non-convex optimization problem. To solve it efficiently, we first provide the optimal task allocation scheme and then divide the original optimization problem into two subproblems based on an alternative optimization method. Although two subproblems are still non-convex, we can apply successive convex approximation method to deal with them and design an iterative algorithm to solve them. Finally, simulation results demonstrate the superiority and effectiveness of our proposed algorithm.

Radio signals are electromagnetic waves that are propagated in freespace. This nature makes it vulnerable to be attacked from eavesdroppers. Fortunately, with the aid of the reconfigurable intelligent surface (RIS), which passively reflects the incident signal, the spatial distribution of the signal strength can be customized to benefit legitimate users. In this work, we propose a RIS aided non-orthogonal multiple access (NOMA) transmission scheme to provide secure links for two users, where the unmanned aerial vehicle (UAV) equipped with RIS serves as a relay to change radio coverage flexibly. In the proposed scheme, the NOMA transmit power of base-station (BS), the UAV's location, and the RIS phase shift are jointly optimized to maximize the secure transmission rate, which is a nonconvex optimization problem. For this non-convex problem, we first decompose it into three subproblems. Then an efficient iterative algorithm is proposed, where the transmit power and UAV's location are optimized through the successive convex approximation (SEA) method, and the phase shift is optimized through the semi-definite relaxation (SDR) strategy. Numerical results verify the secrecy superiority of the proposed scheme compared with the current schemes.

As a promising component of the beyond fifth gener-ation (B5G) and forthcoming sixth generation (6G), massive multi-ple input multiple output (massive MIMO)-based low earth orbit (LEO) communication is facing unprecedented serious doppler frequency shifts and delays due to its fast relative moving speed between the transmitters and receivers, which makes the accurate channel state information (CSI) hard to obtain. In order to solve this problem, we introduce an improved channel prediction method, named Prony-based spatial-delay domain (SDD-Prony) prediction, it not only achieves an accurate CSI acquisition for fast-speed relative motion massive MIMO-based LEO commu-nications, but also greatly reduces the computational complexity. Besides, we find that the prediction error of our method converges to zero when the number of antennas and bandwidth growing large, provided that only two sufficiently accurate channel samples are needed. The validness of our theoretical analysis is verified by numerical results, and the simulations also further demonstrate the effectiveness of our method.

Sparse vector coding (SVC) is emerging as a key enabler for short-packet ultrareliable and low-latency communications (URLLCs), since it displays good block error rate (BLER) performance and can achieve low transmission latency. In this article, we propose an SVC-based sparse superimposed transmission (SVC-ST) coding scheme to further enhance the BLER performance of the SVC scheme. At the encoding side, a portion of transmission bits is represented by nonzero position indices of the sparse vector. The remaining bits are equally split into multiple streams and then mapped into the nonzero positions of sparse vector via quadrature amplitude modulation (QAM) with constellation rotation (CR). We afterward adopt the multipath matching pursuit-based soft decoding (MMP-SD) to recover the transmission packet. The BLER and bit error rate (BER) analyses of the SVC-ST scheme demonstrate the validity and rationality of our study. Moreover, we find from the simulation results that the proposed SVC-ST scheme outperforms SVC and its enhanced version (ESVC) schemes in terms of BLER and latency performance.

Sparse vector coding (SVC) is emerging as a feasible solution for short packet transmission, which can provide better reliability and lower latency. In this article, a uniquely decomposable constellation group (UDCG)-based SVC called UDCG-SVC is proposed. The important idea of UDCG-SVC is to map the bits into the nonzero elements of the sparse vector by using the sub-constellations in the UDCG. After random spreading, the resource blocks carry the unique decomposable superimposed constellations, which ensures that the minimum Euclidean distance (MED) of the superimposed constellation is large. This results in an improved block error rate (BLER) performance of SVC. Numerical results demonstrate the advanced BLER simulation of UDCG-SVC, and the proposed UDCG-SVC scheme is better than constellation rotation-based SVC (CR-SVC) scheme.

Spamming is emerging as a key threat to the Internet of Things (IoT)-based social media applications. It will pose serious security threats to the IoT cyberspace. To this end, artificial intelligence-based detection and identification techniques have been widely investigated. The literature works on IoT cyberspace can be categorized into two categories: 1) behavior pattern-based approaches and 2) semantic pattern-based approaches. However, they are unable to effectively handle concealed, complicated, and changing spamming activities, especially in the highly uncertain environment of the IoT. To address this challenge, in this article, we exploit the collaborative awareness of both patterns, and propose a Collaborative neural network-based spammer detection mechanism (Co-Spam) in social media applications. In particular, it introduces multisource information fusion by collaboratively encoding long-term behavioral and semantic patterns. Hence, a more comprehensive representation of the feature space can be captured for further spammer detection. Empirically, we implement a series of experiments on two real-world data sets under different scenarios and parameter settings. The efficiency of the proposed Co-Spam is compared with five baselines with respect to several evaluation metrics. The experimental results indicate that the Co-Spam has an average performance improvement of approximately 5% compared to the baselines.

The current security method of Internet-of-Vehicles (IoV) systems is rare, which makes it vulnerable to various attacks. The malicious and unauthorized nodes can easily invade the IoV systems to destroy the integrity, availability, and confidentiality of information resources shared among vehicles. Indeed, access control mechanism can remedy this. However, as a static method, it cannot timely response to these attacks. To solve this problem, we propose an intelligent edge-chain-enabled access control framework with vehicle nodes and roadside units (RSUs) in this study. In our scenario, vehicle nodes act as lightweight nodes, whereas RUSs serve as full and edge nodes to provide access control services. Considering the low accuracy of risk prediction due to limited training sets, we leverage a generative adversarial networks (GANs) to convert the risk prediction to a sequence generation. Moreover, aiming at the problems of gradient disappearance and mode collapse existed in the original GANs, we devise a Wasserstein combined GANs (WCGANs). Simulation results demonstrate that WCGAN has higher prediction accuracy than the original GANs. Additionally, it can also improve the accuracy of access control of risk prediction-based access control (RPBAC) model.

In-home health networks greatly rely on the massive connected monitoring devices. Compared to orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA) can connect more monitoring devices and enhance the spectrum efficiency (SE) performance, which makes it an ideal solution to in-home health networks. However, conventional NOMA (C-NOMA) is mostly constrained to single-carrier scenario. The problem of multi-carrier NOMA lies in the inter-carrier interference (ICI) from neighboring carriers. In this article, we propose a sparse vector coding-based NOMA (SVC-NOMA) to suppress the ICI. We give closed-form expressions of capacity and symbol error rate (SER) performances for both C-NOMA and SVC-NOMA within the considered multi-carrier scenario. Simulation results demonstrate that compared to C-NOMA, SVC-NOMA has better capacity and SER performances. In addition, we find from our results that there is a trade-off between SVC-NOMA's ICI suppression ability and the system capacity performance.

This paper investigates the energy efficiency (EE) optimization in downlink multi-cell massive multiple-input multiple-output (MIMO). In our research, the statistical channel state information (CSI) is exploited to reduce the signaling overhead. To maximize the minimum EE among the neighbouring cells, we design the transmit covariance matrices for each base station (BS). Specifically, optimization schemes for this max-min EE problem are developed, in the centralized and distributed ways, respectively. To obtain the transmit covariance matrices, we first find out the closed-form optimal transmit eigenmatrices for the BS in each cell, and convert the original transmit covariance matrices designing problem into a power allocation one. Then, to lower the computational complexity, we utilize an asymptotic approximation expression for the problem objective. Moreover, for the power allocation design, we adopt the minorization maximization method to address the non-convexity of the ergodic rate, and use Dinkelbach’s transform to convert the max-min fractional problem into a series of convex optimization subproblems. To tackle the transformed subproblems, we propose a centralized iterative water-filling scheme. For reducing the backhaul burden, we further develop a distributed algorithm for the power allocation problem, which requires limited inter-cell information sharing. Finally, the performance of the proposed algorithms are demonstrated by extensive numerical results.

Due to the complexity of myocardial infarction (MI) waveform, most traditional automatic diagnosis models rarely detect it, while those able to detect MI often require high computing and storage capacity, rendering them unsuitable for portable devices. Therefore, in order for convenient real-time MI detection, it is essential to design lightweight models suitable for resource-limited portable devices. This paper proposes a novel multi-channel lightweight model (ML-Net), that provides a new solution for portable detection devices with limited resources. In ML-Net, each electrocardiogram (ECG) lead is assigned an independent channel, ensuring data independence and preserve the ECG characteristics of different angles represented by different leads. Moreover, convolution kernels of heterogeneous sizes are utilized to achieve accurate classification with only a small amount of lead data. Extensive experiments over actual ECG data from the PTB diagnostic database are conducted to evaluate ML-Net. The results show that ML-Net outperforms comparable schemes in diagnosing MI, and it requires lower computational cost and less memory, so that portable devices can be more widely used in the field of Internet of Medical Things(IoMT).

Sparse code multiple access (SCMA), which helps improve spectrum efficiency (SE) and enhance connectivity, has been proposed as a non-orthogonal multiple access (NOMA) scheme for 5G systems. In SCMA, codebook design determines system overload ratio and detection performance at a receiver. In this paper, an SCMA codebook design approach is proposed based on uniquely decomposable constellation group (UDCG). We show that there are N+1 ( N ≥ 1) constellations in the proposed UDCG, each of which has M (M ≥ 2) constellation points. These constellations are allocated to users sharing the same resource. Combining the constellations allocated on multiple resources of each user, we can obtain UDCG-based codebook sets. Bit error ratio (BER) performance will be discussed in terms of coding gain maximization with superimposed constellations and UDCG-based codebooks. Simulation results demonstrate that the superimposed constellation of each resource has large minimum Euclidean distance (MED) and meets uniquely decodable constraint. Thus, BER performance of the proposed codebook design approach outperforms that of the existing codebook design schemes in both uncoded and coded SCMA systems, especially for large-size codebooks.

This paper investigates power allocation problem for secure multiple-input single-output transmission with artificial noise (AN). With an unknown eavesdropper, we propose an optimal adaptive power allocation scheme, which adaptively adjust the power allocation factor (PAF) according to the instantaneous channel state information of the legitimate channel. On this basis, we derive a closed-form expression for the optimal PAF aiming to minimize the secrecy outage probability (SOP). A suboptimal fixed power allocation scheme is also proposed to reduce system complexity. Moreover, exact closed-form expressions of SOP for both schemes are also obtained.

Network Function Virtualization (NFV) enables flexible deployment of network services as applications. Network operators expect to use a limited number of Network Function (NF) instances to handle the fluctuating traffic load and provide network services. However, it is a big challenge to guarantee the Quality of Service (QoS) under the unpredictable network traffic while minimizing the processing resources. One typical solution is to realize NF scale-out, scale-in and load balancing by elastically migrating the related traffic flows with SoftwareDefined Networking (SDN). However, it is difficult to optimally migrate flows since many real-time statuses of NF instances should be considered to make accurate decisions. In this paper, we propose DeepMigration to solve the problem by efficiently and dynamically migrating traffic flows among different NF instances. DeepMigration is a Deep Reinforcement Learning (DRL)-based solution coupled with Graph Neural Network (GNN). By taking advantages of the graph-based relationship deduction ability from our customized GNN and the self-evolution ability from the experience training of DRL, DeepMigration can accurately model the cost (e.g., migration latency) and the benefit (e.g., reducing the number of NF instances) of flow migration among different NF instances and generate dynamic and effective flow migration policies to improve the QoS. Experiment results show that DeepMigration requires less migration cost and saves up to 71.6{%} of the computation time than existing solutions.

In this paper, we propose a novel two-dimensional (2D) non-stationary geometry-based stochastic model (GBSM) for wideband multiple-input multiple-output (MIMO) base station-to-vehicle (B2V) channels. The proposed model combines one-ring and multiple ellipses with time-variant parameters, which can capture the channel non-stationary characteristics more precisely. The corresponding stochastic simulation model is then developed with finite number of effective scatterers. In addition, the birth-death process is applied to determine the number of ellipses in the proposed model at different time instants. Afterwards, the time-variant parameters and time-variant space cross-correlation functions (CCFs) are derived and analyzed. The impact of different parameters on the space CCFs such as vehicle traffic density (VTD) is investigated. Numerical results illustrate that the simulation model has great agreement with the reference model at different time instants, which indicates the correctness of our derivations.

The information sharing among vehicles provides intelligent transport applications in the Internet of Vehicles (IoV), such as self-driving and traffic awareness. However, due to the openness of the wireless communication (e.g., DSRC), the integrity, confidentiality and availability of information resources are easy to be hacked by illegal access, which threatens the security of the related IoV applications. In this paper, we propose a novel Risk Prediction-Based Access Control model, named RPBAC, which assigns the access rights to a node by predicting the risk level. Considering the impact of limited training datasets on prediction accuracy, we first introduce the Generative Adversarial Network (GAN) in our risk prediction module. The GAN increases the items of training sets to train the Neural Network, which is used to predict the risk level of vehicles. In addition, focusing on the problem of pattern collapse and gradient disappearance in the traditional GAN, we develop a combined GAN based on Wasserstein distance, named WCGAN, to improve the convergence time of the training model. The simulation results show that the WCGAN has a faster convergence speed than the traditional GAN, and the datasets generated by WCGAN have a higher similarity with real datasets. Moreover, the Neural Network (NN) trained with the datasets generated by WCGAN and real datasets (NN-WCGAN) performs a faster speed of training, a higher prediction accuracy and a lower false negative rate than the Neural Network trained with the datasets generated by GAN and real datasets (NN-GAN), and the Neural Network trained with the real datasets (NN). Additionally, the RPBAC model can improve the accuracy of access control to a great extent.

Optimization problem of transmit power and straight trajectory is addressed in unmanned aerial vehicle (UAV) aided wireless sensor networks. To maximize the energy efficiency (EE) performance, we formulate the transmit power and straight trajectory optimization problem as a non-convex optimization and solve it by iterative method. To facilitate the analysis, we derive an analytical expression of upper bound of the aggregated throughput with straight UAV flight. Based on the upper bound, the original optimization problem can be addressed by solving an alternating sequence of trajectory optimization (TO) and power optimization (PO) sub-problems with closed-form expressions. Furthermore, we propose an alternative algorithm for power allocation and straight trajectory optimization problem and analyze the computing complexity. Numerical results show that the proposed method achieves a significant improvement compared to the existing EE schemes.

We introduce a full duplex non-orthogonal multiple access (FD-NOMA)-based decentralized vehicle to everything (V2X) system model and focus on its capacity performance analysis. In order to solve the computation complicated problems of the involved exponential integral functions and infinite factorial expressions, we give approximate closed-form expressions with controllable arbitrary small errors. We find the accuracy of our approximate expressions is controlled by the division of $\frac{\pi}{2}$ in the urban and crowded (UC) scenario, and the truncation point $T$ in the suburban and remote (SR) scenario. Numerical results manifest 1) Increasing the number of V2X device, NOMA power and Rician factor value yields better capacity performance. 2) Effect of FD-NOMA is determined by the FD self-interference and the channel noise. 3) FD-NOMA has better latency performance compared to other schemes.

In order to meet the requirements of massively connected devices, different quality of services (QoS), various transmit rates, and ultra-reliable and low latency communications (URLLC) in vehicle-to-everything (V2X) communications, we introduce a full duplex non-orthogonal multiple access (FD-NOMA)-based decentralized V2X system model. We, then, classify the V2X communications into two scenarios and give their exact capacity expressions. To solve the computation complicated problems of the involved exponential integral functions, we give the approximate closed-form expressions with arbitrary small errors. Numerical results indicate the validness of our derivations. Our analysis has that the accuracy of our approximate expressions is controlled by the division of π/2 in the urban and crowded scenarios, and the truncation point T in the suburban and remote scenarios. Numerical results manifest that: 1) increasing the number of V2X device, NOMA power, and Rician factor value yields a better capacity performance; 2) effect of FD-NOMA is determined by the FD self-interference and the channel noise; and 3) FD-NOMA has a better latency performance compared with other schemes.

Millimeter wave band (mmWave) integrates with a wide variety of signals under manifold communication standards due to its high-capacity feature, which enables mmWave beam sensing to serve a valuable function in discriminating different signals. In this paper, we propose a novel frame structure consisting of variant beam sensing process and data transmission process. In the beam sensing process, multi-power-level beam sensing method is conducted in every direction to discriminate multi-users under multiple standards. The sensing duration varies with the number of directions. Several performance metrics are correspondingly proposed to quantify the beam sensing for multiple mmWave users, such as the probability of correct detection and the false alarm probability. In the second process, the signal with the biggest received signal-to-noise ratio (SNR) is given priority to communicate. On this base, sensing-throughput tradeoff is analyzed to balance the time division between two processes for throughput maximization. Finally, numerical evaluations and simulations are conducted to verify the correctness of the proposed methods.

Small cell networks (SCNs) have emerged as promising technologies to meet the data traffic demands for the future wireless communications. However, the benefits of SCNs are limited to their hard handovers between base stations (BSs). In addition, the interference is another challenging issue. To solve this problem, this study employs a cooperative transmission mechanism focusing on correlated Rician/Gamma fading channels with zero‐forcing receivers. The analytical expressions for the achievable sum rate, symbol error rate and outage probability are derived, which are applicable to arbitrary Rician factors, correlation coefficients, the number of antennas, and remain tight across entire signal‐to‐noise ratios (SNRs). Asymptotic analyses at the high and low SNR regimes are carried out in order to further reveal the insights of the model parameters on the system performance. Monte‐Carlo simulation results validate the correctness of their derivations. Numerical results indicate that the theoretical expressions provide sufficiently accurate approximation to simulated results.

By analogy with internet of things (IoT), internet of vehicles (IoV) which enables ubiquitous information exchange and content sharing among vehicles with little or no human intervention is a key enabler for the intelligent transportation industry. In this paper, we study how to combine both the physical and social layer information to realize rapid content dissemination in device-to-device vehicle-to-vehicle (D2D-V2V)-based IoV networks under various quality of service (QoS) requirements. In the physical layer, headway distance of vehicles is modeled as a Wiener process, and the connection probability of D2D-V2V links is estimated by employing the Kolmogorov equation. In the social layer, the social relationship tightness that represents content selection similarities is derived by Bayesian nonparametric learning based on real-world social big data, which are collected from Sina Weibo and Youku. Then, a price-rising based iterative matching algorithm is proposed to solve the formulated joint peer discovery, power control, and channel selection problem. Finally, numerical results demonstrate the effectiveness and superiority of the proposed algorithm from the perspectives of weighted sum rate and matching satisfaction gains.

Non-orthogonal multiple access (NOMA), millimeter wave (mmWave), and massive multiple-input-multiple-output (MIMO) have been emerging as key technologies for fifth generation mobile communications. However, less studies have been done on combining the three technologies into the converged systems. In addition, how many capacity improvements can be achieved via this combination remains unclear. In this paper, we provide an in-depth capacity analysis for the integrated NOMA-mmWave-massive-MIMO systems. First, a simplified mmWave channel model is introduced by extending the uniform random single-path model with angle of arrival. Afterward, we divide the capacity analysis into the low signal to noise ratio (SNR) and high-SNR regimes based on the dominant factors of signal to interference plus noise ratio. In the noise-dominated low-SNR regime, the capacity analysis is derived by the deterministic equivalent method with the Stieltjes–Shannon transform. In contrast, the statistic and eigenvalue distribution tools are invoked for the capacity analysis in the interference-dominated high-SNR regime. The exact capacity expression and the low-complexity asymptotic capacity expression are derived based on the probability distribution function of the channel eigenvalue. Finally, simulation results validate the theoretical analysis and demonstrate that significant capacity improvements can be achieved by the integrated NOMA-mmWave-massive-MIMO systems.

The non-regenerative massive multi-input-multi-output (MIMO) non-orthogonal multiple access (NOMA) relay systems are introduced in this paper. The NOMA is invoked with a superposition coding technique at the transmitter and successive interference cancellation (SIC) technique at the receiver. In addition, a maximum mean square error-SIC receiver design is adopted. With the aid of deterministic equivalent and matrix analysis tools, a closed-form expression of the signal to interference plus noise ratio (SINR) is derived. To characterize the performance of the considered systems, closed-form expressions of the capacity and sum rate are further obtained based on the derived SINR expression. Insights from the derived analytical results demonstrate that the ratio between the transmitter antenna number and the relay number is a dominate factor of the system performance. Afterward, the correctness of the derived expressions are verified by the Monte Carlo simulations with numerical results. Simulation results also illustrate that: 1) the transmitter antenna, averaged power value, and user number display the positive correlations on the capacity and sum rate performances, whereas the relay number displays a negative correlation on the performance and 2) the combined massive-MIMO-NOMA scheme is capable of achieving higher capacity performance compared with the conventional MIMO-NOMA, relay-assisted NOMA, and massive-MIMO orthogonal multiple access (OMA) scheme.

Other than separately investing the energy efficiency (EE) merits of information-centric networking’s (ICN’s) caching and sharing (CS) mechanism in wireless communications, here we comprehensively compare the EE performances of ICN’s CS mechanism in different scenarios. A modified system model is first proposed while introducing the CS mechanism into the in-network router, base station (BS), and neighboring user sides. Afterwards, the system achievable sum rate as well as the power consumptions in wireless and wired sections is investigated. The EE performances of different scenarios are finally obtained by dividing the achievable sum rate by the consumed power. While comparing the three scenarios, numerical results demonstrate that the optimal place to cache the content is mainly determined by the distance and hub number of the core routers that passed.

A Pseudo Double Scattering Channel (PDSC) Matrix assumption is proposed here for the downlink Non- Orthogonal Multiple Access (NOMA) within the massive Multi-Input Multi-Output (MIMO) systems. Afterwards, the outage probability analysis of such a system is investigated. That is, with the aid of random matrix and statistics theories, the Cumulative Probability Distribution (CDF) and also the outage probability performance are addressed. After that, the mathematics derivations obtained here are verified through numerical simulation results, wherein we further find out that with antenna number increasing, the system outage probability performance is reduced.

To further enhance the energy efficiency (EE) performance of fifth generation (5G) Internet of Things systems, an integrated structure is proposed in this paper. That is, other than prior studies that separately study the wireless and wired parts, the wireless and wired parts are holistically combined together to comprehensively optimize the EE of the whole system. The integrated system structure is introduced beforehand with the proposed unified control center components for better deployment of the select-and-sleep mechanism. In addition, in the wireless part, one cellular partition zooming (CPZ) mechanism is proposed. In contrast, in the wired part, a precaching mechanism is introduced. With these proposals, the proposed system EE performance is investigated. Comprehensive computer-based simulation results demonstrate that the proposed schemes display better EE performance. This is due to the fact that system power consumption is further reduced with these schemes as compared to the prior work.

Massive Multiple-Input Multiple-Output (Massive MIMO) has been realized as a promising technology element for 5G wireless mobile communications, in which Spectral Efficiency (SE) and Energy Efficiency (EE) are two critical issues. Prior estimates have indicated that 57% energy consumption of cellular system comes from the operator, mostly used to feed the base station (BS). Yet previously, the User Equipment(UE) is focused on while studying the EE issue instead of BS. In this case, in this paper, an EE scheme that focuses on the optimization of BS energy consumption is proposed. Apart from the previous studies, which divides the coverage area by circuit section, the coverage area is divided by fan section with the help of Propagation theory for zoom in or zoom out. In the proposal, transmission model and parameters related to EE is deduced first. Afterwards, the Cellular Partition Zooming (CPZ) scheme is proposed where the BS can zoom in to maintain the coverage area or zoom out to save the energy. Comprehensive simulation results demonstrate that CPZ presents better EE performance with negligible impact on the transmission rate.

Spectrum sensing, as a key technology of cognitive radio (CR), needs to reliably and efficiently detect spectrum holes in wireless environments, which challenges the traditional spectral estimation methods typically operating at or above Nyquist rates. This paper develops a novel compression ratio allocation (CRA) method for wideband spectrum sensing in CR networks. In our scheme, each CR terminal performs compressed sensing with sub-Nyquist rate samples to scan a wide spectrum range at practical signal-acquisition complexity. It can greatly reduce the sensing measurements through fewer sample numbers. Meanwhile, the cognitive base station optimizes the compression ratio at each CR terminal according to their local signal-to-noise ratio (SNR), so the total sample number can be further cut down. Simulation results show that the CRA algorithm provides an optimal performance while requiring a relatively low complexity of sensing process.