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.

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.

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.

Immersive services are the typical emerging services in current IMT-2020 network. With the development of network evolution, real-time interactive applications emerge one after another. This article provides an overview on immersive services which focus on real-time interaction. The scenarios, framework, requirements, key technologies, and issues of interactive immersive service are presented.

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.

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.

To further improve the secrecy rate, a joint optimization scheme for the reconfigurable intelligent surface (RIS) phase shift and the power allocation is proposed in the untrusted relay (UR) networks assisted by the RIS. The eavesdropping on the UR is interfered by a source-based jamming strategy. Under the constraints of unit modulus and total power, the RIS phase shift, the power allocation between the confidential signal and the jamming signal, and the power allocation between the source node and the UR are jointly optimized to maximize the secrecy rate. The complex multivariable coupling problem is decomposed into three sub-problems, and the non-convexity of the objective function and the constraints is solved with semi-definite relaxation. Simulation results indicate that the secrecy rate is remarkably enhanced with the proposed scheme compared with the equal power allocation scheme, the random phase shift scheme, and the no-RIS scheme.

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.

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.

Gray mapping is a well-known way to improve the performance of regular constellation modulation, but it is challenging to be applied directly for irregular alternative. To address this issue, in this paper, a unified bit-to-symbol mapping method is designed for generalized constellation modulation (i.e., regular and irregular shaping). The objective of the proposed approach is to minimize the average bit error probability by reducing the hamming distance (HD) of symbols with larger values of pairwise error probability. Simulation results show that the conventional constellation modulation(i.e., phase shift keying and quadrature amplitude modulation (QAM) with the proposed mapping rule yield the same performance as that of classical gray mapping. Moreover, the recently developed golden angle modulation (GAM) with the proposed mapping method is capable of providing around 1 dB gain over the conventional mapping counterpart and offers comparable performance to QAM with Gray mapping.

The emergence of various commercial and industrial Internet of Things (IoT) devices has brought great convenience to people's life and production. Both low-power, massively connected mMTC devices (MDs) and highly reliable, low-latency URLLC devices (UDs) play an important role in different application scenarios. However, when dense MDs and UDs periodically initiate random access (RA) to connect the base station and send data, due to the limited preamble resources, preamble collisions are likely to occur, resulting in device access failure and data transmission delay. At the same time, due to the high-reliability demands of UDs, which require smooth access and fast data transmission, it is necessary to reduce the failure rate of their RA process. To this end, we propose an intelligent preamble allocation scheme, which uses hierarchical reinforcement learning to partition the UD exclusive preamble resource pool at the base station side and perform preamble selection within each RA slot at the device side. In particular, considering the limited processing capacity and energy of IoT devices, we adopt the lightweight Q-learning algorithm on the device side and design simple states and actions for them. Experimental results show that the proposed intelligent scheme can significantly reduce the transmission failure rate of UDs and improve the overall access success rate of devices.

This paper investigates the bit-interleaved coded generalized spatial modulation (BICGSM) with iterative decoding (BICGSM-ID) for multiple-input multiple-output (MIMO) visible light communications (VLC). In the BICGSM-ID scheme, the information bits conveyed by the signal-domain (SiD) symbols and the spatial-domain (SpD) light emitting diode (LED)-index patterns are coded by a protograph low-density parity-check (P-LDPC) code. Specifically, we propose a signal-domain symbol expanding and re-allocating (SSER) method for constructing a type of novel generalized spatial modulation (GSM) constellations, referred to as SSERGSM constellations , so as to boost the performance of the BICGSM-ID MIMO-VLC systems. Moreover, by applying a modified PEXIT (MPEXIT) algorithm, we further design a family of rate-compatible P-LDPC codes, referred to as enhanced accumulate-repeat-accumulate (EARA) codes, which possess both excellent decoding thresholds and linear-minimum-distance-growth property. Both analysis and simulation results illustrate that the proposed SSERGSM constellations and P-LDPC codes can remarkably improve the convergence and decoding performance of MIMO-VLC systems. Therefore, the proposed P-LDPC-coded SSERGSM-mapped BICGSM-ID configuration is envisioned as a promising transmission solution to satisfy the high-throughput requirement of MIMO-VLC applications.

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.

Space/air communications have been envisioned as an essential part of the next-generation mobile communication networks for providing highquality global connectivity. However, the inherent broadcasting nature of wireless propagation environment and the broad coverage pose severe threats to the protection of private data. Emerging covert communications provides a promising solution to achieve robust communication security. Aiming at facilitating the practical implementation of covert communications in space/air networks, we present a tutorial overview of its potentials, scenarios, and key technologies. Specifically, first, the commonly used covertness constraint model, covert performance metrics, and potential application scenarios are briefly introduced. Then, several efficient methods that introduce uncertainty into the covert system are thoroughly summarized, followed by several critical enabling technologies, including joint resource allocation and deployment/trajectory design, multi-antenna and beamforming techniques, reconfigurable intelligent surface (RIS), and artificial intelligence algorithms. Finally, we highlight some open issues for future investigation.

Extensive research attentions have been devoted to studying cooperative cognitive radio networks (CCRNs), where secondary users (SU) providing cooperative transmissions can be permitted by primary users (PU) to use spectrum. In order to maximize SU’s utility, SU may transmit its own information during the period of cooperative transmission, which stimulates the use of covert transmission against PU’s monitoring. For this sake, this article reviews the motivations of studying covert communications in CCRN. In particular, three intelligent covert transmission approaches are developed for maximizing SU’s utility in CCRNs, namely, intelligent parasitic covert transmission (IPCT), intelligent jammer aided covert transmission (IJCT) and intelligent reflecting surface assisted covert transmission (IRSC). Further, some raw performance evaluations are discussed, and a range of potential research directions are also provided.

Reconfigurable intelligent surface (RIS) for wireless networks have drawn lots of attention in both academic and industry communities. RIS can dynamically control the phases of the reflection elements to send the signal in the desired direction, thus it provides supplementary links for wireless networks. Most of prior works on RIS-aided wireless communication systems consider continuous phase shifts, but phase shifts of RIS are discrete in practical hardware. Thus we focus on the actual discrete phase shifts on RIS in this paper. Using the advanced deep reinforcement learning (DRL), we jointly optimize the transmit beamforming matrix from the discrete Fourier transform (DFT) codebook at the base station (BS) and the discrete phase shifts at the RIS to maximize the received signal-to-interference plus noise ratio (SINR). Unlike the traditional schemes usually using alternate optimization methods to solve the transmit beamforming and phase shifts, the DRL algorithm proposed in the paper can jointly design the transmit beamforming and phase shifts as the output of the DRL neural network. Numerical results indicate that the DRL proposed can dispose the complicated optimization problem with low computational complexity.

In this paper, joint location and velocity estimation (JLVE) of vehicular terminals for 6G integrated communication and sensing (ICAS) is studied. We aim to provide a unified performance analysis framework for ICAS-based JLVE, which is challenging due to random fading, multipath interference, and complexly coupled system models, and thus the impact of channel fading and multipath interference on JLVE performance is not fully understood. To address this challenge, we exploit structured information models of the JLVE problem to render tractable performance quantification. Firstly, an individual closed-form Cramer-Rao lower bound for vehicular localization, velocity detection and channel estimation, respectively, is established for gaining insights into performance limits of ICAS-based JLVE. Secondly, the impact of system resource factors and fading environments, e.g., system bandwidth, the number of subcarriers, carrier frequency, antenna array size, transmission distance, spatial channel correlation, channel covariance, the number of interference paths and noise power, on the JLVE performance is theoretically analyzed. The associated closed-form JLVE performance analysis can not only provide theoretical foundations for ICAS receiver design but also provide a performance benchmark for various JLVE methods.

In this paper, we focus on providing data provenance auditing schemes for distributed denial of service (DDoS) defense in intelligent internet of things (IoT). To achieve effective DDoS defense, we introduce a two-layer collaborative blockchain framework to support data auditing. Specifically, using data scattered among intelligent IoT devices, switch gateways self-assemble a layer of blockchain in the local autonomous system (AS), and the main chain with controller participation can be aggregated by its associated layer of blocks once a cycle, to obtain a global security model. To optimize the processing delay of the security model, we propose a process of data pre-validation with the goal of ensuring data consistency while satisfying overhead requirements. Since the flood of identity spoofing packets, it is difficult to solve the identity consistency of data with traditional detection methods, and accountability cannot be pursued afterwards. Thus, we proposed a $Packet \; Traceback \; Telemetry $ (PTT) scheme, based on in-band telemetry, to solve the problem. Specifically, the PTT scheme is executed on the distributed switch side, the controller to schedule and select routing policies. Moreover, a tracing probabilistic optimization is embedded into the PTT scheme to accelerate path reconstruction and save device resources. Simulation results show that the PTT scheme can reconstruct address spoofing packet forward path, reduce the resource consumption compared with existing tracing scheme. Data tracing audit method has fine-grained detection and feasible performance.

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.

In this paper, we propose an extended hybrid carrier system based on the weighted fractional Fourier transform to ensure the reliability of wireless communication. The proposed scheme improves the dispersion and compensation capabilities of the hybrid carrier system for channel fading through the design of the signal power distribution, which has greatly reduced the probability of high-power distortion of the signal and improved the bit error rate performance as a result. Theoretical analysis has shown the superiority of the extended hybrid carrier system. With a lower cost of computational complexity increment, the proposed scheme obtains a performance improvement without occupying additional time-frequency physical resources. Compared with the existing hybrid carrier scheme, numerical simulation results have shown that the proposed extended hybrid carrier scheme has better anti-fading performance under the doubly-selective channel and improves the reliability of the wireless communication system effectively.

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.

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.

Lower Earth Orbit (LEO) satellite becomes an important part of complementing terrestrial communication due to its lower orbital altitude and smaller propagation delay than Geostationary satellite. However, the LEO satellite communication system cannot meet the requirements of users when the satellite-terrestrial link is blocked by obstacles. To solve this problem, we introduce Intelligent reflect surface (IRS) for improving the achievable rate of terrestrial users in LEO satellite communication. We investigated joint IRS scheduling, user scheduling, power and bandwidth allocation (JIRPB) optimization algorithm for improving LEO satellite system throughput. The optimization problem of joint user scheduling and resource allocation is formulated as a non-convex optimization problem. To cope with this problem, the non-convex optimization problem is divided into resource allocation optimization sub-problem and scheduling optimization sub-problem firstly. Second, we optimize the resource allocation sub-problem via alternating direction multiplier method (ADMM) and scheduling sub-problem via Lagrangian dual method repeatedly. Third, we prove that the proposed resource allocation algorithm based ADMM approaches sublinear convergence theoretically. Finally, we demonstrate that the proposed JIRPB optimization algorithm improves the LEO satellite communication system throughput.

Polarizing beam splitter has rather significant applications in polarization diversity circuits and polarization multiplexing systems. In this paper, we present an asymmetric polarizing beam splitter utilizing hybrid plasmonic waveguide. The special hybrid structure with a hybrid waveguide and a dielectric waveguide can limit the energy of TE and TM modes to a different layer. Therefore, we can achieve beam splitting by adjusting the corresponding parameters of the two waveguides. First, we studied the influences of different structure parameters on the real part of the effective mode refractive index of the two waveguides, and obtained a set of parameters that satisfy the condition of strong coupling of TM mode and weak coupling of TE mode. Then, the performance of our proposed polarizing beam splitter is evaluated numerically. The length of the coupling section is only 4.1 $\mu\mathrm{m}$, and the propagation loss of TM and TE modes is 0.0025 $\mathrm{dB} / \mu \mathrm{m}$ and 0.0031 $\mathrm{dB} / \mu \mathrm{m}$ respectively. Additionally, the extinction ratios of TM and TE modes are 10.62 $\text { dB }$and 12.55$\text { dB }$, respectively. Particularly, the proposed device has excellent wavelength insensitivity. Over the entire C-band, the fluctuation of the whole normalized output power is less than 0.03. In short, our proposed asymmetric polarizing beam splitter features ultra-compactness, low propagation loss, and broad bandwidth, which would provide promising applications in polarization multiplexing system and polarization diversity circuits relevant to optical interconnection.

In this paper, we propose a novel deep learning (DL)-based receiver design for orthogonal frequency division multiplexing (OFDM) systems. The entire process of channel estimation, equalization, and signal detection is replaced by a neural network (NN), and hence, the detector is called a NN detector (${N^2D}$). First, an OFDM signal model is established. We analyze both temporal and spectral characteristics of OFDM signals, which are the motivation for DL. Then, the generated data based on the simulation of channel statistics is used for offline training of bi-directional long short-term memory (Bi-LSTM) NN. Especially, a discriminator (${F}$) is added to the input of Bi-LSTM NN to look for subcarrier transmission data with optimal channel gain (OCG), which can greatly improve the performance of the detector. Finally, the trained ${N^2D}$ is used for online recovery of OFDM symbols. The performance of the proposed ${N^2D}$ is analyzed theoretically in terms of bit error rate (BER) by Monte Carlo simulation under different parameter scenarios. The simulation results demonstrate that the BER of ${N^2D}$ is obviously lower than other algorithms, especially at high signal-to-noise ratios (SNRs). Meanwhile, the proposed ${N^2D}$ is robust to the fluctuation of parameter values.

Non-orthogonal multiple access (NOMA) is viewed as a key technique to improve the spectrum efficiency and solve the issue of massive connectivity. However, for power domain NOMA, the required overall transmit power should be increased rapidly with the increasing number of users in order to ensure that the signal-to-interference-plus-noise ratio reaches a predefined threshold. In addition, since the successive interference cancellation (SIC) is adopted, the error propagation would become more serious as the order of SIC increases. Aiming at minimizing the total transmit power and satisfying each user's service requirement, this paper proposes a novel framework with group-based SIC for the deep integration between power domain NOMA and multi-antenna technology. Based on the proposed framework, a joint optimization of power control and equalizer design is investigated to minimize transmit power consumption for uplink multi-antenna NOMA system with error propagations. Based on the relationship between the equalizer and the transmit power coefficients, the original problem is transformed to a transmit power optimization problem, which is further addressed by a parallel iteration algorithm. It is shown by simulations that, in terms of the total power consumption, the proposed scheme outperforms the conventional OMA and the existing cluster-based NOMA schemes.

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.

The minimum energy per bit (EPB) as the energy efficiency (EE) metric in an automatic retransmission request (ARQ) based multi-hop system is analyzed under power and throughput constraints. Two ARQ protocols including type-I (ARQ-I) and repetition redundancy (ARQ-RR) are considered and expressions for the optimal power allocation (PA) are obtained. Using the obtained optimal powers, the EE-throughput tradeoff (EETT) is analyzed and the EETT closed-form expressions for both ARQ protocols and in arbitrary average channel gain values are obtained. It is shown that how different throughput requirements, especially the high levels, affect the EE performance. Additionally, asymptotic analysis is made in the feasible high throughput values and lower and upper EETT bounds are derived for ARQ-I protocol. To evaluate the EE a distributed PA scenario, as a benchmark, is presented and the energy saving-gain obtained from the optimal PA in comparison with the distributed PA for ARQ-I and ARQ-RR protocols is discussed in different throughput values and node locations.

To improve the recognition ability of communication jamming signals, Siamese Neural Network-based Open World Recognition (SNN-OWR) is proposed. The algorithm can recognize known jamming classes, detect new (unknown) jamming classes, and unsupervised cluseter new classes. The network of SNN-OWR is trained supervised with paired input data consisting of two samples from a known dataset. On the one hand, the network is required to have the ability to distinguish whether two samples are from the same class. On the other hand, the latent distribution of known class is forced to approach their own unique Gaussian distribution, which is prepared for the subsequent open set testing. During the test, the unknown class detection process based on Gaussian probability density function threshold is designed, and an unsupervised clustering algorithm of the unknown jamming is realized by using the prior knowledge of known classes. The simulation results show that when the jamming-to-noise ratio is more than 0dB, the accuracy of SNN-OWR algorithm for known jamming classes recognition, unknown jamming detection and unsupervised clustering of unknown jamming is about 95%. This indicates that the SNN-OWR algorithm can make the effect of the recognition of unknown jamming be almost the same as that of known jamming.

A large amount of mobile data from growing high-speed train (HST) users makes intelligent HST communications enter the era of big data. The corresponding artificial intelligence (AI) based HST channel modeling becomes a trend. This paper provides AI based channel characteristic prediction and scenario classification model for millimeter wave (mmWave) HST communications. Firstly, the ray tracing method verified by measurement data is applied to reconstruct four representative HST scenarios. By setting the positions of transmitter (Tx), receiver (Rx), and other parameters, the multi-scenarios wireless channel big data is acquired. Then, based on the obtained channel database, radial basis function neural network (RBF-NN) and back propagation neural network (BP-NN) are trained for channel characteristic prediction and scenario classification. Finally, the channel characteristic prediction and scenario classification capabilities of the network are evaluated by calculating the root mean square error (RMSE). The results show that RBF-NN can generally achieve better performance than BP-NN, and is more applicable to prediction of HST scenarios.

Spatial covariance matrix (SCM) is essential in many multi-antenna systems such as massive multiple-input multiple-output (MIMO). For multi-antenna systems operating at millimeter-wave bands, hybrid analog-digital structure has been widely adopted to reduce the cost of radio frequency chains. In this situation, signals received at the antennas are unavailable to the digital receiver, and as a consequence, traditional sample average approach cannot be used for SCM reconstruction in hybrid multi-antenna systems. To address this issue, beam sweeping algorithm (BSA) which can reconstruct the SCM effectively for a hybrid uniform linear array, has been proposed in our previous works. However, direct extension of BSA to a hybrid uniform circular array (UCA) will result in a huge computational burden. To this end, a low-complexity approach is proposed in this paper. By exploiting the symmetry features of SCM for the UCA, the number of unknowns can be reduced significantly and thus the complexity of reconstruction can be saved accordingly. Furthermore, an insightful analysis is also presented in this paper, showing that the reduction of the number of unknowns can also improve the accuracy of the reconstructed SCM. Simulation results are also shown to demonstrate the proposed approach.

With the vigorous development of automobile industry, in-vehicle network is also constantly upgraded to meet data transmission requirements of emerging applications. The main transmission requirements are low latency and certainty especially for autonomous driving. Time sensitive networking (TSN) based on Ethernet gives a possible solution to these requirements. Previous surveys usually investigated TSN from a general perspective, which referred to TSN of various application fields. In this paper, we focus on the application of TSN to the in-vehicle networks. For in-vehicle networks, we discuss all related TSN standards specified by IEEE 802.1 work group up to now. We further overview and analyze recent literature on various aspects of TSN for automotive applications, including synchronization, resource reservation, scheduling, certainty, software and hardware. Application scenarios of TSN for in-vehicle networks are analyzed one by one. Since TSN of in-vehicle network is still at a very initial stage, this paper also gives insights on open issues, future research directions and possible solutions.

The numbers of beam positions (BPs) and time slots for beam hopping (BH) dominate the latency of LEO satellite communications. Aiming at minimizing the number of BPs subject to a predefined requirement on the radius of BP, a low-complexity user density-based BP design scheme is proposed, where the original problem is decomposed into two subproblems, with the first one to find the sparsest user and the second one to determine the corresponding best BP. In particular, for the second subproblem, a user selection and smallest BP radius algorithm is proposed, where the nearby users are sequentially selected until the constraint of the given BP radius is no longer satisfied. These two subproblems are iteratively solved until all the users are selected. To further reduce the BP radius, a duplicated user removal algorithm is proposed to decrease the number of the users covered by two or more BPs. Aiming at minimizing the number of time slots subject to the no co-channel interference (CCI) constraint and the traffic demand constraint, a low-complexity CCI-free BH design scheme is proposed, where the BPs having difficulty in satisfying the constraints are considered to be illuminated in priory. Simulation results verify the effectiveness of the proposed schemes.

Covert communications can hide the existence of a transmission from the transmitter to receiver. This paper considers an intelligent reflecting surface (IRS) assisted unmanned aerial vehicle (UAV) covert communication system. It was inspired by the high-dimensional data processing and decision-making capabilities of the deep reinforcement learning (DRL) algorithm. In order to improve the covert communication performance, an UAV 3D trajectory and IRS phase optimization algorithm based on double deep Q network (TAP-DDQN) is proposed. The simulations show that TAP-DDQN can significantly improve the covert performance of the IRS-assisted UAV covert communication system, compared with benchmark solutions.

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) plays an important role in the fifth-generation (5G) mobile communications and beyond wireless communication systems owing to its potential of high capacity. However, channel estimation has become very challenging due to the use of massive MIMO antenna array. Fortunately, the mmWave channel has strong sparsity in the spatial angle domain, and the compressed sensing technology can be used to convert the original channel matrix into the sparse matrix of discrete angle grid. Thus the high-dimensional channel matrix estimation is transformed into a sparse recovery problem with greatly reduced computational complexity. However, the path angle in the actual scene appears randomly and is unlikely to be completely located on the quantization angle grid, thus leading to the problem of power leakage. Moreover, multiple paths with the random distribution of angles will bring about serious inter-path interference and further deteriorate the performance of channel estimation. To address these off-grid issues, we propose a parallel interference cancellation assisted multi-grid matching pursuit (PIC-MGMP) algorithm in this paper. The proposed algorithm consists of three stages, including coarse estimation, refined estimation, and inter-path cyclic iterative interference cancellation. More specifically, the angular resolution can be improved by locally refining the grid to reduce power leakage, while the inter-path interference is eliminated by parallel interference cancellation (PIC), and the two together improve the estimation accuracy. Simulation results show that compared with the traditional orthogonal matching pursuit (OMP) algorithm, the normalized mean square error (NMSE) of the proposed algorithm decreases by over 14dB in the case of 2 paths.

Next-Generation (NextG) wireless communication networks with their widespread applications require high data rates, seamless connectivity and high quality of service (QoS). To cope up with an unprecedented rise of data hungry applications, users demand more spectral resources imposing a limitation on available wireless spectrum. One of the potential solutions to address the spectrum scarce issue is to incorporate in band full duplex (IBFD) or full duplex (FD) paradigm in next generation networks including 5G new radio (NR). Recently, FD has gained the research interest in cellular networks for its potential to double the wireless link capacity and enhancing spectral efficiency (SE). In half duplex (HD) cellular networks, base stations (BSs) can either perform uplink (UL) or downlink (DL) transmission at a particular time instant leading to reduced throughput levels. Due to the advancement in the self interference reduction (SIR) techniques, full duplex base stations (FD-BSs) can be employed to allow simultaneous UL and DL transmissions at the same time-frequency resources as compared to its HD counterpart. It ideally achieves twice the throughput without any additional complexity at user-equipment(UE). This paper covers a detailed survey on FD cellular networks. A series of SIR approaches, UE-UE mitigation techniques are summarized. Various existing MAC protocols and antenna architectures for FD cellular networks are outlined. An overview of security aspects for FD in cellular networks is also presented. Lastly, various open issues and possible research directions are brought up for FD cellular networks.

In coded caching, users cache pieces of files under a specific arrangement so that the server can satisfy their requests simultaneously in the broadcast scenario via eXclusive OR (XOR) operation and therefore reduce the amount of transmission data. However, when users' locations are changing, the uploading of caching information is frequent and extensive that the traffic increase outweighed the traffic reduction that the traditional coded caching achieved. In this paper, we propose mobile coded caching schemes to reduce network traffic in mobility scenarios, which achieve a lower cost on caching information uploading. In the cache placement phase, the proposed scheme first constructs caching patterns, and then assigns the caching patterns to users according to the graph coloring method and four color theorem in our centralized cache placement algorithm or randomly in our decentralized cache placement algorithm. Then users are divided into groups based on their caching patterns. As a benefit, when user movements occur, the types of caching pattern, rather than the whole caching information of which file pieces are cached, are uploaded. In the content delivery phase, XOR coded caching messages are reconstructed. Transmission data volume is derived to measure the performance of the proposed schemes. Numerical results show that the proposed schemes achieve great improvement in traffic offloading.

In this paper, a time-varying channel prediction method based on conditional generative adversarial network (CPcGAN) is proposed for time division duplexing/frequency division duplexing (TDD/FDD) systems. CPcGAN utilizes a discriminator to calculate the divergence between the predicted downlink channel state information (CSI) and the real sample distributions under a conditional constraint that is previous uplink CSI. The generator of CPcGAN learns the function relationship between the conditional constraint and the predicted downlink CSI and reduces the divergence between predicted CSI and real CSI. The capability of CPcGAN fitting data distribution can capture the time-varying and multipath characteristics of the channel well. Considering the propagation characteristics of real channel, we further develop a channel prediction error indicator to determine whether the generator reaches the best state. Simulations show that the CPcGAN can obtain higher prediction accuracy and lower system bit error rate than the existing methods under the same user speeds.

Mobile Crowd Sensing (MCS) is an emerging paradigm that leverages sensor-equipped smart devices to collect data. The introduction of MCS also poses some challenges such as providing high-quality data for upper layer MCS applications, which requires adequate participants. However, recruiting enough participants to provide the sensing data for free is hard for the MCS platform under a limited budget, which may lead to a low coverage ratio of sensing area. This paper proposes a novel method to choose participants uniformly distributed in a specific sensing area based on the mobility patterns of mobile users. The method consists of two steps: (1) A second-order Markov chain is used to predict the next positions of users, and select users whose next places are in the target sensing area to form a candidate pool. (2) The Average Entropy (DAE) is proposed to measure the distribution of participants. The participant maximizing the DAE value of a specific sensing area with different granular sub-areas is chosen to maximize the coverage ratio of the sensing area. Experimental results show that the proposed method can maximize the coverage ratio of a sensing area under different partition granularities.

Road Side Units (RSUs) are the essential component of vehicular communication for the objective of improving safety and mobility in the road transportation. RSUs are generally deployed at the roadside and more specifically at the intersections in order to collect traffic information from the vehicles and disseminate alarms and messages in emergency situations to the neighborhood vehicles cooperating with the network. However, the development of a predominant RSUs placement algorithm for ensuring competent communication in VANETs is a challenging issue due to the hindrance of obstacles like water bodies, trees and buildings. In this paper, Ruppert's Delaunay Triangulation Refinement Scheme (RDTRS) for optimal RSUs placement is proposed for accurately estimating the optimal number of RSUs that has the possibility of enhancing the area of coverage during data communication. This RDTRS is proposed by considering the maximum number of factors such as global coverage, intersection popularity, vehicle density and obstacles present in the map for optimal RSUs placement, which is considered as the core improvement over the existing RSUs optimal placement strategies. It is contributed for deploying requisite RSUs with essential transmission range for maximal coverage in the convex map such that each position of the map could be effectively covered by at least one RSU in the presence of obstacles. The simulation experiments of the proposed RDTRS are conducted with complex road traffic environments. The results of this proposed RDTRS confirmed its predominance in reducing the end-to-end delay by 21.32%, packet loss by 9.38% with improved packet delivery rate of 10.68%, compared to the benchmarked schemes.