This paper studies the problem of jamming decision-making for dynamic multiple communication links in wireless communication networks (WCNs). We propose a novel jamming channel allocation and power decision-making (JCAPD) approach based on multi-agent deep reinforcement learning (MADRL). In high-dynamic and multi-target aviation communication environments, the rapid changes in channels make it difficult for sensors to accurately capture instantaneous channel state information. This poses a challenge to make centralized jamming decisions with single-agent deep reinforcement learning (DRL) approaches. In response, we design a distributed multi-agent decision architecture (DMADA). We formulate multi-jammer resource allocation as a multi-agent Markov decision process (MDP) and propose a fingerprint-based double deep Q-Network (FBDDQN) algorithm for solving it. Each jammer functions as an agent that interacts with the environment in this framework. Through the design of a reasonable reward and training mechanism, our approach enables jammers to achieve distributed cooperation, significantly improving the jamming success rate while considering jamming power cost, and reducing the transmission rate of links. Our experimental results show the FBDDQN algorithm is superior to the baseline methods.

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.

Quantum computing is a promising technology that has the potential to revolutionize many areas of science and technology, including communication. In this review, we discuss the current state of quantum computing in communication and its potential applications in various areas such as network optimization, signal processing, and machine learning for communication. First, the basic principle of quantum computing, quantum physics systems, and quantum algorithms are analyzed. Then, based on the classification of quantum algorithms, several important basic quantum algorithms, quantum optimization algorithms, and quantum machine learning algorithms are discussed in detail. Finally, the basic ideas and feasibility of introducing quantum algorithms into communications are emphatically analyzed, which provides a reference to address computational bottlenecks in communication networks.

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 vehicle edge computing (VEC), asynchronous federated learning (AFL) is used, where the edge receives a local model and updates the global model, effectively reducing the global aggregation latency. Due to different amounts of local data, computing capabilities and locations of the vehicles, renewing the global model with same weight is inappropriate. The above factors will affect the local calculation time and upload time of the local model, and the vehicle may also be affected by Byzantine attacks, leading to the deterioration of the vehicle data. However, based on deep reinforcement learning (DRL), we can consider these factors comprehensively to eliminate vehicles with poor performance as much as possible and exclude vehicles that have suffered Byzantine attacks before AFL. At the same time, when aggregating AFL, we can focus on those vehicles with better performance to improve the accuracy and safety of the system. In this paper, we proposed a vehicle selection scheme based on DRL in VEC. In this scheme, vehicle's mobility, channel conditions with temporal variations, computational resources with temporal variations, different data amount, transmission channel status of vehicles as well as Byzantine attacks were taken into account. Simulation results show that the proposed scheme effectively improves the safety and accuracy of the global model.

In this paper, we propose an active reconfigurable intelligent surface (RIS) enabled hybrid relaying scheme for a multi-antenna wireless powered communication network (WPCN), where the active RIS is employed to assist both wireless energy transfer (WET) from the power station (PS) to energy-constrained users and wireless information transmission (WIT) from users to the receiving station (RS). For further performance enhancement, we propose to employ both transmit beamforming at the PS and receive beamforming at the RS. We formulate a sum-rate maximization problem by jointly optimizing the RIS phase shifts and amplitude reflection coefficients for both the WET and the WIT, transmit and receive beamforming vectors, and network resource allocation. To solve this non-convex problem, we propose an efficient alternating optimization algorithm with the linear minimum mean squared error criterion, semi-definite relaxation (SDR) and successive convex approximation techniques. Specifically, the tightness of applying the SDR is proved. Simulation results demonstrate that our proposed scheme with 10 reflecting elements (REs) and 4 antennas can achieve 17.78% and 415.48% performance gains compared to the single-antenna scheme with 10 REs and passive RIS scheme with 100 REs, respectively.

By pushing computation, cache, and network control to the edge, mobile edge computing (MEC) is expected to play a leading role in fifth generation (5G) and future sixth generation (6G). Nevertheless, facing ubiquitous fast-growing computational demands, it is impossible for a single MEC paradigm to effectively support high-quality intelligent services at end user equipments (UEs). To address this issue, we propose an {a}ir-{g}round {c}ollaborative {MEC} (AGC-MEC) architecture in this article. The proposed AGC-MEC integrates all potentially available MEC servers within air and ground in the envisioned 6G, by a variety of collaborative ways to provide computation services at their best for UEs. Firstly, we introduce the AGC-MEC architecture and elaborate three typical use cases. Then, we discuss four main challenges in the AGC-MEC as well as their potential solutions. Next, we conduct a case study of collaborative service placement for AGC-MEC to validate the effectiveness of the proposed collaborative service placement strategy. Finally, we highlight several potential research directions of the AGC-MEC.

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.

Recently, orthogonal time frequency space (OTFS) was presented to alleviate severe Doppler effects in high mobility scenarios. Most of the current OTFS detection schemes rely on perfect channel state information (CSI). However, in real-life systems, the parameters of channels will constantly change, which are often difficult to capture and describe. In this paper, we summarize the existing research on OTFS detection based on data-driven deep learning (DL) and propose three new network structures. The presented three networks include a residual network (ResNet), a dense network (DenseNet), and a residual dense network (RDN) for OTFS detection. The detection schemes based on data-driven paradigms do not require a model that is easy to handle mathematically. Meanwhile, compared with the existing fully connected-deep neural network (FC-DNN) and standard convolutional neural network (CNN), these three new networks can alleviate the problems of gradient explosion and gradient disappearance. Through simulation, it is proved that RDN has the best performance among the three proposed schemes due to the combination of shallow and deep features. RDN can solve the issue of performance loss caused by the traditional network not fully utilizing all the hierarchical information.

Recently, anomaly detection (AD) in streaming data gained significant attention among research communities due to its applicability in finance, business, healthcare, education, etc. The recent developments of deep learning (DL) models find helpful in the detection and classification of anomalies. This article designs an oversampling with an optimal deep learning-based streaming data classification (OS-ODLSDC) model. The aim of the OS-ODLSDC model is to recognize and classify the presence of anomalies in the streaming data. The proposed OS-ODLSDC model initially undergoes pre-processing step. Since streaming data is unbalanced, support vector machine (SVM)-Synthetic Minority Over-sampling Technique (SVM-SMOTE) is applied for oversampling process. Besides, the OS-ODLSDC model employs bidirectional long short-term memory (BiLSTM) for AD and classification. Finally, the root means square propagation (RMSProp) optimizer is applied for optimal hyperparameter tuning of the BiLSTM model. For ensuring the promising performance of the OS-ODLSDC model, a wide-ranging experimental analysis is performed using three benchmark datasets such as CICIDS 2018, KDD-Cup 1999, and NSL-KDD datasets.

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.

Low earth orbit (LEO) satellites with wide coverage can carry the mobile edge computing (MEC) servers with powerful computing capabilities to form the LEO satellite edge computing system, providing computing services for the global ground users. In this paper, the computation offloading problem and resource allocation problem are formulated as a mixed integer nonlinear program (MINLP) problem. This paper proposes a computation offloading algorithm based on deep deterministic policy gradient (DDPG) to obtain the user offloading decisions and user uplink transmission power. This paper uses the convex optimization algorithm based on Lagrange multiplier method to obtain the optimal MEC server resource allocation scheme. In addition, the expression of suboptimal user local CPU cycles is derived by relaxation method. Simulation results show that the proposed algorithm can achieve excellent convergence effect, and the proposed algorithm significantly reduces the system utility values at considerable time cost compared with other algorithms.

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.

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.

With the rapid development of cloud computing, edge computing, and smart devices, computing power resources indicate a trend of ubiquitous deployment. The traditional network architecture cannot efficiently leverage these distributed computing power resources due to computing power island effect. To overcome these problems and improve network efficiency, a new network computing paradigm is proposed, i.e., Computing Power Network (CPN). Computing power network can connect ubiquitous and heterogenous computing power resources through networking to realize computing power scheduling flexibly. In this survey, we make an exhaustive review on the state-of-the-art research efforts on computing power network. We first give an overview of computing power network, including definition, architecture, and advantages. Next, a comprehensive elaboration of issues on computing power modeling, information awareness and announcement, resource allocation, network forwarding, computing power transaction platform and resource orchestration platform is presented. The computing power network testbed is built and evaluated. The applications and use cases in computing power network are discussed. Then, the key enabling technologies for computing power network are introduced. Finally, open challenges and future research directions are presented as well.

In recent times, various power control and clustering approaches have been proposed to enhance overall performance for cell-free massive multiple-input multiple-output (CF-mMIMO) networks. With the emergence of deep reinforcement learning (DRL), significant progress has been made in the field of network optimization as DRL holds great promise for improving network performance and efficiency. In this work, our focus delves into the intricate challenge of joint cooperation clustering and downlink power control within CF-mMIMO networks. Leveraging the potent deep deterministic policy gradient (DDPG) algorithm, our objective is to maximize the proportional fairness (PF) for user rates, thereby aiming to achieve optimal network performance and resource utilization. Moreover, we harness the concept of “divide and conquer” strategy, introducing two innovative methods termed alternating DDPG (A-DDPG) and hierarchical DDPG (H-DDPG). These approaches aim to decompose the intricate joint optimization problem into more manageable sub-problems, thereby facilitating a more efficient resolution process. Our findings unequivocally showcase the superior efficacy of our proposed DDPG approach over the baseline schemes in both clustering and downlink power control. Furthermore, the A-DDPG and H-DDPG obtain higher performance gain than DDPG with lower computational complexity.

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.

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.

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.

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.

With the development of wireless communication technology, an urgent problem to be solved is co-site broadband interference on independent communication platforms such as satellites, space stations, aircrafts and ships. Also, the problem of strong self-interference rejection should be solved in the co-time co-frequency full duplex mode which realizes spectrum multiplication in 5G communication technology. In the research of such interference rejection, interference cancellation technology has been applied. In order to reject multipath interference, multitap double LMS (Least Mean Square) loop interference cancellation system is often used for cancelling RF (Radio Frequency) domain interference cancelling. However, more taps will lead to a more complex structure of the cancellation system. A novel tap single LMS loop adaptive interference cancellation system was proposed to improve the system compactness and reduce the cost. In addition, a mathematical model was built for the proposed cancellation system, the correlation function of CP2FSK (Continuous Phase Binary Frequency Shift Keying) signal was derived, and the quantitative relationship was established between the correlation function and the interference signal bandwidth and tap delay differential. The steady-state weights and the expression of the average interference cancellation ratio (ICR) were deduced in the scenes of LOS (Line of Sight) interference with antenna swaying on an independent communication platform and indoor multipath interference. The quantitative relationship was deeply analyzed between the interference cancellation performance and the parameters such as antenna swing, LMS loop gain, and interference signal bandwidth, which was verified by simulation experiment. And the performance of the proposed interference cancellation system was compared with that of the traditional double LMS loop cancellation system. The results showed that the compact single LMS loop cancellation system can achieve an average interference rejection capability comparable to the double LMS loop cancellation system.

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.

Automatic modulation classification (AMC) aims at identifying the modulation of the received signals, which is a significant approach to identifying the target in military and civil applications. In this paper, a novel data-driven framework named convolutional and transformer-based deep neural network (CTDNN) is proposed to improve the classification performance. CTDNN can be divided into four modules, i.e., convolutional neural network (CNN) backbone, transition module, transformer module, and final classifier. In the CNN backbone, a wide and deep convolution structure is designed, which consists of 1$\times$15 convolution kernels and intensive cross-layer connections instead of traditional 1$\times$3 kernels and sequential connections. In the transition module, a 1$\times$1 convolution layer is utilized to compress the channels of the previous multi-scale CNN features. In the transformer module, three self-attention layers are designed for extracting global features and generating the classification vector. In the classifier, the final decision is made based on the maximum a posterior probability. Extensive simulations are conducted, and the result shows that our proposed CTDNN can achieve superior classification performance than traditional deep models.

In recent years, deep learning-based semantic communications have shown great potential to enhance the performance of communication systems. This has led to the belief that semantic communications represent a breakthrough beyond the Shannon paradigm and will play an essential role in future communications. To narrow the gap between current research and future vision, after an overview of semantic communications, this article presents and discusses ten fundamental and critical challenges in today's semantic communication field. These challenges are divided into theory foundation, system design, and practical implementation. Challenges related to the theory foundation including semantic capacity, entropy, and rate-distortion are discussed first. Then, the system design challenges encompassing architecture, knowledge base, joint semantic-channel coding, tailored transmission scheme, and impairment are posed. The last two challenges associated with the practical implementation lie in cross-layer optimization for networks and standardization. For each challenge, efforts to date and thoughtful insights are provided.

In this paper, we examine an illegal wireless communication network consisting of an illegal user receiving illegal signals from an illegal station and propose an active reconfigurable intelligent surface (ARIS)-assisted multi-antenna jamming (MAJ) scheme denoted by ARIS-MAJ to interfere with the illegal signal transmission. In order to strike a balance between the jamming performance and the energy consumption, we consider a so-called jamming energy efficiency (JEE) which is defined as the ratio of achievable rate reduced by the jamming system to the corresponding power consumption. We formulate an optimization problem to maximize the JEE for the proposed ARIS-MAJ scheme by jointly optimizing the jammer's beamforming vector and ARIS's reflecting coefficients under the constraint that the jamming power received at the illegal user is lower than the illegal user's detection threshold. To address the non-convex optimization problem, we propose the Dinkelbach-based alternating optimization (AO) algorithm by applying the semidefinite relaxation (SDR) algorithm with Gaussian randomization method. Numerical results validate that the proposed ARIS-MAJ scheme outperforms the passive reconfigurable intelligent surface (PRIS)-assisted multi-antenna jamming (PRIS-MAJ) scheme and the conventional multi-antenna jamming scheme without RIS (NRIS-MAJ) in terms of the JEE.

Orthogonal time frequency space (OTFS) modulation has been proven to be superior to traditional orthogonal frequency division multiplexing (OFDM) systems in high-speed communication scenarios. However, the existing channel estimation sche- mes may results in poor peak to average power ratio (PAPR) performance of OTFS system or low spectrum efficiency. Hence, in this paper, we propose a low PAPR channel estimation scheme with high spectrum efficiency. Specifically, we design a multiple scattered pilot pattern, where multiple low power pilot symbols are superimposed with data symbols in delay-Doppler domain. Furthermore, we propose the placement rules for pilot symbols, which can guarantee the low PAPR. Moreover, the data aided iterative channel estimation was invoked, where joint channel estimation is proposed by exploiting multiple independent received signals instead of only one received signal in the existing scheme, which can mitigate the interference imposed by data symbols for channel estimation. Simulation results shows that the proposed multiple scattered pilot aided channel estimation scheme can significantly reduce the PAPR while keeping the high spectrum efficiency.

With the development of artificial intelligence (AI) and 5G technology, the integration of sensing, communication and computing in the Internet of Vehicles (IoV) is becoming a trend. However, the large amount of data transmission and the computing requirements of intelligent tasks lead to the complex resource management problems. In view of the above challenges, this paper proposes a tasks-oriented joint resource allocation scheme (TOJRAS) in the scenario of IoV. First, this paper proposes a system model with sensing, communication, and computing integration for multiple intelligent tasks with different requirements in the IoV. Secondly, joint resource allocation problems for real-time tasks and delay-tolerant tasks in the IoV are constructed respectively, including communication, computing and caching resources. Thirdly, a distributed deep Q-network (DDQN) based algorithm is proposed to solve the optimization problems, and the convergence and complexity of the algorithm are discussed. Finally, the experimental results based on real data sets verify the performance advantages of the proposed resource allocation scheme, compared to the existing ones. The exploration efficiency of our proposed DDQN-based algorithm is improved by at least about 5%, and our proposed resource allocation scheme improves the mAP performance by about 0.15 under resource constraints.

Due to the limited computational capability and the diversity of the Internet of Things devices working in different environment, we consider few-shot learning-based automatic modulation classification (AMC) to improve its reliability. A data enhancement module (DEM) is designed by a convolutional layer to supplement frequency-domain information as well as providing nonlinear mapping that is beneficial for AMC. Multimodal network is designed to have multiple residual blocks, where each residual block has multiple convolutional kernels of different sizes for diverse feature extraction. Moreover, a deep supervised loss function is designed to supervise all parts of the network including the hidden layers and the DEM. Since different model may output different results, cooperative classifier is designed to avoid the randomness of single model and improve the reliability. Simulation results show that this few-shot learning-based AMC method can significantly improve the AMC accuracy compared to the existing methods.

As conventional communication systems based on classic information theory have closely approached Shannon capacity, semantic communication is emerging as a key enabling technology for the further improvement of communication performance. However, it is still unsettled on how to represent semantic information and characterise the theoretical limits of semantic-oriented compression and transmission. In this paper, we consider a semantic source which is characterised by a set of correlated random variables whose joint probabilistic distribution can be described by a Bayesian network. We give the information-theoretic limit on the lossless compression of the semantic source and introduce a low complexity encoding method by exploiting the conditional independence. We further characterise the limits on lossy compression of the semantic source and the upper and lower bounds of the rate-distortion function. We also investigate the lossy compression of the semantic source with two-sided information at the encoder and decoder, and obtain the corresponding rate distortion function. We prove that the optimal code of the semantic source is the combination of the optimal codes of each conditional independent set given the side information.

Connected and autonomous vehicle (CAV) vehicle to infrastructure (V2I) scenarios have more stringent requirements on the communication rate, delay, and reliability of the Internet of vehicles (IoV). New radio vehicle to everything (NR-V2X) adopts link adaptation (LA) to improve the efficiency and reliability of road safety information transmission. In order to solve the problem that the existing LA scheduling algorithms cannot adapt to the Doppler shift and complex fast time-varying channel in V2I scenario, resulting in low reliability of information transmission, this paper proposes a deep Q-learning (DQL)-based massive multiple-input multiple-output (MIMO) LA scheduling algorithm for autonomous driving V2I scenario. The algorithm combines deep neural network (DNN) with Q-learning (QL) algorithm, which is used for joint scheduling of modulation and coding scheme (MCS) and space division multiplexing (SDM). The system simulation results show that the algorithm proposed in this paper can fully adapt to the different channel environment in the V2I scenario, and select the optimal MCS and SDM for the transmission of road safety information, thereby the accuracy of road safety information transmission is improved, collision accidents can be avoided, and bring a good autonomous driving experience.

Directional modulation (DM) is one of the most promising secure communication techniques. However, when the eavesdropper is co-located with the legitimate receiver, the conventional DM has the disadvantages of weak anti-scanning capability, anti-deciphering capability, and low secrecy rate. In response to these problems, we propose a two-dimensional multi-term weighted fractional Fourier transform aided DM scheme, in which the legitimate receiver and the transmitter use different transform terms and transform orders to encrypt and decrypt the confidential information. In order to further lower the probability of being deciphered by an eavesdropper, we use the subblock partition method to convert the one-dimensional modulated signal vector into a two-dimensional signal matrix, increasing the confusion of the useful information. Numerical results demonstrate that the proposed DM scheme not only provides stronger anti-deciphering and anti-scanning capabilities but also improves the secrecy rate performance of the system.

In recent years, as giant satellite constellations grow rapidly worldwide, the co-existence between constellations has been widely concerned. In this paper, we overview the co-frequency interference (CFI) among the giant non-geostationary orbit (NGSO) constellations. Specifically, we first summarize the CFI scenario and evaluation index among different NGSO constellations. Based on statistics about NGSO constellation plans, we analyse the challenges in mitigation and analysis of CFI. Next, the CFI calculation methods and research progress are systematically sorted out from the aspects of interference risk analysis framework, numerical calculation and link construction. Then, the feasibility of interference mitigation technologies based on space, frequency domain isolation, power control, and interference alignment mitigation in the NGSO mega-constellation CFI scenario are further sorted out. Finally, we present promising directions for future research in CFI analysis and CFI avoidance.

In LEO satellite communication networks, the number of satellites has increased sharply, the relative velocity of satellites is very fast, then electronic signal aliasing occurs from time to time. Those aliasing signals make the receiving ability of the signal receiver worse, the signal processing ability weaker, and the anti-interference ability of the communication system lower. Aiming at the above problems, to save communication resources and improve communication efficiency, and considering the irregularity of interference signals, the underdetermined blind separation technology can effectively deal with the problem of interference sensing and signal reconstruction in this scenario. In order to improve the stability of source signal separation and the security of information transmission, a greedy optimization algorithm can be executed. At the same time, to improve network information transmission efficiency and prevent algorithms from getting trapped in local optima, delete low-energy points during each iteration process. Ultimately, simulation experiments validate that the algorithm presented in this paper enhances both the transmission efficiency of the network transmission system and the security of the communication system, achieving the process of interference sensing and signal reconstruction in the LEO satellite communication system.

This paper proposes a high-throughput short reference differential chaos shift keying cooperative communication system with the aid of code index modulation, referred to as CIM-SR-DCSK-CC system. In the proposed CIM-SR-DCSK-CC system, the source transmits information bits to both the relay and destination in the first time slot, while the relay not only forwards the source information bits but also sends new information bits to the destination in the second time slot. To be specific, the relay employs an $N$-order Walsh code to carry additional ${{\log }_{2}}N$ information bits, which are superimposed onto the SR-DCSK signal carrying the decoded source information bits. Subsequently, the superimposed signal carrying both the source and relay information bits is transmitted to the destination. Moreover, the theoretical bit error rate (BER) expressions of the proposed CIM-SR-DCSK-CC system are derived over additive white Gaussian noise (AWGN) and multipath Rayleigh fading channels. Compared with the conventional DCSK-CC system and SR-DCSK-CC system, the proposed CIM-SR-DCSK-CC system can significantly improve the throughput without deteriorating any BER performance. As a consequence, the proposed system is very promising for the applications of the 6G-enabled low-power and high-rate communication.

Orthogonal time frequency space (OTFS), as a novel 2-D modulation technique, has been proposed to achieve better BER performances over delay-Doppler channels. In this paper, we propose two different power allocation (PA) algorithms in OTFS systems with zero forcing (ZF) or minimum mean square error (MMSE) equalization, where general formulas with PA are derived in advance under the condition of minimum BER (MBER) criterion. On one hand, a suboptimal MBER power allocation method is put forward to achieve better BER performances, and then analytical BER expressions are derived with proposed PA strategy. Considering the case of MMSE equalization, a combined subsymbol allocation (SA) and PA strategy is raised, where some subsymbols may be abandoned due to worse channel conditions, and then it is proven effectively to improve BER performances through theoretical and simulation results. Furthermore, BER performances with proposed joint SA and PA strategy are also investigated in delay-Doppler channels, where an improved message passing (MP) receiver based on equivalent channel matrix with PA is given.