To meet heterogeneous IoT service requirements in the era of ubiquitous connectivity, flexible and real-time beam management and power allocation have become a critical challenge for sixth-generation (6G) non-geostationary orbit (NGSO) satellite networks. Conventional model-driven approaches, although analytically tractable, exhibit limited adaptability and scalability in large-scale, dynamic satellite environments. Reinforcement learning (RL)-based solutions offer great flexibility, but still face scalability and robustness challenges due to the massive decision space, complex system dynamics and interference conditions. Moreover, most existing RL frameworks overlook model-based priors essential for facilitating policy training and improving generalization. To address these limitations, this paper proposes a Graph-embedded Deep Deterministic Policy Gradient (Graph-DDPG) beam management framework that jointly optimizes the Quality of Service (QoS) for delay-sensitive (DS) and throughput-sensitive (TS) services while ensuring satellite load balancing. By incorporating model-informed guidance into the learning process, the proposed method substantially enhances scalability and convergence. Simulations verify that the proposed framework achieves up to 56.12% throughput satisfaction increase and at least 19.47% queuing latency reduction over baseline approaches, underscoring its potential for practical and effective beam management in large-scale NGSO satellite networks.

In this work, a multiple waveguide pinching antenna system (PASS) assisted integrated sensing and communication (ISAC) system is proposed, where the base station (BS) is equipped with transmitting pinching antennas (PAs) and receiving uniform linear array (ULA) antennas. The PASS-transmitting- ULA-receiving (PTUR) BS transmits the communication and sensing signals through the PAs on waveguides and collects the echo sensing signals with the mounted ULA. Based on this configuration, a target sensing Cramèr–Rao Bound (CRB) minimization problem is formulated under communication quality-of-service (QoS) constraints, power budget constraint, and PA deployment constraints. To tackle the resulting non-convex problem, an alternating optimization (AO) framework is developed, which decomposes the problem into a digital beamforming sub-problem and a pinching beamforming sub-problem. The digital beamforming design is optimized via semidefinite relaxation (SDR), while the PA deployment is updated using penalty-based method. Simulation results demonstrate that: 1) the proposed PASS assisted ISAC framework achieves superior performance over benchmark schemes; and 2) the PASS assisted ISAC is less affected by stringent communication constraints compared to conventional MIMO-ISAC, and benefits from increasing the number of waveguides and PAs per waveguide.

The dynamic resource allocation between enhanced mobile broadband (eMBB) and ultra-reliable low-latency communications (URLLC) traffic is a challenging problem. While eMBB strives for high data rates using slots as transmission time intervals, URLLC emphasizes reliability and low latency using mini-slots. Puncturing and superposition schemes are introduced by 3GPP. The existing works considering puncturing and/or superposition overlook the frequency-selective fading and consequently neglect the differences in channel state information of resource blocks (RBs) occupying different sub-carriers. Under the frequency-selective fading, it is crucial to perform RB-specific puncturing and/or superposition by determining which specific RBs will be punctured and/or superposed. In this paper, a deep reinforcement learning (DRL)-based dynamic resource slicing scheme on mini-slot-level timescale for eMBB and URLLC traffic under frequency-selective fading is proposed, considering both puncturing and superposition schemes. In particular, an optimization problem is designed to determine the transmission power allocation ratio of each RB in each mini-slot, where the objective is to maximize the comprehensive performance of eMBB users considering data rate satisfaction, fairness and data rate stability simultaneously under URLLC latency and reliability constraint. Simulation results demonstrate that the proposed DRL-based algorithm employing deep Q-network, with lower complexity, achieves near-optimal performance and outperforms benchmark algorithms.

Thousands of service units interact through dependency chains in the Microservice Application (MA) to handle various business functions. As a result, microservice applications feature complex interaction structures. Furthermore, these service units are distributed across multiple devices in the network, making them highly susceptible to cascading failures from single points of failure. Considerable efforts have been made to address and mitigate the significant risks posed by cascading failures. However, as an emerging network architecture, microservices have not yet been fully studied in terms of cascading failure modeling specific to microservice applications. This paper leverages the characteristics of the MA and proposes the Business Sink Node-Oriented Cascading Failure (BSN-OCF) model. The model extracts the network and application layers to describe the MA and models the load and capacity of service units and physical devices. The concept of a business sink node is introduced to address the challenge of directly calculating load. Furthermore, the Assessment Method of Structural Loss (AMSL) is proposed to quantify the vulnerability of the MA. This method overcomes the limitations of previous approaches, which focused solely on the loss of topology. Experiments and results validate the effectiveness of the proposed model. This work provides a self-assessment method for the MA and offers valuable support for optimizing its deployment structure in the future.

As embedded devices become increasingly popular, monolithic firmware, known for its execution efficiency and simplicity, is widely used in resource-constrained devices. Different from ordinary firmware, the monolithic firmware image is packed without the file that indicates its format, which challenges the reverse engineering of monolithic firmware. Function identification is the prerequisite of monolithic firmware's analysis. Prior works on function identification are less effectiveness when applied to monolithic firmware due to their heavy reliance on file formats. In this paper, we propose Moye, a novel method to identify functions in monolithic firmware. We leverage the important insight that the use of registers must conform to some constraints. In particular, our approach segments the firmware, locate code sections and output the instructions. We use a masked language model to learn hiding relationships among the instructions to identify the function boundaries. We evaluate Moye using 1,318 monolithic firmware images, including 48 samples collected from widely used devices. The evaluation demonstrates that our approach significantly outperforms current works, achieving a precision greater than 98 % and a recall rate greater than 97 % across most datasets, showing robustness to complicated compilation options.

To address the issues of single point of failure and malicious attacks faced by existing federated learning-based spec-trum sensing models in cognitive radio, a cooperative spectrum sensing model based on blockchain and federated learning is proposed. A novel consensus mechanism based on Proof of Rapid Aggregation (PoRA) is first introduced, which effectively avoids the block generation delay caused by the mining process. Additionally, an adaptive weight aggregation algorithm is proposed using smart contract to improve accuracy. Experimental results demonstrate that the proposed model achieves efficient and accurate cooperative spectrum sensing while ensuring security.

Abstract With the growing demand for seamless connectivity and intelligent communication, the integration of artificial intelligence (AI) and sixth-generation (6G) communication networks has emerged as a transformative paradigm. By embedding AI capabilities across various network layers, this integration enables optimized resource allocation, improved efficiency, and enhanced system robust performance. This paper presents a comprehensive overview of AI and communication for 6G networks, with a focus on their foundational principles, inherent challenges, and future research opportunities. We first review the integration of AI and communications in the context of 6G, exploring the driving factors behind incorporating AI into wireless communications, as well as the vision for the convergence of AI and 6G. The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks, divided into three progressive stages. The first stage, AI for network, focuses on employing AI to augment network performance, optimize efficiency, and enhance user service experiences. The second stage, network for AI, highlights the role of the network in facilitating and buttressing AI operations and presents key enabling technologies. We compare wireless network large models with conventional large language models (LLMs), and identify key design principles and components for building wireless network architectures. In the final stage, AI as a service, it is anticipated that future 6G networks will innately provide AI functions as services, supporting application scenarios like immersive communication and intelligent industrial robots. Specifically, we define the quality of AI service, which refers to a framework for measuring AI services within the network. We further summarize the standardization process of AI for wireless networks, highlighting key milestones and ongoing efforts. In addition, we analyze the critical challenges faced by the integration of AI and communications in 6G. Finally, we outline promising future research opportunities that are expected to drive the development and refinement of AI and 6G communications.

Successive cancellation list (SCL) decoding for polar codes has the ability to achieve Shannon capacity over the binary discrete memoryless channel. However, a large list size is required to achieve the Shannon capacity limit. Consequently, this paper proposes a novel iterative method for the CA-SCL decoder based on frozen bits to improve the error-correction performance of CA-SCL decoding for polar codes with smaller list sizes. The proposed technique determines the vulnerable channel log-likelihood ratios (LLRs) for frozen bits that degrade the CA-SCL decoding error correction performance and corrects them for the next CA-SCL decoding attempt. We analyze the behavior of wrongly estimated LLRs of frozen bits during CA-SCL decoding and design an attenuation factor set using a greedy algorithm. The proposed technique employs iterative CA-SCL decoding and revises the wrongly estimated frozen bit LLRs using the attenuation factor set during the decoding process. The simulation results show that the proposed technique achieves a block error rate (BLER) performance gain of up to 0.25 compared with the CA-SCL decoder, with a minor increase in computational complexity. Moreover, the attenuation factor set can be adjusted arbitrarily to balance performance and complexity.

In high-stakes process industries, the Basic Process Control System (BPCS) relies on conventional control to enhance productivity, while the Safety Instrumented System (SIS) uses safety functions to maintain safety. Compared to the BPCS, attackers targeting the SIS can modify safety function activation conditions to trigger them prematurely or to evade the activation of the safety function. While various attack detection methods focus on the BPCS, they often overlook the SIS. This can lead to undetected safety breaches, significantly increasing the risk of catastrophic fault. Recent methods face three key limitations that hinder their practical application to SIS. First, both attackers and engineers can exploit the hot update mechanism of SIS to add or modify control logic. However, current methods lack verification for the newly added or modified logic. Second, current methods are unable to assess the rationality of dangerous value ranges. Third, these methods struggle to distinguish between faults and attacks, making it difficult to determine the appropriate time to activate the SIS’s safety function. To overcome these limitations, we propose SecureSIS, a method for securing SIS safety functions by leveraging the safety attributes of the SIS and incorporating information from the BPCS. The core of SecureSIS includes: 1) using the safety attributes of the SIS to verify automatically extracted candidate control logic detection rules; 2) utilizing information from the BPCS to verify automatically extracted candidate value range detection rules; and 3) distinguishing between safety function attacks and industrial process faults with validated rules and integration of process data from BPCS. Our scheme was evaluated using a Tricon SIS controller deployed on a gas pipeline network platform. The results indicate that SecureSIS achieved 97.3% accuracy in detecting data injection attacks and a detection accuracy of 96.0% for control logic modification attacks. Compared with the other representative detection approaches, our scheme has better detection performance.

A novel multiple-input multiple-output (MIMO) cognitive radio (CR) system is proposed in this work. Specifically, the underly secondary network in the proposed CR system reuses the same frequency resources occupied by the primary network with the help of the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). The secondary base station (SBS) beamformers and the STAR-RIS coefficients are jointly designed to maximize the sum rate of secondary users (SUs) considering the power limitation at the SBS, interference limitation at the primary users (PUs), and the coefficients constraint for the STAR-RIS. The block coordinate descent method is invoked to tackle the formulated optimization problem. In each iteration, the beamformers at the SBS are optimized by solving a quadratically constrained quadratic program problem, and the passive STAR beamforming problem is solved with the successive convex approximation-based algorithm. Simulation results show that the proposed STAR-RIS aided CR communication framework can significantly enhance the sum rate of the secondary system.

A downlink near-field integrated sensing and communication (ISAC) framework is proposed. A novel double-array structure at the BS is proposed, where an assisting receiver (AR) is attached to the main transmitter (MT) to enable the near-field communication (NFC) system with the ability of target positioning. The joint angle and distance Cramér-Rao bound (CRB) is derived and then minimized subject to the communication quality of ser-vice (QoS) requirement and the hybrid-analog-and-digital (HAD) structure constraint. A double-loop iterative algorithm utilizing the penalty dual decomposition (PDD) framework is proposed to tackle the non-convex problem. The numerical results show that: 1) The proposed ISAC system can locate the target in both angle and distance domains; 2) The performance of the HAD ISAC approaches the performance of fully digital (FD) ISAC when the communication QoS requirement is not stringent.

As the Industrial Control System (ICS) increasingly merges with the Internet, the security threats have been increasing from internal users and external hackers. These challenges are further intensified by the facts: industrial control devices and protocols, leading to the inadequacy of traditional access control models in tackling the intricacies of ICS. We identify attributes that are optimally aligned with the specific needs of the ICS environment and propose the Multidimensional Industrial Control Attribute-Based Access Control Model (MICABAC) as a customized solution. MICABAC model significantly improves access control security and is finer granularity by selecting and evaluating required attributes within various ICS. We have been validated in two real-world ICS environments: the Gas Pipe Network System (GPNS) and the Computer Numerical Control (CNC) machine tool. Experiments indicate that by integrating MICABAC into the existing system, the maximum delay for access requests is 63.83 ms. In terms of accuracy in defending against malicious attacks, the GPNS achieves 96.49% and the CNC reaches 94.86%. Finally, we discuss the advantages and limitations of MICABAC and explore potential directions for future research.

As an important component of self-healing technology, outage detection is of great significance for the smooth operation of subsequent fault diagnosis and outage compensation operations. In this paper, we propose an outage detection algorithm that combines a hybrid Generative Adversarial Network (GAN) with an overlap-sensitive Artificial Neural Network (ANN) to solve the data imbalance problem as well as the overlap problem between data classes in outage detection. The proposed algorithm first synthesizes the outage data and adjusts the data distribution by hybrid GAN outage data distribution features. Then, based on the distribution of the samples in the feature space, the K-nearest neighbor algorithm is used to calculate the degree of overlap between the classes of the samples, and the samples are assigned weights accordingly. Finally, the resulting weight set is combined with the calibrated dataset and weighted to train the artificial neural network classifier. Simulation results show that when compared with the existing outage detection algorithm, the proposed algorithm improves the outage detection performance significantly, and can accurately detect multiple classes of outage cells.

The coexistence of Enhanced Mobile Broad-band (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC) services is a common scenario in 5G. While eMBB strives for high data rates using slots as transmission time intervals (TTIs), URLLC emphasizes reliability and low latency using mini-slots. Puncturing scheme is introduced by 3GPP, which means puncturing a set of Resource Blocks (RBs) from ongoing eMBB transmissions and reallocating them for URLLC traffic. In this paper, a DRL-based dynamic resource slicing scheme for eMBB and URLLC traffic considering puncturing is proposed, where the data rate, Quality of Service (QoS) satisfaction and rate stability for eMBB users are simultaneously optimized on mini-slot-level timescale, by employing an improved Deep Q-learning (DQN) algorithm. Simulation results demonstrate that the proposed algorithm outperforms the baseline algorithms while ensuring the latency and reliability requirements of URLLC and successfully protects eMBB users under adverse conditions.

Reconfigurable intelligent surface (RIS) is a promising candidate technology for the upcoming sixth-generation (6G) communication system for its ability to manipulate the wireless communication environment by controlling the coefficients of reflection elements (REs). However, since the RIS usually consists of a large number of passive REs, the pilot overhead for channel estimation in the RIS-aided system is prohibitively high. In this article, the channel estimation problem for an RIS-aided multiuser multiple-input–single-output (MISO) communication system with clustered users is investigated. First, to describe the correlated feature for RIS–user channels, a beam-domain channel model is developed for RIS–user channels. Then, a pilot reuse strategy is put forward to reduce the pilot overhead and decompose the channel estimation problem into several subproblems. Finally, by leveraging the correlated nature of RIS–user channels, an eigenspace projection (EP) algorithm is proposed to solve each subproblem, respectively. Simulation results show that the proposed EP channel estimation scheme can achieve accurate channel estimation with lower pilot overhead than existing schemes.

In recent years, industrial control systems (ICSs) security incidents have revealed vulnerabilities in the system hardware, user programs, and communication protocols. The various components of the ICS are connected by the Industrial Internet of Things (IIoT) protocol. Nevertheless, malicious attackers can exploit vulnerabilities in IIoT protocol to manipulate the ICS, potentially causing damage to the associated ICS equipment. This work focuses on the challenge of identifying vulnerabilities in IIoT protocols, aiming to enhance the system security through advanced fuzz testing techniques. To address the limitations of current fuzz testing in IIoT protocols, such as short prediction sequence lengths and low recognition rates, this work proposes a novel fuzz testing model based on the long attention mechanism, named TXL-Fuzz. This model is capable of handling longer protocol sequences and improving the diversity of the generated test cases. Experimental results demonstrate that the model outperforms the existing fuzz testers in test case recognition rate (TCRR) for the protocols of different lengths. Notably, TXL-Fuzz achieves a bits-per-character (BPC) of approximately 0.5, significantly lower by nearly 0.3 compared to the Anti-Sample Fuzzer, the long short-term memory network (LSTM)-based model, and GRU-based model. Furthermore, it exhibits a TCRR that is 5% to 15% higher than Peach Fuzzer, Anti-Sample Fuzzer, and BLSTM-DCNNFuzz under similar conditions.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided integrated sensing, computing, and communication (ISCC) Internet of Robotic Things (IoRT) framework is proposed. Specifically, the full-duplex (FD) base station (BS) simultaneously receives the offloading signals from decision robots (DRs) and carries out target robot (TR) sensing. A computation rate maximization problem is formulated to optimize the sensing and receive beamformers at the BS and the STAR-RIS coefficients under the BS power constraint, the sensing signal-to-noise ratio constraint, and STAR-RIS coefficients constraints. The alternating optimization (AO) method is adopted to solve the proposed optimization problem. With fixed STAR-RIS coefficients, the subproblem with respect to sensing and receiving beamformer at the BS is tackled with the weighted minimum mean-square error method. Given beamformers at the BS, the subproblem with respect to STAR-RIS coefficients is tacked with the penalty method and successive convex approximation method. The overall algorithm is guaranteed to converge to at least a stationary point of the computation rate maximization problem. Our simulation results validate that the proposed STAR-RIS aided ISCC IoRT system can enhance the sum computation rate compared with the benchmark schemes.

A near-field integrated sensing, positioning, and communication (ISPAC) framework is proposed, where a base station (BS) simultaneously serves multiple communication users and carries out target sensing and positioning. A novel double-array structure is proposed to enable the near-field ISPAC at the BS. Specifically, a small-scale assisting transceiver (AT) is attached to the large-scale main transceiver (MT) to empower the communication system with the ability of sensing and positioning. Based on the proposed framework, the joint angle and distance Cramér-Rao bound (CRB) is first derived. Then, the CRB is minimized subject to the minimum communication rate requirement in both downlink and uplink ISPAC scenarios: 1) For downlink ISPAC, a downlink target positioning algorithm is proposed and a penalty dual decomposition (PDD)-based double-loop algorithm is developed to tackle the non-convex optimization problem. 2) For uplink ISPAC, an uplink target positioning algorithm is proposed and an efficient alternating optimization algorithm is conceived to solve the non-convex CRB minimization problem with coupled user communication and target probing design. Both proposed optimization algorithms can converge to a stationary point of the CRB minimization problem. Numerical results show that: 1) The proposed ISPAC system can locate the target in both angle and distance domains merely relying on single BS and limited bandwidths; and 2) the positioning performance achieved by the hybrid-analog-and-digital ISPAC approaches that achieved by fully digital ISPAC when the communication rate requirement is not stringent.

Industrial control technology faces serious communication security threats. Authenticated key agreement(AKA) protocols are essential to secure communication between industrial control nodes, but they must be efficient and robust due to resource constraints. Existing AKA protocols based on encryption algorithms have limitations such as vulnerability to clone attacks. Also, there is a lack of protocol’s research that fully integrate advantages of physical and upper layer. We propose a novel cross-layer AKA protocol that leverages channel state information(CSI), which has uniqueness and real randomness, and is unforgeable, to enhance security and reduce computational overhead. Our protocol only requires simple operations such as algebraic, Hash and XOR. We introduce a new security verification model, superCK, which extends the well-known eCK and relaxes assumptions on attackers. Our protocol achieves 12 fully provable key security capabilities, and has 75.91% less computational overhead and 6.99% less communication overhead than the best ones, achieving an optimal trade-off between security and efficiency.

The development of sixth-generation (6G) communication technologies is confronted with the significant challenge of spectrum resource shortage. To alleviate this issue, we propose a novel simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided multiple-input multiple-output (MIMO) cognitive radio (CR) system. Specifically, the underlying secondary network in the proposed CR system reuses the same frequency resources occupied by the primary network with the help of the STAR-RIS. The secondary network sum rate maximization problem is first formulated for the STAR-RIS aided MIMO CR system. The adoption of STAR-RIS necessitates an intricate beamforming design for the considered system due to its large number of coupled coefficients. The block coordinate descent method is employed to address the formulated optimization problem. In each iteration, the beamformers at the secondary base station (SBS) are optimized by solving a quadratically constrained quadratic program (QCQP) problem. Concurrently, the STAR-RIS passive beamforming problem is resolved using tailored algorithms designed for the two phase-shift models: 1) For theindependent phase-shift model, a successive convex approximation-based algorithm is proposed; 2) For thecoupled phase-shift model, a penalty dual decomposition-based algorithm is conceived, in which the phase shifts and amplitudes of the STAR-RIS elements are optimized using closed-form solutions. Simulation results show that: 1) The proposed STAR-RIS aided CR communication framework can significantly enhance the sum rate of the secondary system; 2) The coupled phase-shift model results in limited performance degradation compared to the independent phase-shift model.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided near-field multiple-input multiple-output (MIMO) communication framework is proposed. A weighted sum rate maximization problem for the joint optimization of the active beamforming at the base station (BS) and the transmission/reflection-coefficients (TRCs) at the STAR-RIS is formulated. The resulting non-convex problem is solved by the developed block coordinate descent (BCD)-based algorithm. Numerical results illustrate that the near-field beamforming for the STAR-RIS aided MIMO communications significantly improve the achieved weighted sum rate.

Abstract The authors propose a secure intelligent reflecting surface (IRS) assisted transmission system in the presence of a ground eavesdropper, where the source in the ground transmits confidential information to unmanned aerial vehicle (UAV), IRS is deployed to promote the transmission rate of the source to UAV. Closed‐form expression for secure transmission non‐outage probability is derived, and secure transmission performance is analyzed. Simulation validates the correctness of the derivation. Compared with benchmarks, results show that the IRS can improve the secure transmission of ground to UAV.

Nowadays, multi-sensor technologies are applied in many fields, e.g., Health Care (HC), Human Activity Recognition (HAR), and Industrial Control System (ICS). These sensors can generate a substantial amount of multivariate time-series data. Unsupervised anomaly detection on multi-sensor time-series data has been proven critical in machine learning researches. The key challenge is to discover generalized normal patterns by capturing spatial-temporal correlation in multi-sensor data. Beyond this challenge, the noisy data is often intertwined with the training data, which is likely to mislead the model by making it hard to distinguish between the normal, abnormal, and noisy data. Few of previous researches can jointly address these two challenges. In this paper, we propose a novel deep learning-based anomaly detection algorithm called Deep Convolutional Autoencoding Memory network (CAE-M). We first build a Deep Convolutional Autoencoder to characterize spatial dependence of multi-sensor data with a Maximum Mean Discrepancy (MMD) to better distinguish between the noisy, normal, and abnormal data. Then, we construct a Memory Network consisting of linear (Autoregressive Model) and non-linear predictions (Bidirectional LSTM with Attention) to capture temporal dependence from time-series data. Finally, CAE-M jointly optimizes these two subnetworks. We empirically compare the proposed approach with several state-of-the-art anomaly detection methods on HAR and HC datasets. Experimental results demonstrate that our proposed model outperforms these existing methods.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

We propose a legal full‐duplex unmanned aerial vehicle (UAV) surveillance system in the presence of the ground‐to‐ground suspicious link with antisurveillance technology. UAV performs passive surveillance and active jamming simultaneously, and the suspicious source with multiantenna employs artificial noise to avoid being monitored. In order to ensure effective surveilling, we adopt two beamforming schemes, namely, maximum ratio transmission (MRT)/receiving zero‐forcing (RZF) and transmitting zero‐forcing (TZF)/maximum ratio combing (MRC), for MIMO UAV. For the two beamforming schemes, we derive the surveilling nonoutage probability in a closed‐form expression and analyze the surveilling performance under different system environments. Monte Carlo (MC) simulation validates the correctness of the formula.

Device‐to‐device (D2D) communication provides the communication of the users in the vicinity and thereby decreases end‐to‐end delay and power consumption. More importantly, D2D communication enables offloading the traffic load of the base station (BS), and it is very suitable for caching, especially in ultra dense networks (UDNs). In this paper, a joint optimisation problem of collaborative caching and sleeping strategy based on energy‐delay tradeoff for D2D‐aided UDN is investigated. To solve the joint optimization problem, we decompose it into sleeping sub‐problem and collaborative caching sub‐problem. For sleeping subproblem, the optimal sleeping ratio is first derived, then, a delay‐aware sleeping strategy is proposed to obtain the local optimal sleeping scheme. For the collaborative caching subproblem, the suboptimal solution is obtained by distributed iterative method. In each iteration step, the suboptimal solution is derived by solving the combinatorial optimisation problem under Karush‐Kuhn‐Tucker conditions. Simulation results show that the proposed algorithms can coverage to the global solution. It also demonstrate that increasing caching capacity shortened mean delay, and benefitting from collaborative caching, delay performance and energy saving can be improved significantly. Moreover, it can be seen that combining the sleeping strategy with collaborative caching further reduced energy consumption by a considerable amount.

In this paper, a wavelet domain based algorithm is proposed for the image detail enhancement. The proposed algorithm works on the principle of residual proximity measure between the image and it's residual. The detail features of an image are enhanced by using a pixel and patch-based fast search mechanism. The unique properties of the stationary wavelet transform such as redundancy and approximate scale invariance are utilized along with the fast patchbased search mechanism. The proposed model works well for natural images. By incorporating the wavelet analysis along with the residual structure similarity, we are able to mine the fine detail features required for the image enhancement. The proposed algorithm is compared with benchmark algorithms. The experimental analysis is carried out to show the efficacy of the proposed algorithm. The visual, as well as quantitative results show that proposed algorithm is on par with the other detail enhancement algorithms.

Fatigue is not only common in clinical patients but is also prevalent in the healthy population. A lot of studies have been done to detect mental fatigue. Most of the current fatigue detection systems are based on individual questionnaire and computer vision. In this paper, we proposed a deep temporal model named Deep Convolutional Autoencoding Memory network to detect mental fatigue in real time based on biometrics (e.g., GSR, HR, R-R intervals and skin temperature) collected with smart band. The model can be regarded as a One-Class classifier consisted of two sub-networks: Characterization Network and Memory Network that can be co-optimized to extract features from raw data automatically and generate baseline model which can describe the sequential patterns of non-fatigue data. The major contributions of this paper are as follows: First, we solved the class imbalance problem between normal data and fatigue data through performing One-Class classification; Second, Characterization Network can extract features from the heterogeneous biometrics automatically; Third, Memory Network can generate a temporal model which accurately describes the normal pattern of sequential data; Last, our model uses a novel decision criterion to make the detection decision. Performance of the proposed method is validated on a realistic environmental dataset containing the biometrics of 6 participants collected in 6 weeks. Experimental results show that the accuracy of fatigue detection attains 82.9%, which is superior to the representative off-the-shelf methods.

In this paper, we investigate the energy-delay tradeoff (EDT) problem to reduce energy consumption while ensure packet delay performance for wireless-backhauling ultra-dense network (UDN). We formulate the EDT problem as a cost minimization problem to find the optimal base stations (BS) sleeping ratio for sleeping mechanism. To solve the problem, a dynamic gradient descent iteration algorithm is used to obtain a locally optimal sleeping ratio. We prove it can converge to the global optimal BS sleeping ratio. The impacts of BS sleeping ratio on EC and packet delay are investigated and validated by simulation and numerical results.

In this paper, a home Internet-of-Things system is analyzed by dividing it into four layers, i.e., the node layer, gateway layer, service layer, and open layer. The gateway layer, which supports a variety of wireless technologies and is the core of home wireless networks access unit, together with the node layer constitutes the home wireless network. A gateway prototype following the proposed architecture has been implemented. A testbed of an interference-aware wireless network which includes the gateway prototype has also been created for testing its user interaction performances. The experimental results show that both Wi-Fi and Bluetooth have an impact on the ZigBee communication. Considering the complex scene of home and building, ZigBee multihop communications are set to reduce the packet loss probability. In addition, an event-level-based transmission control strategy is proposed, in which the packet loss probability of wireless network is reduced by controlling the transmission priority of different levels of monitoring events, and optimizing the ZigBee wireless network channel occupancy.

Advanced networking technology and increasing information services have led to extensive interconnection between Building Automation Systems (BAS) communication protocols and Internet, which makes Fog computing service a potential solution for automation of building end devices. However, the connection to Internet and public networks increases significantly the risk of the BAS networks being attacked due mainly to the significant increase in the attack surface. In this paper, we present an anomaly based Intrusion Detection System (IDS) that combines context awareness and Cyber DNA techniques to detect network misbehavior from security and functionality perspectives. We developed runtime models for service interactions and functionality patterns by modeling the information that is continuously acquired from building assets into two novel data structures: Protocol Context Aware and sensor-DNA. Our IDS uses Anomaly Behavior Analysis techniques to accurately detect anomalous events triggered by cyber-attacks or any failure. A classification of detected attacks allow our IDS to automatically launch protective countermeasures. We evaluate our approach in the Smart Building testbed developed at the University of Arizona Center for Cloud and Autonomic Computing, by launching several cyber-attacks that exploit the generic vulnerabilities of BAS.

Wireless Network Virtualization (WNV) enables high resource utilization, inter-slice isolation and customizable resource allocation, and has attracted much interest. However, most of works on WNV did not consider the business model between Infrastructure Providers (InPs) and Service Providers (SPs) nor did they focus on the isolation and customization issues. This paper proposes a WNV framework for LTE where hypervisor does not have direct access to user information, and Resource Block (RB) allocation problem in this framework is investigated by adopting the Vickrey-Clarke-Groves (VCG) mechanism to model auction games between InPs and Mobile Virtual Network Operators (MVNOs) where MVNOs have the incentive to bid truthfully to compete for RBs. A unified utility function is utilized to enable MVNOs to determine their own scheduling strategies by setting the parameters in it. The RB allocation problem is formulated to divide the total RB set into different subsets to maximize the summation of reporting valuations of all MVNOs. Due to its complexity, a heuristic algorithm is proposed. Simulation results show that the intra-slice customization and inter-slice isolation of virtual networks are satisfied in our model.

Self-healing is an interesting topic in SON (Self- Organizing Networks). In this paper, we investigate cell outage detection problem, and propose an improved TCM (Transductive confidence machines) based automatic cell outage detection algorithm. By incorporating a hypothesis test with the Neyman-Pearson criterion to improve the detection accuracy, the improved TCM can effectively detect cell outage using normal data for training. The simulation results demonstrate that the proposed scheme has lower false alarm rate while ensuring high detection accuracy than the traditional outage detection methods.

We study the Enhanced Inter-Cell Interference Coordination (eICIC) for co-channel deployments of pico cells in macro cells. In particular, we consider a non- uniform topology where the number of pico cells within the coverage area of each macro cell is different. To alleviate the interference caused by the co-channel deployment, the macro cells employ low-power almost- blank subframe. We consider the joint problem of power and resource allocation,where the goal is to maximize the proportional fairness utility of the system. A convergent iterative algorithm is proposed and simulation results demonstrate that our proposed algorithm improves both the system throughput and user fairness compared to existing schemes.

In this paper, energy saving of two-tier network: macrocells overlaid by randomly distributed picos is considered. A pico on/off algorithm based on offload bandwidth is proposed to save energy and improve the energy efficiency. Pico on/off selection will be triggered when the traffic load of macro base station (BS) exceeds thresholds. The objective of the algorithm is to minimize the number of active picos. Simulation results show that the proposed algorithm can save more energy compared with state of the art scheme.

Advanced networking technology and increasing information services have led to extensive interconnection between Building Automation and Control (BAC) networks and Internet. The connection to Internet and public networks massively elevates the risk of the BAC networks being attacked. In this paper, we present a framework for a rule based anomaly detection of Building Automation and Control networks. We develop an anomaly based intrusion detection system to the building network by training the system with dataflows that are dynamically captured from the Fire Alarm System testbed using the BACnet Protocol Monitoring module. The rules acquired from the offline data mining procedure can detect attacks against the BACnet protocol with an extremely low false positive rate. We evaluate our approach by launching several attacks that exploit the generic vulnerabilities of the BACnet Protocol. A classification of detected attacks is introduced at the end.

In ultra dense heterogeneous networks (HetNets), the interference becomes more serious since multiple small cells coexist in the coverage area of the macrocells and share the same spectrum. An efficient interference coordination method is adjusting the transmit powers of all cells in a cooperative way. However, under practical serving cell selection rules in which serving cells of users alter with the variation of cell powers, finding optimal cell powers becomes a difficult problem. In this paper, an improved modified particle swarm optimization (MPSO) is proposed to tackle this difficult problem caused by the altering of serving cell. Local search and multi-restart process are introduced to guarantee the local and then global optimality, and the convergence conditions and global optimality are proved by mathematical deduction to guide the selection of the parameters. Simulations show that the proposed algorithm can significantly improve system throughput compared with existing algorithms which do not consider the alteration of serving cells. By improving MPSO, the proposed algorithm can spend less iteration time to achieve higher system throughput, and exhibit similar performance as exhaustive search in both system throughput and the signal to interference plus noise ratio (SINR) of users.

The intensive downlink (DL) inter-cell interference created by cell range expansion (CRE) of picocells is an urgent problem needing to be solved under macro-pico heterogeneous network (HetNet) scenario. A promising approach is taking advantage of additional degree of freedom brought by carrier aggregation (CA). However, poorly arranged carrier configurations or interference coordination schemes can lead to a degradation of system overall performance. In this paper, we propose a novel dynamic interference coordination scheme based on carrier aggregation to alleviate the DL interference from macrocells to users located in the cell range expansion area. Novel carrier configuration pattern and dynamic power control scheme based on price algorithm are applied to mitigate detrimental interference to picocell-edge users and improve the availability of macrocells. Simulation results show that the proposed scheme can boost the picocell-edge throughput significantly while ameliorating the overall system throughput.

In this paper, we propose a novel opportunistic scheduling algorithm in multi-cell cooperation scenario, i.e., joint multi-user diversity (MUD) exploitation and inter-cell interference (ICI) mitigation based scheduling (MEIMS). Our algorithm jointly considers the intended channel condition of the scheduled user from its serving cell and the orthogonality between that and the corresponding interference channels to concurrently scheduled users in neighboring cells so as to exploit MUD and mitigate ICI simultaneously. The performance of our algorithm is evaluated through simulation. Results show that, our scheme can significantly enhance the received signal to interference plus noise ratio (SINR) with relatively better fairness guarantee, thus achieves the largest throughput and utility comparing to several well-known scheduling algorithms.

3GPP LTE networks can provide a higher capacity by adopting advanced physical layer techniques and serve users with different Quality of Service (QoS) requirements. However, unbalanced user distributions and strong inter-cell interference (ICI) still deteriorate network performances severely. Since fractional frequency reuse (FFR) technique is recommended to mitigate ICI, we investigate the load balancing problem in a 3GPP LTE multi-cell FFR network with heterogenous services in this paper. Firstly we formulate a multi-objective optimization problem, whose objectives are intra- and inter-cell load balancing index for users with QoS requirements and total utility function for users without QoS requirements. Then we analyze the complexity of the problem and propose a practical algorithm which includes QoS aware intra- and inter-cell handover and call admission control. Extensive simulations are conducted, the results show that our algorithm can lead to significantly better performances, i.e., a lower new call blocking rate for users with QoS requirements, a larger utility for users without QoS requirements at the cost of a bit degradation of total throughput.

In this paper, we propose a novel opportunistic scheduling algorithm, joint useful and interference channel based scheduling (JUICS), in coordinated multi-point (CoMP) transmission scenario. Our scheme jointly considers the useful channel condition of the scheduled user from its serving cell and the orthogonality between that and the corresponding interference channels to concurrently scheduled users in neighboring CoMP cells, thus to exploit multi-user diversity (MUD) and mitigate inter-cell interference (ICI) simultaneously. The performance of the proposed algorithm is evaluated through simulation in terms of the cumulative distribution performance of the received signal to interference plus noise ratio (SINR) and that of the scheduled times of all CoMP users. Results show that, with limited complexity and overhead, our scheme can significantly enhance the received SINR with relatively better fairness guarantee, thus to achieve the largest throughput and utility comparing to several well-known scheduling algorithms.

In this paper, we investigate call admission control (CAC) with opportunistic scheduling and propose a novel CAC algorithm for users with quality of service (QoS) requirements. Our main contribution is threefold. First, we verify that, compared with several other scheduling schemes, cumulative distributed function based scheduling (CS) makes the best tradeoff between efficiency and fairness in full-load scenario and exploits the best opportunism with absolutely fair resource allocation. Then we deduce and validate the multi-user diversity gain (MDG) of CS, which determines its long-term average performance and is used for estimation of resource occupation in CAC algorithm design. After that, we use opportunistic round robin (ORR) method to calculate the statistical low performance bound of CS, and propose CS/ORR based CAC (COCAC) algorithm, which guarantees the heterogeneous minimum rate requirement (MRRs) of both new access users and existing ones. Finally, we evaluate the performance of the proposed COCAC algorithm via simulation. Results show that COCAC can significantly reduce new call block probability, effectively make use of system resources, as well as strictly guarantee all users' MRRs.

In this paper, we investigate load balancing problem in 3GPP Long Term Evolution (LTE) networks and propose our solution which considers users with different Quality-of-Service (QoS) requirements. Load unbalance among neighboring cells often yields negative impacts on user experience and network performance, and it has mainly been considered for only data services without QoS guarantee. However, 3GPP LTE network aims to support multi-class services with different QoS requirements, on which the influence of load unbalance is quite different. For those with minimum rate requirements, it may result in high block probability, while for others without rate requirements, the throughput of boundary users may be degraded. In this paper, we incorporate all the differences into a network utility maximization framework and formulate it as a multi-objective optimization problem. The objectives in the problem are load balancing index of services with QoS requirements and the total utility of other services, and the constraints are physical resource limits and QoS demands. Then we analyze the complexity of the problem, and propose our solution, which includes a QoS guaranteed hybrid scheduling scheme, handover of users with and without QoS requirements, and a call admission control algorithm. Extensive simulation is conducted and the results show that the proposed framework leads to significantly better load balancing, and thus the decrease in call block probability of users with QoS requirements, and the increase in throughput of boundary best effort users.

The total energy minimization in wireless multihop networks using two-way network coding is investigated in this paper. The problem is first formulated as a linear programming problem, then decomposed into two sub-problems using the La grangian decomposition, and finally solved with the subgradient method. After that, the backpressure based algorithm is proposed to solve the problem in a distributed manner. The performance of the algorithm is evaluated first in a simple topology for analysis and then in a random topology with different number of flows for practical consideration. Simulation results show that the convergence time increases as the number of nodes in the network, and the energy cost per packet can be saved up to 30% by using two-way network coding.

This paper deals with lifetime maximization for energy constrained wireless networks with inter-session network coding. The problem of lifetime maximization is first formulated, and then transformed into a linear programming problem. Using Lagrangian dual decomposition, it is further converted into a dual problem that consists of two subproblems: lifetime optimization and network optimization, and then it is solved by utilizing dual subgradient method. Convex combination is used for primary recovery from the dual solutions. Performance of the algorithm is evaluated through simulation. Results illustrate that theoretical analysis matches practical calculation well, and that inter-session network coding can prolong the network lifetime up to 50% compared with the one without network coding.

Load imbalance that deteriorates the system performance is a severe problem existing in 3GPP LTE networks. To deal with this problem, we propose in this paper a load balancing framework, which aims at balancing the load in the entire network, while keeping the network throughput as high as possible. In this framework, the objective is formulated as a network-wide utility function balancing network throughput and load distribution, and then it is transformed to an integer optimization problem under resource allocation constraints. After that, the complexity of the problem is analyzed, network structure constraints are presented, and a practical suboptimal algorithm, called Heaviest-First Load Balancing (HFLB), is proposed. Extensive simulation is made and the results show that using the HFLB algorithm the network can get significantly better load balancing while maintaining the same network throughput at the price of a bit more handovers compared with the traditional signal strength-based handover algorithm.

We investigate channel estimation for timeslot-structured single-carrier block transmission (SCBT) over space-, time-, and frequency-selective fading multiple-input multiple-output (MIMO) channels. A MIMO-SCBT with a dual cyclic timeslot structure is presented first. Then, an optimal channel estimation in the minimal mean square error (MMSE) sense on the timeslot basis is investigated. It is shown that the optimal pilots for the timeslot-based MMSE channel estimation are related to the statistical channel state information in eigenmode. Under the assumption that the transmit correlation is unknown at the transmitter, the optimal pilots satisfy the same condition as reported for the block-based least-square (LS) channel estimation in literature, and the channel estimation can be simplified to initial block-based LS channel estimation followed by space-time postprocessing. Particularly, for spatially uncorrelated channels, the space-time postprocessing can be reduced to pathwise processing. A new design of the pilot sequences is given, which leads to an efficient implementation of the channel estimation. Later on, a more efficient implementation for the initial channel estimation is obtained by using the structure of the pilot sequences, and discrete cosine transform (DCT)-based implementation is developed for the space-time postprocessing to approximate the optimal solution with low implementation complexity. Finally, the performance of the proposed channel estimation is verified via simulations.

In this paper, we present a generalized multi-carrier (GMC) transmission technique in multi-input and multi-output (MIMO) channel environments for beyond 3G mobile communications. Several key techniques including the design and implementation of the GMC synthesis/analysis system, adaptive double cyclic timeslot structure, pilot design and channel estimation, and iterative detection/decoding are briefly discussed. Link-level simulation results in Rayleigh fading MIMO channels are provided