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.

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.

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.

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.

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

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

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

With the 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.

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.

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 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.

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.

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.

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.

The sixth generation (6G) of mobile communication system is witnessing a new paradigm shift, i.e., integrated sensing-communication system. A comprehensive dataset is a prerequisite for 6G integrated sensing-communication research. This paper develops a novel simulation dataset, named M³SC, for mixed multi-modal (MMM) sensing-communication integration, and the generation framework of the M³SC dataset is further given. To obtain multi-modal sensory data in physical space and communication data in electromagnetic space, we utilize AirSim and WaveFarer to collect multi-modal sensory data and exploit Wireless InSite to collect communication data. Furthermore, the in-depth integration and precise alignment of AirSim, WaveFarer, and Wireless InSite are achieved. The M³SC dataset covers various weather conditions, multiplex frequency bands, and different times of the day. Currently, the M³SC dataset contains 1500 snapshots, including 80 RGB images, 160 depth maps, 80 LiDAR point clouds, 256 sets of mmWave waveforms with 8 radar point clouds, and 72 channel impulse response (CIR) matrices per snapshot, thus totaling 120,000 RGB images, 240,000 depth maps, 120,000 LiDAR point clouds, 384,000 sets of mmWave waveforms with 12,000 radar point clouds, and 108,000 CIR matrices. The data processing result presents the multi-modal sensory information and communication channel statistical properties. Finally, the MMM sensing-communication application, which can be supported by the M³SC dataset, is discussed.

In this paper, ambient IoT is used as a typical use case of massive connections for the sixth generation (6G) mobile communications where we derive the performance requirements to facilitate the evaluation of technical solutions. A rather complete design of unsourced multiple access is proposed in which two key parts: a compressed sensing module for active user detection, and a sparse interleaver-division multiple access (SIDMA) module are simulated side by side on a same platform at balanced signal to noise ratio (SNR) operating points. With a proper combination of compressed sensing matrix, a convolutional encoder, receiver algorithms, the simulated performance results appear superior to the state-of-the-art benchmark, yet with relatively less complicated processing.

In order to achieve dependable and efficient data acquisition and transmission in the Internet of Remote Things (IoRT), we investigate the optimization scheme of IoRT data acquisition under the unmanned aerial vehicle (UAV)-low earth orbit (LEO) satellite integrated space-air-ground network, in which the UAV acquires data from massive Internet of Things (IoT) devices in special scenarios. To combine with the actual scenario, we consider two different data types, that is, delay-sensitive data and delay-tolerant data, the transmission mode is accordingly divided into two types. For delay-sensitive data, the data will be transmitted via the LEO satellite relay to the data center (DC) in real-time. For delay-tolerant data, the UAV will store and carry the data until the acquisition is completed, and then return to DC. Due to non-convexity and complexity of the formulated problem, a multi-dimensional optimization Rate Demand based Joint Optimization (RDJO) algorithm is proposed. The algorithm first uses successive convex approximation (SCA) technology to solve the non-convexity, and then based on the block coordinate descent (BCD) method, the data acquisition efficiency is maximized by jointly optimizing UAV deployment, the bandwidth allocation of IoRT devices, and the transmission power of the UAV. Finally, the proposed RDJO algorithm is compared with the conventional algorithms. Simulation consequences demonstrate that the efficiency of IoRT data acquisition can be greatly improved by multi-parameter optimization of the bandwidth allocation, UAV deployment and the transmission power.

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.

The unreasonable observation arrangements in the satellite operation control center (SOCC) may result in the observation data cannot be downloaded as scheduled. Meanwhile, if the operation instructions released by the satellite telemetry tracking center (STTC) for the on-board payloads are not injected on the specific satellites in time, the corresponding satellites cannot perform the observation operations as planned. Therefore, there is an urgent need to design an integrated instruction release, and observation task planning (I-IRO-TP) scheme by efficiently collaborating the SOCC and STTC. Motivated by this fact, we design an interaction mechanism between the SOCC and the STTC, where we first formulate the I-IRO-TP problem as a constraint satisfaction problem aiming at maximizing the number of completed tasks. Furthermore, we propose an interactive imaging task planning algorithm based on the analysis of resource distribution in the STTC during the previous planning periods to preferentially select the observation arcs that not only satisfy the requirements in the observation resource allocation phase but also facilitate the arrangement of measurement and control instruction release. We conduct extensive simulations to demonstrate the effectiveness of the proposed algorithm in terms of the number of completed tasks.

As the demands of massive connections and vast coverage rapidly grow in the next wireless communication networks, rate splitting multiple access (RSMA) is considered to be the new promising access scheme since it can provide higher efficiency with limited spectrum resources. In this paper, combining spectrum splitting with rate splitting, we propose to allocate resources with traffic offloading in hybrid satellite terrestrial networks. A novel deep reinforcement learning method is adopted to solve this challenging non-convex problem. However, the never-ending learning process could prohibit its practical implementation. Therefore, we introduce the switch mechanism to avoid unnecessary learning. Additionally, the QoS constraint in the scheme can rule out unsuccessful transmission. The simulation results validates the energy efficiency performance and the convergence speed of the proposed algorithm.

Massive machine type communication aims to support the connection of massive devices, which is still an important scenario in 6G. In this paper, a novel cluster-based massive access method is proposed for massive multiple input multiple output systems. By exploiting the angular domain characteristics, devices are separated into multiple clusters with a learned cluster-specific dictionary, which enhances the identification of active devices. For detected active devices whose data recovery fails, power domain nonorthogonal multiple access with successive interference cancellation is employed to recover their data via re-transmission. Simulation results show that the proposed scheme and algorithm achieve improved performance on active user detection and data recovery.

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.

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 investigates a wireless powered and backscattering enabled sensor network based on the non-linear energy harvesting model, where the power beacon (PB) delivers energy signals to wireless sensors to enable their passive backscattering and active transmission to the access point (AP). We propose an efficient time scheduling scheme for network performance enhancement, based on which each sensor can always harvest energy from the PB over the entire block except its time slots allocated for passive and active information delivery. Considering the PB and wireless sensors are from two selfish service providers, we use the Stackelberg game to model the energy interaction among them. To address the non-convexity of the leader-level problem, we propose to decompose the original problem into two sub-problems and solve them iteratively in an alternating manner. Specifically, the successive convex approximation, semi-definite relaxation (SDR) and variable substitution techniques are applied to find a near-optimal solution. To evaluate the performance loss caused by the interaction between two providers, we further investigate the social welfare maximization problem. Numerical results demonstrate that compared to the benchmark schemes, the proposed scheme can achieve up to 35.4% and 38.7% utility gain for the leader and the follower, respectively.

In commercial unmanned aerial vehicle (UAV) applications, one of the main restrictions is UAVs' limited battery endurance when executing persistent tasks. With the mature of wireless power transfer (WPT) technologies, by leveraging ground vehicles mounted with WPT facilities on their proofs, we propose a mobile and collaborative recharging scheme for UAVs in an on-demand manner. Specifically, we first present a novel air-ground cooperative UAV recharging framework, where ground vehicles cooperatively share their idle wireless chargers to UAVs and a swarm of UAVs in the task area compete to get recharging services. Considering the mobility dynamics and energy competitions, we formulate an energy scheduling problem for UAVs and vehicles under practical constraints. A fair online auction-based solution with low complexity is also devised to allocate and price idle wireless chargers on vehicular proofs in real time. We rigorously prove that the proposed scheme is strategy-proof, envy-free, and produces stable allocation outcomes. The first property enforces that truthful bidding is the dominant strategy for participants, the second ensures that no user is better off by exchanging his allocation with another user when the auction ends, while the third guarantees the matching stability between UAVs and UGVs. Extensive simulations validate that the proposed scheme outperforms benchmarks in terms of energy allocation efficiency and UAV's utility.

The ultra-dense low earth orbit (LEO) integrated satellite-terrestrial networks (UDLEO-ISTN) can bring lots of benefits in terms of wide coverage, high capacity, and strong robustness. Meanwhile, the broadcasting and open natures of satellite links also reveal many challenges for transmission security protection, especially for eavesdropping defence. How to efficiently take advantage of the LEO satellite's density and ensure the secure communication by leveraging physical layer security with the cooperation of jammers deserves further investigation. To our knowledge, using satellites as jammers in UDLEO-ISTN is still a new problem since existing works mainly focused on this issue only from the aspect of terrestrial networks. To this end, we study in this paper the cooperative secrecy communication problem in UDLEO-ISTN by utilizing several satellites to send jamming signal to the eavesdroppers. An iterative scheme is proposed as our solution to maximize the system secrecy energy efficiency (SEE) via jointly optimizing transmit power allocation and user association. Extensive experiment results verify that our designed optimization scheme can significantly enhance the system SEE and achieve the optimal power allocation and user association strategies.

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.

Modulation recognition becomes unreliable at low signal-to-noise ratio (SNR) over fading channel. A novel method is proposed to recognize the digital modulated signals with frequency and phase offsets over multi-path fading channels in this paper. This method can overcome the effects of phase offset, Gaussian noise and multi-path fading. To achieve this, firstly, the characteristic parameters search is constructed based on the cyclostationarity of received signals, to overcome the phase offset, Gaussian white noise, and influence caused by multi-path fading. Then, the carrier frequency of the received signal is estimated, and the maximum characteristic parameter is searched around the integer multiple carriers and their vicinities. Finally, the modulation types of the received signal with frequency and phase offsets are classified using decision thresholds. Simulation results demonstrate that the performance of the proposed method is better than the traditional methods when SNR is over 5dB, and that the proposed method is robust to frequency and phase offsets over multi-path channels.

This paper investigates the low earth orbit (LEO) satellite-enabled coded compressed sensing (CCS) unsourced random access (URA) in orthogonal frequency division multiple access (OFDMA) framework, where a massive uniform planar array (UPA) is equipped on the satellite. In LEO satellite communications, unavoidable timing and frequency offsets cause phase shifts in the transmitted signals, substantially diminishing the decoding performance of current terrestrial CCS URA receiver. To cope with this issue, we expand the inner codebook with predefined timing and frequency offsets and formulate the inner decoding as a tractable compressed sensing (CS) problem. Additionally, we leverage the inherent sparsity of the UPA-equipped LEO satellite angular domain channels, thereby enabling the outer decoder to support more active devices. Furthermore, the outputs of the outer decoder are used to reduce the search space of the inner decoder, which cuts down the computational complexity and accelerates the convergence of the inner decoding. Simulation results verify the effectiveness of the proposed scheme.

Due to the fading characteristics of wireless channels and the burstiness of data traffic, how to deal with congestion in Ad-hoc networks with effective algorithms is still open and challenging. In this paper, we focus on enabling congestion control to minimize network transmission delays through flexible power control. To effectively solve the congestion problem, we propose a distributed cross-layer scheduling algorithm, which is empowered by graph-based multi-agent deep reinforcement learning. The transmit power is adaptively adjusted in real-time by our algorithm based only on local information (i.e., channel state information and queue length) and local communication (i.e., information exchanged with neighbors). Moreover, the training complexity of the algorithm is low due to the regional cooperation based on the graph attention network. In the evaluation, we show that our algorithm can reduce the transmission delay of data flow under severe signal interference and drastically changing channel states, and demonstrate the adaptability and stability in different topologies. The method is general and can be extended to various types of topologies.

In this paper, a differential scheme is proposed for reconfigurable intelligent surface (RIS) assisted spatial modulation, which is referred to as RIS-DSM, to eliminate the need for channel state information (CSI) at the receiver. The proposed scheme is an improvement over the current differential modulation scheme used in RIS-based systems, as it avoids the high-order matrix calculation and improves the spectral efficiency. A mathematical framework is developed to determine the theoretical average bit error probability (ABEP) of the system using RIS-DSM. The detection complexity of the proposed RIS-DSM scheme is extremely low through the simplification. Finally, simulations results demonstrate that the proposed RIS-DSM scheme can deliver satisfactory error performance even in low signal-to-noise ratio environments.

The high reliability of the communication system is critical in metro and mining applications for personal safety, channel optimization, and improving operational performance. This paper surveys the progress of wireless communication systems in underground environments such as tunnels and mines from 1920 to 2022, including the evolution of primitive technology, advancements in channel modelling, and realization of various wireless propagation channels. In addition, the existing and advanced channel modeling strategies, which include the evolution of different technologies and their applications; mathematical, analytical, and experimental techniques for radio propagation; and significance of the radiation characteristics, antenna placement, and physical environment of multiple-input multiple-output (MIMO) communication systems, are analyzed. The given study introduces leaky coaxial cable (LCX) and distributed antenna system (DAS) designs for improving narrowband and wideband channel capacity. The paper concludes by figuring out open research areas for the future technologies.

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.

To provide reliability in distributed systems, combination property (CP) is desired, where $k$ original packets are encoded into $n \geq k$ packets and arbitrary $k$ are sufficient to reconstruct all the original packets. Shift-and-add (SA) encoding combined with zigzag decoding (ZD) obtains the CP-ZD, which is promising to reap low computational complexity in the encoding/decoding process of these systems. As densely coded modulation is difficult to achieve CP-ZD, research attentions are paid to sparse coded modulation. The drawback of existing sparse CP-ZD coded modulation lies in high overhead, especially in widely deployed setting $m<k$, where $m \triangleq n-k$. For this scenario, namely, $m<k$, a sparse reverse-order shift (Rev-Shift) CP-ZD coded modulation is designed. The proof that Rev-Shift possesses CP-ZD is provided. A lower bound for the overhead, as far as we know is the first for sparse CP-ZD coded modulation, is derived. The bound is found tight in certain scenarios, which shows the code optimality. Extensive numerical studies show that compared to existing sparse CP-ZD coded modulation, the overhead of Rev-Shift reduces significantly, and the derived lower bound is tight when $k$ or $m$ approaches 0.

Connected autonomous vehicles (CAVs) are a promising paradigm for implementing intelligent transportation systems. However, in CAVs scenarios, the sensing blind areas cause serious safety hazards. Existing vehicle-to-vehicle (V2V) technology is difficult to break through the sensing blind area and ensure reliable sensing information. To overcome these problems, considering infrastructures as a means to extend the sensing range is feasible based on the integrated sensing and communication (ISAC) technology. The mmWave base station (mmBS) transmits multiple beams consisting of communication beams and sensing beams. The sensing beams are responsible for sensing objects within the CAVs blind area, while the communication beams are responsible for transmitting the sensed information to the CAVs. To reduce the impact of inter-beam interference, a joint multiple beamwidth and power allocation (JMBPA) algorithm is proposed. By maximizing the communication transmission rate under the sensing constraints. The proposed non-convex optimization problem is transformed into a standard difference of two convex functions (D.C.) problem. Finally, the superiority of the proposed JMBPA algorithm is verified by iterative solutions. The average transmission rate of communication beams remains over 3.4 Gbps, showcasing a significant improvement compared to other algorithms. Moreover, the satisfaction of sensing services remains steady.

In this paper, the problem of abnormal spectrum usage between satellite spectrum sharing systems is investigated to support multi-satellite spectrum coexistence. Given the cost of monitoring, the mobility of low-orbit satellites, and the directional nature of their signals, traditional monitoring methods are no longer suitable, especially in the case of multiple power level. Mobile crowdsensing (MCS), as a new technology, can make full use of idle resources to complete a variety of perceptual tasks. However, traditional MCS heavily relies on a centralized server and is vulnerable to single point of failure attacks. Therefore, we replace the original centralized server with a blockchain-based distributed service provider to enable its security. Therefore, in this work, we propose a blockchain-based MCS framework, in which we explain in detail how this framework can achieve abnormal frequency behavior monitoring in an inter-satellite spectrum sharing system. Then, under certain false alarm probability, we propose an abnormal spectrum detection algorithm based on mixed hypothesis test to maximize detection probability in single power level and multiple power level scenarios, respectively. Finally, a Bad out of Good (BooG) detector is proposed to ease the computational pressure on the blockchain nodes. Simulation results show the effectiveness of the proposed framework.

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.

By multiplexing information symbols in the delay-Doppler (DD) domain, orthogonal time frequency space (OTFS) is a promising candidate for future wireless communication in high-mobility scenarios. In addition to the superior communication performance, OTFS is also a natural choice for radar sensing since the primary parameters (range and velocity of targets) in radar signal processing can be inferred directly from the delay and Doppler shifts. Though there are several works on OTFS radar sensing, most of them consider the integer parameter estimation only, while the delay and Doppler shifts are usually fractional in the real world. In this paper, we propose a two-step method to estimate the fractional delay and Doppler shifts. We first perform the two-dimensional (2D) correlation between the received and transmitted DD domain symbols to obtain the integer parts of the parameters. Then a difference-based method is implemented to estimate the fractional parts of delay and Doppler indices. Meanwhile, we implement a target detection method based on a generalized likelihood ratio test since the number of potential targets in the sensing scenario is usually unknown. The simulation results show that the proposed method can obtain the delay and Doppler shifts accurately and get the number of sensing targets with a high detection probability.