Orthogonal Time Frequency and Space (OTFS) modulation is expected to provide high-speed and ultra-reliable communications for emerging mobile applications, including low-orbit satellite communications. Using the Doppler frequency for positioning is a promising research direction on communication and navigation integration. To tackle the high Doppler frequency and low signal-to-noise ratio (SNR) in satellite communication, this paper proposes a Red and Blue Frequency Shift Discriminator (RBFSD) based on the pseudo-noise (PN) sequence. The paper derives that the cross-correlation function on the Doppler domain exhibits the characteristic of a Sinc function. Therefore, it applies modulation onto the Delay-Doppler domain using PN sequence and adjusts Doppler frequency estimation by red-shifting or blue-shifting. Simulation results show that the performance of Doppler frequency estimation is close to the Cramér-Rao Lower Bound when the SNR is greater than -15dB. The proposed algorithm is about $1/D$ times less complex than the existing PN pilot sequence algorithm, where $D$ is the resolution of the fractional Doppler.

In the 6G era, Space-Air-Ground Integrated Network (SAGIN) are anticipated to deliver global coverage, necessitating support for a diverse array of emerging applications in high-mobility, hostile environments. Under such conditions, conventional orthogonal frequency division multiplexing (OFDM) modulation, widely employed in cellular and Wi-Fi communication systems, experiences performance degradation due to significant Doppler shifts. To overcome this obstacle, a novel two-dimensional (2D) modulation approach, namely orthogonal time frequency space (OTFS), has emerged as a key enabler for future high-mobility use cases. Distinctively, OTFS modulates information within the delay-Doppler (DD) domain, as opposed to the time-frequency (TF) domain utilized by OFDM. This offers advantages such as Doppler and delay resilience, reduced signaling latency, a lower peak-to-average ratio (PAPR), and a reduced-complexity implementation. Recent studies further indicate that the direct interplay between information and the physical world in the DD domain positions OTFS as a promising waveform for achieving integrated sensing and communications (ISAC). In this article, we present an in-depth review of OTFS technology in the context of the 6G era, encompassing fundamentals, recent advancements, and future directions. Our objective is to provide a helpful resource for researchers engaged in the field of OTFS.

This paper investigates the security performance of a cooperative multicast-unicast system, where the users present the feature of high mobility. Specifically, we develop the non-orthogonal multiple access (NOMA) based orthogonal time frequency space (OTFS) transmission scheme, namely NOMA-OTFS, in order to combat Doppler effect as well as to improve the spectral efficiency. Further, we propose a power allocation method addressing the trade-off between the reliability of multicast streaming and the confidentiality of unicast streaming. Based on that, we utilize the relay selection strategy, to improve the security of unicast streaming. In the context of multicast-unicast streaming, our simulation findings validate the effectiveness of the NOMA-OTFS based cooperative transmission, which can significantly outperform the existing NOMA-OFDM in terms of both reliability and security.

In this paper, an integrated substrate gap waveguide (ISGW) filtering antenna is proposed at millimeter wave band, whose surface wave and spurious modes are simultaneously suppressed. A second-order filtering response is obtained through a coupling feeding scheme using one uniform impedance resonator (UIR) and two stepped-impedance resonators (SIRs). To increase the stopband width of the antenna, the spurious modes are suppressed by selecting the appropriate sizes of the ISGW unit cell. Furthermore, the ISGW is implemented to improve the radiation performance of the antenna by alleviating the propagation of surface wave. And an equivalent circuit is investigated to reveal the working principle of ISGW. To demonstrate this methodology, an ISGW filtering antenna operating at a center frequency of 25 GHz is designed, fabricated, and measured. The results show that the antenna achieves a stopband width of 1.6$f_0$ (center frequency), an out-of-band suppression level of 21 dB, and a peak realized gain of 8.5 dBi.

Since orthogonal time-frequency space (OTFS) can effectively handle the problems caused by Doppler effect in high-mobility environment, it has gradually become a promising candidate for modulation scheme in the next generation of mobile communication. However, the inter-Doppler interference (IDI) problem caused by fractional Doppler poses great challenges to channel estimation. To avoid this problem, this paper proposes a joint time and delay-Doppler (DD) domain based on sparse Bayesian learning (SBL) channel estimation algorithm. Firstly, we derive the original channel response (OCR) from the time domain channel impulse response (CIR), which can reflect the channel variation during one OTFS symbol. Compare with the traditional channel model, the OCR can avoid the IDI problem. After that, the dimension of OCR is reduced by using the basis expansion model (BEM) and the relationship between the time and DD domain channel model, so that we have turned the underdetermined problem into an overdetermined problem. Finally, in terms of sparsity of channel in delay domain, SBL algorithm is used to estimate the basis coefficients in the BEM without any priori information of channel. The simulation results show the effectiveness and superiority of the proposed channel estimation algorithm.

With the rapid development of the Industrial Internet of Things (IIoT), the traditional centralized cloud processing model has encountered the challenges of high communication latency and high energy consumption in handling industrial big data tasks. This paper aims to propose a low-latency and low-energy path computing scheme for the above problems. This scheme is based on the cloud-fog network architecture. The computing resources of fog network devices in the fog computing layer are used to complete task processing step by step during the data interaction from industrial field devices to the cloud center. A collaborative scheduling strategy based on the particle diversity discrete binary particle swarm optimization (PDBPSO) algorithm is proposed to deploy manufacturing tasks to the fog computing layer reasonably. The task in the form of a directed acyclic graph (DAG) is mapped to a factory fog network in the form of an undirected graph (UG) to find the appropriate computing path for the task, significantly reducing the task processing latency under energy consumption constraints. Simulation experiments show that this scheme's latency performance outperforms the strategy that tasks are wholly offloaded to the cloud and the strategy that tasks are entirely offloaded to the edge equipment.

Federated edge learning (FEEL) technology for vehicular networks is considered as a promising technology to reduce the computation workload while keeping the privacy of users. In the FEEL system, vehicles upload data to the edge servers, which train the vehicles' data to update local models and then return the result to vehicles to avoid sharing the original data. However, the cache queue in the edge is limited and the channel between edge server and each vehicle is time-varying. Thus, it is challenging to select a suitable number of vehicles to ensure that the uploaded data can keep a stable cache queue in edge server while maximizing the learning accuracy. Moreover, selecting vehicles with different resource statuses to update data will affect the total amount of data involved in training, which further affects the model accuracy. In this paper, we propose a vehicle selection scheme, which maximizes the learning accuracy while ensuring the stability of the cache queue, where the statuses of all the vehicles in the coverage of edge server are taken into account. The performance of this scheme is evaluated through simulation experiments, which indicates that our proposed scheme can perform better than the known benchmark scheme.

In this paper, average bit error probability (ABEP) bound of optimal maximum likelihood (ML) detector is first derived for ultra massive (UM) multiple-input-multiple-output (MIMO) system with generalized amplitude phase modulation (APM), which is confirmed by simulation results. Furthermore, a minimum residual criterion (MRC) based low-complexity near-optimal ML detector is proposed for UM-MIMO system. Specifically, we first obtain an initial estimated signal by a conventional detector, i.e., matched filter (MF), or minimum mean square error (MMSE) and so on. Furthermore, MRC based error correction mechanism (ECM) is proposed to correct the erroneous symbol encountered in the initial result. Simulation results are shown that the performance of the proposed MRC-ECM based detector is capable of approaching theoretical ABEP of ML, despite only imposing a slightly higher complexity than that of the initial detector.

Wireless channel characteristics have significant impacts on channel modeling, estimation, and communication performance. While the channel sparsity is an important characteristic of wireless channels. Utilizing the sparse nature of wireless channels can reduce the complexity of channel modeling and estimation, and improve system design and performance analysis. Compared with the traditional sub-6 GHz channel, millimeter wave (mmWave) channel has been considered to be more sparse in existing researches. However, most research only assume that the mmWave channel is sparse, without providing quantitative analysis and evaluation. Therefore, this paper evaluates the sparsity of mmWave channels based on mmWave channel measurements. A vector network analyzer (VNA)-based mmWave channel sounder is developed to measure the channel at 28 GHz, and multi-scenario channel measurements are conducted. The Gini index, Rician $K$ factor and root-mean-square (RMS) delay spread are used to measure channel sparsity. Then, the key factors affecting mmWave channel sparsity are explored. It is found that antenna steering direction and scattering environment will affect the sparsity of mmWave channel. In addition, the impact of channel sparsity on channel eigenvalue and capacity is evaluated and analyzed.

Integrating the blockchain technology into mobile-edge computing (MEC) networks with multiple cooperative MEC servers (MECS) providing a promising solution to improving resource utilization, and helping establish a secure reward mechanism that can facilitate load balancing among MECS. In addition, intelligent management of service caching and load balancing can improve the network utility in MEC blockchain networks with multiple types of workloads. In this paper, we investigate a learning-based joint service caching and load balancing policy for optimizing the communication and computation resources allocation, so as to improve the resource utilization of MEC blockchain networks. We formulate the problem as a challenging long-term network revenue maximization Markov decision process (MDP) problem. To address the highly dynamic and high dimension of system states, we design a joint service caching and load balancing algorithm based on the double-dueling Deep Q network (DQN) approach. The simulation results validate the feasibility and superior performance of our proposed algorithm over several baseline schemes.

The routing protocols are paramount to guarantee the Quality of Service (QoS) for Flying Ad Hoc Networks (FANETs). However, they still face several challenges owing to high mobility and dynamic topology. This paper mainly focuses on the adaptive routing protocol and proposes a Three Dimensional Q-Learning (3DQ) based routing protocol to guarantee the packet delivery ratio and improve the QoS. In 3DQ routing, we propose a Q-Learning based routing decision scheme, which contains a link-state prediction module and routing decision module. The link-state prediction module allows each Unmanned Aerial Vehicle (UAV) to predict the link-state of Neighboring UAVs (NUs), considering their Three Dimensional mobility and packet arrival. Then, UAV can produce routing decisions with the help of the routing decision module considering the link-state. We evaluate the various performance of 3DQ routing, and simulation results demonstrate that 3DQ can improve packet delivery ratio, goodput and delay of baseline protocol at most 71.36%, 89.32% and 83.54% in FANETs over a variety of communication scenarios.

To implement the multi-way phase shifting maintaining the compact size and simplicity in structure, the uniform reference line concept was proposed for the differential phase shifter. However, the performance in bandwidth and phase range deteriorates with the additional requirements considered. To solve this problem, a quarter wavelength coupled line section loaded with open/short stubs is proposed as the basic element to implement the main line and also reference line. According to the theoretical analysis on this basic element, the loading stubs can be used to control the phase shift and also the phase slope of the basic element without affecting the amplitude property. With the predetermined parameters of the uniform reference line, only two parameters are required for the implementation of different differential phase shifts. This demonstrates the high simplicity of the proposed structure. For demonstration, an eight-way differential phase shifter operating at 3.5 GHz was implemented using the vertically installed planar structure. The prototype was further fabricated and measured. Good agreement between simulation and measurement can be observed. The implemented phase shifter can provide a wide range of phase shifting values from 45° to 315° with reference to the uniform reference line over a relative bandwidth of 62.3%.

Motivated by 5th generation wireless systems (5G), a large number of emerging applications appear, which put forward higher requirements for the task's transmission determinacy, which refers to the delay and jitter. To satisfy the deterministic requirement, mobile edge computing (MEC) is envisioned as a promising technique for reducing the end-to-end delay significantly. In this paper, we consider delay-sensitive task and jitter-sensitive task, and then formulate the joint communications and computing optimization problem under the latency, the total bandwidth, the total transmission power of base station (BS) and the computing ability of the MEC server constraints to minimize the delay and jitter in a multi-user MEC system. Because of the problems are non-convex, we decouple them into some subproblems and propose the corresponding algorithms to obtain a suboptimal solution. Finally, numerical results show that the proposed algorithms have a significant performance gain over the traditional solution in terms of the delay and the jitter.

This paper addresses sparse channels estimation problem for the generalized linear models (GLM) in the orthogonal time frequency space (OTFS) underwater acoustic (UWA) system. OTFS works in the delay-Doppler domain, where time-varying channels are characterized as delay-Doppler impulse responses. In fact, a typical doubly spread UWA channel is associated with several resolvable paths, which exhibits a structured sparsity in the delay-Doppler domain. To leverage the structured sparsity of the doubly spread UWA channel, we develop a structured sparsity-based generalized approximated message passing (GAMP) algorithm for reliable channel estimation in quantized OTFS systems. The proposed algorithm has a lower computational complexity compared to the conventional Bayesian algorithm. In addition, the expectation maximum algorithm is employed to learn the sparsity ratio and the noise variance. Simulation and experimental results show that the proposed algorithm has superior performance and low computational complexity for quantized OTFS systems.

Integration of digital twin (DT) and wireless channel provides new solution of channel modeling and simulation, and can assist to design, optimize and evaluate intelligent wireless communication system and networks. With DT channel modeling, the generated channel data can be closer to realistic channel measurements without requiring a prior channel model, and amount of channel data can be significantly increased. Artificial intelligence (AI) based modeling approach shows outstanding performance to solve such problems. In this work, a channel modeling method based on generative adversarial networks is proposed for DT channel, which can generate identical statistical distribution with measured channel. Model validation is conducted by comparing DT channel characteristics with measurements, and results show that DT channel leads to fairly good agreement with measured channel. Finally, a link-layer simulation is implemented based on DT channel. It is found that the proposed DT channel model can be well used to conduct link-layer simulation and its performance is comparable to using measurement data. The observations and results can facilitate the development of DT channel modeling and provide new thoughts for DT channel applications, as well as improving the performance and reliability of intelligent communication networking.

As the sixth generation network (6G) emerges, the Internet of remote things (IoRT) has become a critical issue. However, conventional terrestrial networks cannot meet the delay-sensitive data collection needs of IoRT networks, and the Space-Air-Ground integrated network (SAGIN) holds promise. We propose a novel setup that integrates non-orthogonal multiple access (NOMA) and wireless power transfer (WPT) to collect latency-sensitive data from IoRT networks. To extend the lifetime of devices, we aim to minimize the maximum energy consumption among all IoRT devices. Due to the coupling between variables, the resulting problem is non-convex. We first decouple the variables and split the original problem into four subproblems. Then, we propose an iterative algorithm to solve the corresponding subproblems based on successive convex approximation (SCA) techniques and slack variables. Finally, simulation results show that the NOMA strategy has a tremendous advantage over the OMA scheme in terms of network lifetime and energy efficiency, providing valuable insights.

RFPA (radio frequency power amplifier) based on PWM (pulse width modulation) can be perfect linear without using complicated DPD (digital pre-distortion) hardware. However, PWM RFPA need ultra-high PWM time resolution (less than 10 ps) for wide-band 5G, which is limited by CMOS technologies (can reach 40 ps). It limits the complexity of modulation so that PWM RFPA cannot be really commercialized so far in wideband radio/mobile technologies. This paper presents our contribution by introducing jitter into PWM. For the first time, we demonstrate that jitter can improve the PWM time resolution equivalently reaching less than 2.6 ps. Hence PWM RFPA can be used in 5G supporting 256QAM OFDM modulation based on currently available digital CMOS technologies. We demonstrate that jitter can refine the inherent resolution of PWM hardware circuit modulation. The jitter makes the MCS (modulation and coding scheme) of the bandwidth OFDM system improved from 16QAM to 256QAM in the band n28 of 3GPP 38.101. We also verified that 5G 256QAM OFDM modulation is achievable in the 3GPP band n20, Softbank band28 and NTT Docomo band28. The RMS EVM (root mean square error vector magnitude) of 256QAM OFDM modulation is about -44 dB.

In this paper, a beamforming scheme to improve the coverage in high-speed railway communication systems is investigated. A dedicated coverage model, where the coverage cell is an ellipse rather than the traditional circular or linear, is considered. Based on the elliptical coverage cell, an optimization problem for the beamforming design is formulated to maximize the percentage of railway coverage, subject to the constraints on equal expected designed propagation gain (the gain obtained by a combination of designed beam and propagation channel) on the elliptical curve, i.e., the expectation of designed propagation gain on the elliptical curve are all equal. Considering that the coverage can be improved by increasing the minimum designed propagation gain on the railway, the problem can be recast to maximizing the equal expected designed propagation gain on the elliptical curve. Subsequently, a beamforming design with an improved β-fairness power allocation, where the optimization problem is formulated to maximize the minimum expected received power over time with the constraints on elliptical cell based beamforming and mobile service amount, is proposed to further improve the coverage. An alternating iteration algorithm is developed to find the optimal beamforming vector and the instantaneous transmit power. Through numerical results, it is found that the beamforming designed on the elliptical curve covers longer railway than beamforming designed on the railway directly, and the coverage of elliptical cell based beamforming can be increased with the eccentricity. In addition, beamforming with the improved β-fairness power allocation can further improve the railway coverage and mobile service amount simultaneously. Moreover, it is shown that the larger eccentricity of the ellipse with appropriately chosen BS location, the larger coverage distance.

To satisfy diversified service demands of vertical industries, network slicing enables efficient resource allocation of a common infrastructure by creating isolated logical networks. However, uncertainty and dynamics of service demands will cause performance degradation. Due to operation costs and resource constraints, it is challenging to maintain high quality of user experience while obtaining high revenue for service providers (SPs). This paper develops an optimal and fast slice reconfiguration (OFSR) framework based on reinforcement learning, where a novel scheme is proposed to offer optimal decisions for reconfiguring diverse slices. A demand prediction model is proposed to capture changes in resource requirements, based on which the OFSR scheme is triggered to determine whether to perform slice reconfiguration. Considering the large state and action spaces generated from uncertain service time and resource requirements, deep dueling architecture is adopted to improve the convergence rate. Extensive simulations validate the effectiveness of the proposed framework in achieving higher long-term revenue for SPs.

Traditional underwater acoustic communication networks (UACNs) generally use omnidirectional transmission technology that causes a large number of data-packet collisions, thus resulting in low network throughput and high end-to-end delays. Compared with omnidirectional transmission technology, directional technology only sends and receives data packets in a specified direction. This can significantly reduce the probability of collisions and improve network performance. However, it also causes a deafness problem, which occurs when the sending node sends a data packet to the receiving node but the receiving node is unable to reply to the sender, because its antenna beam is closed. To resolve this issue, this study proposes a collision classification media access control (CC-MAC) protocol for UACNs. With this protocol, the underwater acoustic channel is divided into two subchannels, and the nodes transmit corresponding data types on them. The sending node can estimate the current status of the receiving node (i.e., no collision, normal collision, deafness) according to the type of the data packet received and the sub-channel it arrived on, and it can choose correct options to improve network efficiency. Finally, we verify the performance of CC-MAC via simulations, showing that the protocol achieved higher network throughput and lower end-toend delays.

At present, there is a problem of false positives caused by the too vast mimic scope in mimic transformation technology. Previous studies have focused on the “compensation” method to deal with this problem, which is expensive and cannot fundamentally solve it. This paper provides new insights into coping with the situation. Firstly, this study summarizes the false-positive problem in the mimic transformation, analyzes its possible harm and the root causes. Secondly, three properties about the mimic scope are proposed. Based on the three properties and security quantification technology, the best mimic component set theory is put forward to solve the false-positive problem. There are two algorithms, the supplemental method and the subtraction method. The best mimic component set obtained by these two algorithms can fundamentally solve the mimic system's false-positive problem but reduce the cost of mimic transformation. Thus make up for the lack of previous researches.

When Internet of Things (IoT) nodes access the network through wireless channels, the network is vulnerable to spoofing attacks and the Sybil attack. However, the connection of massive devices in IoT makes it difficult to manage and distribute keys, thus limiting the application of traditional high-level authentication schemes. Compared with the high-level authentication scheme, the physical layer authentication scheme realizes the lightweight authentication of users by comparing the wireless channel characteristics of adjacent packets. However, traditional physical layer authentication schemes still adopt the one-to-one authentication method, which will consume numerous network resources in the face of large-scale IoT node access authentication. In order to realize the secure access authentication of IoT nodes and regional intrusion detection with low resource consumption, we propose a physical layer authentication mechanism based on convolution neural network (CNN), which uses the deep characteristics of channel state information (CSI) to identify sending nodes in different locations. Specifically, we obtain the instantaneous CSI data of IoT sending nodes at different locations in the pre-set area, and then feed them into CNN for training to procure a model for IoT node authentication. With its powerful ability of data analysis and feature extraction, CNN can extract deep Spatio-temporal environment features of CSI data and bind them with node identities. Accordingly, an authentication mechanism which can distinguish the identity types of IoT nodes located in different positions is established to authenticate the identity of unknown nodes when they break into the pre-set area. Experimental results show that this authentication mechanism can still achieve 94.7% authentication accuracy in the case of a low signalto- noise ratio (SNR) of 0 dB, which means a significant improvement in authentication accuracy and robustness.

With the rapid development of the sixth generation (6G) network and Internet of Things (IoT), it has become extremely challenging to efficiently detect and prevent the distributed denial of service (DDoS) attacks originating from IoT devices. In this paper we propose an innovative trust model for IoT devices to prevent potential DDoS attacks by evaluating their trustworthiness, which can be deployed in the access network of 6G IoT. Based on historical communication behaviors, this model combines spatial trust and temporal trust values to comprehensively characterize the normal behavior patterns of IoT devices, thereby effectively distinguishing attack traffic. Experimental results show that the proposed method can efficiently distinguish normal traffic from DDoS traffic. Compared with the benchmark methods, our method has advantages in terms of both accuracy and efficiency in identifying attack flows.

Unmanned aerial vehicle (UAV) communications are subject to the severe spectrum scarcity problem. Cognitive UAV networks are promising to tackle this issue while the confidential information is susceptible to be eavesdropped. A UAV jamming assisted scheme is proposed. A joint resource allocation and trajectories optimization problem is formulated in a UAV-assisted jamming cognitive UAV network subject to diverse power and trajectory constraints. An alternative optimization algorithm is proposed to solve the challenging non-convex joint optimization problem. Extensive simulation results demonstrate the superiority of our proposed scheme and many meaningful insights are obtained for the practical design of cognitive UAV networks.

A scanning and uniform array architecture with large spacing, low complexity and high scalability is presented for high integration massive array applications. It is constructed by offset phase center elements arranged in a uniform and regular way, but its spacing can be larger than that of traditional arrays. An ideal model of the offset phase center element is established and its far-field distribution is derived. To suppress grating lobes, the phase center of any element is designed to be movable without changing its physical position. Using genetic algorithm (GA), a new constraint condition limiting the number of phase center changes is proposed to solve the objective function of the minimum values of grating lobes (GLs) and side lobes (SLs). It is shown that the optimal results can be achieved by two changes of phase centers. A multimode circular patch is developed and designed, and characteristics of the offset phase center are analyzed and verified. A prototype array of 12×12 offset phase center elements is implemented based on multi-mode circular patches. Full wave simulation results of radiation patterns show that the level of grating lobes is suppressed at least 7dB with 1.12λ spacing, while the scanning angle is 20°.

Time series data is a kind of data accumulated over time, which can describe the change of phenomenon. This kind of data reflects the degree of change of a certain thing or phenomenon. The existing technologies such as LSTM and ARIMA are better than convolutional neural network in time series prediction, but they are not enough to mine the periodicity of data. In this article, we perform periodic analysis on two types of time series data, select time metrics with high periodic characteristics, and propose a multi-scale prediction model based on the attention mechanism for the periodic trend of the data. A loss calculation method for traffic time series characteristics is proposed as well. Multiple experiments have been conducted on actual data sets. The experiments show that the method proposed in this paper has better performance than commonly used traffic prediction methods (ARIMA, LSTM, etc.) and 3%-5% increase on MAPE.

Blockage and imperfect beam alignment are two principal difficulties in high-frequency bands directional transmissions. In this paper, the coverage performance of downlink directional transmissions in ultra-dense networks is analyzed, with the consideration of beam alignment error and link blockage through stochastic geometry. Numerical experiments demonstrate that narrower beam leads to higher coverage probability with perfect beam alignment, but it is not the case with imperfect beam alignment. Therefore, the optimal beamwidth that maximize the coverage probability is characterized and a closed-form approximation of the optimal beamwidth is derived under imperfect beam alignment, accordingly. Furthermore, the optimal beamwidth is a monotonically increasing function of the standard deviation of the beam alignment error, and a monotonically decreasing function of the beamwidth of correspondent communication end, indicating that the beamwidth of the communication pairs ought to be jointly designed.

This paper introduces bit-interleaved polar coded modulation with iterative detection/decoding (BIPCM-ID). In order to enable the soft output of successive cancellation list (SCL) decoding, two types of re-encoders are proposed, namely the max-re-encoder and min-re-encoder, respectively. Regarding the iterative decoding, we analytically verify that the average mutual information (AMI) between the transmitted and decoded symbols can approach the Shannon bound with the proposed schemes. Moreover, bit error rate (BER) and block error rate (BLER) in single-user and multi-user scenarios are studied. Finally, simulation results show that the performance of BIPCM-ID outperforms other bit-interleaved coded modulation (BICM) systems with LDPC and Turbo codes, while also reducing the computational complexity.

Service function chains (SFC) mapping takes the responsibility for managing virtual network functions (VNFs). In SFC mapping, existing solutions duplicate VNFs with redundant instances to provide high availability in response to failures. However, as a compromise, these solutions result in high resource consumption due to device maintenance. In this paper, we propose a novel method named dynamic backup sharing (DBS) that allows SFCs to dynamically share backups to reduce resource consumption. DBS formulates the problem of sharing backups among different VNFs as an integer linear programming (ILP). Thereafter, we design a novel online algorithm based on dynamic programming to solve the problem. The experimental results indicate that DBS outperforms state-ofthe- art works by reducing resource consumption and improving the number of accepted requests.

This paper investigates the anti-jamming communication scenario where an intelligent reflecting surface (IRS) is mounted on the unmanned aerial vehicle (UAV) to resist the malicious jamming attacks. Different from existing works, we consider the dynamic deployment of IRS-UAV in the environment of the mobile user and unknown jammer. Therefore, a joint trajectory and passive beamforming optimization approach is proposed in the IRS-UAV enhanced networks. In detail, the optimization problem is firstly formulated into a Markov decision process (MDP). Then, a dueling double deep Q networks multi-step learning algorithm is proposed to tackle the complex and coupling decision-making problem. Finally, simulation results show that the proposed scheme can significantly improve the anti-jamming communication performance of the mobile user.

According to the boot process of modern computer systems, whoever boots first will gain control first. Taking advantage of this feature, a malicious code called bootkit can hijack the control before the OS bootloader and bypass security mechanisms in boot process. That makes bootkits difficult to detect or clean up thoroughly. With the improvement of security mechanisms and the emergence of UEFI, the attack and defense techniques for bootkits have constantly been evolving. We first introduce two boot modes of modern computer systems and present an attack model of bootkits by some sophistical samples. Then we discuss some classic attack techniques used by bootkits from their initial appearance to the present on two axes, including boot mode axis and attack phase axis. Next, we evaluate the race to the bottom of the system and the evolution process between bootkits and security mechanisms. At last, we present the possible future direction for bootkits in the context of continuous improvement of OS and firmware security mechanisms.

With the rapid development of information technology, the cyberspace security problem is increasingly serious. Kinds of dynamic defense technology have emerged such as moving target defense and mimic defense. This paper aims to describe the architecture and analyze the performance of Cyberspace Mimic DNS based on generalized stochastic Petri net. We propose a general method of anti-attacking analysis. For general attack and special attack model, the available probability, escaped probability and nonspecial awareness probability are adopted to quantitatively analyze the system performance. And we expand the GSPN model to adjust to engineering practice by specifying randomness of different output vectors. The result shows that the proposed method is effective, and Mimic system has high anti-attacking performance. To deal with the special attack, we can integrate the traditional defense mechanism in engineering practice. Besides, we analyze the performance of mimic DNSframework based on multi-ruling proxy and input-output desperation, the results represent we can use multi ruling or high-speed cache servers to achieve the consistent cost of delay, throughput compared with single authorized DNS, it can effectively solve 10% to 20% performance loss caused by general ruling proxy.

In this study, a real-time rotor temperature monitoring system for large turbogenerators using SmartMesh IP wireless network communication technology was designed and tested. The system is capable of providing comprehensive, accurate, continuous, and reliable real-time temperature monitoring for turbogenerators. Additionally, it has demonstrated satisfactory results in a real-time monitoring test of the rotor temperature of various famous large-scale turbogenerators and giant nuclear power half-speed turbogenerators designed and manufactured in China. The development and application of this wireless temperature measurement system would aid in improving the intelligent operation quality, safety, and stability of China's large turbine generators and even the entire power system.

To design an efficient protocol for sharing the encrypted lock keys in the renting house system, we introduce a new notion called time- and identitybased proxy reencryption (TIPRE) and the blockchain platform. Our CPA secure TIPRE scheme is constructed from Green et al.'s identity-based proxy reencryption scheme by adding the time property. In every time period, a time stamp authority generates a public key embedded with the current time stamp for each user. In our protocol for the renting house system, the TIPRE scheme is the primary building block, and the blockchain platform serves instead of a trusted third party, such as a real estate agency between landlords and tenants. The TIPRE scheme allows the landlord to change the lock key at each time period for safety. The blockchain platform allows the landlords and tenants to directly interact, and all of the interactions are recorded in the blockchain database to provide the desired security requirements, such as nonrepudiation and unforgeability. Finally, we provide the secure analysis of our protocol and test its performance by implementing it in the MacBook Pro and the Intel Edison development platforms.

The cognitive network has become a promising method to solve the spectrum resources shortage problem. Especially for the optimization of network slicing resources in the cognitive radio access network (RAN), we are interested in the profit of the mobile virtual network operator (MVNO) and the utility of secondary users (SUs). In cognitive RAN, we aim to find the optimal scheme for the MVNO to efficiently allocate slice resources to SUs. Since the MVNO and SUs are selfish and the game between the MVNO and SUs is difficult to reach equilibrium, we consider modeling this scheme as a Stackelberg game. Leveraging mathematical programming with equilibrium constraints (MPEC) and Karush-Kuhn-Tucker (KKT) conditions, we can obtain a single-level optimization problem, and then prove that the problem is a convex optimization problem. The simulation results show that the proposed method is superior to the non-cooperative game. While guaranteeing the Quality of Service (QoS) of primary users (PUs) and SUs, the proposed method can balance the profit of the MVNO and the utility of SUs.

In this paper, we consider a full-duplex (FD) millimeter wave (mmWave) multiuser integrated access and backhaul (IAB) system with massive MIMO, %Editor: Abbreviations and acronyms are often defined the first time they are used within the abstract and again in the main text and then used throughout the remainder of the manuscript. Please consider adhering to this convention. The target journal may have a list of abbreviations that are considered common enough that they do not need to be defined. and the system asymptotic performance and interference cancellation schemes are investigated. First, the asymptotic performance of the IAB system with massive MIMO is analyzed. As the number of macro base station (MBS) and small base station (SBS) antennas approaches infinity, the FD self-interference (SI), inter-tier interference and noise can be eliminated, which means that only multiuser interference remains in the system. Then, multiuser interference can be suppressed by the base band (BB) precoders. Since all interference and noise are suppressed, the spectral efficiency of the SBS and users are infinite in theory. Then, two interference suppression precoding schemes are proposed. A block diagonalization (BD)-based interference cancellation scheme is designed based on the channel characteristics and null space projection. The FD SI, inter-tier interference and multiuser interference are eliminated by BB precoders. Instead of eliminating interference completely, a signal to leakage and noise ratio (SLNR)-based precoding scheme is derived to suppress both interference and noise. By utilizing the Rayleigh-Ritz theorem, the SLNRs of the SBS and users are optimized. Simulation results show that all the interference can be effectively eliminated by the BD-based scheme at the cost of spectral efficiency performance loss, while the SLNR-based scheme can balance interference and noise and achieve higher spectral efficiency with comparatively low interference level. Therefore, the BD-based scheme is more suitable for interference elimination cases, and the SLNR-based scheme can improve the system performance in low interference scenarios.

With the development of the transportation industry, the effective guidance of aircraft in an emergency to prevent catastrophic accidents remains one of the top safety concerns. Undoubtedly, operational status data of the aircraft play an important role in the judgment and command of the Operational Control Center (OCC). However, how to transmit various operational status data from abnormal aircraft back to the OCC in an emergency is still an open problem. In this paper, we propose a novel Telemetry, Tracking, and Command (TT&C) architecture named Collaborative TT&C (CoTT&C) based on mega-constellation to solve such a problem. CoTT&C allows each satellite to help the abnormal aircraft by sharing TT&C resources when needed, realizing real-time and reliable aeronautical communication in an emergency. Specifically, we design a dynamic resource sharing mechanism for CoTT&C and model the mechanism as a single-leader-multi-follower Stackelberg game. Further, we give an unique Nash Equilibrium (NE) of the game as a closed form. Simulation results demonstrate that the proposed resource sharing mechanism is effective, incentive compatible, fair, and reciprocal. We hope that our findings can shed some light for future research on aeronautical communications in an emergency.

Fast data synchronization in wireless ad hoc networks is a challenging and critical problem. It is fundamental for efficient information fusion, control and decision in distributed systems. Previously, distributed data synchronization was mainly studied in the latency-tolerant distributed databases, or assuming the general model of wireless ad hoc networks. In this paper, we propose a pair of linear network coding (NC) and all-to-all broadcast based fast data synchronization algorithms for wireless ad hoc networks whose topology is under operator's control. We consider both data block selection and transmitting node selection for exploiting the benefits of NC. Instead of using the store-and-forward protocol as in the conventional uncoded approach, a compute-and-forward protocol is used in our scheme, which improves the transmission efficiency. The performance of the proposed algorithms is studied under different values of network size, network connection degree, and per-hop packet error rate. Simulation results demonstrate that our algorithms significantly reduce the times slots used for data synchronization compared with the baseline that does not use NC.

This paper investigates the tradeoff of the communication link and the eavesdropping link in covert communication in the presence of a full-duplex (FD) receiver. When a warden (Willie) attempts to detect the signal transmitted from a legitimate transmitter (Alice), the controllable FD receiver (Bob) can transmit with random power to impose interference uncertainty to Willie and force it to make an incorrect decision. To maximize the average transmission rate (ATR) of Alice-Bob and the average covert probability (ACP) for Willie, we propose a multi-objective optimization framework to optimize Bob's power uncertainty range (PUR) and spatial position jointly, subject to the sufficient condition for covert communication and the none-deployed-zone (NDZ). Due to the presence of multiple optimization objectives and nonconvex constraints, the nondominated sorting genetic algorithm II (NSGA-II) is utilized to explore the Pareto front and to give a set of solutions that reflect tradeoffs between the two conflicting objectives. Simulation results reveal that the solutions determined by the NSGA-II have larger values for both ATR and ACP than the other two baselines. Simulations also show the positive effect of the width of the PUR of Bob on the Pareto front.

Due to the large amount of unused and unexplored spectrum resources, the so-called sub-Terahertz (sub-THz) frequency bands from $100$ to $300$ GHz are seen as promising bands for the next generation of wireless communication systems. Channel modeling at sub-THz bands is essential for the design and deployment of future wireless communication systems. Channel measurement is a widely adopted method to obtain channel characteristics and establish mathematical channel models. Channel measurements depend on the design and construction of channel sounders. Thus, reliable channel sounding techniques and accurate channel measurements are required. In this paper, the requirements of an ideal channel sounder are discussed and the main channel sounding techniques are described for the sub-THz frequency bands. The state-of-the-art sub-THz channel sounders reported in the literature and respective channel measurements are presented. Moreover, a vector network analyzer (VNA) based channel sounder, which supports frequency bands from $220$ to $330$ GHz is presented and its performance capability and limitation are evaluated. This paper also discussed the challenge and future outlook of the sub-THz channel sounders and measurements.