Artificial intelligence-empowered low earth orbit (LEO) satellite networks have the great potential to provide robust and ubiquitous communications capabilities for Internet of Everything (IoE) services. Due to the intrinsic high decentralization, it is expected for LEO satellite nodes to deploy decentralized federated learning (DFL) for model training collaboratively while preserving data privacy. However, the challenges lie in the latency management of the DFL process under communications reliability constraints. To address the issue, we propose a novel DFL framework that incorporates halving and doubling to balance the load of networks. Considering the dependency of aggregation, the latency of DFL clients is recursively derived. Under constraints of energy consumption and network reliability, an optimization problem is formulated that aims to minimize the mean latency, solved using the particle swarm optimization algorithm. Additionally, we introduce a latency-aware scheduling strategy to further improve DFL latency-efficiency by leveraging overlapping inter-satellite links among DFL clients. Simulation results show that the proposed methods significantly accelerate the DFL process by up to 16.2% and enhance its efficiency compared to the alternatives.

Low Earth Orbit (LEO) satellite networks are expected to be seamlessly integrated with terrestrial infrastructures, providing high-quality, globally accessible communication services. Given the diverse service requirements of users, the traffic transmitted through these networks exhibits varying levels of delay tolerance. However, the highly dynamic topology of LEO satellite networks and the limited satellite resources present significant challenges for traditional multipath routing schemes in providing delay differentiated services. To address these issues, this paper proposes a novel Ant Colony Optimization (ACO)-based multipath routing algorithm, which enables each satellite to forward ant packets based on local information to discover paths. This algorithm allows the source satellite to obtain information about multiple disjoint paths that satisfy the delay differentiated service transmission requirements without collecting global information. Furthermore, a dynamic delay differentiated traffic scheduling algorithm is designed to allocate delay-sensitive and delay-tolerant traffic across multiple paths by considering predicted traffic ratios as well as path delay, bandwidth, and energy factors to achieve balanced utilization of network resources. Simulation results show that the proposed routing scheme can provide differentiated services for both types of traffic while reducing both delay and packet loss rate and maintaining balanced utilization of network resources.

In recent years, radio frequency fingerprinting (RFF), which leverages artificial intelligence (AI) to exploit the unique hardware characteristics of wireless devices, has emerged as a promising method for device identification. However, the complex and time-varying nature of wireless channels makes it difficult to acquire large-scale datasets, leading to small-sample scenarios that hinder the generalization and identification performance of RFF models. To address these challenges, we propose a novel approach called the deep transfer triplet network (DTTN). Specifically, DTTN integrates triplet loss and cross-entropy loss to simultaneously enhance feature discriminability and robustness by leveraging sample similarity while mitigating channel-induced distortions in radio frequency features. Furthermore, to cope with data distribution shifts, DTTN incorporates a parameter transfer strategy that improves the model’s generalization across varying channel conditions. For efficient deployment, we evaluate several quantization schemes and experimental results show that DTTN significantly improves recognition accuracy and robustness in small-sample, time-varying channel scenarios, while also supporting lightweight model deployment. With approximately 100 samples per client, DTTN achieves a 13% improvement in recognition accuracy compared to conventional convolutional neural networks based methods.

With the rapid development of distributed edge intelligence (DEI) within Internet of Vehicle (IoV) network, it is required to support heterogeneous rapid, reliable and lightweight authentication which prevents eavesdropping, tampering and replay attacks. Radio frequency fingerprinting (RFF), which leverages unique and tamper-proof hardware characteristics, is an emerging deep learning-based physical layer technology poised to achieve excellent authentication within DEI enhanced heterogeneous IoV. However, centralized collection of critical datasets will bring severe privacy concerns as well as huge communication overheads toward resources-constrained IoV nodes. In this article, we propose a deep federated fractional scattering fingerprinting network (FFSFNet) which amalgamates fractional wavelet scattering and federated learning to achieve excellent identification. Particularly, we first exploit fractional wavelet scattering to extract RFF characteristics from nonstationary waveform, eliminate redundancies and enhance interpretability. To improve the training efficiency and privacy protection capability, we design a novel federated framework, which not only completes distributed training, reduces overhead but also protects privacy. Furthermore, we conducted a comprehensive comparative analysis of different model quantization schemes and validated the proposed scheme with field programmable gate array (FPGA) accelerators. Experimental results demonstrate that the proposed FFSFNet can maintain excellent identification performance with only 5.08% of original samples. The model size and inference latency can be effectively improved by quantization with limited degradation. Moreover, the identification testing accuracy of FFSFNet can eventually converge to 99.4% with 0.64 ms inference latency per sample.

With the continuous advancement of Industrial Internet of Things (IIoT) technology and the demand for ultra-high-speed wireless transmission, unmanned aerial vehicle (UAV) capable of effectively enhancing communication quality have become one of the primary applications for sixth generation (6G) wireless communications. However, UAV face challenges such as traffic burst and significant jitter when used for data transmission. Time-sensitive networking (TSN) has emerged to address real-time and deterministic transmission problems in UAV communications. However, existing scheduling algorithms struggle to adapt to the dynamic network changes and easily lead to scheduling failure and delay redundancy. This paper proposes a deep reinforcement learning (DRL) based dynamic time slot allocation (DTA) algorithm to optimize TSN scheduling in UAV communications. The key point of this approach is to use DRL to evaluate the historical information of scheduling result and predict subsequent decisions to ensure the completion of dynamic scheduling. The scheme firstly uses constraints to limit flows to complete scheduling, and dynamically allocates time slot lengths to flows with changing periods and lengths. It then uses the evaluation results to make path decisions and update the deep neural network (DNN) to complete the dynamic scheduling. When compared with other scheduling algorithms (No-DTA, Traverse-Edge, Link-Traverse-Edge), proposed scheme consistently outperforms them across various network scenarios. It achieves high scheduling success rates and maintains stable average scheduling delays for flows, making it a more effective scheduling solution.

In recent years, the field of wireless communications supported by reconfigurable intelligent surface (RIS) has emerged as a cutting-edge area of research. A primary challenge in this domain is the accurate and efficient channel estimation, especially under conditions of low pilot overhead. This work introduces a system model and a DNN-based channel estimation solution with the goal of improving the efficiency and accuracy of channel estimation under low pilot overhead in RIS-assisted communication systems. A significant highlight is the reduction in pilot overhead required for downlink channel estimation, which was accomplished by leveraging statistical correlation among different users' channels. Mainly, the research emphasizes the collaborative training of the DNN model, where both the Base Station (BS) and users iteratively exchange data and model updates, resulting in a jointly learned model that offers improved performance. The findings show that the proposed approach not only substantially reduces the pilot overhead but also ensures efficient channel state information learning, paving the way for more efficient RIS-assisted wireless communications. Simulation outcomes reveal that, when compared with conventional estimation techniques like least squares (LS) and minimum mean square error (MMSE), the suggested deep neural network (DNN) model attains enhanced estimation performance while reducing the required pilot overhead for all users.

Federated learning (FL) has emerged in sparse low earth orbit (LEO) satellite constellations as a promising architecture for on-board machine learning (ML) model training, aimed at preserving Internet of Things (IoT) data privacy in specialized and sophisticated tasks. However, the user in FL who spends the longest time in a FL round significantly hinders efficiency. Furthermore, intermittent satellite connectivity, rapidly changing network topologies of sparse LEO satellite constellations and a dearth of information including computation capabilities and positions of satellites greatly obstacle the efficient implementation of FL. To address this challenge, we propose a deep reinforcement learning (DRL)-based server selection scheme for FL in sparse LEO satellite constellations. The optimization problem to minimize the overall FL latency is formulated. A Markov decision process (MDP) is subsequently established and the corresponding double Q-learning agent is trained to make sequential FL server selection decisions to figure it out. Simulation results demonstrate that the proposed scheme reduces latency compared to other server selection schemes.

In recent years, due to the rise of semantic communication in sixth generation (6G) wireless communication systems, the research on semantic transmission has gained increasing attention in Internet of Things (IoT). In this paper, we develop a software-defined radio (SDR) based long range(LoRa) communication platform, which utilize the universal software radio peripheral (USRP) and GNU Radio to implement an intelligent end-to-end semantic communication system, comprising two key levels: the Semantic Level and the LoRa Transmission Level. Specifically, we generate semantic vectors at the sending end by extracting the meaning of the data. In this case, the data transmitted by the LoRa physical layer is the semantic information with redundancy removed. We set the spreading factor (SF) as 7, the signal bandwidth as 250 kHz, the preamble length as 8 and the sampling rate as 1 MHz, under these configured LoRa parameters, we successfully transmit 10 sentences in a single transmission event. Experimental results demonstrate that at the receiving end, the bilingual evaluation understudy 1-grams (BLEU) score achieve 0.86, and the bidirectional encoder representations from transformers (BERT) similarity score achieve 0.96. This high-lights the exceptional performance of our intelligent end-to-end LoRa-Semantic communication system in conveying semantic information. Even when simultaneously transmitting multiple sentences, the receiver accurately comprehends the conveyed meanings.

The combination of simultaneously transmitting and reflecting-reconfigurable intelligent surface (STAR-RIS) and nonorthogonal multiple access (NOMA) brings the necessary full-space degrees of freedom and spatial multiplexing gains for the Internet of Things (IoT) networks. The inherent network heterogeneity and sharing of wireless channels may however increase the exposure of the information interactions to the third party. To address this issue, we propose a covert communication scheme in STAR-RIS assisted NOMA networks over Nakagami-m fading channels, where both downlink and uplink IoT scenarios are considered. Under the NOMA protocol with imperfect successive interference cancelation (SIC), an IoT access point interacts with two IoT users aided by a STAR-RIS without being detected by two wardens. In this scenario, the two IoT users are located on both sides of the STAR-RIS which adopts coherent phase shifting and operates according to the mode switching protocol. To evaluate the wardens’ detection performance, the Kullback-Leibler (KL) divergence is used. Furthermore, the cascaded channel gains of IoT users and wardens are, respectively, characterized as Gamma and complex Gaussian random variables. The closed-form expressions of the expectations of KL divergence and the interruption probabilities for downlink and uplink are derived. To further improve the performance, we formulate the effective covert rate maximization as the joint optimization problems of the transmit power and power allocation coefficient for downlink and uplink, subject to the constraints for covertness, reliability and power budget, which are, respectively, resolved analytically. Extensive simulation results indicate that the proposed scheme improves covertness compared with the benchmarks.

Internet of Vehicles (IoV) is an emerging paradigm with significant potential to improve traffic efficiency and driving safety. Here, we focus on the design of a novel visible light communication (VLC)-assisted scheme to enable driving safety-related Internet of Vehicles (IoV) services that require ultrareliable and low-latency communications (URLLC). Specifically, the Vehicle-to-Vehicle (V2V) communication mode is adopted to satisfy the ultralow latency requirement of URLLC in roadside infrastructure-less IoV systems. In the outdoor V2V- VLC scenarios, the quality of the received optical signal is degraded by path loss, atmospheric turbulence and additive noise. In addition, the short-packet feature of URLLC introduces inevitable data decoding errors and imperfect channel state information (CSI). With this background, we aim to investigate the reliability performance of URLLC in outdoor V2V- VLC systems, which is described by the average packet loss probability under given user-plane transmission latency. First, we consider the ideal case of a perfect CSI at the receiver, and derive an analytical expression of average packet loss probability. Further, a closed-form approximation is provided to simplify the numerical calculation. Next, we extend the theoretical analysis to a practical V2V- VLC system with imperfect CSI at the receiver. Through numerical results, we validate the accuracy of our designed theoretical framework and propose ideas to enable driving safety-related IoV services in outdoor V2V- VLC systems.

Healthcare systems, empowered by the integration of Artificial Intelligence (AI) and Internet of Things networks, are undergoing significant advancements, ushering in a new era of enhanced treatment experiences and improved quality of life. Edge computing plays a pivotal role as an architectural enabler; however, it also presents numerous energy-related challenges spanning sensors, communication, and edge devices. One of the most formidable challenges is the proliferation of complex communication protocols across various devices, including sensors, reconfigurable intelligent surfaces, smart devices, and edge servers, leading to substantial carbon emissions and energy consumption. To address this challenge, this paper introduces a low-carbon, sustainable edge architecture leveraging AI techniques. Specifically, we develop a deep learning-based radio frequency fingerprint access protocol to facilitate real-time and energy-efficient device access between smart devices and edge gateways. Building upon this foundation, we propose a hybrid quantum-classical optimization algorithm to achieve green data transmission at lower layers for artificial intelligence of things healthcare systems. Simulation results demonstrate that our optimized architecture achieves over 99% identification accuracy using a signal dataset of 50GB obtained from real-world smart devices and practical gateways in a real-world environment, all while maintaining energy-efficient data delivery.

Artificial intelligence enhanced Internet of unmanned aerial vehicles (UAVs) is a promising network to achieve the complicated vehicular tasks and construct intelligent communication networks. One of the critical tasks is to guarantee a secure network access while achieving trade-off between accuracy and latency through lightweight deployment on resource-limited and hardware-constrained UAVs. To address this issue, a novel noise-tolerant radio frequency fingerprinting (NT-RFF) based on tiny machine learning (TinyML) scheme is proposed, which amalgamates contrastive learning and data augmentation, aiming to improve the generalization ability of unauthorized device identification (UDI). Particularly, we first exploit the augmentation technique to enhance the legitimate training datasets under the circumstance of varying signal-to-noise ratios, facilitating an enhanced and diversified datasets. Second, a synthesis of contrastive learning and supervised learning is employed to attain comprehensive global learning. We design a new contrastive loss criteria to capture relevant information from the samples collected over the air. Besides, we design a categorical cross-entropy loss criteria by which supervisory information can be leveraged from associated labels. Finally, quantification is utilized to enhance model efficiency and achieve an optimal balance between accuracy and latency within computing and energy resource-limited UAVs. Experimental results demonstrate that the proposed tiny NT-RFF which only contains about 25-30% quantitative parameters can maintain excellent performance and improve the UDI accuracy greatly compared with the traditional machine learning-based RFF schemes. Moreover, the remarkable results showcase that our proposed framework attains a substantial increase in identification accuracy compared to the DACL-RFF and DASL-RFF methods, exhibiting improvements of 14.16% and 5.17%, respectively.

With the rapid integration of Internet of Things (IoT) devices and artificial intelligence (AI) function, the data management and privacy issue has drawn great attentions in intelligent IoT systems where communication infrastructures frequently exchange open data flows over the air. Therefore, lightweight and private access over radio communication pipes becomes a critical but challengeable need for resource-constrained IoT devices due to the limited memory capacity, computing, and energy consumption. In this article, we develop the concept of deep federated scattering fingerprinting aided by differential privacy (DFSF-DP) in which a deep fingerprinting data learning network exploits fingerprinting data to realize lightweight intelligent access and incorporates federated learning with differential privacy to guarantee the data privacy in a way of distributed training. Particularly, first, we employ a wavelet scattering network for the efficient radio frequency fingerprinting (RFF) feature extraction and construct a high information density database. Subsequently, the implementation of distributed learning minimizes the demand for computing resources, by exploiting the full potential of edge and cloud nodes to aggregate the global model. To bolster the data privacy and security, adaptive clipping and gradient noising are incorporated into DFSF-DP. Experimental results demonstrate that DFSF-DP obtains outstanding performance and achieves equivalent advancements while utilizing a mere 25% of the original data set. Moreover, it attains a 93% identification accuracy with 0.1 noise multiplier which confirms the remarkable performance of DFSF-DP while upholding privacy and security considerations.

The rapid advancement of wireless communication and artificial intelligence (AI) has led to a plethora of emerging applications that require exceptional connectivity, minimal latency, and substantial computing resources. The widespread adoption of cloud-edge intelligence is propelling the development of future networks capable of supporting intelligent computing. Mobile edge computing (MEC) technology facilitates the movement of computing resources and storage to the network’s edge, enabling cost-effective offloading of computational tasks for related applications which needs for reduced latency and improved energy efficiency. However, the offloading efficiency is hindered by limitations of wireless transmission capacity. This paper aims to address this issue by integrating reconfigurable intelligent surfaces (RISs) into a cell-free network within an intelligent cloud-edge system. The core idea is to strategically deploy passive RISs around base stations (BSs) to reconstruct the transmission channel and improve the corresponding capacity. Subsequently, we formulate an optimal problem involving joint beamforming for RISs and BSs, which is characterized by non-convexity and complexity. To tackle this challenge, we employ an alternating optimization scheme to ensure the effectiveness of joint beamforming. In particular, deep reinforcement learning (DRL) is leveraged to reduce the computational complexity involved in optimizing task offloading. Additionally, Lyapunov optimization is utilized to model the latency queue and improve the learning efficiency of the offloading framework. We conduct comprehensive evaluations on the wireless system’s capacity, average latency, and energy consumption, considering the integration of RIS with the DRL offloading framework. Experimental results demonstrate that our proposed scheme achieves superior efficiency and robustness.

Driven by the ever-increasing requirements of ultrahigh spectral efficiency, ultralow latency, and massive connectivity, the forefront of wireless research calls for the design of advanced next-generation multiple access schemes to facilitate the provisioning of these stringent demands. This inspires the embrace of nonorthogonal multiple access (NOMA) in future wireless communication networks. Nevertheless, the support of massive access via NOMA leads to additional security threats due to the open nature of the air interface, the broadcast characteristic of radio propagation, and the intertwined relationship among paired NOMA users. To address this specific challenge, the superimposed transmission of NOMA can be explored as new opportunities for security-aware design; for example, multiuser interference inherent in NOMA can be constructively engineered to benefit communication secrecy and privacy. The purpose of this tutorial is to provide a comprehensive overview of the state-of-the-art physical layer security techniques that guarantee wireless security and privacy for NOMA networks, along with the opportunities, technical challenges, and future research trends.

In this paper, we propose a platoon access authentication system for initial access process in autonomous vehicle platoons (AVPs) in which post-quantum encryption and signal processing techniques are employed to protect against both active and passive cyber-physical attacks. To avoid passive quantum cyber attacks, a quasi-cyclic moderate-density parity-check code is used to encode and decode AVP messages. Moreover, an independent component analysis-based signal separation technique is employed to eliminate the effect of high-power active cyber attacks on AVP messages. To measure the reliability of the system, we derive an analytical expression for the system failure probability, taking into account the influence of both the cyber and physical planes. The simulations show that the proposed system is effective against attacks and can help reduce system failures caused by intentional and unintentional adverse cyber-physical effects. The proposed system offers a potential solution to the challenge of protecting initial access while maintaining ultra-reliable low-latency communications between AVPs and the infrastructure.

Federated learning (FL) has been deployed on low earth orbit (LEO) satellites Internet of Things (IoT), where learning models can be trained collaboratively, thus preserving IoT data privacy without centralizing. However, the efficiency of FL is significantly hindered by the straggler that cause maximum latency. The data allocation strategy that parallelizes learning could potentially increase efficiency of FL for LEO satellite IoT networks since multiple LEO satellites can access a terrestrial IoT gateway concurrently. However, modeling and optimizing data allocation poses a significant challenge. To address this challenge, this paper proposes a collaborative learning method with latency-aware data allocation for LEO satellite IoT networks. Particularly, we formulate the data allocation strategy as an optimization problem of minimizing the maximum latency which is the sum of training time of the learning model and signal propagation delay, while considering the constraint of limited energy at each satellite. Next, we use a line search sequential quadratic programming (SQP) method to decompose the problem into a sequence of quadratic programming (QP) subproblems, which are further solved by the active-set algorithm. Simulation results show that nearly the half of the maximum latency per round can be decreased and the procedure of convergence is accelerated about 25 % in a large LEO satellite constellation with 1000 satellites and 10 IoT gateways.

Reconfigurable intelligent surfaces (RIS) assisted unmanned aerial vehicle (UAV) communications are an effective way to enhance communication and effectively improve rescue efficiency in disaster scenarios. This provides a good communication guarantee for the collection and transmission of big data. Beam training is the key method to solve the problem of beam alignment between the receiving and transmitting ends. However, existing schemes rely on feedback from uniform or finite precision codebooks, which are not suitable for complex electromagnetic environments, resulting in large beam training overhead. We consider a non-uniform codebook-based beam training scheme under Karush-Kuhn-Tucker (KKT) conditions to optimize the energy consumption of rescued user. Specifically, we consider the RIS assisted UAV communication system with emergency rescue. Then, considering the constraints of RIS phase-shift, the system communication rate and energy efficiency, we propose an optimization problem to minimize the system transmission power. In addition, we propose an optimization algorithm of successive approximation codebook iteration with Karush-Kuhn-Tucker (KKT) to solve this problem with low precision non-uniform codebook. Finally, the simulation results show that the proposed optimization algorithm can effectively reduce the transmission power of the system.

Wireless power communication networks(WPCN) can be used in disaster scenarios to provide reliable communication and power supply, helping to improve disaster response efforts. Using reconfigurable intelligent surface(RIS) to assist WPCN can improve signal quality and extend the transmission distance where traditional infrastructure is damaged or unavailable. However, it is important to transmit an emergency response in disaster scenarios as soon as possible, which is little consideration before. In this paper, we propose a time allocation scheme whose key idea is to find the minimum time for disaster scenario information dissemination while optimizing the time threshold for downlink energy harvest to confirm uplink information transmit requirements. We propose a new optimization problem of minimizing the transmit time duration by jointly optimizing the RIS shifts, downlink time for energy havest, uplink time and power for information transmission, subject to the constraints on harvesting power at user, the phase shift module 1, and the minimum Quality of Service(QoS) constraints. We find that our proposed optimization problem is multi variables and non-convex. To solve this, we apply alternating optimization to divide it into two sub-problems. Then Successive Convex Approximation and penalty-based algorithm are introduced to solve the sub-problems, respectively. Simulation results show that we can find the least time for information transmission in disaster scenarios.

Reconfigurable intelligent surface (RIS) aided unmanned aerial vehicle (UAV) has been deemed as a critical wireless communication paradigm in emergency communication system. However, channel estimation is particularly challenging since the wireless channels via RIS cascade with each other, causing high-dimensional computation of channel estimation parameters. In this work, we design a cascaded channel estimation protocol based on auxiliary pilot training to estimate each separate wireless channel accurately. Specifically, a secondary UAV is configured to provide auxiliary pilot training. We improve the traditional pilot training with singular value decomposition technique such that the cascaded channels can be combined subtly and estimated accurately with the auxiliary pilot training. Then we propose a novel channel training protocol and design a transmission scheme to concentrate estimated channel parameters. We show that gathered parameters can construct a ternary system of equations and decouple cascaded channels. Based on the protocol, we formulate closed-form expressions of the performance of estimation for each separate channel. Numerical results show that proposed channel estimation protocol can achieve high accuracy and keep reliable in disaster scenarios.

The rapid development of the Internet of Things (IoT) has highlighted the critical importance of security and privacy in cognitive cities. In this context, radio frequency fingerprinting (RFF) identification has emerged as an excellent authentication scheme that provides intelligent and efficient identification in IoT systems. By leveraging RFF, we can improve the security and privacy of cognitive cities while also enhancing their operational efficiency. The RF nonlinear features are unique and unchanging, operating at the hardware level. This attribute renders them amenable to sufficient learning through convolution neural networks (CNNs), which have demonstrated remarkable identification accuracy. Nonetheless, CNNs suffer from a lack of strong interpretability and necessitate vast quantities of training data. Additionally, the enormous amount of data required for training imposes greater demands on computing resources, which are often inadequate in IoT. Moreover, traditional training schemes employ centralized datasets, which cannot ensure corresponding privacy. More recently, federated learning and fractional wavelet scattering network have been proposed to solve the problems above. To address this issue, we in this paper proposed a deep federated radio fingerprinting based on fractional wavelet scattering network (DFFNet) which can acquire the subtle features from non-stationary signals. The advantage of DFFNet is that the federated learning is applied to achieve privacy preserving during the learning process. Meanwhile, fractional wavelet is suitable for non-stationary signal’s features extraction with high interpretability. The representative experiment results demonstrate that hybrid federated framework DFFNet achieve about 99.1% identification accuracy under practical application.

The Internet of Vehicles (IoV) industry has developed rapidly in recent years. However, the information security of IoV needs more attention. The use of cross-layer secure transmission technology can improve the security of IoV communication, but the existing cross-layer schemes have some shortcomings. To this end, we propose a packet encoding scheme based on encrypted Raptor codes to improve the secure capacity of IoV communication by utilizing fountain codes and physical layer Low-density parity-check (LDPC) codes. Specifically, we choose Raptor codes which combine LDPC codes and fountain codes for secure encoding. With a sparser degree distribution, Raptor codes make decoding faster and more accurate at the legitimate receiver. In the transmission, the transmitter encrypts and sends the coding control information corresponding to the packets received by the legitimate receiver, rather than sending the generating matrix directly. We found that confidentiality can be improved by this encrypting. The simulation results show that the proposed scheme has higher security than the comparison schemes.

Automatic modulation recognition(AMC) is an intermediate process between signal detection and signal demodulation, which is an important technology in wireless communication systems. Its main purpose is to determine the modulation mode of the received wireless communication signal, thereby realizing demodulation and subsequent processing of the signal. AMC is considered as the promising methods to improve the quality of the service in cognitive radio(CR). However, AMC suffers from the phase offset of the signal and low recognition accuracy. Therefore, we proposed deformation-insensitive color constellation(DICC) to improve the recognition accuracy in AMC. In this paper, DICC is insensitive to the deformation of the constellation caused by the phase offset and able to represent the density information of points in the constellation diagram. Firstly, we use the method of phase difference to prevent the phase offset. Particularly, we use different colors to match with density information of constellation diagrams, and use deep learning models, VGG-19 and GoogleNet for classification. The results show that for the received signal constellation with carrier phase offset, it still has a high recognition accuracy and the classification accuracy is 3%-4% higher than previous methods.

Physical layer key generation technology which leverages channel randomness to generate secret keys has attracted extensive attentions in long range (LoRa)-based networks recently. We in this paper develop a software-defined radio (SDR) based LoRa communications platform using GNU Radio on universal software radio peripheral (USRP) to implement and evaluate typical physical layer key generation schemes. Thanks to the flexibility and configurability of GNU Radio to extract LoRa packets, we are able to obtain the fine-grained channel frequency response (CFR) through LoRa preamble based channel estimation for key generation. Besides, we propose a low-complexity preprocessing method to enhance the randomness of quantization while reducing the secret key disagreement ratio. The results indicate that we can achieve 367 key bits with a high level of randomness through just a single effective channel probing in an indoor environment at a distance of 2 meters under the circumstance of a spreading factor (SF) of 7, a preamble length of 8, a signal bandwidth of 250 kHz, and a sampling rate of 1 MHz.

Reconfigurable intelligent surfaces (RIS) assisted unmanned aerial vehicle (UAV) communication can enhance wireless communication in disaster scenarios. However, the critical challenge lies in improving the communication throughput under the joint design of channel acquisition and beamforming with limited percision codebook. In this paper, we consider using cascaded double-RIS assisted UAV of multi-hop communications with limited feedback multiple-input single-output (MISO). And the steepest descent method (SDM) based beamforming design scheme and a codebook based channel quantization feedback mechanism were proposed. Then, we establish an optimization problem of maximizing the system throughput to realize the design of beamforming, with the constraints of transmission power and RISs phase-shift. The SDM based alternating iterative optimization algorithm is proposed to solve this problem with low complexity. The final simulation results show that our proposed optimization algorithm can improve the system throughput.

In emergency rescue scenarios, unmanned aerial vehicle (UAV) communications with reconfigurable intelligent surfaces (RIS) is a way to enhance the communications link. However, the key challenges lie in the acquisition of channel information and the design of beamforming due to the limited energy storage and high density integrated antenna array. To solve this problem, we propose an energy minimization (EM) based beam training scheme to optimize the energy consumption of the communication system. Specifically, we consider the RIS assisted UAV communication system with emergency rescue. Considering the constraints of RIS phase-shift, system communication rate and energy efficiency, we propose an optimization problem to minimize the system transmission power, and obtain the optimal solution. The simulation results show that the proposed optimization algorithm can ensure the minimum communication requirements and reduce the transmission power of the system.

The reduced-capability new radio (NR RedCap) was introduced in 3GPP Rel-17 to cater to the use cases that are not yet best served by current NR specifications, such as smart grid and industrial wireless sensors. For the grant-free access system in smart grid, the resistance to impulse noise is a key issue. By using fractional low-order covariance and constant modulus based tensor decomposition, this paper skillfully enables user identification in this scenario while suppressing the effect of impulse noise. The proposed scheme uses spread spectrum signal as the pilot signal. And the user identity is represented jointly by the spread spectrum sequence and information codes. In this condition, we start by transforming the pilot signals into a tensor. The fractional low-order covariance is then used to suppress the impulse noise, and the constant modulus is used to improve the performance of the algorithm during the iterative process of tensor decomposition. Finally the sensor identity is confirmed by the decomposition result. Simulation results show that the proposed scheme can greatly improve the performance of user identification under impulse noise channel. Specifically, the identification rate of the proposed algorithm valued 99.815% outperformed that of AMP valued 89.1471% when generalized signal-to-noise ratio GSNR = 0 dB. In addition, the proposed scheme can also correctly estimate the channel gain from the sensors to the base station in impulsive noise environment.

For the grant-free random access system in the Internet of Thing (IoT) scenario, the recovery of the pilot sequence and the identification of the IoT device is a crucial issue. Contrapose the problem that the existing grant-free access schemes cannot accurately recover the pilot sequence in the intensive industrial zone with impulse noise, this paper proposes to use spread spectrum signal as pilot signal and proposes an estimation algorithm based on joint k-means and M estimation accordingly. This algorithm dynamically suppresses the influence of noise with adaptive weighted function according to the estimated noise energy in the iterative process. First, the received signal is segmented to obtain samples. Second, the samples are clustered using the K-means algorithm. In the iterative process of the algorithm, cluster centers are used to estimate the energy of signal noise. According to the estimation result of the noise energy, the adaptive weighted function is used to dynamically update the cluster centers and the similarity between samples and cluster centers. Finally, assigning +1 or −1 to the samples according to the clustering results, and then the estimation of pseudo-code sequence (PN sequence) is realized while impulse noise is suppressed. Simulation results show that the proposed algorithm can greatly improve the performance of PN sequence estimation under impulse noise channel. The bit error ratio (BER) of the proposed algorithm valued 0.008 outperformed that of EVD valued 0.3 when the generalized signal-to-noise ratio (GSNR) is −4dB. In particular, the proposed algorithm has better performance when the noise distribution has heavier tails, which is different from traditional algorithms.

With the deployment of 5G and large-scale Internet of Things (IoT), the equipment identification and authentication scheme based on RF fingerprint shows unique advantages in terms of lightweight and uniqueness. However, traditional RF fingerprint identification scheme based on machine learning has the disadvantages of high computational complexity and low accuracy. Meanwhile, this scheme requires large-scale labeled datasets to realize network learning, and due to the nonlinearity of the cascade, we can not well understand the properties and optimal configurations of these networks. To solve above problems, in this paper, we propose an RF fingerprint identification method based on wavelet scattering network in the small-scale dataset. Specifically, in this method, we first design a hybrid network model of wavelet scattering network combined with deep residual network (Resnet18). Then, since one of the main problems of RF fingerprinting is the diversity of signal information at different time scales, we choose to use the construction of scattering network based on wavelet basis to complete the accurate feature decomposition of the nonlinear features of RF fingerprint. These features are stable against deformations and retain high frequency information for identification. Finally, we can use the obtained detailed features to realize the accurate identification of RF radiation source equipments. The experimental results show that our scheme can better suppress the interference of noise in the signal, improve the feature representation ability, and it can obtain higher identification accuracy than other comparison schemes.

Deep learning (DL) based identification systems are deemed as the scalable, accurate and lightweight authentication mechanisms to handle the security provisioning of massive Internet of Things (IoT) systems by leveraging the hardware-level radio frequency fingerprints. However, the conventional DL-based methods perform poor generalization in the practical time-varying signal-to-noise ratio (SNR) scenarios. In this paper, we propose a data augmentation and contrastive learning based radio frequency fingerprinting (DACL-RFF) with the joint optimization of samples agreement and labels agreement. First, we expand the SNR variations of training dataset with data augmentation, and then we propose a novel framework of contrastive learning. Specifically, we employ the original samples as the supervisory information of augmented samples and the label information of original samples is leveraged to guide the training process. Experimental results demonstrate that our proposal can increase the average accuracy by up to 51.74% in comparison with the case of none augmentation as the conventional DL-based methods. Additionally, we show that our framework of contrastive learning yields 5.27% improvement compared to the case of data augmentation with supervised learning.

In this article, we focus on the joint design of channel training and data transmission for secure ultrareliable and low-latency communications (URLLCs) in mission-critical Internet of Things (IoT) scenarios, e.g., intelligent transportation and remote control. Specifically, we consider a multiple-input multiple-output multiantenna eavesdropper (MIMOME) system, and the role of artificial noise (AN) for securing URLLC in this system is studied. In the channel training phase, we use the two-way discriminatory channel estimation (DCE) protocol with AN injection to suppress the channel estimation accuracy at eavesdropper. Meanwhile, the AN-assisted secrecy beamforming scheme is adopted to mask the confidential data signals. Following by the security enhancements above, we provide a quantitative definition of achievable effective secrecy rate (AESR) to measure the performance of our URLLC system with imperfect channel state information (CSI) and short-packet feature. Then, a nonasymptotic closed-form lower bound of AESR is provided to make the numerical calculation tractable, and the asymptotic system performance in the high-SNR regime is also studied to gain a comprehensive insight. Based on the cyclic coordinated search method, we propose an iterative resource allocation algorithm to maximize the AESR of our system, where the blocklength and transmit power assigned to the reverse/forward pilots, confidential data signals, and AN are jointly optimized. In addition, numerical results reveal the AESR performance of our URLLC system for different system parameters, and demonstrate the convergence and superiority of our proposed algorithm.

Secure ultrareliable low-latency communication (URLLC) has become a crucial requirement of mission-critical Internet of Things (IoT) applications and use cases, including automotive driving, remote surgery, and many others. However, it is still challenging to protect initial access of massive IoT devices over wireless channels, especially when malicious quantum adversaries paralyze the initial access by overhearing and tampering critical wireless messages, i.e., preambles. We propose a nested hash access system with post-quantum encryption to solve this issue. The system performs random repetition coding and nested hash coding on multidomain physical-layer resources to encode and decode preambles precisely and resiliently. Particularly, a subtle compression and encryption mechanism based on quasi-cyclic (QC)-moderate-density parity-check (MDPC) code is proposed between repetition and hashing operations to avoid passive eavesdropping during the preamble encoding process. We show that the code information can be maintained at 128-bit or higher privacy level, depending on the length of repetition code. Besides, the preamble decoding process can be proved secure agaisnt active attacks with a tolerable loss of decoding errors. Then, we formulate two nonconvex integer programming problems, each problem corresponding to the minimization of upper bound of preamble decoding error in an example application scenario. Finally, we can derive the expressions of system failure probability to evaluate the reliability of URLLC system under mission-critical IoT scenarios. Simulation results show the effectiveness of our proposed scheme despite attack.

Resilient and secure ultrareliable low-latency communications (URLLCs) over radio interface is expected to play a crucial role in next-generation Industrial Internet of Things scenarios. However, attacking wireless pilot signals has been a potential easy way to interrupt URLLC services. In this work, we propose a random structured code to encode and decode pilot signals on multidimensional physical resources, and also design a quantum learning framework to make this code secure and reliable. Specifically, the code suggests using random encoding with little structures to disperse the effect of attacks. We find that the decoding process can be modeled as a computing trap if the group spatial channel features are employed. The security problem is, therefore, transformed as random computing with redundancy. We employ a quantum algorithm to learn the computing trap model such that the computing redundancy can be removed quickly while the dispersed attack can be eliminated. In this respect, we can prove the existence of the quantum black-box model corresponding to the computing trap, and derive a precise expression of computing performance. Based on the result, we can formulate novel analytical closed-form expressions of system failure probability to characterize the reliability of the URLLC. Numerical results show that the proposed system can maintain ultrahigh reliability and low latency against attacks on wireless pilots.

Symbiotic communications have been deemed as a critical technology for Internet of Things (IoT) communications owing to its high spectrum and energy efficiency. Reconfigurable intelligent surface (RIS), which can tune wireless transmission channels by manipulating incident waves through the corresponding electromagnetic elements, is a promising enabler of various symbiotic communications scenarios in IoT. However, when the full electromagnetic elements of RIS are activated, system capacity will be improved and energy efficiency will be reduced inevitably, also with undesirable power consumption. To address this issue, an intelligent dynamic subarray RIS framework based on deep reinforcement learning (DRL) has been proposed. The key idea is to divide RIS electromagnetic elements into several groups and optimize power amplifier factor, independent phase shifts to improve the system energy efficiency under the premise of user’s basic requirements. In particular, we formulate a hybrid optimization problem of RIS subarray partition and beamforming to maximize system energy efficiency. It can be proved that this hybrid optimization is a mixed nonconvex integer programming problem. To solve this issue, we proposed a comprehensive DRL framework, including two parts, i.e., 1) a Markov decision process (MDP) to model the subarray partition design, amplitude, and phase shifts of RIS and 2) an active RIS subarray optimization scheme based on deep deterministic policy gradient. Numerical results have demonstrated that, compared with the conventional fully-connected RIS, the system energy efficiency can be significantly improved.

One of the greatest challenges in vehicle-to-everything(V2X) communications lies in protecting vehicle authentication during initial access against advanced quantum cyber-physical attack (CPA), as well as maintaining ultra-reliable low-latency communications with each other and the infrastructure. In this paper, we propose a vehicle initial access authentication system which exploits post-quantum encryption and signal processing technique to defend against both active and passive quantum CPA. The system encodes/decodes the physical layer bits of messages using quasi-cyclic moderate-density parity-check code to avoid quantum passive cyber attacks while eliminating the influence of high-power active cyber attacks on bits by employing the independent component analysis based signal separation technique. We define the system failure probability to measure the reliability, by considering both the cyber and physical plane influence, such as asynchronous access delay caused by mobility and communications, wireless channel fading and the uncertain bit generation induced by traffic hazards. Simulations show the effectiveness of our proposed system against attacks and indicate how the adverse cyber-physical effects, either being intentional or unintentional, can be avoided subtly to reduce system failures.

Radio frequency fingerprints (RFF) identification is a critical enabling technology to support rapid and scalable device identification in long rang (LoRa) based Internet of Things (IoT). In recent years, the identification precision of RFF has been significantly improved by leveraging artificial intelligence (AI) technologies to deeply exploit RFF features which are hardware-level, unique and resilient. However, traditional AI technologies lack strong interpretability, require massive amounts of training data and occupy huge computing resources. To address above challenges, we in this paper propose a deep fractional scattering network (DFSNet) to extract the RFF features hidden in non-stationary LoRa chirp signal through linear translation-variant multiscale fractional wavelet filters. Due to the fractional-domain deformation stability in DFSNet, the influence of noise on feature extraction can be reduced to the greatest extent by fractional transformation. Firstly, we apply DFSNet to build a hybrid RFF identification interpretability framework where the scattering coefficients of input can be calculated and characterized. Ben-efiting from the application of fractional wavelet transform, we can clearly explain the features represented by each coefficient. Then, the robustness characteristic of the fractional deformation is analyzed. Finally, experiment results show that our proposed hybrid DFSNet can achieve up to about 98.5% recognition accuracy rate with only about 5000 LoRa practical training samples per device.

Fountain code, as a linear random code without a fixed bit rate, can be introduced into multi-path transmission control protocol (TCP) to improve throughput and reduce the bottleneck effect caused by path quality diversity. However, the multiple-hop transmission of data and huge feedback delay of acknowledge character (ACK) will lead to excessive transmission of fountain-coded packets on multiple paths, posing serious privacy concerns. To tackle with this problem, we propose a secure cross-locking enabled multi-route fountain coding scheme in this paper which can utilize the diversity of transmission quality among different paths to improve the security of the multi-route fountain code transmission system. Specifically, the fountain-coded packet data on the superior path of the legal receiver is employed to protect the fountain-coded packets on the inferior path, forming a cross-interlocking structure between multiple paths. This design can reduce the probability that the eavesdropper receives enough fountain-coded packets and decode successfully. Besides, the data received by the legal receiver is used to support implicit transmission of control information, further reducing the interception probability of eavesdropper. The simulation results finally verify the effectiveness of this scheme against eavesdropping attack.

Ambient backscatter communications, a promising technique to realize massive machine type communication (mMTC), has recently attracted great attentions, due to its spectrum-and-energy-efficient characteristics. In ambient backscatter communications, multiple tags will transmit signals asynchronously to the target reader, which however imposes huge challenges to the radio access and signal detection at the reader. To tackle these problems, we propose an independent component analysis (ICA) based blind signal separation, identification and detection scheme. Specifically, each of tag signals is randomly and independently encoded to reduce the collision of the tags. Then a novel ICA algorithm is applied at the reader to separate, identify and detect the signals. The results show that the proposed scheme, compared with existing schemes, provides higher detection accuracy with lower cost even under a large number of tags.

The development of 6G communication is now putting higher performance requirements on the mobile satellite system. How to achieve comprehensive coverage by adding satellites to mobile communication system has become a popular topic on integrated satellite-ground network. However, using traditional methods to analyze coverage performance is limited by the topology of the satellite constellation. In this paper we use the stochastic geometry to model the LEO constellation. The stochastic geometry analysis method weakens the influence of constellation topology and provides a new representation of the coverage performance characteristics. This paper considers the models of beam coverage angle and atmospheric attenuation on coverage performance and proposes a new interference simplification method. It provides a new tool for the future optimization analysis of coverage performance of LEO satellite constellation and gives a new general expression for the coverage probability. The simulation shows that the coverage analysis model established in this paper can clearly represent the characteristics of the variation of the coverage probability with different parameters, which is conforms to the changing trends of the actual satellite constellation. For example, it can obtain the number of satellites with optimal coverage probability at different orbital altitudes.

Beam hopping technology can flexibly allocate system resources to achieve on-demand coverage in satellite communication system. However, most of the current research mainly focused on the improvement of system capacity while ignoring the optimization of user delay and quality of service (QoS). In this paper, we propose a maximal user service weight gain (maxUSWG) resource allocation algorithm in beam hopping satellite communication system. Specifically, the user service weight gain is determined by the current traffic demand and delay sensitivity of the cell. We propose to use the combination weights method for multiple attribute decision making based on maximizing deviations to calculate the weight of traffic demand and delay sensitivity. After the weighted decision attributes are obtained, the user service weight gain can be combined and evaluated. In order to maximize the user service weight gain, we allocate resources to the cell with larger weight gain until the power is exhausted or the number of working beams reaches a certain threshold. The simulation results show that the algorithm maxUSWG can effectively improve the QoS, increase the system throughput and improve the resource utilization.

Lightweight device authentication is a critical aspect in edge computing to guarantee the rightness of edge device identities and services. Radio frequency fingerprinting (RFF) is such an enabling technology able to provide robust and affordable security by employing the unique device and channel features which are usually extracted via machine learning (ML) method. The challenge is how to realize strong interpretability and support sufficient generalization ability under non-stationary channel characteristics. To solve this, we in this paper propose a novel hybrid network named FWSResNet which exploits fractional wavelet scattering transform and residual neural network to deal with the device and channel features subtlety. In particular, the proposed FWSResNet uses the scattering network based on fractional domain wavelet transform to extract the low and high-frequency features of the input signal through multiscale fractional wavelet. We find that this design can be robust to non-stationary signals and can extract the key features of noise signals. We also present a comprehensive theoretical analysis of the performance of FWSResNet under non-stationary signal distortion. Finally, we evaluate the hybrid network under largescale long term evolution (LTE) data in the practical application scenario and show that our proposed FWSResNet can achieve 93% recognition accuracy rate with only 280 training samples per device, and achieve up to 99.5% when training samples increase to 4200 per device.

In this paper, we investigate the issue of secure image transmission over point-to-point flat-fading channels under an eavesdropper. We propose an adaptive noise aggregation (ANA) scheme to worsen the eavesdropper’s reception by exploiting the inherent noise of wireless channels. Particularly, data packets to be sent are equally separated into multiple groups which are then correlated with each other for transmission in different time slots. An ANA scheme is proposed to encode even and odd packets in such a way that the correlation between data packets can be adaptively adjusted according to the average signal-to-noise ratio (SNR) fed back by the legal receiver. We prove that this scheme can significantly improve the receiving signal quality of legitimate receiver under low SNR, while paralyzing the decoding process of eavesdropper. Finally, simulation results show the superiority of our scheme over existing noise aggregation schemes.

Industrial Internet of Things (IIoT), a core enabler of Industry 4.0, is evolving rapidly to tackle the challenges imposed by explosive real-time manufacturing data in the context of Internet and telecommunication industry. 5G technology is the key to addressing such challenges. This is done by bypassing upper authentication protocols and supporting small data transmission during initial access, which, however, causes serious security breaches in IIoT device authentication. To solve this, in this article propose a secure cross-layer authentication framework based on quantum walk on circles. The system performs random hash coding on multidomain physical-layer resources to encode and decode device identifiers securely, while using a quantum walk based privacy-preserving protocol to maintain code privacy at arbitrary high level, being controlled by the number of occupied physical resources. The upper bound of decoding errors is derived and a nonconvex integer programming problem of minimizing the bound is formulated to characterize the security performance. The space of one-time keys for encryption is also derived that show how high privacy and scalability advantage is maintained against classical and quantum computers. Finally, we derive novel expressions of failure probability of this new authentication system and numerically show that our scheme can bring ultrahigh level of security and privacy protection with low latency despite attack.

A hidden security issue in OFDM based wireless communications system is the denial of OFDM pilots by spoofing/jamming/nulling regular pilot tones on time-frequency resource grids (TFRG), which cause denial of service (DoS). Lots of literatures have elaborated on solving this problem efficiently by coding on time-frequency domain, but ignored the huge decoding overheads caused by unknown disturbance from attacker. We in this paper discover an interesting fact: the decoding process of information coding that captures signal features can be accelerated doubly under random disturbance by a quantum circuit that exploit quantum phase kick-back. Firstly and most importantly, we show how to formulate the decoding process as a black-box model which can be resolved by a well-designed quantum circuit with two distinguishable quantum states as its inputs. Then we model the searching process of desirable codewords as decision making of the phase information of two quantum state inputs. By employing the quantum phase kick back, we finally prove that one measurement of output of the quantum circuit is enabled to reflect the global information of two inputs and the searching complexity of codewords can be reduced by half under random disturbance.

Secure wireless access in ultra-reliable low-latency communications (URLLC), which is a critical aspect of 5G security, has become increasingly important due to its potential support of grant-free configuration. In grant-free URLLC, precise allocation of different pilot resources to different users that share the same time-frequency resource is essential for the next generation NodeB (gNB) to exactly identify those users under access collision and to maintain precise channel estimation required for reliable data transmission. However, this process easily suffers from attacks on pilots. We in this article propose a quantum learning based nonrandom superimposed coding method to encode and decode pilots on multidimensional resources, such that the uncertainty of attacks can be learned quickly and eliminated precisely. Particularly, multiuser pilots for uplink access are encoded as distinguishable subcarrier activation patterns (SAPs) and gNB decodes pilots of interest from observed SAPs, a superposition of SAPs from access users, by joint design of attack mode detection and user activity detection though a quantum learning network (QLN). We found that the uncertainty lies in the identification process of codeword digits from the attacker, which can be always modelled as a black-box model, resolved by a quantum learning algorithm and quantum circuit. Novel analytical closed-form expressions of failure probability are derived to characterize the reliability of this URLLC system with short packet transmission. Simulations how that our method can bring ultra-high reliability and low latency despite attacks on pilots.

In this paper, we investigate the uplink transmission performance of URLLC in multi-cell interference channel, which is characterized by the delay-bound violation probability under given end-to-end (E2E) latency bound. To avoid extra channel estimation and feedback overheads, we assume that each device transmits with fixed rate and power, without the knowledge of instantaneous channel state information (CSI). Moreover, the statistical queuing behaviour of each device is described by the tool of stochastic network calculus. Due to the stringent performance metrics and short codeword blocklength of URLLC, we develop a performance optimization framework to maximize the transmission reliability of URLLC in the finite blocklength regime, via optimizing the fixed transmission rate. Numerical and simulation results demonstrate the effectiveness of our proposed performance optimization framework, in terms of improving the transmission reliability of URLLC.

As device-to-device (D2D) caching technology allows a number of devices to cache some particular contents, requesters can obtain these contents directly from these neighbor devices rather than the base station (BS) and thus the burden can be efficiently reduced. However, the required time consumptions for caching contents, which may significantly affect the network delay performance, are ignored in the existing schemes. Consequently, a delay-driven caching and sharing strategy is proposed in this paper. Specifically, in the proposed strategy, each D2D device can obtain contents from BS and play as the cache device (CD). Moreover, the consumed time for CDs is integrated into the strategy design. Then, three kinds of delay, which are the delay for CDs to cache contents and the delay for requesters to get the required files from BS and CDs, respectively, are considered simultaneously. We formulate an optimization problem, which aims at minimizing the overall average network delay subject to the successful transmission probability as well as the content cache and request constraints. To solve the formulated complex non-convex problem, the original problem is divided into three subproblems and efficiently solved in an iterative manner. Moreover, as the convergence for solving the three subproblems are proved, the convergence of the developed iterative algorithm can be guaranteed. Simulation results demonstrate that the proposed strategy can efficiently reduce the overall average network delay as compared to the existing schemes.

To accommodate massive machine-type communication (mMTC) in the networks originally designed for human-to-human (H2H) communication, we investigate the resource allocation for the mMTC/H2H coexisting network where the conventional random access (RA) and data transmission procedures are tailored for mMTC. The resource allocation strategy jointly consider the resource allocation of physical random access channel (PRACH) and physical uplink shared channel (PUSCH), aiming to support more MTC users while protecting the quality-of-service (QoS) of traditional H2H communication. A Markov chain is utilized to explicitly model the RA and data transmissions of H2H, and H2H's success probability of data transmission is derived under the analysis of stationary distribution. Then, we formulate a nonlinear integer programming (NLIP) problem which aims to maximize MTC throughput while guaranteeing H2H's success probability of data transmission. By solving the optimization problem with a modified particle swarm optimization method, we obtain the resource allocation strategy that achieves a balance between PRACH and PUSCH in terms of resource efficiency. Simulation results demonstrate the superiority of our proposed resource allocation strategy over traditional LTE strategy in the scenario of mMTC/H2H coexistence.

In this paper, we propose a power back-off based non-orthogonal random access (NORA) scheme for massive machine-type communications (mMTC) networks. Specifically, by employing the technique of tagged preambles (PA), multiple machine-type communication devices (MTCD) choosing the same PA can be distinguished and regarded as a non- orthogonal multiple access (NOMA) group, which enables multiple MTCDs to share the same physical uplink shared channel (PUSCH) for transmissions by multiplexing in power domain. Then, we adopt the Sukhatme's classic theory and characteristic function to formulate the optimization problem that aims at maximizing the throughput subject to the constraints on the power back-off factor, the number of MTCDs included in a NOMA group, and the successful transmission probability. By using the particle swarm optimization (PSO) algorithm, the formulated optimization problem is efficiently solved. We further adjust the access class barring (ACB) factor such that more MTCDs can obtain the access opportunities. Moreover, a low-complexity solution is also developed, which can achieve near PSO-based performance under high data rate requirement. Simulation results show that our proposed scheme can efficiently improve the network performance as compared with the existing schemes.

Unlike many existing studies on physical layer security which use multi-antenna techniques to improve the transmission security, this paper exploits the multipath wireless channel to achieve secure transmission. Specifically, we consider a time-reversal transmission system where the signal waveform is designed as the conjugated time reversed counterpart of the wireless multipath channel. Thus, the wireless channel acts as a matched filter that can boost the signal power at the intended receiver. To further improve the transmission security, we also inject a time-domain artificial noise which causes no interference to the intended receiver's signal detection. As for the eavesdroppers, we assume that their specific number and locations are unknown and use the homogenous Poisson point process (HPPP) to model the distribution of them. First, we study the average achievable rate of the intended user and a given eavesdropper, respectively. Then, based on the HPPP model, we can obtain the secrecy outage probability under a given density of eavesdropper. With a given requirement on the secrecy outage probability, we can finally obtain the optimal energy allocation parameter and the corresponding maximum secure transmission rate. Numerical results are presented to show that the proposed scheme can guarantee the transmission security under various system parameters.

The relay networks face with such a circumstance that an eavesdropper overhears every hop of the relay networks. To solve the issue, we propose a full-duplex friendly-jamming relaying (FDFJR) scheme to secure the information transmissions for a two hop relay network, where a malicious eavesdropper attempts to overhear the private messages transferred by the transmitter and the relay. In the scheme, we take advantage of the capacity of simultaneous reception and transmission of fullduplex (FD) relay. Specifically, in the first hop, the full-duplex jamming relay (FDJR) receives the private information from the transmitter while transmitting the jamming signals to the malicious eavesdropper. And in the second hop, FDJR continues transferring the jamming signals to the eavesdropper, rather than merely amplifying and forwarding the information signals previously received. Furthermore, the ergodic secrecy capacity (ESC) of the networks is characterized and its asymptotical expression is derived. To maximize ESC, we formulate a novel power scheduling problem satisfying several key power and rate requirements. By solving the problem, an exact analytic solution regarding power allocation factor is acquired. Finally, numerical results are provided to validate the accuracy of analytical results and the superiority of our proposed scheme.

This paper investigates the physical layer security of non-orthogonal multiple access (NOMA) based cooperative device-to-device (D2D) communications in cellular networks. In the networks, D2D transmitter acts as a relay of cellular networks while transmitting its own information over the spectrum of cellular networks with NOMA technique. The information transmitted by D2D transmitter is overheard by an external eavesdropper. To prevent eavesdropping, a joint design of jamming and beamforming is performed from an aspect of the power control. Particularly, the full-duplex (FD) receiver of cellular networks emits the jamming signals to deteriorate the eavesdropper's channel while receiving the confidential signals from D2D transmitter. Beamforming is designed to protect the legitimate receivers against the jamming signals. Furthermore, the secrecy outage probability of the system (SOPS) is characterized and its closed-form expression is derived. Finally, numerical results are employed to validate the accuracy of the analytical results and the superiority of the proposed scheme in terms of security performance.

In this paper, we investigate the power-efficient resource allocation strategy with Quality-of Service (QoS) provisioning in uplink ultra- reliable and low-latency communication (URLLC) networks. By adopting the finite-blocklength information theory, the QoS requirement is described for uplink URLLC transmissions. Then, we formulate an optimization problem concerning joint bandwidth assignment, subchannel allocation and transmit power control, which aims at minimizing the required total transmit power consumption with QoS provisioning. To solve this non-convex optimization problem, we design a traffic-aware resource allocation scheme, including the adaptive bandwidth assignment strategy based on the traffic load information and the joint subchannel allocation and transmit power control strategy based on nearest-neighbor searching. What's more, the impact of spatial diversity on the QoS provisioning and the power consumption are also analyzed. Simulation results demonstrate that our proposed resource allocation scheme can achieve better performance as compared to the conventional schemes.

The non-orthogonal multiple access (NOMA) technology can improve spectral efficiency by introducing tolerable inter-user interferences. However, the applicability of NOMA suffers from co-channel interferences and intentional jamming (CIIJ) signals, which severely degrading the estimation and detection performances of NOMA. To overcome this problem, we propose an interference/jamming-immune receiver design, featured by optimal manifold filtering (OMF) for MIMO-NOMA systems. In particular, we formulate an optimization problem of minimizing mean-squared-error (MSE) of filtered symbols. We verify the optimization problem is non-convex and its solutions of filters lie on the Stiefel manifold. Here, an insightful observation is that the problem can be transformed to be convex if a variable transformation is performed. Following this discovery, we prove that the optimal filtering matrices for symbol estimation form a Grassmann manifold on the matrix space. An explicit expression of signal-to-interference- plus-noise ratio (SINR) under those filters is further presented to show the superiority of our proposed receiver in terms of system spectral efficiency, bit-error-rate (BER), and interference and jamming signal suppression. These results demonstrate the capability of NOMA in combating CIIJ signals, thus offering an effective approach for implementation of practical NOMA systems.

Wireless channel state information (CSI) from intelligent vehicles to the roadside unit (RSU) is a must for vehicleto-infrastructure (V2I) communications in Internet of Vehicles, but easily suffers the risks of being attacked due to the publicly known and deterministic characteristic of PHY-layer pilots that are employed for CSI acquisition. This incurs the issue of wireless pilot authentication (WPA), that is, verifying the authenticity of pilots and claimed CSIs. In this paper, we, for multiantenna V2I orthogonal frequency division multiplexed communications, develop a PHY-layer cover-free (PHY-CF) coding theory to build up a secure WPA (SWPA) protocol. Here, we encode and convey vehicle pilot signals into diversified subcarrier activation patterns (SAPs) on the time-frequency domain by employing cover-free coding. We redesign the decoding procedure using the signal independence characteristic such that those encoded SAPs, though camouflaged by malicious signals and superimposed onto each other in wireless environment, could be separated, identified and decoded into the original pilots securely. For this protocol, we prove that perfect pilot conveying and separation could be both guaranteed. We formulate the pilot identification error probability (IEP) and show how PHY-CF coding could help position the location of attacker and reduce IEP to further achieve ultrasecurity. Considering 20 MHz long-term evolution bandwidth, we prove that the number of co-time co-frequency vehicles that are securely authenticated achieves up to 19 × X for X serving sectors of base station type RSU and the latency time of uplink data access is up to 1.5 ms, thus furthering the autonomous driving. Computer simulations comprehensively verify those benefits of proposed SWPA protocol.

In wireless orthogonal frequency-division multiplexing communications systems, pilot tones, due to their publicly known and deterministic characteristic, suffer significant jamming/nulling/spoofing risks. Thus, the convectional channel training protocol using pilot tones could be attacked and paralyzed, which raises the issue of anti-attack channel training authentication (CTA), i.e., verifying the claims of identities of pilot tones and channel estimation samples. In this paper, we consider one-ring scattering scenarios with large-scale uniform linear arrays (ULA) and develop an independence-checking coding (ICC) theory to build a secure and stable CTA protocol, namely, ICC-based CTA (ICC-CTA) protocol. In this protocol, the pilot tones are not only merely randomized and inserted into subcarriers but also encoded as diversified subcarrier activation patterns (SAPs) simultaneously. Those encoded SAPs, though camouflaged by malicious signals, can be identified and decoded into original pilots for high-accuracy channel impulse response (CIR) estimation. The CTA security is first characterized by the error probability of identifying legitimate CIR estimation samples. We prove that the identification error probability (IEP) is equal to zero under the continuously distributed mean angle of arrival (AoA) and also derive a closed-form expression of IEP under the discretely distributed case. The CTA instability is formulated as the function of probability of stably estimating CIR against all available diversified SAPs. A realistic tradeoff between the CTA security and instability under the discretely distributed AoA is identified and an optimally stable tradeoff problem is formulated, with the objective of optimizing the code rate to maximize security while maintaining maximum stability for ever. Solving this, we derive the closed-form expression of optimal code rate. Numerical results finally validate the resilience of proposed ICC-CTA protocol.

Due to the publicly known and deterministic characteristic of pilot tones, pilot authentication (PA) in multi-user multi-antenna orthogonal frequency-division multiplexing systems is very susceptible to the jamming/nulling/spoofing behaviors. To solve this, in this paper, we develop a hierarchical 2-D feature (H2DF) coding theory that exploits the hidden pilot signal features, i.e., the energy feature and independence feature, to secure pilot information coding which is applied between legitimate parties through a well-designed five-layer hierarchical coding model to achieve secure multiuser PA (SMPA). The reliability of SMPA is characterized using the identification error probability (IEP) of pilot encoding and decoding with the exact closed-form upper and lower bounds. However, this phenomenon of non-tight bounds brings about the risk of long-term instability in SMPA. Therefore, a reliability bound contraction theory is developed to shrink the bound interval, and practically, this is done by an easy-to-implement technique, namely, codebook partition within the H2DF code. In this process, a tradeoff between the upper and lower bounds of IEP is identified and a problem of optimal upper and lower bound tradeoff is formulated, with the objective of optimizing the cardinality of sub-codebooks such that the upper and lower bounds coincide. Solving this, we finally derive an exact closed-form expression for IEP, which realizes a stable and highly reliable SMPA. Numerical results validate the stability and resilience of H2DF coding in SMPA.

Due to the publicly-known deterministic character- istic of pilot tones, pilot-aware attack, by jamming, nulling and spoofing pilot tones, can significantly paralyze the uplink channel training in large-scale MISO-OFDM systems. To solve this, we in this paper develop an independence-checking coding based (ICCB) uplink training architecture for one-ring scattering scenarios allowing for uniform linear arrays (ULA) deployment. Here, we not only insert randomized pilots on subcarriers for channel impulse response (CIR) estimation, but also diversify and encode subcarrier activation patterns (SAPs) to convey those pilots simultaneously. The coded SAPs, though interfered by arbitrary unknown SAPs in wireless environment, are qualified to be reliably identified and decoded into the original pilots by checking the hidden channel independence existing in sub- carriers. Specifically, an independence-checking coding (ICC) theory is formulated to support the encoding/decoding process in this architecture. The optimal ICC code is further devel- oped for guaranteeing a well-imposed estimation of CIR while maximizing the code rate. Based on this code, the identification error probability (IEP) is characterized to evaluate the reliability of this architecture. Interestingly, we discover the principle of IEP reduction by exploiting the array spatial correlation, and prove that zero- IEP, i.e., perfect reliability, can be guaranteed under continuously-distributed mean angle of arrival (AoA). Besides this, a novel closed form of IEP expression is derived in discretely-distributed case. Simulation results finally verify the effectiveness of the proposed architecture.

Physical layer security (PLS) has emerged as a promising technique to guarantee the secrecy of wireless communication against eavesdroppers. Although extensive studies have been devoted to the PLS-based transmission design, the combination of this physical layer technique and network layer mechanisms such as routing still remains an open problem. This paper concentrates on the security issue for the cognitive radio networks. Specifically, we focus on the routing protocol design for the artificial-noise-aided multi-hop multi-antenna secondary communication against randomly distributed eavesdroppers. We first derive the closed-form expression of secure connection probability (SCP) for a single hop link. We optimize the power allocation between the information signal and artificial noise to obtain the maximum SCP of each link. Then a secure routing problem is formulated whose objective is to find the multi-hop path having the highest secrecy throughput. An optimal routing strategy based on a revised Bellman-Ford algorithm is proposed. We also propose a suboptimal strategy based on the Dijkstra algorithm with lower complexity. From numerical results we find that the selected route always makes a detour around the primary user to enhance the secrecy throughput.

Conventional time-division duplex (TDD) orthogonal frequency division multiplexing (TDD-OFDM) system is vulnerable to the pilot spoofing attack as it mainly relies on employing the deterministic pilot to capture the channel state information (CSI). To solve the abovementioned issue, we in this paper propose an independent component analysis (ICA) based channel identification and estimation (ICA-CIE) mechanism which uses randomized pilot to paralyze the pilot spoofing attack. Specifically, we first convert the pilot spoofing attack to the pilot jamming attack by exploiting pilot randomization. Then, an eigenstructure-based detector (EID) is proposed to realize efficient pilot jamming attack detection. In particular, on one hand, Least Square (LS) method is adopted if no jamming attack is detected. On the other hand, if jamming attack is detected, we further design a channelseparation oriented joint approximate diagonalization of eigenmatrices (CS-JADE) algorithm for channel estimation and identification. Therein, an optimization problem is first formulated to optimize the fourth-order statistical information of the observation data, after which a pilot signal reconstruction scheme is further developed to help identify true sub-channels from the optimization output. Simulation results demonstrate that our proposed ICA-CIE mechanism can achieve highly-accurate channel estimation for the legitimate transceiver pair only by using 6 OFDM symbols within the coherence time. Moreover, we also observe that the estimation accuracy of our proposed mechanism is immune to Eve's transmit power, which further verifies superiority of our proposed mechanism.

A pilot spoofer can paralyze the channel estimation in multi-user orthogonal frequency-division multiplexing (OFDM) systems by using the same publicly known pilot tones as legitimate nodes. This causes the problem of pilot authentication (PA). To solve this, we propose, for a two-user multi-antenna OFDM system, a code-frequency block group (CFBG) coding-based PA mechanism. Here multi-user pilot information, after being randomized independently to avoid being spoofed, is converted into activation patterns of subcarrier-block groups on code-frequency domain. Those patterns, though overlapped and interfered mutually in the wireless transmission environment, are qualified to be separated and identified as the original pilots with high accuracy, by exploiting CFBG coding theory and channel characteristic. Particularly, we develop the CFBG code through two steps, i.e., 1) devising an ordered signal detection technique to recognize the number of signals coexisting on each subcarrier block, and encoding each subcarrier block with the detected number and 2) constructing a zero-false-drop code and block detection-based code via k-dimensional Latin hypercubes and integrating those two codes into the CFBG code. This code can bring a desirable pilot separation error probability, inversely proportional to the number of occupied subcarriers and antennas with a power of k. To apply the code to PA, a scheme of pilot conveying, separation, and identification is proposed. Based on this novel PA, a joint channel estimation and identification mechanism is proposed to achieve high-precision channel recovery and simultaneously enhance PA without occupying extra resources. Simulation results verify the effectiveness of our proposed mechanism.

We in this paper introduce an advanced eavesdropper that aims to paralyze the artificial-noise-aided secure communications. We consider the M-1-2 Gaussian MISO wiretap channel, which consists of a M-antenna transmitter, a single-antenna receiver, and a two-antenna eavesdropper. This type of eavesdropper, by adopting an optimal Grassmann manifold (OGM) filtering structure, can reduce the maximum achievable secrecy rate (MASR) to be zero by using only two receive antennas, regardless of the number of antennas at the transmitter. Specifically, the eavesdropper exploits linear filters to serially recover the legitimate information symbols and intends to find the optimal filter that minimizes the mean-square error (MSE) in estimating the symbols. During the process, a convex semidefinite programming (SDP) problem with constraints on the filter matrix can be formulated and solved. Interestingly, the resulted optimal filters constitute a complex Grassmann manifold on the matrix space. Based on the filters, a novel expression of MASR is derived and further verified to be zero under the noiseless environment. Besides this, an achievable variable region (AVR) that induces zero MASR is presented analytically in the noisy case. Numerical results are provided to illustrate the huge disaster in the respect of secrecy rate.

Pilot spoofing attack is a serious threat to timedivision duplex (TDD) orthogonal frequency division multiplexing (TDD-OFDM) system. By employing identical pilot tones as a legitimate receiver, an adversary can contaminate the uplink channel estimation between a transceiver pair. To solve this problem, we in this paper propose an independent component analysis (ICA) based channel estimation and identification mechanism with a subcarrier-block discriminating coding (SBDC) technique (ICA-SBDC). Firstly, a receiver randomizes the values of its pilot tones to avoid contamination, which however incurs pilot jamming attack. A minor-component-based detector (MCD) is devised to detect the attack efficiently. Secondly, the transmitter exploits the fourth-order statistical information of received signals to extract a linear-mixing channel. We can prove that given previously used legitimate pilots, both legitimate and attack sub-channels can be recovered from the obtained channel. Finally, the receiver maps its utilized pilots into various uplink transmission strategies on subcarrier-blocks which can be ultimately identified by the transmitter in a jamming environment. The mapping therein is formulated via a public-known codebook with discriminating algebraic property and the identification is achieved by decoding the codebook according to the results of MCD-based detection for each subcarrier-block. Simulation results verify the effectiveness of our proposed mechanism.

Physical layer security has emerged as a promising technique to safeguard the information security in wireless networks. In this paper, we investigate the physical layer security issue for a wireless network where there coexist multiple users with security concerns. Specifically, we tackle the problem from a distributed perspective and formulate the secure transmissions at different users as a non-cooperative game. Consider the practical situation that the legitimate transmitter may not always have the perfect information regarding the channel state information of the eavesdropper, we adopt the robust secrecy rate to combat the potential worst cases. Accordingly, the robust Nash equilibrium is employed as the solution to the resource competition game among the users. Further, we analyze properties of the equilibrium and derive the optimal transmission strategy for each individual user to maximize its own robust secrecy rate, following which the distributed algorithm is proposed for the network-wide competition to reach the equilibrium. Finally, simulation results are provided to corroborate our theoretical findings.

Conventional methods of codebook design in limited-feedback multi-antenna systems aim to quantize the single-user channel but without considering secrecy requirements. Thus, the information leakage is inevitably aggravated due to the eavesdropping behaviors in limited-feedback multiple-input single-output single-antenna- eavesdropper (MISOSE) systems. To reduce the information leakage without any extra cost in antenna resources and feedback overheads, the statistical distribution of the channel matrix of both the legitimate receiver and the eavesdropper needs to be jointly exploited. Accordingly, this paper studies the novel codebook design method by further utilizing the statistical relationship between channel direction vectors and codeword vectors. Particularly, we formulate a codeword update mechanism on the weighted Voronoi diagram (WVD) where weighted codeword vectors are iteratively updated for improving the non-zero secrecy rates. Ultimately, an implementing algorithm is devised to determine those codewords with both of the secrecy-rate gains and beamforming gains. Simulation results further validate the superiority of our proposed method over conventional single-user-oriented codebooks in the respect of both average secrecy rates and average rates.

We consider secure communications over MISO wiretap channels, in the presence of a passive eavesdropper with multiple antennas. In this scenario, an artificial-noise-resistant (ANR) eavesdropping behavior is introduced to invalidate the artificial noise (AN) scheme proposed by Goel et al. This novel eavesdropper, by exploiting the statistical filter derived from the collected signals, can completely eliminate the influence of AN by using only two receive antennas. In particular, the received signals are firstly syphered to eliminate the power influence of AN by a linear weight which is generated from the statistical estimation for the signal covariance matrix. In comparison with the original legitimate signals, the weighted signals are however imposed by a phase difference which can be then erased by a weight vector inferred from the available information at the eavesdropper. Based on the two filtering processes, we derive a novel expression of achievable secrecy rate and give an analytical expression for the zero-secrecy-rate distance of eavesdropper to the transmitter. Finally, we characterize the expression of maximum achievable secrecy rate (MASR) and show that the optimal power allocation strategy under an ANR eavesdropping behavior is transformed into no allocation of transmission power to AN. Numerical results are presented to illustrate the damage caused by the investigated eavesdropping behavior. Interestingly, the consequence of AN elimination is not influenced even under large number of transmit antennas.

In this paper, we propose a joint amplify-and-forward (AF)-based channel estimation and user selection (J-ACES) scheme for multi-user MIMO (MU-MIMO) systems. Firstly, without channel state information (CSI) quantization, the transmitter estimates the downlink CSI for each receiver by adopting AF-based CSI feedback mechanism, in which the receiver amplifies and feeds back its own received downlink pilot symbols to the transmitter. An analytic user normalized mean square error (UNMSE) characterization of the uplink channel estimation and a lower bound of UNMSE for the downlink channel estimation are respectively derived and verified by Monte Carlo simulations. We show how the performance of UNMSE is influenced by the uplink and downlink pilot signal to noise ratio (SNR). Secondly, we obtain a corresponding suboptimal beamformer and estimated Signal to Interference Plus Noise Ratio (SINR) for each receiver to minimize the influence of estimation error on the subsequent user selection based on semi-orthogonal user selection (SUS). Finally, a mechanism that each selected receiver is informed of all the selected receiver beamformers to adopt the minimum mean square error (MMSE) detector is performed for further reducing the error caused by the uncertainty of the user selection. Simulation results show that the proposed scheme can improve the system spectral efficiency and has robust spectral efficiency gain even under the condition that the uplink pilot SNR is 20dB lower than the downlink pilot SNR, as compared to Quasi-MMSE Weight (QMW) scheme.