Symbol detection is critical for ambient backscatter communication (AmBC) and its optimal detection threshold is sensitive to the distribution of the signal from the ambient radio frequency source. Existing work mainly focuses on symbol detection under the assumption of a single ambient RF source (S-ARF). However, in the space-air-ground integrated networks, multiple ambient RF sources (M-ARFs), including satellites, unmanned aerial vehicles and terrestrial base stations, often reuse the same RF resources, which makes the distribution of M-ARFs signals differ from that of an S-ARFs signal. In this paper, we investigate symbol detection for AmBC in the presence of M-ARFs. To this end, we model the M-ARFs signal as an aggregate signal with log-normally distributed power. On this basis, we formulate a maximum likelihood (ML) detector and obtain a closed-form approximate threshold using Hermite-Gauss Quadrature, which is effective under specific parameter conditions. Furthermore, to enhance generality and analyze the impact of aggregate signal parameters, we construct an energy detector (ED) and propose a novel log-energy detector (LED), for which we derive the expressions for bit error rate (BER) and near-optimal thresholds. Simulation results show that the aggregate signal can significantly degrade the BER performance when directly using the detection threshold assuming an S-ARF. In contrast, the ML and ED based on the proposed detection framework can mitigate the performance degradation, and LED can achieve superior BER performance.

Millimeter-wave rate-splitting multiple access (mmWave-RSMA) technology combines the wideband characteristics of millimeter waves with the rate-splitting mechanism of RSMA, significantly improving the communication efficiency of the system. However, this feature also poses greater challenges to its secure transmission, especially in networks with dual-identity nodes which act as legitimate users while also being able to eavesdrop on information from other users. This behavior will simultaneously weaken the security of both public and private streams. In this regard, the active reconfigurable intelligent surface (RIS) offers a promising method by suppressing eavesdropper channels and enhancing the quality of legitimate links. Focusing on the physical layer security (PLS) challenge posed by dual-identity users in mmWave-RSMA networks, this paper proposes an active RIS assisted secure beamforming scheme integrated with resource allocation optimization. Specifically, while ensuring the quality of service (QoS) for dual-identity user and power amplification constraints of the active RIS, the sum secrecy rate is maximized by jointly optimizing the transmit beamforming vector, reflection coefficient matrix, common rate allocation, and power allocation coefficients. To solve this non convex problem, we divide it into three subproblems. Then, successive convex approximation (SCA) and semidefinite relaxation (SDR) are used to solve these subproblems. Simulation results validated the critical role of active RIS in reducing eavesdropping, and emphasized the importance of joint resource allocation for secure beamforming in mmWave-RSMA.

This paper investigates the uplink massive grant-free random access in low-earth-orbit (LEO) satellite-based Internet-of-Things systems, where accurate joint activity detection and channel estimation (JADCE) is essential for reliable data recovery. However, the resource constraint and high-dynamic characteristic of LEO scenarios pose significant challenges to traditional JADCE schemes in terms of both estimation accuracy and computational complexity. To overcome these limitations, we propose a novel deep-unfolding JADCE framework based on the synergistic multi-stage optimization iterative shrinkage thresholding algorithm network, referred to as SMO-ISTA-Net, to facilitate efficient massive device access. Specifically, we first develop a synergistic attention module, where an inertia-guided optimization strategy is introduced into the gradient descent process to improve convergence stability and adaptability to fast-varying satellite channels. In order to mitigate the temporal feature inconsistency caused by asynchronous access and multi-path propagation, we further design a lightweight cross-attention mechanism that enables efficient channel feature fusion and facilitates multi-stage information interaction. Moreover, we propose a memory-enhanced proximal-mapping module that incorporates a high-throughput short-term memory mechanism into the unfolded structure, so as to significantly reduce information loss and maximize memory retention of the network. Extensive simulations under diverse LEO scenarios demonstrated that our scheme can achieve superior convergence speed, estimation accuracy, and preamble efficiency, while maintaining low computational complexity and short runtime, compared to the state-of-the-art model-driven JADCE schemes.

Ambient backscatter communication (AmBC) enables ultra-low-power, low-cost and massive connectivity. However, practical AmBC systems suffer from symbol timing offset (STO) due to propagation delay and backscatter receiver (BR) activation latency, while conventional correlation-based synchronization methods are inapplicable because ambient radio frequency sources are non-cooperative. Moreover, residual STO (RSTO) inevitably remains due to the finite synchronization sequence, which degrades symbol detection performance. To address these challenges, we first design a specialized synchronization sequence with alternating “0” and “1” bits at the backscatter device to induce observable sampling errors at the BR. Based on this, we propose a pilot-aided, sampling-error-aware maximum likelihood estimation (PSE-MLE) method for STO estimation and compensation, which exploits the statistical variations in the received synchronization signal. After STO compensation, the remaining RSTO is statistically modeled as a discrete bilateral Laplace distribution, with its parameter estimated via ridge regression. Leveraging this prior information, we further develop a Bayesian average energy detector (ave-ED) and derive closed-form expressions for both the detection threshold and bit error rate. Simulation and experimental results on a practical AmBC platform validate the effectiveness of the proposed methods.

Mutualistic symbiotic radio (MSR) that allows a primary link and a backscatter link to share resources and benefit each other has been investigated mainly for long-packet communications. In the Internet of Things (IoT), where short packets are often transmitted, the design of MSR needs to consider the distinct features of short packet transmissions. In this work, we study a multi-IoT-node MSR network supporting both long and short packet transmissions, where a primary transmitter sends long packets to a cooperative receiver (CR), while multiple IoT nodes sequentially backscatter their short packets to the CR. We first derive the lower bound expression for the primary transmission rate by considering the error probabilities (EPs) of the IoT nodes’ short-packet communications. On this basis, we propose a resource allocation scheme to maximize the weighted sum throughput of all IoT nodes, subject to the constraints on the quality-of-service requirement and energy causality for each IoT node, as well as the throughput gain for the primary transmission. Specifically, the weighted sum throughput maximization problem is formulated by jointly optimizing the short-packet blocklength, power reflection coefficient and EP of each IoT node, and is solved by proposing an iterative algorithm based on the block coordinate decent method. Simulation results confirm the quick convergence of the proposed iterative algorithm and show that our proposed scheme outperforms the existing scheme in terms of the weighted sum throughput of all IoT nodes.

In a single-antenna fluid antenna system (FAS), the transceiver dynamically selects the antenna port with the strongest instantaneous channel to enhance link reliability. However, deriving accurate yet tractable performance expressions under the fully correlated fading channel remains challenging, primarily due to the absence of a closed-form distribution for the FAS channel. To address this gap, this paper develops a novel performance evaluation framework for FAS under fully correlated Rayleigh fading by modeling the FAS channel using extreme value distributions (EVDs). We first justify the suitability of EVDs and model the FAS channel as a Gumbel distribution, whose parameters, estimated via the maximum likelihood (ML) criterion, are expressed as functions of the number of ports and antenna aperture size. Closed-form approximate expressions for the outage probability (OP) and the ergodic capacity (EC) are then derived. Simulation results show that the Gumbel distribution provides simple yet sufficiently accurate expressions for EC, although slight deviations in OP accur in the low-probability region of practical interest. To further improve accuracy, the FAS channel is modeled using the generalized extreme value (GEV) distribution, yielding closed-form expressions for OP and EC based on ML-estimated parameters. Simulation results confirm that the GEV distribution provides superior accuracy compared to the Gumbel, particularly for OP, while both EVD-based approaches offer computationally efficient and analytically tractable tools for evaluating FAS performance under the fully correlated fading channel.

Generalizing to unseen graph tasks without task-specific supervision remains challenging.Graph Neural Networks (GNNs) are limited by fixed label spaces, while Large Language Models (LLMs) lack structural inductive biases.Recent advances in Large Reasoning Models (LRMs) provide a zero-shot alternative via explicit, long chain-of-thought reasoning.Inspired by this, we propose a GNN-free approach that reformulates graph tasks-node classification, link prediction, and graph classification-as textual reasoning problems solved by LRMs.We introduce the first datasets with detailed reasoning traces for these tasks and develop GRAPH-R1, a reinforcement learning framework that leverages task-specific rethink templates to guide reasoning over linearized graphs.Experiments demonstrate that GRAPH-R1 outperforms state-of-the-art baselines in zero-shot settings, producing interpretable and effective predictions.Our work highlights the promise of explicit reasoning for graph learning and provides new resources for future research.Codes are available at https://github.com/lgybuaa/Graph-R1.

Generalizing to unseen graph tasks without task-specific supervision is challenging: conventional graph neural networks are typically tied to a fixed label space, while large language models (LLMs) struggle to capture graph structure. We introduce UniGTE, an instruction-tuned encoder–decoder framework that unifies structural and semantic reasoning. The encoder augments a pretrained autoregressive LLM with learnable alignment tokens and a structure-aware graph–text attention mechanism, enabling it to attend jointly to a tokenized graph and a natural-language task prompt while remaining permutation-invariant to node order. This yields compact, task-aware graph representations. Conditioned solely on these representations, a frozen LLM decoder predicts and reconstructs: it outputs the task answer and simultaneously paraphrases the input graph in natural language. The reconstruction objective regularizes the encoder to preserve structural cues. UniGTE is instruction-tuned on five datasets spanning node-, edge-, and graph-level tasks across diverse domains, yet requires no fine-tuning at inference. It achieves new state-of-the-art zero-shot results on node classification, link prediction, graph classification and graph regression under cross-task and cross-domain settings, demonstrating that tight integration of graph structure with LLM semantics enables robust, transferable graph reasoning.

Fluid antennas (FA) offer a revolutionary breakthrough in wireless communications by dynamically regulating antenna positions within confined spaces to significantly enhance system capacity and link reliability. In this paper, a FA integrated and reconfigurable intelligent surface empowered receive spatial modulation (FA-RIS-RSM) scheme is proposed. Specifically, due to the unique physical properties of FA, we propose two port selection strategies, namely the random port selection (RPS) and optimal port selection (OPS), to assist the transmission achieving with distinct extents of reliability. Furthermore, we derive closed-form expressions for the average bit error probability (ABEP), via utilizing the Gamma approximation analysis and correlated fading analogism analysis methods, respectively. Simulated and numerical performance results corroborate the correctness of the derivations, as well as demonstrate the superiority and effectiveness of the proposed FA-RIS-RSM in enhancing the wireless transmission reliability.

To address the physical layer security issues for the next generation wireless, we introduce and investigate a novel fluid antenna assisted secure joint transmitter-spatial and receiver-port index modulation (FA-SJIM) system. Specifically, we detail two schemes to enhance both the transmission efficiency and secrecy. The schemes are based on the continuous random phase and discrete random phase distortions at the transmitter and the constructive phase-aligning manipulations at the RIS, respectively. Furthermore, we derive the semi-closed-form secrecy outage probability (SOP) results of the proposed schemes with the maximum likelihood detection, so as to facilitate the evaluation of the system secrecy. Finally simulated and numerical performance results are characterized to reveal the high accuracy of SOP derivations, and the superiority of the FA-SJIM in terms of transmission efficiency and secrecy.

To address the increasing demand for transmission systems with enhanced efficiency and flexibility, two-way (TW) full-duplex (FD) systems have become a vital research focus in wireless communications. In this paper, a novel reconfigurable intelligent surfaces (RISs) assisted TW-FD spatial modulation (SM) model is proposed to improve the transmission reliability performance. With this proposal, we introduce the transmit-side SM to activate single antenna for fulfilling the transmission of amplitude and phase modulated symbols. The transmission is then reflected by the corresponding RIS to the antenna at the receiver end. Moreover, the prewired phase-offset (PPO) is included in the transmission process for attaining enhanced detecting reliability. To evaluate the performance boundaries of the proposed system, we consider the maximum likelihood detection based analytical derivations. More specifically, upon utilizing the moment generating function method, we derive the closed-form results for the system average bit error probability. Finally, with the simulation based comparative analysis, we verify the reliability and efficiency of the proposed TW-FD-RIS-PPOSM system.

Symbiotic backscatter communication radio (SBCR) systems enable spectrum-efficient communication by allowing backscatter devices (BDs) to modulate and reflect primary transmitter (PT) signals. However, prior studies often assume circularly symmetric complex Gaussian (CSCG) signals for BDs, which is impractical for low-complexity BDs. This paper addresses this gap by investigating SBCR systems where the BD adopts one of two popular modulation schemes in wireless communications, i.e., binary phase-shift keying (BPSK) and quadrature phase-shift keying (QPSK) modulation schemes. We derive the PT’s rates for these modulation schemes, highlighting the critical role of BD’s modulation phase in enhancing the PT’s rate. We then formulate two optimization problems to maximize the PT’s rate by optimizing the BD’s reflection phases for BPSK and QPSK, respectively, and derive closed-form expressions for the optimal phases. Simulations validate our theoretical analysis, and reveal that BPSK modulation achieves a higher PT’s rate than that of QPSK.

Reconfigurable intelligent surface (RIS) and nonorthogonal multiple access (NOMA) are two promising technologies for future wireless communication networks. This paper proposes a new transmission design for short-packet communications (SPCs) in a downlink RIS-assisted NOMA system, considering hardware impairments at the transceiver nodes, non-optimal continuous phase shifts (CPSs) with phase estimation errors at the RIS, and imperfect successive interference cancellation (SIC) for a pair of NOMA users. Using the method of moment matching, we approximate the end-to-end channel gain with a Gamma distribution, and derive the closed-form approximate expressions for the average block error rates (BLERs) for NOMA users. Moreover, the system throughput is obtained to characterize the transmission efficiency of the network. Analytical results of the proposed scheme indicate that the phase estimation errors can seriously affect the system performance. Therefore, when conducting theoretical analysis, the non-optimal CPSs should be considered for RIS deployment. Numerical results validate our theoretical analysis, and confirm the performance improvement of transmission reliability and effectiveness of the proposed scheme, as compared to existing transmission schemes.

Although coexisting passive reconfigurable intelligent surface (RIS) and active relay have been considered for enhancing long-packet communications, their potential in supporting short-packet communications (SPC) where the direct link is blocked has not been sufficiently studied. Motivated by this, this work investigates the performance of the coexisting passive RIS and active relay-assisted SPC network, where a source (S) transmits its short-packet data to the destination (D) with the help of a passive RIS and an active relay (R). More specifically, in the first time slot of the two-hop transmission, S transmits its short-packet signal to both the RIS and R. The RIS then reflects the incident signal towards D. If D is unable to decode the short-packet information while R successfully decodes it, R forwards the decoded signal to D with the assistance of the RIS in the second time slot. Following a decode-and-forward (DF) relaying protocol, we analytically derive the closed-form expression of the block error rate (BLER) for the short-packet transmission. Simulation results verify the correctness of the derived expression and show that the combination of the passive RIS and active relay can enhance the BLER performance as compared with the benchmarks that employ only a passive RIS or an active relay.

Reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) are two promising technologies for future wireless communication networks. This paper proposes a new transmission design for short-packet communications (SPCs) in a downlink RIS-assisted NOMA system, considering hardware impairments at the transceiver nodes, non-optimal continuous phase shifts (CPSs) and discrete phase shifts (DPSs) at the RIS, and imperfect successive interference cancellation (SIC) for a pair of NOMA users. Using the method of moment matching, we approximate the end-to-end channel gain with a Gamma distribution, and derive the closed-form approximate expressions for the average block error rates (BLERs) for NOMA users, along with the diversity order and the asymptotic expressions for the average BLERs at the high signal-to-noise ratio (SNR) region. Inspired by the asymptotic average BLER, the minimum blocklength is derived, and the optimal power allocation factor is obtained. Then, the system throughput is obtained to characterize the transmission efficiency of the RIS-assisted short-packet NOMA network. Analytical results of the proposed scheme indicate that: 1) The phase estimation error of the RIS has a significant impact on the system performance, and thus the non-optimal CPSs should be used in the theoretical analysis; 2) The DPSs with 3-bit quantization can effectively achieve a comparable performance to the optimal CPS scheme in practical applications. Numerical results validate our theoretical analysis, and clearly show the performance improvement of transmission reliability and effectiveness of the proposed scheme, as compared to existing transmission schemes.

Existing works on mutualistic backscatter communications (MBC) rely on the existence of direct links connecting both the primary user (PU) and the backscatter device (BD) to the destination node (D), rendering them ineffective when these links are blocked. While relay technology offers a solution, the performance of relay-assisted MBC and whether the backscatter link still benefits the primary link or not remain unclear. Additionally, the energy-harvesting capability of BD has been largely ignored in the study of MBC. This paper investigates a relay-assisted MBC network, where a decode-and-forward relay (R) forwards the messages of a PU and an energy constrained BD to D. We propose three forwarding schemes for R, i.e., orthogonal time-division multiple access (OMA), non-orthogonal multiple access (NOMA) with static power allocation (PA), and NOMA with dynamic PA. Considering the energy-causality constraint of BD, we derive the outage probabilities and diversity gains of the primary and backscatter links for each forwarding scheme. Computer simulation verifies the correctness of our analytical results and reveals that different from conventional MBC, in the relay-assisted MBC, whether or not the backscatter link improves the primary link’s outage performance depends on the specific forwarding scheme employed, as well as the location of R and the power reflection coefficient of BD.

Compared with terrestrial reconfigurable intelligent surface (RIS), aerial RIS provides improved transmission performance owing to its high adaptability in three-dimensional space and the existence of established line-of-sight links. In this paper, we study an aerial RIS-aided over-the-air federated learning (AirFL) system and propose an effective resource allocation scheme to maximize the energy efficiency (EE) of the AirFL system, where the transmit power of the Internet-of-Things devices, the receive scalar at the base station (BS), and the phase shifts of the RIS are jointly optimized. Since the formulated EE maximization problem consists of coupled variables, it is essentially non-convex. The original problem is then transformed into several sub-problems, which are addressed by adopting Dinkelbach's method, successive convex approximation and difference-of-convex programming approach. Finally, simulation results demonstrate that the proposed scheme is superior in terms of the EE performance for RIS-aided AirFL system compared with the existing scheme.

In the emergency communication scenario, unmanned aerial vehicle (UAV)-based relaying and mobile edge computing (MEC) networks can quickly collect ground users' (GUs) information and provide high-quality computing services. In this paper, we investigate a UAV and reconfigurable intelligent surface (RIS) assisted uplink non-orthogonal multiple access MEC networks for emergency communications, where the UAV serves as a relay to assist the transmission from GUs to base station and provides MEC services. For the considered system, the computation efficiency (CE) is maximized by jointly optimizing the phase shift of RIS, communication and computation resources, bandwidth allocation coefficients, and the space location of UAV. The formulated problem is non-convex, and we divide the problem into four sub-problems. An alternating optimization-based algorithm is proposed to deal with it. In particular, with the aid of Dinkelbach's method, semi-definite relaxation, first-order Taylor expansion, successive convex approximation and improved particle swarm optimization, the decoupled non-convex sub-problems are effectively solved. Simulation results show that the proposed scheme can effectively improve the CE of the system, as compared with the conventional schemes.

The rise of deep learning enables spectrum sensing to make decisions only based on the observed data which brings about a lot of flexibility. However, the existing data-driven spectrum detection methods handle the data in Euclidean space well but ignore the laten structure connections of the signal. To characterize this information, we propose a graph construction mechanism based on the similarity measure of the random signal and further convert the received signal into graph topology. On this basis, we propose a graph neural network based detector that consists of the construction of graph topology, offline training and online detection. The proposed data-driven method does not need any priori knowledge of the observed signal. Simulation results demonstrate that whether there is noise uncertainty or not, the proposed method is robust and performs better than the existing deep learning based methods.

Automatic Modulation Recognition (AMR) is becoming increasingly crucial in the industrial internet, and it ensures more reliable communication for devices within this vast and intricate network. Although recent application of neural networks, notably Graph Convolutional Network (GCN) in the AMR domain shows promising results, it fails to account for temporal features within modulation signals, resulting in a loss of recognition precision. Given that modulation signals possess both time and space features, this paper proposes a hybrid deep learning model named LAGNet, which combines Long Short-Term Memory (LSTM) and GCN. The model uses an attention mechanism to map the LSTM outputs into a graph and then employs the GCN to extract the spatial features of the signal. Subsequently, the temporal features and the spatial features are combined together for modulation signal classification. Experimental results show that LAGNet not only outperforms several advanced models in classification accuracy across all signal-to-noise ratios, but also requires fewer learnable parameters.

This paper investigates the secrecy communications of a downlink wireless-powered cooperative non-orthogonal multiple access (NOMA) system with a source node, a relay, a pair of near and far users, and an eavesdropper (Eve). A wireless-powered relay assists the transmission from the source to the far user, and the Eve intends to wiretap the confidential message transmitted to the near user. We propose a dynamic power allocation and friendly jamming based secure transmission scheme. Specifically, the optimal power allocation factors at the source node for the paired NOMA users are obtained to guarantee the perfect successive interference cancellation performance and improve the transmission reliability of the pair of NOMA users. In the direct transmission phase, the source transmits a superimposed signal to the near user and the wireless-powered relay, and the far user serves as a friendly jammer to emit artificial noise to confuse the Eve. In the cooperative transmission phase, the relay utilizes the harvested energy to assist the transmission to the far user. By applying the Gaussian-Chebyshev and Gaussian-Laguerre quadratures, closed-form expressions for the secrecy outage probability of the near user and the connection outage probability of the far user are derived. Simulation results verify the correctness of our theoretical analysis. The superiority of the proposed scheme is also demonstrated for improving the secrecy performance, as compared to the existing cooperative NOMA schemes.

In this paper, we investigate a hybrid reconfigurable intelligent surface (RIS) enabled integrated sensing and commu-nication (ISAC) system, in which a hybrid RIS is employed to assist a base station (BS) to sense a specified target, while interacting with multiple communication users (CUs) simultaneously. In particular, a hybrid RIS is introduced such that each of its surface module is able to switch between active and passive modes, reducing the system's power consumption. Subsequently, an optimization problem is formulated with the aim of maximizing the radar output signal-to-noise ratio while satisfying communication requirement for each CU, transmit power constraint for BS and the active RIS elements, by jointly optimizing radar recieve filter, BS's transmit beamforming matrix, RIS reflection coefficients, and the selection matrix that determines the working modes for each unite of the hybrid RIS. Since this design problem is not convex, we propose an alternating optimization based method to solve this problem. Eventually, upon the simulation analysis, we demonstrate that the performance achievable by the proposed scheme is significantly better than the counterparts assisted solely by the active or passive RIS.

Abstract In recent years, deep artificial neural networks have attracted much attention and have been applied in various fields because they surpass the parameter fitting effect of traditional methods under the condition of data convergence. On the other hand, limited transmission bandwidth and storage capacity make image compression necessary in communication. Here, a compression algorithm that combines the K‐means clustering algorithm with the neural network algorithm is proposed. First, the pixel points of the image are clustered by K‐means algorithm in order to reduce the amount of data input to the neural network algorithm. Secondly, neural network is used to extract image features which realizes further compression. The experiment results show that the peak signal‐to‐noise ratio (PSNR) is 33.48 dB at most with compression ratio at 32:1. The ablation experiment shows that the run time speeds up 9.5% compared to the algorithm without K‐means clustering. Comprehensive comparison experiment shows that the average PSNR is 30.09 dB, which is larger than other baseline approaches. The proposed algorithm is an efficient solution for image compression.

With the emergence of the fifth generation (5G) era, the development of the Internet of Things (IoT) network has been accelerated with a new impetus, making it imperative to strive for a more reliable and efficient network environment. To accomplish this, we introduce and investigate a novel proposal for the intelligent reflecting surface (IRS) enabled uplink secure receive spatial modulation (SM), named IRS-USRSM, to resolve the security issues arising from the open wireless transmission environment in the 5G IoT network. In the IRS-USRSM scheme, we assume that the passive eavesdropper is directly connected to the uplink user and occasionally connected to the IRS. To achieve enhanced secrecy with finite alphabet inputs, a joint transmitter perturbation and IRS reflection design for physical layer security is proposed to guarantee secure and reliable transmission of IRS-USRSM. Specifically, two categories of IRS-based random phase compensation strategies, namely, random perturbation compensation and random path synthesize, along with maximum likelihood detection and suboptimal detection are proposed to meet the variant design requirements between achieved performance and system cost. Furthermore, in order to evaluate the performance limits of the IRS-USRSM, the closed-form results of average bit error probabilities and discrete-input continuous-output memoryless channel capacities are derived using the method of moment generating function. Simulation results are presented to verify the correctness of our theoretical analyses, as well as to demonstrate the efficiency and superiority of the proposed IRS-USRSM scheme.

In this paper, we investigate a covert cooperative backscatter communication (BackCom) network under residual hardware impairments (RHIs) that are unavoidable in practical radio frequency transceivers and may have an impact on the communication covertness and reliability. By exploiting the availability of statistical channel state information, we derive analytic expressions for the eavesdropper's detection error probability (DEP) and system outage probability (SOP) to evaluate the system covertness and communication reliability, respectively. Using the derived results, we prove that RHIs can improve the communication covertness but significantly degrade the outage performance. We also show that under RHIs and extremely high transmit signal-to-noise levels, the system outage performance has a non-zero asymptote, while the DEP still approaches one, similar to the scenario with perfect hardware. Computer simulations corroborate our analytical results.

In this paper, we investigate the physical layer security for simultaneous transmission and reflection (STAR) reconfigurable intelligent surface (RIS) aided systems. We consider a more practical case where multiple eavesdroppers can wiretap the transmitted signals from the access point (AP) to the legitimate users located in the transmission and reflection regions, respectively. An artificial noise (AN) aided secrecy transmission scheme is then proposed, aiming to improve the secrecy performance. The transmit beamforming and AN vectors of AP as well as the transmission and reflection coefficients are jointly optimized to maximize the sum secrecy rate. To effectively solve the formulated problem, it is firstly divided into two subproblems, and then the methods of semi-definite relaxation and Sion’s minimax theorem are employed to deal with the subproblems. By alternatively solving the sub-problems, the optimal solutions are obtained. Simulation results demonstrate the effectiveness of the proposed scheme compared to the conventional scheme without AN and STAR-RIS, and also bring useful insights into the design of secrecy communications in STAR-RIS aided systems with multiple eavesdroppers.

To address the security issues in wireless transmission communication systems with finite-alphabet inputs, a secure uplink reception scheme based on receiver spatial modulation is proposed to ensure high security of the wireless transmission system while achieving high spectrum efficiency. The proposed scheme introduces disturbance phase updates at the transmitter end and disturbance compensation at the intelligent reflecting surface (IRS) to ensure both the spectrum efficiency and physical layer security of the wireless transmission system. This enables the legitimate receiver to correctly receive the signal at maximum power while preventing eavesdroppers from accurately intercepting the signal. In this paper, we propose two phase compensation schemes, namely, element-wise random disturbance compensation (ERDC) and group-wise random disturbance compensation. Furthermore, two eavesdropping scenarios, referred to as ideal eavesdropping and jamming eavesdropping, are thoroughly investigated and considered. We introduce the maximum likelihood detection to reliably detecte the indices of the designed receive antenna and the based-band modulated signal, and the closed-form expressions of error performance with ERDC scheme are deduced. Finally, in the simulated and numercial results, the performance analysis verifies error performance discrepancy based security performance evaluation of the proposed scheme, demonstrating its effectiveness and superiority.

In this article, we propose a novel system model for a wireless-powered mobile edge computing (MEC) network, where the Internet of Things (IoT) nodes perform partial offloading to the MEC server via hybrid backscatter communication (BackCom) and active radio (AR) following an orthogonal frequency division multiple access protocol, and maximize the system computation bits (SCBs). For the case of the system having more subchannels than IoT nodes, we formulate the SCB maximization problem that requires the joint optimization of the transmit power and time, subchannel allocation, computation frequency, and time of the MEC server, as well as the IoT nodes’ BackCom time and reflection coefficients, transmit power and time for AR-based offloading, local computing time and frequencies, subject to the MEC server’s computation capacity and the Quality of Service (QoS) and energy-causality constraints of each IoT node. By applying the proof by contradiction and time-sharing relaxation, we transform the formulated problem into a convex one and then solve it by using the existing convex tools. For the case of the system having less subchannels than IoT nodes, we propose a dynamic subchannel allocation scheme that allows each IoT node to choose one task-offloading mode from three modes: 1) HAPR; 2) BackCom only; and 3) AR only, while ensuring that no more than one IoT node occupies a subchannel at any time. The SCB is maximized by first determining the subchannel allocation and mode selection of each IoT node and then optimizing the remaining resource allocation for the MEC server and all IoT nodes under the obtained subchannel assignment and mode selection. Simulations validate the superior performance of the proposed schemes over several benchmark schemes from the SCB perspective.

Satellite-ground integrated vehicular network can provision ubiquitous and unlimited network connectivity for massive vehicles, and is expected to play a vital role in 6G-supported intelligent transportation systems (ITS). However, due to its high-dynamic channel environments and limited satellite payload, the uplink synchronization has become a major bottleneck to restrict vehicular communication performance. Focusing on maintaining reliable uplink synchronization, we propose an efficient timing advance (TA) update approach in this paper. Specifically, an enhanced preamble format is first presented based on the periodical pairing sounding reference signals (SRSs), which enables the satellite to continuously track uplink timing variation with a low signaling overhead. By taking full advantage of all the fourth-order autocorrelation produces from the received preamble, we further design a novel timing metric consisted of the correlation and differential normalization functions, which is capable of having a considerably increased correlation length and shaper mainlobe, as compared to the existing ones. Through theoretical performance analysis, it is indicated that the proposed approach not only notably promotes class distance between the correct and wrong timing indexes, but also can achieve the immunity to multi-path effect and large carrier frequency offset (CFO), while having a reduced computational complexity. Simulation results in a typical low-earth-orbit (LEO) scenario reveal the superiority of our approach in terms of the false alarm probability, the missed detection probability, as well as the timing mean square error.

Reconfigurable intelligent surface (RIS) assisted spatial-domain pattern index modulations (PIMs) are recently emerged as promising transmission candidates for the next generation wireless. In this research, we are motivated to investigate a novel augmented PIM (APIM) framework empowered by RIS, such that the potentials of PIM and RIS can be merged for realizing energy efficient high data rate transmissions. In contrast to the spatial-domain PIM, the proposed APIM is implemented by utilizing the indices of activated patterns of the transmitter, receiver and the RIS to modulate information. To maximize the signal power observed with the desired receive pattern, the element-wise and subset-wise phase alignment based reflection designs are provided for the RIS, which may be imposed with different requirements regarding the reflection implementation complexity. Based on the principle of maximum-likelihood estimation, we introduce the optimal joint detection (OJD) to accomplish detecting of APIM signals. Furthermore, we analyze the error and capacity performance of the APIM systems employing the OJD, so as to evaluate the performance limits achievable by the proposed APIM. Finally, simulation results demonstrate the efficiency of our APIM scheme and verify the accuracy of the performance evaluation.

In mutualistic cooperative ambient backscatter communications (AmBC), Internet-of-Things (IoT) device sends its information to a desired receiver by modulating and backscattering the primary signal, while providing beneficial multipath diversity to the primary receiver in return, thus forming a mutualism relationship between the AmBC and primary links. We note that the hardware impairments (HIs), which are unavoidable in practical systems and may significantly affect the transmission rates of the primary and AmBC links and their mutualism relationships, have been largely ignored in the study of mutualistic cooperative AmBC networks. In this paper, we consider a mutualistic cooperative AmBC network under HIs, and study the impacts of HIs on the achievable rates of the primary link and the AmBC link. In particular, we theoretically prove that although HIs degrades the rate of each link, the mutualism relationship between the AmBC and primary links is maintained, i.e., the rate of the primary link in the mutualistic cooperative AmBC network is still higher than that without the AmBC link. The closed-form rate expressions of both the AmBC and primary links are derived. Computer simulations are provided to validate our theoretical analysis.

In the emergency communication scenario, how to quickly collect ground users (GUs) information and carry out reliable transmission has become a difficult point. In this paper, we consider the unmanned aerial vehicle (UAV)-assisted emergency communication system based on uplink non-orthogonal multiple access (NOMA). For the considered system, an alternative optimization scheme for the space location of UAV and resource allocation is proposed to maximize the uplink throughput from the UAV to base station (BS). The original joint optimization problem turns out to be non-convex and is divided into two sub-problems. Firstly, we optimize the space location of UAV by using particle swarm optimization algorithm, which has significant advantages in solving the optimal space location problem. Secondly, by using the tools of the first-order Taylor expansion and successive convex approximation, we jointly optimize the bandwidth allocation coefficients and transmit powers of GUs to maximize the throughput from the UAV to BS. Finally, simulation results verify our proposed scheme is superior in terms of the throughput for UAV-assisted emergency communication system compared with the existing scheme and the baseline schemes.

In this paper, the non-orthogonal multiple access (NOMA) and simultaneous wireless information and power transfer (SWIPT) are applied to wireless caching networks (WCN). Particularly, in the content pushing stage, base station sends the most popular contents to helpers with NOMA and the helpers harvest energy from the received signals. Then, the helpers send the requested files to the users with NOMA in the content delivery stage. In the content pushing stage, we minimize the maximum delay of the helpers by jointly optimizing the power allocation and time switching factors. Due to the non-convexity of the problem, the bisection method and two-layer iterative algorithm are devised to solve the problem. In the content delivery stage, the energy efficiency (EE) optimization problem is formulated under the quality-of-service, energy harvesting, and transmit power constraints. By using the nonlinear fractional programming theory and the Lagrange dual method, the original optimization problem is solved and the optimal power and time allocation coefficients are obtained. Simulation results show that the proposed algorithm reduces the delay of the helpers and improves the EE performance of helpers compared to the benchmark schemes.

Unmanned aerial vehicle (UAV) carrying intelligent reflecting surface (IRS) can serve as an aerial platform to improve the coverage area and transmission performance of traditional wireless network. In this paper, we investigate the weighted sum rate maximization problem for a UAV-assisted and IRS-based multi-user communication system. Specifically, by applying the alternating optimization approach, we propose a two-phase approach to effectively optimize the number of activated reflective elements, the precoding matrix, the phase shift, and the UAV location. Numerical results validate that the proposed approach can converge at a faster rate and improve the weighted sum rate performance.

In this article, we propose a wireless powered opportunistic cooperative backscatter communication network, where an Internet of Things (IoT) node conveys information to its associated receiver via backscatter communications with the help of a hybrid access point (HAP) in each transmission block. The HAP provides energy signals for the IoT node in the first half transmission block, and continues to do or relays the IoT node’s signal to the receiver via decode-and-forward protocol in the second half transmission block. We investigate under which condition the HAP serves as the relay node in the second half transmission block in terms of the achievable throughput. To this end, a mixed integer non-convex optimization is formulated to maximize the throughput of the IoT node by optimizing the power reflection coefficient (PRC) of the IoT node and the operation mode of the HAP during the second half transmission block, while meeting the energy-causality constraint of the IoT node. We derive the closed-form expressions for the optimal PRC and operation mode, based on which a scheme is proposed to determine the optimal operation mode of the HAP. Simulations validate the derived results and study the impacts of various parameters on the optimal operation mode and throughput.

Mutualistic cooperative ambient backscatter communications (AmBC) have been proposed to improve the spectrum and energy efficiencies of Internet-of-Things (IoT) systems, where a primary link (from a primary transmitter to a primary receiver) and an AmBC link (from an IoT device to the same primary receiver) form a mutualism relationship. We note that hardware impairments (HIs), which are unavoidable in practical systems and may significantly affect the transmission rates of the primary and AmBC links and their mutualism relationships, have been largely ignored in the study of mutualistic cooperative AmBC networks. In this paper, we study a mutualistic cooperative AmBC network with HIs at all the active transceivers and a non-linear energy harvesting circuit at each IoT device. We derive closed-form rate expressions for both the AmBC and primary links and theoretically prove that the mutualism relationship between the AmBC and primary links is maintained under HIs, i.e., the rate of the primary link in the mutualistic cooperative AmBC network is still higher than that without the AmBC link. To maximize the weighted sum rate of all links in a cooperative AmBC network under HIs, we propose two resource allocation schemes for two scenarios with a single link and multiple AmBC links, respectively. For the single AmBC link case, we derive the optimal transmit power of the primary transmitter and the optimal power reflection coefficient of the IoT device in closed forms. For the scenario with multiple AmBC links, the weighted-sum-rate maximization problem is transformed into a convex one and solved with convex optimization tools. Computer simulations validate our theoretical results and that our proposed schemes outperform the benchmark schemes in terms of the weighted sum rate.

In this paper, we consider a backscatter-assisted wireless powered mobile edge computing (MEC) network, where multiple Internet-of-Things (IoT) nodes harvest energy from the energy signals transmitted by a power beacon (PB) and utilize the harvested energy for local computing and task offloading via hybrid backscatter communication (BackCom) and active transmission (AT). Considering the limited computation capacity of the MEC server and the quality-of-service (QoS) and energy-causality constraints per IoT node, we propose two resource allocation schemes to maximize the total computation bits of all the IoT nodes and the system computation energy efficiency (EE), respectively, by jointly optimizing the computation frequency and time of the MEC server and each IoT node, the transmit power of the PB and each IoT node, and the BackCom power reflection coefficient and the time for energy harvesting (EH), BackCom, and AT of each IoT node. The non-convex computation bits maximization problem is transformed to a convex one by introducing a series of auxiliary variables and proof by contradiction, and then solved by the existing convex tools. The system computation EE maximization is a non-convex nonlinear programming problem. We propose a two-layer iterative algorithm to solve it optimally and devise a reduced-complexity iterative algorithm to solve it sub-optimally by leveraging the block coordinate decent technique. Computer simulations validate the convergence of the proposed iterative algorithms and their superior performance over the benchmark schemes in terms of the computation bits or EE.

Transmission delay is critical to time-sensitive and power-limited Internet of Things (IoT) systems. This work proposes a hybrid transmission scheme, which enables energy-constrained sensor nodes (SNs) to deliver data to an information fusion via a hybrid of backscatter communications (BackCom) and wireless powered active communications (WPAC). Considering a non-linear energy harvesting (EH) model for each SN, we formulate a non-convex optimization problem to minimize the total transmission time of all SNs while satisfying the minimum throughput requirement for each SN, by jointly optimizing the time allocation between BackCom and active communications (AC), the transmit power and the power reflection coefficients of each SN, and the transmit power of the energy source (ES). We first determine the optimal transmit power of the ES by contradiction and then transform the non-convex problem into a convex one by introducing a series of auxiliary variables. We theoretically prove that the minimum total transmission time is achieved when each SN exhausts all the harvested energy and does not work in the pure BackCom mode. Simulation results show that the proposed scheme achieves a much shorter total transmission time than the existing schemes, e.g., binary transmission scheme, WPAC, and pure BackCom.

Mobile-edge computing (MEC) is considered as a promising technology to enable low latency applications while consuming less energy, and nonorthogonal multiple access (NOMA) is regarded as a hopeful method of increasing spectrum efficiency and the wireless network capacity. In this article, we consider a NOMA-MEC-based Internet-of-Things (IoT) network, and propose a joint optimization framework to maximize the effective system capacity, i.e., the number of IoT devices whose tasks are processed successfully, and meanwhile to maximize the total energy saving. First, we concentrate on improving the effective system capacity from the wireless side by introducing NOMA, and from the IoT device side by task offloading decision optimization, where distributed optimization is conducted and closed-form solution is obtained. Then, we maximize the total energy saving also from two aspects, i.e., the device-side computation resource allocation, and the wireless side joint admission control, user clustering, orthogonal subcarrier assignment, and transmit power control, where we resort to graph theory and propose a low-complexity heuristic algorithm to solve it. Abundant simulation results demonstrate our proposed joint optimization algorithm performs well in both effective system capacity optimization and energy saving maximization.

Emerging smart Internet-of-Things (IoT) applications are increasingly relying on mobile-edge computing (MEC) networks, where the energy efficiency (EE) of computation is one of the most pertaining issues. In this article, considering the limited computation capacity at the MEC server and a practical nonlinear energy harvesting (EH) model for IoT devices, we propose a scheme to maximize the system computation EE (CEE) of a wireless power transfer (WPT) enabled nonorthogonal multiple access (NOMA)-based MEC network by jointly optimizing the computing frequencies and execution time of the MEC server and the IoT devices, the offloading time, the EH time and the transmit power of each IoT device, as well as the transmit power of the power beacon (PB). We formulate the joint optimization into a nonlinear fractional programming problem and devise a Dinkelbach-based iterative algorithm to solve it. By means of convex theory, we derive closed-form expressions for parts of the optimal solutions, which reveal several instrumental insights into the maximization of the system CEE. In particular, the system CEE increases as the optimal computing frequencies of both the IoT devices and the MEC server decrease, and the system CEE is maximized when the MEC server and the IoT devices use the maximum allowed time to complete their computing tasks. Simulation results demonstrate the superiority of the proposed scheme over benchmark schemes in terms of system CEE.

Blockchain technology has been widely used in many fields. However, the proof of work (PoW) problem in the mining process of mobile devices requires a large amount of computing resources and energy consumption, which brings huge challenges to mobile devices. Mobile edge computing (MEC) can effectively solve the above problems, allowing mobile devices to offload tasks to edge servers to relieve the pressure of limited computing resources on mobile devices. Nonorthogonal multiple access (NOMA) is good at improving spectrum efficiency, so that the system can accommodate more users. In this paper, we propose a new NOMA-based MEC-enabled blockchain framework. Under the conditions of a given task execution deadline, the decision of offloading, local computing resource allocation, user clustering and admission control, and transmit power control is jointly optimized to minimize the total cost of the system. Since the problem is hard to solve, we decouple it into subproblems for low-complexity solutions. First, we propose two heuristic algorithms to obtain the binary offloading decision and user association, and then closed-form solutions of local resource allocation and transmit power control are obtained under the required delay constraints. Simulation results show that our proposed algorithms perform good in cost reduction compared with other baseline algorithms.

Filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) has been studied as an alternative waveform for post-orthogonal frequency division multiplexing (OFDM) wireless communications. However, due to the intrinsic imaginary inter-carrier/inter-symbol interference of FBMC/OQAM signals, accurate channel estimation and equalization remain open issues for FBMC/OQAM systems in dispersive channels. In this article, two new signal models of the analysis filter bank (AFB) output are first derived in the absence of any channel assumption and in the presence of a relaxed channel assumption, respectively. Based on the new-built models, initial channel estimation with least squares (LS) (IniCE-LS) is presented and its output is used as the initial condition of recursive weighted LS (RWLS) estimation; moreover, non-iterative and iterative channel equalization combined with RWLS channel estimation (i.e., N-ICE-RWLS and ICE-RWLS) are proposed by using the real-time reconstructed FBMC/OQAM data symbols as pilots. The proposed N-ICE-RWLS and ICE-RWLS schemes not only allow to improve channel estimation and equalization performance, but also achieve high transmission efficiency. Considering the requirements of practical application, we further develop the low-complexity implementation methods of N-ICE-RWLS and ICE-RWLS. All these schemes are validated by analyzing the channel estimation accuracy and bit error rate (BER) over various frequency selective channels.

Merchants of e-commerce Websites expect recommender systems to entice more consumption which is highly correlated with the customers' purchasing propensity. However, most existing recommender systems focus on customers' general preference rather than purchasing propensity often governed by instant demands which we deem to be well conveyed by the questions asked by customers. A typical recommendation scenario is: Bob wants to buy a cell phone which can play the game PUBG. He is interested in HUAWEI P20 and asks “can PUBG run smoothly on this phone?” under it. Then our system will be triggered to recommend the most eligible cell phones to him. Intuitively, diverse user questions could probably be addressed in reviews written by other users who have similar concerns. To address this recommendation problem, we propose a novel Question-Driven Attentive Neural Network (QDANN) to assess the instant demands of questioners and the eligibility of products based on user generated reviews, and do recommendation accordingly. Without supervision, QDANN can well exploit reviews to achieve this goal. The attention mechanisms can be used to provide explanations for recommendations. We evaluate QDANN in three domains of Taobao. The results show the efficacy of our method and its superiority over baseline methods.

Immersive virtual reality (VR) video is becoming increasingly popular owing to its enhanced immersive experience. To enjoy ultrahigh resolution immersive VR video with wireless user equipments, such as head-mounted displays (HMDs), ultralow-latency viewport rendering, and data transmission are the core prerequisites, which could not be achieved without a huge bandwidth and superior processing capabilities. Besides, potentially very high energy consumption at the HMD may impede the rapid development of wireless panoramic VR video. Multiaccess edge computing (MEC) has emerged as a promising technology to reduce both the task processing latency and the energy consumption for HMD, while bandwidth-rich terahertz (THz) communication is expected to enable ultrahigh-speed wireless data transmission. In this article, we propose to minimize the long-term energy consumption of a THz wireless access-based MEC system for high quality immersive VR video services support by jointly optimizing the viewport rendering offloading and downlink transmit power control. Considering the time-varying nature of wireless channel conditions, we propose a deep reinforcement learning-based approach to learn the optimal viewport rendering offloading and transmit power control policies and an asynchronous advantage actor-critic (A3C)-based joint optimization algorithm is proposed. The simulation results demonstrate that the proposed algorithm converges fast under different learning rates, and outperforms existing algorithms in terms of minimized energy consumption and maximized reward.

Ambient backscatter communications (AmBackComs) have been recognized as a spectrum- and energy-efficient technology for the Internet of Things, as it allows passive backscatter devices (BDs) to modulate their information into the legacy signals, e.g., cellular signals, and reflect them to their associated receivers while harvesting energy from the legacy signals to power their circuit operation. However, the co-channel interference between the backscatter link and the legacy link and the nonlinear behavior of energy harvesters at the BDs have largely been ignored in the performance analysis of AmBackComs. Taking these two aspects, this article provides a comprehensive outage performance analysis for an AmBackCom system with multiple backscatter links, where one of the backscatter links is opportunistically selected to leverage the legacy signals transmitted in a given resource block. For any selected backscatter link, we propose an adaptive reflection coefficient (RC), which is adapted to the nonlinear energy harvesting (EH) model and the location of the selected backscatter link, to minimize the outage probability of the backscatter link. In order to study the impact of co-channel interference on both backscatter and legacy links, for a selected backscatter link, we derive the outage probabilities for the legacy link and the backscatter link. Furthermore, we study the best and worst outage performances for the backscatter system where the selected backscatter link maximizes or minimizes the signal-to-interference-plus-noise ratio (SINR) at the backscatter receiver. We also study the best and worst outage performances for the legacy link where the selected backscatter link results in the lowest and highest co-channel interference to the legacy receiver, respectively. Computer simulations validate our analytical results and reveal the impacts of the co-channel interference and the EH model on the AmBackCom performance. In particular, the co-channel interference leads to the outage saturation phenomenon in AmBackComs, and the conventional linear EH model results in an overestimated outage performance for the backscatter link.

Non-orthogonal multiple access (NOMA) based mobile edge computing (MEC) networks can enhance delay-constrained computation. Most of the existing works focus on the energy or delay minimization, and the study on NOMA based MEC in terms of successful computation probability has been limited, which motivates this work. In particular, randomly deployed edge computing users are modeled as the homogeneous Poisson Point Process and are divided into two groups namely center group (C-group) and edge group (E-group) for uplink NOMA grouping. We propose a new hybrid offloading scheme in a NOMA-based MEC network that can operate in three different modes, namely partial offloading, complete local computation, and complete offloading. We firstly consider a NOMA grouping scenario where a user from the C-group and a user from the E-group are each given a fixed location. The probability that the computation can be completed within the given delay budget is derived and the optimal parameters, i.e., the time for offloading, the power allocation, and the offloading ratios for the two fixed users, are obtained. It reveals that the optimal offloading ratios are determined by the difference of the computational capability between the edge computing user and the MEC server, and that the locations of users have a big impact on the successful computation probability. Inspired by this, we further study three distance-dependent NOMA grouping schemes in the MEC offloading. Specifically, we provide closed-form mathematical expressions of the successful computation probability and its partial optimal solutions for these three schemes. Simulation results verify the accuracy of the analytical results and compare the performance of the proposed offloading scheme with the existing schemes. Insights on the pros and the cons of different user selection schemes are also provided.

Mobile edge computing enabled vehicular networking has appeared as a promising solution to the emerging resource hungry vehicular applications. In this paper, we study the computation offloading in a cognitive vehicular network that reuses the TV white space (TVWS) bands. We propose to maximize the average economical profit of the service provider by jointly considering communication and computation resource allocation, while guaranteeing network stability and the QoS of TVWS primary users. Based on Lyapunov optimization, we design an per-frame algorithm to tackle the joint optimization problem, where we first derive the closed-form solution for computation resource allocation, and then develop a continuous relaxation and Lagrangian dual decomposition based iterative algorithm for radio resource allocation. Simulation results demonstrate that the proposed algorithm can flexibly balance the profit-delay tradeoff, and can improve the economical profit of the service provider significantly as compared with the existing schemes.

Traffic-related messages exchange (TME) is considered as a powerful approach to improve traffic safety and efficiency in vehicular networks. However, TME assumes all vehicles always are honest, and thus offering opportunities for attackers to fake traffic-related messages. To combat such threat, recent efforts have been made to trust mechanism. In this article, a vulnerability for trust mechanism is found, that is, the ratings from initiator vehicles (IVs) are generally unchecked. Such ratings corresponding to the truth of traffic-related events can be exploited by attackers to disturb trust mechanism. Specially, attackers would form a clique to help with each other in an inside-and-outside collusive (IOC) manner. One of the IOC attackers can disguise as an IV who sends the rating in accordance with the traffic-related messages of his conspirators, result in promoting their trust value quickly. With high trust value, attackers can escape the detection of trust mechanism. We conduct an in-depth investigation on IOC attack and propose a defense scheme called TFAA from the design ideas of trust fluctuation association analysis. In addition, the trust data management of central and distributed trust mechanism may be unsuitable for vehicular networks. To support the trust data management for the TFAA scheme, we also design a semi-distributed trust data storage scheme called TruChain with the combination of consortium blockchain and vehicular regions partition. The simulation results show that the TFAA scheme can enhance the accuracy of trust value evaluation, and thus successfully reducing the power of IOC attack against TME.

This paper studies a cognitive small cell downlink network, where one cognitive base station (CBS) transmits information to a cognitive user and transfers energy to energy harvesting receivers (EHRs). The spectrum sensing interval, the spectrum sensing time, and the beamforming matrices of the CBS are jointly optimized to maximize the energy efficiency (EE) of the CBS and to minimize the energy cost of the CBS under both the bounded channel state information (CSI) model and the probabilistic CSI model. The interference constraints of the macrocell users, the secrecy rate constraint, the transmit power constraint of the CBS and the energy harvesting constraints of the EHRs are all considered in this paper. All the three formulated optimization problems are nonconvex, for which semidefinite relaxation, a 2-D line search method, S-Procedure, and Bernstein-type inequalities are exploited. This paper also derives the conditions under which the EE maximization problem and the energy cost of the CBS minimization problem using the bounded CSI model have rank-one solutions. Simulation results demonstrate that the proposed algorithms have significant gain on the CBS EE under the perfect CSI and also gain on the CBS energy cost under imperfect CSI, all compared to the benchmark scheme.

This paper investigates an energy‐constrained two‐way multiplicative amplify‐and‐forward (AF) relay network, where a practical nonlinear energy harvesting (NLEH) model is equipped at the relay to realize simultaneous wireless information and power transfer (SWIPT). We focus on the design of dynamic power splitting (DPS) strategy, in which the PS ratio is able to adjust itself according to the instantaneous channel state information (CSI). Specifically, we first formulate an optimization problem to maximize the outage throughput, subject to the NLEH. Since this formulated problem is nonconvex and difficult to solve, we further transfer it into an equivalent problem and develop a Dinkelbach iterative method to obtain the corresponding solution. Numerical results are given to verify the quick convergence of the proposed iterative method and show the superior outage throughput of the designed DPS strategy by comparing with two peer strategies designed for the linear energy harvesting (LEH) model.

In this paper, we consider the simultaneous wireless information and power transfer assisted cooperative nonorthogonal multiple access (SWIPT-CNOMA) system, in which the near NOMA users with strong channel conditions serve as energy harvesting (EH) relays to help the far NOMA users with poor channel conditions. For the considered system, a new power splitting (PS) protocol for EH relay is proposed and its impact on the outage performance of both near and far NOMA users is studied. The analytical expressions of both outage probability and system throughput are further derived for delay-sensitive transmission mode. Simulation results are provided to verify the derived results and reveal the advantages of the proposed protocol.

Inspired by random matrix theory, a quantity of eigenvalue based cooperative spectrum sensing methods have been proposed. The results are based on the asymptotical assumptions in need of large numbers of users and samples, which result in inferior performance with a few users. In this paper, sensing methods based on maximum eigenvalue and minimum eigenvalue of LDLT decomposition are proposed respectively with a view to improve the accuracy of decision threshold by means of hard decision criterion. The corresponding expressions of false alarm probability are also derived. Finally, both theoretical analyses and simulations demonstrate that the proposed two methods perform better than the existing eigenvalue based sensing methods for accurate decision threshold.

Spectrum sensing based on absolute value cumulating (AVC sensing) has attracted much attention due to its effectiveness in the presence of Laplaican noise and simpleness of implementation. In this paper, we investigate its detection performance and optimal detection threshold. Specifically, based on the derived mean and variance of the test statistic, an accurate expression of the detection probability is given. Using the accurate expression, an optimization problem is formulated to minimize the total error rate of AVC sensing by optimizing the detection threshold with a constraint on false alarm probability, and the optimal detection threshold is derived. Numerical results are provided to support our work.

Cooperative spectrum sensing CSS for vacant spectrum is one of the key techniques in cognitive radio networks. However, most of CSS schemes assume secondary users SUs to always tell the truth, and thus offering opportunities for malicious SUs to launch the spectrum-sensing data falsification attack SSDF attack. To combat such vicious behaviors, recent efforts have been made to trust evaluation. In this paper, we argue that powering CSS with traditional trust schemes is not enough. The intermittent SSDF attack ISSDF attack is found in this paper. Unlike SSDF, ISSDF attackers can maintain high trustworthiness in an alternant process of reporting true or false sensing data, resulting in difficultly detecting malicious SUs in trust schemes. To defend against ISSDF attack for CSS, a novel-trust scheme using dynamic evaluation is proposed in this paper. Simulation results show that this scheme can successfully reduce the power of ISSDF and thus can ensure the performance of CSS. Copyright © 2015John Wiley & Sons, Ltd.

For micromotion targets with rotating parts, the inverse synthetic-aperture-radar image of the main body may be shadowed by the micro-Doppler. To solve this problem, this paper proposes an imaging algorithm based on the complex-valued empirical-mode decomposition. First, the radar echoes are decomposed into a series of complex-valued intrinsic-mode functions (IMFs). Then, the IMFs from the rotating parts and those from the main body are separated according to the characteristics of their zero-crossings. Finally, the well-focused imaging of the main body via traditional imaging algorithm and the accurate parameter estimation of the rotating part can be obtained. Both the imaging results for the simulated and measured data are given to verify the validity of the proposed algorithm.

A 3-D inverse synthetic aperture radar imaging method for rapidly spinning targets, i.e., a generalized Radon transform (GRT)-CLEAN algorithm, is proposed in this paper. The signal model is first changed into an equivalent high-speed turntable model after the compensation of the translational motion. Second, based on the relationship between the range profile variation of the spinning targets and the scatterers' positions, the GRT is utilized to estimate the scatterers' positions. Finally, combining the GRT with the modified CLEAN approach, the parameters of each scatterer and, thus, the 3-D image of the targets can be obtained. In addition to the development of the GRT-CLEAN algorithm, the estimation and compensation of the linear translational motion error in the imaging process is also considered in this paper. Good images yielded confirm the effectiveness of the GRT-CLEAN algorithm in the simulations.

Two approaches for reducing peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) are proposed that are relied on a set of cyclically shifted phase sequences (CSPS) and implemented using the time domain circular convolution. After multiplying CSPS with the frequency domain data, the signal candidates can be expressed as weighted sum of the circularly shifted OFDM time domain data in the first method, which is called CSPS method. In the second method, weighted coefficients for generating the signal candidates in CSPS method are optimally selected to improve its performance; thus, the second method is referred to as optimised CSPS (OCSPS) method. The performances of the CSPS and OCSPS methods are evaluated using simulated data and compared with those of selective mapping (SLM) and partial transmit sequences (PTS). The simulation results show that both the CSPS and OCSPS methods can reduce the PAPR effectively, and that the OCSPS performs even better than the CSPS. The OCSPS can achieve the same performance as compared to the PTS. A distinct feature of the proposed methods is that only one inverse discrete Fourier transform is needed, and thus, the candidates can be calculated in time domain directly.

In the recent literature, only single-range Doppler interferometry (SRDI) has been considered for radar imaging of space debris with dimensions smaller than the radar range resolution. Considering the typical trajectories and dynamics of space debris, a single-range matching filtering (SDMF) approach is proposed in this letter. SDMF permits obtaining a 2-D image of space debris by using range unit cross-range echo data and by matched filtering the signals with different turning radii. The analysis and simulations show that, compared with SRDI, the proposed approach has less computational load and better images, particularly, for targets with smaller size.