Signal classification plays a pivotal role in modern digital communications by ensuring efficient and secure data transmission across critical tasks, such as automatic modulation classification (AMC) and wireless technology classification (WTC). In recent years, the advent of Deep Learning (DL) has revolutionized signal classification by automating feature extraction and significantly improving classification accuracy in dynamic and noisy environments. Despite these advancements, effectively addressing the challenges of multi-task signal classification in complex scenarios remains an open problem. To tackle this, this paper presents a novel knowledge-and-data-driven multi-scale gated multi-layer perceptron (KDD-MSGMLP) framework, specifically designed to concurrently handle AMC and WTC tasks. The framework combines environmental side information as prior knowledge with advanced deep features, thereby substantially enhancing both robustness and accuracy in classification. At its core, the KDD-MSGMLP framework is powered by an innovative MSGMLP backbone network. This backbone effectively integrates multi-scale convolution with residual connections to enable shared feature embedding across tasks, while its gated multi-layer perceptron modules focus on refining task-specific features. Such a design ensures robust and efficient signal classification, making it well-suited for complex wireless communication environments. Simulation results demonstrate that the proposed framework achieves performance improvements of over 1% in AMC and between 2% and 4% in WTC. Moreover, it shows significant potential for enhancing performance and efficiency in complex wireless scenarios. The codes supporting this work are available on GitHub1.

The external power supply requirement of intelligent reflecting surfaces (IRS) restricts its deployment and application. To address this issue, self-sustainable IRS that can harvest energy from wireless signals significantly enhances the convenience and feasibility of IRS deployment. This paper investigates a novel IRS-aided simultaneous wireless information and power transfer (SWIPT) system, where IRS adopts power splitting (PS) protocol, time switching (TS) protocol or element splitting (ES) protocol to extract energy from the incident signals for sustaining its operation. Our aim is to maximize the weighted sum rate (WSR) while accounting for the constraints in terms of maximum transmit power, IRS reflection coefficients, the energy harvesting requirements of both users and the IRS. The WSR maximization problem is non-convex due to the coupling among variables. For the formulated problem, we propose an alternating optimization (AO) framework to transform it into several subproblems and solves them iteratively. Specifically, we utilize successive convex approximation (SCA) technique to tackle the non-convex optimization challenges in both transmit and reflective beamforming subproblems. Finally, simulation results demonstrate that employing a self-sustainable IRS together with the proposed algorithm yields a WSR improvement of 32%–60%.

Unmanned aerial vehicles (UAVs) assisted communications have become one of the crucial approaches to enable the reliable and flexible data transmissions, particularly in ultra-reliable and low-latency scenarios, such as remote sensing, emergency response, and military long-range command transmission. In this paper, we investigate the secrecy performance of UAV-assisted short-packet transmission via non-orthogonal multiple access (NOMA), where a UAV serves as an aerial relay to forward mission-critical information from a base station to two remote users in the presence of a ground-based eavesdropper. Both the base station and UAV relay use beamforming for generating the artificial noise to disrupt the eavesdropping and enhance the security, and the UAV operates in half-duplex mode to meet resource constraints and avoid self-interference. The weighted effective secrecy rates of the two users are maximized by jointly optimizing the blocklength, transmission rate, power allocation coefficients, power-sharing factors and UAV position, which is shown to be non-convex and difficult to be solved directly. Accordingly, we decompose the problem into four sub-problems by applying the block coordinate descent (BCD) algorithm to maximize the weighted effective secrecy rate. Then, slack variables are introduced to further solve the sub-problems via successive convex approximation (SCA). Finally, simulation results are presented to demonstrate the effectiveness of the proposed scheme.

To satisfy the communication, localization, and energy demands of low-power IoT nodes, combining integrated sensing and communication (ISAC) and simultaneous wireless information and power transfer (SWIPT) can offer an innovative solution. However, carrying private information in wireless sensing beams critically increases the vulnerability of being eavesdropped by the target. In this paper, we propose a non-orthogonal multiple access (NOMA)-enhanced secure transmission strategy to counteract the internal eavesdropping for SWIPT-ISAC. We jointly optimize the transmit beamforming and power splitting to maximize the secrecy rate towards the eavesdropping by the target, ensuring the nonlinear energy harvesting (EH) and precise sensing beampatterns. Our approach enables the nodes to manage the interference through successive interference cancellation and to harvest energy from the transmitted signal. The original optimization problem is non-convex and difficult to tackle. Using the semidefinite relaxation, we convert it to convex form, and propose alternating optimization algorithm to derive the solutions. Simulation results indicate that the proposed scheme can effectively counteract the internal eavesdropping, while ensuring the nonlinear$\mathbf{E H}$and sensing performance.

Vision-Language Models (VLM) can generate plausible high-level plans when prompted with a goal, the context, an image of the scene, and any planning constraints. However, there is no guarantee that the predicted actions are geometrically and kinematically feasible for a particular robot embodiment. As a result, many prerequisite steps such as opening drawers to access objects are often omitted in their plans. Robot task and motion planners can generate motion trajectories that respect the geometric feasibility of actions and insert physically necessary actions, but do not scale to everyday problems that require common-sense knowledge and involve large state spaces comprised of many variables. We propose VLM-TAMP, a hierarchical planning algorithm that leverages a VLM to generate both semantically-meaningful and horizon-reducing intermediate subgoals that guide a task and motion planner. When a subgoal or action cannot be refined, the VLM is queried again for replanning. We evaluate VLMTAMP on kitchen tasks where a robot must accomplish cooking goals that require performing 30-50 actions in sequence and interacting with up to 21 objects. VLM-TAMP substantially outperforms baselines that rigidly and independently execute VLM-generated action sequences, both in terms of success rates (50 to 100 % versus 0 %) and average task completion percentage (72 to 100 % versus 15 to 45 %). See project site https://zt-yang.github.io/vlm-tamp-robot/ for more information.

As unmanned aerial vehicles (UAVs) increasingly operate in large-scale clusters, traditional routing protocols struggle to ensure efficient and time-sensitive packet path planning due to the growing network size and inherent mobility of UAVs. Meanwhile, despite deep learning (DL) based routing methods have shown promise in small UAV networks, their computational demands and limited scalability to large numbers of UAV nodes pose significant challenges. To address the challenges of scalability and computational demands in large-scale UAV networks, this paper proposes a novel decentralized global guided routing algorithm based on potential field. First, a potential field is constructed using a harmonic function to represent the current network status. Subsequently, leveraging this potential field, a global route is derived to define the overarching direction for data transmission. Finally, a compact neural network deployed at each node utilizes the global guidance direction and the local potential field information obtained from its surroundings to establish a specific data forwarding path within its maximum perception range. Simulation results illustrate the advantages of our proposed approach for establishing UAV paths in large-scale UAV networks.

The external power supply requirement of intelligent reflecting surfaces (IRS) restricts its deployment and application. To address this issue, self-sustainable IRS that can harvest energy from wireless signals significantly enhances the convenience and feasibility of IRS deployment. This paper investigates a novel IRS-aided simultaneous wireless information and power transfer (SWIPT) system, where the IRS adopts a power splitting (PS) protocol to extract energy from the incident signals for sustaining its operation. Our aim is to maximize the weighted sum rate (WSR) while considering the constraints in terms of maximum transmit power, IRS reflection, the harvesting energy requirements of users and IRS. The formulated problem of maximizing WSR is non-convex, which stems from the coupling among variables, making the problem complicated. To solve this problem, we propose an alternating optimization (AO) framework to transform the formulated problem into three subproblems, which enables an iterative solution approach. Specifically, we utilize successive convex approximation (SCA) technique to tackle the non-convex optimization challenges in both transmit and reflective beamforming subproblems. Simulation results demonstrate the efficacy of the proposed algorithm and substantiate the advantages of implementing self-sustained IRS for improving the WSR performance compared to other benchmark schemes.

Multimodal sentiment analysis has emerged as a critical research area. However, existing methods face significant challenges: (1) Unimodal feature extraction techniques often fail to capture the topological structure within data, and do not effectively integrate local and global information, leading to information loss. (2) Traditional multimodal fusion methods, such as concatenation, addition, and multiplication, struggle to model modality differences and inter-modal correlations. In this paper, we propose a novel multi-task hypergraph-attention framework (MTHA) to improve feature discrimination and model performance. Experimental results demonstrate that MTHA outperforms most baseline models in both sentiment classification and regression.

The openness of wireless communication poses significant privacy risks to systems enabled by unmanned aerial vehicle (UAV). In this paper, we investigate multi-antenna UAV-enabled covert communication system against an uncertainly located ground warden. The location-uncertain warden presents a heightened challenge to covert communication. We propose an effective scheme to deal with the covert communication against uncertainly located warden. In the first stage, we consider the worst-case scenario, assuming the warden is in the ideal detection threshold and the optimal position. First, we derive the ideal threshold and then use sequential quadratic programming (SQP) algorithm to determine the optimal position. In the second stage, we enhance the performance of UAV-enabled covert communication with SQP algorithm by jointly optimizing the transmission rate, transmission power, and artificial jamming power of full-duplex receiver. Our simulation results demonstrate that covert communication is achievable in the location-uncertain scenario through the proposed scheme.

To satisfy the communication, localization, and energy demands of low-power IoT nodes, combining integrated sensing and communication (ISAC) and simultaneous wireless information and power transfer (SWIPT) can offer an innovative solution. However, carrying private information in wireless sensing beams critically increases the vulnerability of being eavesdropped. In this paper, we propose two non-orthogonal multiple access (NOMA)-enhanced secure transmission strategies to counteract the internal and external eavesdropping for SWIPT-ISAC. First, we jointly optimize the transmit beamforming and power splitting to maximize the secrecy rate towards the internal eavesdropping by the target, ensuring the nonlinear energy harvesting (EH) and precise sensing beampatterns. Then, facing a more severe scenario with L external eavesdroppers, we exploit the artificial jamming with the highest power allocation to mitigate both the internal and external eavesdropping. Our approach enables the nodes to manage the interference through successive interference cancellation and to harvest energy from the jamming. The original optimization problems in both scenarios are non-convex and difficult to tackle. Using the semidefinite relaxation, we convert them to convex forms, and propose alternating optimization algorithms to derive the solutions. Simulation results indicate that the proposed schemes can effectively counteract the internal and external eavesdropping, while ensuring the nonlinear EH and sensing performance.

Physical Layer Authentication (PLA) emerges as a promising security solution, offering efficient identity verification for the Internet of Things (IoT). The advent of 5G/6G technologies has ushered in an era of extensive device connectivity, diverse networks, and complex application scenarios within IoT ecosystems. These advancements necessitate PLA systems that are highly secure, robust, capable of online processing, and adaptable to unknown channel conditions. In this paper, we introduce a novel two-stage PLA framework that synergizes channel prediction with power-delay attributes, ensuring superior performance in mobile and time-varying channel environments. Specifically, our approach employs Sparse Variational Gaussian Processes (SVGP) to accurately model and track real-time channel variations, leveraging historical data for online predictions without incurring significant computational or storage overhead. The second stage of our framework enhances the robustness of the authentication process by incorporating power-delay features, which are inherently resistant to temporal fluctuations, thereby eliminating the need for additional feature extraction in noisy settings. Moreover, our authentication scheme is designed to be distribution-agnostic, utilizing Kernel Density Estimation (KDE) for non-parametric threshold determination in hypothesis testing. Theoretical analysis underpins the generalization capabilities of our proposed method. Simulation results in mobile scenarios reveal that our two-stage PLA framework reduces complexity and significantly improves identity authentication performance, particularly in scenarios with low signal-to-noise ratios.

Although deploying unmanned aerial vehicles (UAVs) can provide line-of-sight (LoS) links to extend the coverage, it also poses severe challenges for covert air-ground transmission. ln this paper, we investigate the covert air-ground finite-blocklength communication with ground blockages, where the UAV acts as an aerial relay to achieve the long-distance transmission. First, we analyze the warden detection performance with its optimal detection threshold derived, which is the worst case for the legitimate transmission. To maximize the covert transmission rate while satisfying the covertness constraint, the blocklength, the transmit power, and the position of UAV are jointly optimized. By analyzing the monotonicity of transmit power and blocklength with respect to the relative entropy, we derive their closed-form solutions. Then, we analyze the optimal hovering position of UAV from two perspectives of the distance and the elevation angle between UAV and transmitter. Specifically, we obtain an optimal elevation angle with fixed distance and the optimal distance with fixed elevation angle through analyzing the received signal-to-noise ratio and the feasible hovering regions of UAV. Numerical results demonstrate the effectiveness of the proposed covert UAV relaying scheme with ground blockages.

Integrated sensing and communication (ISAC), by combining the communication and sensing functions in shared frequency bands, emerges as a promising technology for future wireless networks. However, the performance of ISAC may be affected by the channel fading and massive connections. In this paper, we propose a non-orthogonal multiple access (NOMA) aided ISAC scheme via intelligent reflective surface (IRS) to set up virtual line-of-sight links for multi-user communication and target sensing. Specifically, our goal is to maximize the sum rate through the joint optimization of active transmit beamforming at the base station and passive phases for reflecting at the IRS, while satisfying the sensing requirement for the target user. Since the original optimization problem is non-convex, we first decompose it into two subproblems, which are converted into convex ones by applying the successive convex approximation. Then, an alternating optimization algorithm is proposed to derive a solution to the original problem. Simulation results validate that the proposed scheme can effectively enhance the multi-user NOMA communication performance while guaranteeing the sensing quality by introducing IRS.

Cell-free network and intelligent reflecting surface (IRS) are considered as promising technologies for future network capacity and coverage improvement. They offer advantages such as low cost, low energy consumption, and meeting the requirements of green communication. However, the fixed location of IRS limits the flexibility of the entire network. To address this issue, we propose a more comprehensive cell-free network that enhances network capacity and signal coverage by utilizing the reflected signals from an airborne IRS. Our objective is to maximize the weighted transmission rate for users by jointly optimizing the base station (BS) beamforming, passive beamforming of the IRS, and the location of the UAV. Owing to the non-convex and intricate nature of the problem, we decompose it into three subproblems, employing the principles of Lagrangian duality, multi-ratio fractional programming, and the successive convex approximation (SCA) technique for resolution. Simulation results demonstrate that the proposed scheme can significantly improve the weighted transmission rate and effectively enhance the network coverage compared to the benchmarks.

Integrated sensing and communication (ISAC), as an emerging technology for 6G, raises a critical security issue that the sensing waveform may expose the private information to suspicious detection targets. In this paper, we design to utilize intelligent reflecting surface (IRS) in ISAC to enhance the secure transmission for non-orthogonal multiple access nodes, and establish an additional line-of-sight link for the detection. An IRS-aided secure transmission scheme is proposed to jointly optimize the jamming, the active transmit precoding at the base station and the passive phase reflecting at the IRS to maximize the sum secrecy rate, subject to the echo signal requirement towards the target. To address the non-convexity of the proposed problem, it is decomposed into two subproblems, enabling the optimization of the transmit jamming and precoding vectors and the phase reflecting matrix, respectively. Then, with the help of successive convex approximation, these subproblems are derived to be convex, and an alternating optimization algorithm is introduced to address the original problem. Simulations verify that the proposed scheme significantly outperforms the benchmarks, and can guarantee the sensing functioning while greatly enhancing the communication security.

Multi-hop packet routing is critical for unmanned aerial vehicle (UAV) networks to enable efficient communication between terminals in diverse environments. However, the complexity of routing design exacerbates due to interference from the environment and link instability caused by high-speed mobility. To address this challenge, we propose a harmonic function-based potential field to assess the impact of interference on UAV networks quantitatively. The proposed field maps the communication quality in terms of interference and mobility onto a virtual three-dimensional plane, providing a metric to establish routing paths. Based on this, two routing algorithms are designed to address two distinct routing requirements of UAV networks, timeliness and losslessness. Leveraging the natural adaptation to the potential field, the two proposed routing algorithms can effectively avoid interference while meeting different requirements. Simulation results demonstrate the effectiveness of the proposed potential field in representing the influences of interference and mobility. Additionally, the results validate the ability of the two routing algorithms to fulfill data communication requirements in terms of delay and accuracy while effectively mitigating interference.

Multi-hop packet routing is critical for unmanned aerial vehicle (UAV) networks to enable efficient communication between terminals in diverse environments. However, the complexity of routing design exacerbates due to interference from the environment and link instability caused by high-speed mobility. To address this challenge, we propose a harmonic function-based potential field to assess the impact of interference on UAV networks quantitatively. The proposed field maps the communication quality in terms of interference and mobility onto a virtual three-dimensional plane, providing a metric to establish routing paths. Based on this, two routing algorithms are designed to address two distinct routing requirements of UAV networks, timeliness and losslessness. Leveraging the natural adaptation to the potential field, the two proposed routing algorithms can effectively avoid interference while meeting different requirements. Simulation results demonstrate the effectiveness of the proposed potential field in representing the influences of interference and mobility. Additionally, the results validate the ability of the two routing algorithms to fulfill data communication requirements in terms of delay and accuracy while effectively mitigating interference.

Integrated sensing and communication (ISAC) systems can suffer from malicious jamming attacks due to the open nature of wireless channels. Deploying aerial intelligent reflecting surface (AIRS) can flexibly configure the propagation environment of ISAC to address this threat. In this paper, we propose an anti-jamming scheme for ISAC via AIRS. Our goal is to maximize the achievable sum rate by jointly optimizing the transmitting beamforming at the dual-function base station, as well as the phase shift matrix and deployment of AIRS, while satisfying the echo signal-to-interference-plus-noise ratio requirement of target sensing. To handle this non-convex problem with multiple coupled variables, we decompose it into three sub-problems and solve them via the alternate optimization. We first introduce auxiliary variables to convert the transmit beamforming sub-problem into a convex counterpart and solve it via semi-definite relaxation. Then, the IRS phase-shift design is transformed into an equivalent rank-constrained problem, and the penalty-based method and the first-order Taylor expansion are leveraged to calculate the passive beamforming. Finally, with the optimized active and passive beamformings, we resort to successive convex approximation to optimize the AIRS deployment. Simulation results are presented to verify the feasibility and effectiveness of the proposed scheme.

Integrated sensing and communication (ISAC), as an emerging technology for 6G, raises a critical security issue that the sensing waveform may expose the private information to suspicious detection targets. In this paper, we design to utilize intelligent reflecting surface (IRS) in ISAC to enhance the secure transmission for non-orthogonal multiple access nodes, and establish an additional line-of-sight link for the detection. An IRS-aided secure transmission scheme is proposed to jointly optimize the jamming, the active transmit precoding at the base station and the passive phase reflecting at the IRS to maximize the sum secrecy rate, subject to the echo signal requirement towards the target. To address the non-convexity of the proposed problem, it is decomposed into two subproblems, enabling the optimization of the transmit jamming and precoding vectors and the phase reflecting matrix, respectively. Then, with the help of successive convex approximation, these subproblems are derived to be convex, and an alternating optimization algorithm is introduced to address the original problem, which is guaranteed to converge. Simulations verify that the proposed scheme significantly outperforms the benchmarks, and can guarantee the sensing functioning while greatly enhancing the communication security.

In this paper, an anti-jamming design for integrated sensing and communication (ISAC) via aerial intelligent reflecting surface (AIRS) is proposed. We aim to maximize the achievable sum rate by jointly optimizing the active and passive beamformings, as well as the deployment of AIRS, while satisfying the echo signal-to-interference-plus-noise ratio requirement of target sensing. To address the non-convex problem, we decompose it into three sub-problems and solve them via the alternate optimization. First, the active beamforming is calculated via semi-definite relaxation. Then, the penalty-based method and the first-order Taylor expansion are leveraged to solve the passive beamforming. With the optimized active and passive beamformings, we resort to successive convex approximation to optimize the AIRS deployment. Numerical results validate the effectiveness of the proposed scheme.

The fifth-generation Internet of Things (5G-IoT) requires more capable channel coding methods to obtain low-latency and high-reliability communication systems. Hence, an improved ordered tree-based single-parity-check (OT-SPC) list decoder for polar code is designed in this article to reduce the complexity and keep the reliability. The proposed method introduces the ordered error pattern set of the single-parity-check (SPC) list decoder, which collects all the necessary error patterns. We first define a relationship between these patterns and present the concepts of the necessary error pattern and the ordered error pattern set. Then, we adapt the ordered tree data structure and the asynchronous tournament sorters to relieve the complexity increase of the ordered error pattern set’s introduction. The reliability relationships of different patterns are stored in an ordered tree, and the tournament sorting algorithm is modified separately for the log-likelihood ratio values of the received sequence and the path metrics of the decoding paths. In this scheme, the candidate codewords of the SPC list decoder are constructed in the descending order of their probability, which significantly reduces the calculation and comparison consumption. Eventually, the results show that the OT-SPC list decoder obtains a superior performance in decoding throughput and latency while preserving the error–correction performance.

Rate-splitting multiple access (RSMA) is an emerging multiple access strategy, with non-orthogonal multiple access (NOMA) and space division multiple access (SDMA) as its two special cases. RSMA divides the messages required by users into the private and common parts, and the private streams are naturally suitable for the secure transmission. In this paper, we establish a unified rate-splitting framework to ensure the secure transmission for the three multiple access systems mentioned above. The precoders at the multi-antenna transmitter are conjointly optimized to improve the transmission rate of common message. Successive interference cancellation (SIC) is utilized, and the private message for the downlink broadcasting RSMA network can be effectively hidden in the high-power common message. Simulation results demonstrate that the proposed rate-splitting framework can effectively guarantee the secure transmission of the secrecy information.

Estimating the location parameters of moving target is an important part of intelligent surveillance for the Internet of Vehicles (IoV). Satellite has the potential to play a key role in many applications of space–air–ground-integrated networks (SAGINs). In this article, a novel passive location parameter estimator using multiple satellites for the moving aerial target is proposed. In this estimator, the direct wave signals in reference channels are first filtered by a band-pass filter, followed by a sequence cancelation algorithm to suppress the direct-path interference and multipath interference. Then, the fourth-order cyclic cumulant cross ambiguity function (FOCCCAF) of the signals in the reference channels and the four-weighted fractional Fourier transform FOCCCAF (FWFRFT-FOCCCAF) of signals in the surveillance channels are derived. Using them, the time difference of arrival (TDOA) and the frequency difference of arrival (FDOA) are estimated and the distance between the target and the receiver and the velocity of the moving aerial target are estimated by using multiple satellites. Finally, the Cramer–Rao lower bounds of the proposed location parameter estimators are derived to benchmark the estimator. The simulation results show that the proposed method can effectively and precisely estimate the location parameters of the moving aerial target.

Integrated sensing and communication (ISAC) is an up-and-coming technique for future 6G networks. However, the communication message carried by the detection waveform will face the risk of being eavesdropped, which leads to the challenge of wireless security for ISAC networks. In this paper, we leverage non-orthogonal multiple access (NOMA) to support more users for the ISAC network, with the precoding well designed to guarantee the security. Specifically, we formulate a joint precoding optimization problem to maximize the sum secrecy rate for multiple users via artificial jamming, where the superimposed signal for NOMA users can be concurrently employed for the target detection. Since the optimization problem is non-convex, it is transformed into a convex one based on successive convex approximation (SCA), where the Taylor’s approximation and second-order cone (SOC) constraint are further applied. Then, we propose an iterative algorithm, through which the original optimization problem can be solved effectively. Simulation results show that the proposed secure NOMA-ISAC scheme can guarantee the secure transmission while ensuring the sensing performance.

Hardware impairment is inevitable in many wireless systems. It is particularly severe in low-cost applications due to the imperfect components used. In this paper, the channel estimation and non-coherent detection problems of hardware impaired signals are studied for a single-carrier, single-antenna and single-hop system. Specifically, three different cases are investigated: signals with additive distortion only, signals with in-phase and quadrature imbalance only, and signals with both impairments. The maximum likelihood and Gaussian approximation methods are used to derive the new non-coherent detectors for amplitude modulated signals, while the maximum likelihood and moment-based methods are employed to design the new channel estimators for all signals. Numerical results show that the new non-coherent detectors outperform the existing non-coherent detectors in the presence of hardware impairment. The performance gain can be as high as 8 dB. They also show that the new channel estimators have much higher accuracy than the existing estimator. In some conditions, the accuracy of the new estimator is about 100 times that of the existing estimator.

Non-orthogonal multiple access (NOMA) aided unmanned aerial vehicle (UAV) is becoming a promising technique for future wireless networks. However, its security remains a great challenge due to the line-of-sight in UAV communications and high transmit power for weak users in NOMA. Thus, in this paper, we propose a power allocation (PA) scheme for NOMA-UAV networks with circular trajectory, to maximize the sum rate of common users while guaranteeing the security for a specific user. To achieve this, we consider three cases based on the distance from the UAV to the secure user. Specifically, the lowest transmit power is assigned to the secure user in each time slot to guarantee its security, with the remaining power allocated to common users to maximize their sum rate. To further improve the transmission rate of the secure user, we also derive the upper bound for its decoding threshold, and analyze the linear relationship between the secure decoding threshold and the sum rate of common users. Simulation results are demonstrated to evaluate the effectiveness of the proposed secure PA scheme in NOMA-UAV networks.

Industrial Internet of Things (IIoT) is regarded as the basis of the future industrial system. The interaction between nodes in IIoT shows strong regularity or sociality, which has the potential to improve the efficiency of IIoT. This article focuses on the investigation of the relationship of social activity and data transmission, and proposes a novel routing protocol to enhance QoS of social and cognitive IIoT networks. In accordance with practical requirements of an application, the routing protocol owns two purposes: one is to meet the requirement of delay-sensitive information transmission, and the other focuses on the load balance of the social and cognitive IIoT network. As a special feature of social and cognitive IIoT, the mechanism for protection of social activities is proposed while data transmission is adopted in the proposed protocol. System-level evaluation shows that the proposed protocol can achieve better performances compared with existing routing protocols for social and cognitive IIoT networks.

Modern intelligent transport systems focus on the integration of multiple sensors to obtain hybrid navigation schemes. A key issue of a hybrid scheme is distribution of the information sharing coefficients (ISCs) of subsystems and the fusion of parallel multiple observations of navigation sensors. Recently, deep learning methods, particularly convolutional neural networks (CNNs), have achieved great success in image processing tasks. However, there has been limited work in using deep learning for multisensor-based integrated navigation solutions. In this letter, we propose an ensemble learner-based classification and information fusion method, in which estimation error covariance matrices provided by local adaptive filters are used as input for the classifier, and the triple numbers of ISCs are determined by the proposed scheme. The results validate the effectiveness of the proposed scheme, in which the adequately trained ensemble learner can detect the degradation of a subsystem that may suffer atypical observations or faults and consequently can adjust the corresponding ISC in real time.

Automatic modulation classification (AMC) plays an important role in cognitive radio and is widely studied recent years. However, most existing AMC schemes must be deployed under their training SNRs, which makes them highly dependent on the accuracy of channel estimation. The classifiers may need to be re-trained to fit the varying channel condition. To address this problem, a feature combination method aiming to find noise-robust features under fading channels is proposed in this paper. Stacked auto encoder is deployed to explore robust features from an extracted feature set, and these new features is then used to train a support vector machine (SVM). Numerical results shows that the generalization ability of SVMs trained with new features can be significantly improved; therefore the method is robust to SNR variation.

Digital self-interference cancellation is a potential technique for full-duplex (FD) communication system. However, for digital self- interference (SI) cancellation, limited dynamic range of analog-to-digital converters (ADCs) is a severe challenge. Against this background, a dynamic range (DR) expanding scheme for ADC based on detail information compensation is proposed in this paper, in order to improve the DR of ADCs. Moreover, channel estimation is performed on the basis of half-duplex (HD) training for digital SI cancellation. Analytical and simulation results demonstrate the effectiveness of the proposed scheme as a viable solution for full-duplex communication system.

With the capacity of overcoming radio spectrum shortages for wireless communications in smart grids, cognitive radio enabled Advanced Metering Infrastructure (CR-AMI) networks are expected to enhance the efficiency and practicability of future smart grids. As an integral component of the smart grid ecosystem, CR-AMI networks are practically deployed as a static multi-hop wireless mesh network. This paper focuses on the investigation of an novel RPL-based routing protocol for enhancing the energy efficiency in CR-AMI networks. In accordance with practical requirements of green communications in smart grids, the proposed routing protocol adopts the energy efficiency over virtual distance as the core of routing mechanism such that the energy-efficient route can be achieved. In addition, the protocol has the mechanism for primary (licensed) users protection whilst meeting the utility requirements of cognitive radio users. System-level evaluation shows that the proposed routing protocol has better performances compared with existing routing protocols for cognitive radio- enabled AMI networks.

It is expected that the use of cognitive radio for smart grid communication will be indispensable in near future. Recently, RPL for cognitive radio enabled Advanced Metering Infrastructure (AMI) networks is attractive. Our objective in this paper is to propose an enhance RPL to improve efficiency and reliability of cognitive radio enabled AMI networks. Our protocol is receiver-based in nature, which can achieve better reliability of the network along with protecting the primary users as well as meeting the utility requirements of secondary network. System level performance evaluation shows the effectiveness of proposed protocol as a viable solution for practical cognitive AMI networks.