With the development of the sixth generation wireless communication, the increasingly scarce spectrum resources limit the further increase in data rate and exacerbate the interference problem among different users and applications. To address this issue, rate-splitting multiple access (RSMA) provides a flexible framework that unifies existing orthogonal and non-orthogonal multiple access schemes. In this work, we analyze the interference scenario of RSMA-based space-terrestrial transmission systems, with multiple satellite users independently and uniformly distributed in the coverage area of the serving satellite. Specifically, the outage performance at the satellite users (resp. terrestrial base station (BS) users) is assessed while considering the interference from the BS users (resp. the satellite and the BSs of other cells). Approximate analytical expressions of the outage probability at each satellite user/BS user are derived, numerically evaluated, and verified through simulation results. The impacts of RSMA power allocation factors, fading parameters, interference severity, and satellite altitude on outage performance are thoroughly analyzed, and the trade-off between outage performance and user fairness is also illustrated.

In this paper, we investigate the outage probability of a multi-tier dual-hop terrestrial-low earth orbit (LEO) satellite-geostationary earth orbit (GEO) satellite hybrid wireless communication system. The system comprises multi-tier LEOs and one GEO act as relays in the uplink, which help the terrestrial ground station (S) transmit information to the terrestrial destination (D). In uplink transmission, we introduce a one-dimensional hardcore point process to model different altitudes of LEOs’ different tiers. We also use a generalized selection combining technique to achieve a trade-off between maximum ratio combining and selection combining. In downlink, GEO offers the maximum coverage to randomly distributed D. Moreover, the independent identically distributed Nakagami-m fading and shadowed Rician distribution are brought to model the different channels. Finally, Monte-Carlo simulations are presented to affirm the precision and accuracy of the derived analytical models and the proposed analysis. This framework offers crucial insights for system designers and network operators, enabling the optimization of resource allocation, relay strategies, and overall reliability in terrestrial-satellite hybrid networks.

This paper investigates the internal secrecy and external covertness of a mixed-trust autonomous aerial vehicle (AAV) communication system assisted by rate-splitting multiple access (RSMA). In this setting, a semi-trusted user with partial decoding capability poses an internal eavesdropping threat, while multiple distributed wardens attempt to detect the transmission from the AAV to the semi-trusted user, creating an external covertness challenge. To characterize these security aspects, a unified analytical framework is developed. First, the internal eavesdropping capability of the semi-trusted user is quantified by deriving a closed-form expression for its eavesdropping success probability. Based on the outcome of the eavesdropping attempt, tractable expressions for the secrecy outage probability of the legitimate user are obtained. Furthermore, the external covertness performance is analyzed by deriving closed-form false alarm probability, missed detection probability, and detection error probability (DEP) for an individual warden, together with the optimal detection threshold and the corresponding minimum DEP. The cooperative global detection performance with multiple wardens is further characterized under conservative fusion rules. Extensive Monte Carlo simulations validate the analytical results and, through a joint evaluation of secrecy, reliability, and covertness metrics, illustrate the feasible operating regions enabled by RSMA power allocation in comparison with a NOMA baseline. The results provide a comprehensive theoretical basis for the design of secure and covert AAV communication strategies in mixed-trust environments.

The Internet of Things (IoT) supported by satellites is becoming indispensable for remote sensing in the forthcoming sixth-generation (6G) communication network. However, it is tempting and easy for unauthorized users to exploit the Direct Sequence Spread Spectrum (DSSS) technique to quietly complete their own transmissions due to the open nature of propagation and the publicly available satellite orbits and frequencies. Therefore, it is necessary to conduct a blind estimation of the DSSS signal to take a more proactive approach to protect satellites against illegal use. However, the modulation information is unknown, and the spreading code structure varies, making the blind estimation of spreading codes a significant challenge. Additionally, under data modulation, the dimensionality of the received spread spectrum sequence increases, greatly raising the complexity of spreading code estimation. Against this background, we propose a hybrid learning-based blind estimation algorithm for spreading codes, which combines K-means clustering and Convolutional Neural Networks (CNN). This algorithm achieves low-complexity blind estimation of spreading codes with unknown modulation information and low signal-to-noise ratio. Specifically, the K-means clustering algorithm uncouples the data modulation from the spreading code, reducing the dimensionality of the estimation process. On this basis, the CNN-based parallel convolution architecture is employed to achieve low-complexity and accurate estimation of the spreading code. Simulation results demonstrate that our proposed algorithm outperforms existing algorithms in both computational complexity and estimation performance.

The rapid expansion of Low Earth Orbit (LEO) satellite constellations has brought significant spectrum management challenges, including spectrum scarcity and complex interference issues. Traditional algorithms and prior Artificial Intelligence (AI) methods fail to meet LEO’s demands for managing extreme dynamics, massive scale, and multi-objective optimization. This paper introduces an innovative Large Language Model (LLM) framework for intelligent spectrum sharing and dynamic resource allocation in satellite-terrestrial down-link systems. First, we established a geometric model for satellite-terrestrial down-link communication, and accurately derived the statistical distribution function of satellites within the space enclosed by a specific orbital line by combining the stochastic geometry theory. Under this geometric model, a communication scenario was introduced, and an adaptive modulation transmission mechanism based on orthogonal frequency division multiplexing signals was designed. Then, the system combines the real-time spectrum sensing results with the natural language description of the quality of service of multi-service data using prompt engineering techniques, and delivers the comprehensive information to the LLM for resource allocation and generation of a transmission scheme. Finally, the resource allocation and transmission scheme determined by the LLM is applied to the established communication model, and the system performance is comprehensively evaluated by analyzing indicators such as outage probability, system throughput, and transmission and waiting delays. Primary contributions include novel dynamic service-to-strategy generation, an LLM-centric prompt-driven architecture, and a new paradigm that positions the LLM as an intelligent “spectrum orchestration brain” for complex global LEO resource management. Collectively, these advancements enhance spectrum utilization intelligence, adaptability, and efficiency, offering a transformative approach to overcome the limitations of prior methods in demanding LEO environments.

Achieving secure communication with a low probability of detection (covertness) is critical yet challenging, particularly when employing practical digital modulations that can compromise the statistical indistinguishability assumed in theoretical models. This paper introduces a novel end-to-end framework leveraging digitally modulated covert signal modeling, obfuscation, and deep learning to attain simultaneous covertness and reliability. Firstly, we propose a novel approach to covert performance evaluation for modulated covert signals against detection. To address the deteriorated covertness considering modulation schemes, we further propose generating Gaussianized camouflage signals via a multi-stage transmitter pipeline encompassing spreading, jitter, filtering, and non-linear transformations, designed to mimic noise statistics effectively. At the receiver, a specialized deep learning architecture, CovertSyncNet, performs robust joint dynamic synchronization and symbol recovery. This receiver incorporates dedicated components to precisely estimate time-varying chip offsets and invert the complex, nonlinear distortions inherent in the camouflaged signal, enabling accurate demodulation. Extensive simulations rigorously validate our approach, demonstrating that high reliability is maintained despite the heavy camouflage. Concurrently, enhanced covertness is confirmed through metrics indicating low statistical distinguishability from Gaussian noise. This work highlights the significant potential of deep learning to bridge the gap between theory and practice, realizing communication systems that are simultaneously reliable, secure, and highly covert, even under realistic operational conditions.

Next-generation satellite systems require efficient collaboration in terms of wide area coverage and signal augmentation, enabling intelligent allocation of available wireless resources to ensure the security of information. Meanwhile, machine learning (ML) is widely considered well-suited to massive, real-time data scenarios in satellite communication networks, and graph neural network (GNN) is a specific branch for processing the irregular data within such networks. In this paper, we propose physical layer security for a multi-satellite collaborative (MSC) system involving LEO satellites, users, and eavesdroppers. Specifically, the GNN-based security communication of the MSC (G-MSC-SC) architecture is designed to maximize the secrecy rate. Since heterogeneous and isomorphic methods can effectively solve multi-type node mapping and complex communication problems, the G-MSC-SC architecture is divided into two steps: A heterogeneous graph pruning attention coefficient network (HGPAN) and an isomorphic graph eavesdropper as an auxiliary node network (IGEAN). In the HGPAN architecture, different types of device nodes are embedded in the same dimensional space, addressing the challenge of matching LEO satellites to users. The IGEAN architecture maps user channel state information (CSI) to beamforming (BF) vectors through attention aggregation and an improved loss function. Moreover, the corresponding conventional optimization algorithms are designed as test and comparison baselines. Simulation results show that 1) the G-MSC-SC architecture outperforms neural networks and heuristic algorithms in terms of accuracy and efficiency; 2) as the numbers of users and virtual eavesdroppers increase, the directional alignment between the BF vectors and the LEO satellite-user channels shows an improvement; and 3) with imperfect CSI, the G-MSC-SC architecture still achieves an excellent balance between user secrecy rate and communication rate.

Low Earth Orbit (LEO) satellite communications provide uninterrupted coverage and seamless services, which are becoming a crucial element in the Sixth-Generation of wireless communications. However, LEO communications are subject to high path loss and Doppler frequency shift, which presents significant challenges for such large-distance and time-changeable satellite-terrestrial transmission links. Direct Sequence Spread Spectrum (DSSS) has been widely adopted as a robust modulation technique in LEO satellite systems. Traditional DSSS signal detection methods, which rely on 2D searching, fail to effectively acquire the weak uplink signals due to the extremely low signal-to-noise ratio and pronounced channel dynamics. In light of these considerations, this study proposes an algorithm designated as time synchronization-aided signal detection, with the objective of enhancing the signal detection probability in low signal-to-noise ratio and high-mobility communication scenarios. Furthermore, we develop a novel transmission system based on time synchronization, with the objective of reducing the signal detection threshold by minimizing the impact of the Doppler frequency shift and time delay searching ranges. We theoretically analyze performance at varying time synchronization precision, numerical simulations and hardware experiments under diverse conditions demonstrate that, in comparison to the conventional algorithm, time synchronization can enhance the probability of signal detection.

Satellite communications, characterized by their wide coverage, flexible deployment, and short construction cycles, play an indispensable role in modern communication systems. However, the inherent openness of satellite channels makes transmitted signals highly susceptible to detection and interception. To address these security challenges, this paper proposes an energy-dispersed random time-hopping (ED-RTH) covert communication scheme based on time-uncertain transmission, which effectively enhances system covertness. To overcome the multi-slot combining challenge in the highly dynamic and low-SNR environment of low Earth orbit (LEO) satellite communications, a cross-entropy-based joint reception (CE-JR) algorithm is developed, achieving minimal performance loss with significantly reduced computational complexity. Furthermore, the detection performance of an eavesdropping satellite against the proposed scheme is analyzed, and closed-form expressions for the miss detection probabilities under two typical detection methods are derived, providing valuable insights for the design and optimization of covert satellite communication systems. Finally, a simple implementation of the CE-JR algorithm was carried out on an FPGA development board, demonstrating the feasibility of the proposed algorithm.

Low Earth Orbit (LEO) satellite communication presents a promising solution for data backhaul from oceanic buoys. However, due to the power-limited buoys and large path loss between the buoy and LEO satellites, the received signal at the LEO satellites is critically weak, which poses severe challenges to both signal detection and localization of the buoys. To make things worse, the high mobility of LEO satellites introduces large Doppler frequency shifts that complicate signal detection, while ocean currents exacerbate the positional uncertainty of buoys. The interaction of these factors highlights the inadequacy of conventional approaches that fail to address detection and localization jointly. To address these challenges, we propose a direct detection and localization algorithm based on the Generalized Likelihood Ratio Test (GLRT). To reduce the complexity of the exhaustive grid search algorithm, we employ Particle Swarm Optimization (PSO) as a low-complexity search algorithm. Furthermore, the theoretical analysis is proposed from the perspective of closed-form expressions for the probability of detectionPDand the Earth-surface-constrained CRLB. Numerical simulation results demonstrate that the proposed algorithms can attain the CRLB at high SNR.

With the rapid development of integrated air-space-ground networks, ensuring secure and covert access for unmanned aerial vehicles (UAVs) to low Earth orbit (LEO) satellite constellations has become a critical challenge. This paper proposes the novel adaptive redundancy contention resolution diversity slotted ALOHA (AR-CRDSA) scheme to provide reliable and covert communication for UAV users. The core of the scheme involves a dynamic pairing strategy that conceals a low-power covert user (CU) transmission under the signal of a high-power legitimate user (LU), thereby shielding it from detection by adversarial reconnaissance satellites. A key innovation is the departure from transmitting identical packet replicas; instead, the AR-CRDSA protocol sends two distinct yet complementary packet versions to adapt to fluctuating channel conditions. This adaptive mechanism, combined with multi-satellite selection diversity and an iterative interference cancellation process, is shown through extensive simulations to significantly enhance the effective covert throughput and overall system reliability when compared to traditional random access protocols.

In recent years, low Earth orbit (LEO) satellite communication has emerged as a focal area of extensive research due to its potential for global broadband connectivity. Multi-carrier direct sequence spread spectrum (MC-DSSS) technology, leveraging the inherent anti-jamming advantages of spread spectrum signals, has shown great promise in enhancing communication reliability. However, the acquisition of MC-DSSS signals in LEO transmission link presents significant challenges, primarily due to the coexistence of extremely low signal-to-noise ratio (SNR) and substantial Doppler effect.To address these issues, this paper proposes an optimal two-dimensional (2D) acquisition framework for MC-DSSS signals. Based on the maximum likelihood (ML) criterion, the proposed framework enables coherent combination of subcarriers with high time resolution, thereby improving the acquisition performance in harsh LEO environments. Furthermore, a two-step low-complexity coherent acquisition algorithm is developed. This algorithm significantly reduces the computational burden while maintaining the performance of fully coherent subcarrier combination, making it more suitable for real-time implementation in resource-constrained LEO communication terminals. Moreover, this paper derives closed-form solutions for the false alarm probability, detection probability, and mean squared error (MSE) in additive white Gaussian noise (AWGN) channel, which are validated through simulations. The results demonstrate that the proposed algorithm achieves 2 dB performance improvement in SNR compared to noncoherent combining method, with a 1024-fold reduction in MSE when the number of subcarriers is 16.

With the rapid development of global satellite Internet and the growing demand for seamless connectivity, the Satellite–Terrestrial Integrated Network (STIN) has become a key architecture for achieving ubiquitous communication coverage. However, STIN faces critical energy efficiency challenges, including the high peak-to-average power ratio (PAPR) that degrades high power amplifier efficiency at the physical layer and frequent satellite handovers that increase network overhead. To address these issues, this paper proposes a mobility-aware model of Multicarrier Direct-Sequence Spread Spectrum (MC-DSSS) STIN. At the physical layer, the Orthogonality-Based Generalized Multicarrier Constant Envelope Multiplexing (CEMIC) technique is adopted, and an energy efficiency maximization problem is formulated under constant-envelope constraints. A joint power allocation algorithm is developed based on the Dinkelbach method and the Alternating Direction Method of Multipliers (ADMM) to solve the non-convex problem efficiently. To overcome the limitations of single-layer optimization, a twolayer collaborative framework is further proposed. At the network layer, an improved binary particle swarm optimization (IBPSO-HO) algorithm is employed to optimize satellite handovers. This joint design enables two-layer energy efficiency optimization. Simulation results demonstrate that the proposed scheme significantly enhances overall energy efficiency across both layers, providing robust theoretical support for the large-scale green deployment of STIN.

The growth of communication technologies has led to a corresponding increase in the need for information security. The use of covert communication techniques has the potential to significantly reduce the likelihood of the transmitted signal being intercepted by unintended receivers, thereby significantly increasing the security of the information. In this paper, we propose an energy-dispersed random time-hopping (ED-RTH) covert communication scheme. Building on existing time uncertainty schemes, this scheme further enhances time uncertainty through random time-hopping, while dispersing signal energy to achieve stronger covertness. We present a model for analyzing the covertness of the ED-RTH scheme. Given the complexity of the likelihood ratio distribution, the Kullback-Leibler (KL) divergence is used to analyze the lower bound of Willie’s detection error probability. Through constructing behavioral-level simulations for validation, we investigated the impact of the number of dispersed time slotsKon both Willie’s detection error probability and the system’s covert throughput. Finally we compared the proposed ED-RTH scheme with existing time uncertainty scheme, and the results demonstrate that the ED-RTH scheme achieves significantly enhanced covertness.

Efficient and reliable neighbor discovery is critical for UAV networks equipped with directional antennas, particularly in dynamic and energy-constrained environments. We present a novel optimization framework that jointly minimizes delay and power consumption using the power-delay product as the optimization metric. The framework is formulated for both synchronous and asynchronous schemes, leveraging upper-bound metrics and a convex-concave procedure to achieve tractable convex formulations. Simulation results validate the theoretical models and show significant improvements over baseline methods. We also conducted real-world experiments, where our method reduced power-delay product by 11% compared to the baseline, though the dual-sector hardware configuration limited the achievable gains. This work provides a comprehensive solution for UAV neighbor discovery, with high potential for scalability in more complex and dynamic environments.

With the rapid advancement of unmanned aerial vehicle (UAV) technology in recent years, cooperative communication in UAV networks (UAV-Ns) has made significant strides. However, the effectiveness of UAV-Ns cooperative communication relies heavily on the accurate estimation of channel state information (CSI). Unlike terrestrial networks, the mobility of UAV introduces time-varying channel characteristics, which can substantially affect the overall system capacity. Therefore, this paper investigates the system capacity of UAV-Ns while accounting for the effect of jitter characteristics of UAV. Specifically, we propose a cooperative transmission model utilizing multiple UAV base stations (UAV-BSs) to enhance the signal quality received by ground users through coordinated multi-point (CoMP) transmission. Additionally, we present a jittering channel model, derive the channel autocorrelation function, and assess the capacity of the proposed system. To deal with the jitter, we introduce a jitter compensation scheme based on long short-term memory networks to counteract the effects of UAV jitter and improve the accuracy of channel precoding. Numerical results demonstrate that our approach significantly enhances the communication performance of UAV-Ns under the impact of jitter. Compared to the traditional method, our scheme improves the estimation accuracy of the channel state by up to 3.8%, highlighting the potential of distributed UAV-BSs with CoMP to strengthen UAV-Ns communication.

Millimeter-wave (mmWave) communications, coupled with directional antenna-based Flying Ad-Hoc Networks (FANETs), have received considerable attention for their potential to provide high-speed, low-latency communications for a variety of applications. However, the high mobility of Unmanned Aerial Vehicles (UAVs) in FANETs leads to dynamic changes in relative positions, resulting in frequent link failures. Effective link maintenance in such networks has become a critical challenge. This paper addresses this issue by developing mathematical models of link disconnections in directional antenna-based FANETs. Specifically, we derive the probability density functions for link disconnections due to distance and angular misalignment in closed-form expressions. Based on these prediction models, we propose the Adaptive Link Breakage Prediction with Directionality (ALBP-D) method, which exploits the high directional gain of directional antennas to extend link lifetime and improve network performance. We compare ALBP-D with two baseline methods, the Periodic Link Maintenance (PLM) method and the Residual Path Lifetime (RPL) method, through extensive simulations. The results show that ALBP-D achieves superior performance, with approximately a 10-fold improvement in both link lifetime and network connectivity duration compared to the baseline methods. In addition, ALBP-D exhibits significant improvements in maintenance overhead efficiency, especially at higher max range adjustment count, achieving a 5 to 7-fold improvement over baseline methods. These results highlight the effectiveness of ALBP-D in directional antenna-based FANETs. We also implemented a prototype system consisting of a directional antenna node and an omnidirectional antenna node using realistic UAV trajectory data. Experimental results show that the prediction models agree well with the real link disconnection data, confirming the practical feasibility and accuracy of the proposed method.

Low Earth Orbit (LEO) satellite systems with sensing capabilities are widely regarded as promoting reliable and efficient communication services globally. This paper proposes a Multi-Satellite Collaborative Security System with Integrated Sensing and Communication (ISAC-MSC). Considering the potential benefits of LEO satellites and ISAC, we exploit sensing performance in the MSC system by jointly optimizing LEO satellite assignments, communication Beamforming (BF) vectors, and sensing BF vectors. Specifically, we design improved Continuous Particle Swarm (CP) optimization and Discrete Particle Swarm (DP) optimization algorithms to maximize the target sensing Signal-to-Noise Ratio (SNR) for LEO satellite assignments. Additionally, with respect to the BF vector optimization, we develop Power Approximation (PA) optimization algorithm, Inner Approximation (IA) optimization algorithm, and Joint Sensing and Communication BF (JSC-BF) optimization algorithm. Multiple algorithms are tightly integrated and alternately iterated. Numerical results show that: 1) the JSC-BF algorithms outperform the PA and IA algorithms in terms of sensing performance and communication secrecy rate; 2) compared to the single satellite case, the ISAC-MSC system performance approximately linear growth, and has strong extensibility; 3) with imperfect MSC synchronization case, the communication secrecy rate appears inflection point and stabilization, but the JSC-BF algorithms still have excellent performance.

The rapid development of the Internet of Things (IoT) has led to exponential growth in wireless network traffic and the number of connected devices, thereby intensifying the demand for scarce spectrum resources. In this context, Wireless signal identification, a key technology in spectrum sensing, is crucial for enhancing spectrum utilization by mitigating interference and ensuring system security. In this study, we treat wireless signal identification as a time series classification task and propose a novel model based on Fourier-segmented attention. In our proposed model, instead of computing point-level attention, we extract sequence dependencies by computing segment-level attention. Moreover, we introduce a method based on the Fourier transform to determine the segment length, ensuring that each segment captures multi-scale features. Experimental results indicate that the proposed method outperforms existing models, achieving an accuracy of approximately 95% on our dataset and representing an improvement of around 1.6% in accuracy over competing approaches. Furthermore, experiments were conducted to evaluate the model’s effectiveness in detecting fake signals and its potential to enhance system security.

Millimeter wave (mmWave) communication is crucial for drones, leading to more being equipped with directional antennas. Consequently, the technology for fast detection of directional antenna neighbors is a major challenge in mmWave Flying Ad-Hoc Networks (FANETs). This paper proposes a fully directional Neighbor Discovery (ND) method called Directional ND with Multi-Token Passing (DNDMTP) Algorithm to work out the aforementioned issue. Specifically, a theoretical derivation of DNDMTP is first presented, followed by a verification of its correctness through simulations that match the theoretical results. Unlike existing methods, DNDMTP is designed to maximize the spatial reuse capability of directional antennas by forwarding tokens across multiple antenna coverage areas, accelerating neighbor discovery in multi-hop topologies. In DNDMTP, the token holder nodes can achieve bidirectional discovery with the neighbor nodes through multiple rounds of broadcasting. Furthermore, the optimal selection of parameters is proposed for different scenarios, e.g., the number of antennas, iterations, and the number of tokens passed. Finally, for comparison purposes, we chose the Scan-Based Algorithm-Deterministic (SBA-D) and Learning Automaton Based ND (LAND) algorithms, which represent deterministic and probabilistic algorithms, respectively. The numerical results from Python show that the proposed DNDMTP can reduce the neighbor discovery time by about 50% compared to existing methods, due to multiple token-holder nodes performing neighbor discovery simultaneously. As the number of nodes and antennas increases, DNDMTP performs better in terms of neighbor discovery time.

With the increasing demands of the Industrial Internet of Things (IIoT), highly dynamic multi-unmanned aerial vehicle (UAV) networks are becoming indispensable to IIoT due to their flexibility, cost-effectiveness, robust safety measures, and real-time data collection capabilities. Accurate time synchronization is crucial for coordinated missions of multi-UAV networks, yet the time-varying nature of clock parameters and the rapid movements of UAVs pose significant challenges to achieving precise synchronization. This article introduces new state and observation models for clock and velocity parameters and proposes a Doppler and timestamp-based distributed algorithm for tracking clock parameters using the Kalman filter. To evaluate the performance of the proposed algorithm, we derive the Bayesian Cramér–Rao lower bound and conduct numerical simulations. The results of the simulations demonstrate that our algorithm surpasses existing methods in terms of accuracy in tracking clock parameters.

Space-air-ground integration has become paramount in the next generation of wireless communication systems in the era marked by the seamless integration of terrestrial and celestial domains. On the other hand, the age of information (AoI) has recently emerged as a vital metric for evaluating the timeliness and freshness of data in these multi-hop communication systems. This paper focuses on investigating the information freshness of multi-hop satellite IoT systems while considering several automatic repeat request (ARQ) and hybrid ARQ (HARQ) schemes over different hops to promise the reliability of data transmissions. Specifically, a group of remote sensors transmits their data to a terrestrial base station (B) via the code-division multiple access (CDMA) strategy to exploit CDMA’s natural merits, e.g., anti-jamming and simultaneous transmissions. Then, B sends these received data to a data destination (D) via a satellite (R) under the transparent forwarding strategy. We derive the closed form of the outage probability for the CDMA protocol considering multi-user interference (MUI) and the closed form of the end-to-end outage probability for the three kinds of ARQ and HARQ schemes on the dual-hopB-R-Dtransmission, and then finally derive the expression of the corresponding AoI. Finally, numerical results show that simulations match well with the theoretical results, proving the analysis’s correctness and the pros and cons of the different ARQ/HARQ schemes.

Low Earth orbit (LEO) Mega-constellations have emerged as a transformative approach to realize enhanced system capacity and improved coverage to satisfy the ever-increasing global demand for data services. Subsequently, the high density of satellites in a confined orbital region poses challenges, including potential interference among neighboring satellites. Further, it is vital to adequately address the impacts of safety distances in satellite communication systems on ensuring proper operation, collision avoidance, and interference management. Inspired by these observations, this work proposes a novel analysis tool, the Poisson arc hardcore point process (PAHPP), by extending the traditional Poisson line hardcore point process to characterize the unique orbiting properties of the satellites in LEO mega-constellations, accounting for factors such as the orbit, the satellite density, and spatial distribution. Specifically, this paper presents the PAHPP by enforcing a minimum separation between satellites operating in the same circular orbit to reflect the practical LEO mega-constellations. The imposed minimum inter-satellite separation in the proposed PAHPP model has also been applied to multi-orbit multi-satellite communication cases. Moreover, the discretization approximation technique is employed to analyze system performance, focusing on serving distance and outage probability. Numerical results provide valuable insights and conclusions for uncovering and recognizing LEO mega-constellations.

Satellite-to-ground communication systems typically operate in environments with high interference levels, complex topologies, and stringent platform constraints. Therefore, intelligent, anti-interference, and low-power systems are required to achieve the desired transmission performance. This paper proposes a system for optimizing high-frequency satellite-to-ground communications using computer vision (CV) technology, like millimeter-wave (mmWave) satellite communication systems. The system uniquely combines CV-based target localization with adaptive beamforming and power control to optimize communication links with ground targets such as base stations, ships, and aircraft. This approach significantly outperforms traditional radio frequency-based methods in accuracy and efficiency, particularly in dynamic mmWave scenarios. Simulation results confirm the superiority of our system in terms of sum rate and energy efficiency, demonstrating its potential to revolutionize high-frequency satellite communications by providing reliable, high-quality service to terrestrial targets. Finally, simulation results are presented to demonstrate the efficiency of the proposed schemes.

This article presents a novel noncoherent multisatellite weak signal acquisition scheme by aggregating the observations at multiple low-Earth orbit (LEO) satellites. Existing aggregation schemes rely on exhaustive search over grids on Earth surface to compensate for the differences in the delay and the Doppler frequency shift experienced at different satellites, which have high-computational complexity due to the wide coverage of LEO satellites. Motivated by this observation, we propose to directly work with satellites’ time-frequency (TF) grid and facilitate efficient delay and Doppler compensation by gradually narrowing down the search space over each satellite’s TF grid with multisatellites observations. Our scheme employs geometric search space reduction scheme to reduce the search space for possible Doppler frequency shift and utilizes hierarchical geometric correlation peak matching to eliminate fake correlation peaks based on multisatellite observations. Through extensive performance evaluation, we demonstrate that, in comparison with existing schemes, our proposed multisatellite signal acquisition scheme can achieves significantly better acquisition performance with a much lower computational complexity.

This paper studies covert MIMO ambient backscatter communication (AmBC). In contrast to the prior covert AmBC works, we equip all devices with multiple antennas, where a backscatter tag works as a beamformer to hide both his transmission behavior and location from a warden. Technically, we derive two key metrics, namely the Kullback-Leibler divergence (KL divergence) measuring the transmission covertness and the covert rate measuring the transmission efficiency. We reveal that they both increase monotonically w.r.t the number of the tag’s antenna. This tradeoff makes the optimum parameter configuration non-trivial. To this end, we optimize the beamformer of the tag to maximize the covert rate subject to the covertness constraint under perfect knowledge of the warden’s channel state information (WCSI). We use the matrix permutation and semi-definite relaxation (SDR) method to make the formulated non-convex problem convex, and obtain the solution with the barrier method. We also study the impact of imperfect WCSI. To address the uncertain estimate error, we use the S-Procedure to reformulate the constraints linearly and the primal-dual interior-point method for a near-optimal solution. We conduct extensive numerical experiments and confirm that our work outperforms the state-of-the-art ones.

This paper presents a covert ambient backscatter communication (AmBC) system aided by the intelligent reflecting surface (IRS), where an IRS is used as a beamformer to help Tag for covert AmBC. Technically, we derive the expression of the Kullback-Leibler (KL) divergence to measure the detection performance of the warden. To fight against the warden’s detection, we propose a joint IRS’s beamforming and Tag’s reflection coefficient optimization scheme to maximize the covert AmBC rate subject to a key constraint metric of the KL divergence. To solve the non-convex problem, we formulate it as Fractional Programming (FP) for the linear iterations, and use the Majorization-Minimization (MM) algorithm to obtain the optimal parameters of IRS and Tag. Moreover, we investigate the covert performance with the imperfect channel state information of the warden’s link (WCSI) with Tag and IRS. Numerical results show the covert performance of the system, and illustrate the superiority of the IRS’s assistance to the covert backscatter efficiency. The simulations also show that the channel estimated error of Tag-Willie link has a negative impact on the covert backscatter efficiency, while the channel estimated errors of IRS-Willie link have the positive influence on the contrary.

For mission-critical URLLC applications, timely status updates are essential. This paper investigates the age of information (AoI) of the three HARQ schemes specified in 5G R16, targeting to provide guidelines for future grant-free access design in 5G-Advanced and beyond. Specifically, we analyze two packet management policies: First-come-first-serve (FCFS) and preemption policy where new packets always preempt the buffer. We also study the AoI in a latency-sensitive scenario where expired packets are discarded. We derive exact expressions of AoI and peak AoI for all schemes and their lower bounds, revealing that the number of the maximum consecutive transmissions is critical for information freshness. Simulation results validate the theoretical analysis and show that Proactive HARQ scheme outperforms K-repetition HARQ scheme unconditionally and Reactive HARQ scheme with moderate system load or above. And discarding expired packets enhances system robustness for overload systems but yields larger AoI.

Efficient and secure group writing plays a crucial role in RFID-enabled multi-task backscatter systems. The prior works lay emphasis on the time efficiency of the group data transmission, but neglect its security. This paper is devoted to providing anonymous group writing. We propose the Overlapped Bloom Filter-based protocol (OBF) and its enhanced version (OBF+). The core is to construct an approximately random sequence as a noise by making transmission data for different tag groups overlap with each other, thus hiding the original information with a low computational complexity. The compact filter can guarantee the time efficiency while improving the security of the group writing. To make tags aware of the correctness of the decoded group data, the OBF+ introduces the complementary code-based check mechanism to eliminate the fault data. We prototype the system with USRP and programmable WISP tags, and conduct extensive simulations to evaluate our approaches in terms of the time efficiency, the accuracy, and the anonymity of the data transmission.

Underwater exploration, inspection, and surveillance have become more popular thanks to recent advancements in underwater wireless communication technologies, of which underwater wireless optical communication (UWOC) boasts increased bandwidth, reduced latency, and heightened security. Normally, once underwater sensors collect the data, it is transmitted to remote offices through a relay node stationed on the sea surface. In this study, our focus is on establishing and analyzing the performance of secrecy outage in an uplink hybrid radio frequency (RF)-UWOC space-ocean system that comprises a collection of underwater sensors, a relay floating on the ocean surface, a legitimate satellite receiver, and an eavesdropping satellite. In the considered system, the transmission performance from end-to-end is influenced by several factors. These include path loss, small-scale fading, position randomness of the satellites in RF links, propagation loss, turbulence-induced fading, and random location distribution of the underwater sensors in UWOC links. To enhance our understanding of system design, geometric probability theory is employed to investigate the end-to-end secrecy outage performance of the dual-hop RF-UWOC space-ocean uplink transmission while comprehensively considering the impacts of these factors. Finally, the accuracy of the proposed analysis models is verified through numerical results.

Due to the unique merits of unmanned aerial vehicle (UAV) systems, they have already been harnessed for military, public, and civil applications. Time synchronization is a significant premise of formatting and applying UAV networks. However, the irregular high-speed mobile UAVs pose new challenges to time synchronization, especially when external time references are unavailable in some rigid scenarios. Therefore, in these harsh cases, self-time-synchronization (STS) without any external assistance should be concerned to overcome the relative velocity between UAVs caused by irregular high-speed motion. In this paper, a realistic timestamps model for the multi-UAV networks is established, and then a dynamic topology-based maximum likelihood estimator will be developed to carry out the STS. Furthermore, by introducing the information on frequency offset, a new estimator with the closed-form expression is proposed based on a two-way message exchange framework. After that, a tracking algorithm with the assistance of estimation results will be introduced to compensate for the time-varying change of the clock parameters for the dynamic topology UAV networks. To evaluate the performance of the estimator, both the estimation error and Cramér-Rao lower bound are analyzed. Numerical results show that the proposed algorithm exhibits its superiority in STS performance and computational complexity compared to the existing two-way message exchange algorithm using timestamps only.

Unmanned aerial vehicles (UAVs) have found extensive applications across diverse domains owing to their cost-effectiveness, uncomplicated structure, and adaptable takeoff and landing functionalities. Multi-UAV network systems can be coordinated to improve system performance significantly. Network time synchronization is a crucial prerequisite for establishing and operating multi-UAV networks, whereas the high-speed movement of UAVs presents challenges for time synchronization within multi-UAV networks. In this paper, we develop a practical information exchange model with high relative radial velocity and Gaussian distribution random transfer delay. By introducing Doppler information and a novel clock skew and clock offset consensus model, a Doppler and timestamp joint (DATJ) network time synchronization algorithm has been developed. Assessing the performance of the algorithms, we provide rigorous theoretical proof of network time synchronization convergence. Simulation results further validate the theoretical analysis and demonstrate that the proposed algorithm outperforms similar approaches in terms of synchronization performance.

OFDM backscatter is crucial in passive IoT. Most of the existing works adopt phase-modulated schemes to embed tag data, which suffer from three drawbacks: symbol-level modulation limitation, heavy synchronization accuracy reliance, and small symbol time offset (STO) / carrier frequency (CFO) offset tolerability. We introduce SubScatter, the first subcarrier-level frequency-modulated OFDM backscatter which is able to tolerate bigger synchronization errors, STO, and CFO. The unique feature that sets SubScatter apart from the other backscatter systems is our subcarrier shift keying (SSK) modulation. This method pushes the modulation granularity to the subcarrier by encoding and mapping tag data into different subcarrier patterns. We also design a tandem frequency shift (TFS) scheme that enables SSK with low cost and low power. For decoding, we propose a correlation-based method that decodes tag data from the correlation between the original and backscatter OFDM symbols. We prototype and test SubScatter under 802.11g OFDM WiFi signals. Comprehensive evaluations show that our SubScatter outstands prior works in terms of effectiveness and robustness. Specifically, SubScatter has 743kbps throughput, 3.1× and 14.9× higher than RapidRider and MOXcatter, respectively. It also has a much lower BER under noise and interferences, which is over 6× better than RapidRider or MOXcatter.

This paper presents TiScatter, a timespan-based WiFi backscatter system that provides high-throughput communication with a single COTS receiver used. It outperforms the prior works that tradeoff between considerable data rate and practical deployment. To improve the data rate, TiScatter introduces a symbol-level times-pan modulation method that encodes tag data into the timespan between two modulated WiFi codewords in two successive WiFi packets. For decoding, TiScatter for the first time employs the injective feature between the checksum and the modulated codeword positions, which enables the demodulation of both the tag and original WiFi data using only one COTS receiver. This makes TiScatter more practical. Furthermore, we design TiScatter+ that shows these advantages while providing an even higher throughput under 802.11b excitations. We prototype our design, and comprehensive evaluations demonstrate that TiScatter shows a throughput over 100× higher than prior single-receiver backscatter systems like FS-Backscatter. It even has a better BER and throughput than the prior double-receiver backscatter systems like MOXcatter. Specifically, TiScatter provides 1) 2× higher peak throughput than MOXcatter and 2) an order of magnitude lower BER than MOXcatter with the presence of substantial interferences. In addition, TiScatter+ can deliver a throughput 3× higher than TiScatter under 802.11b ambient excitations. Our evaluation also confirms that TiScatter is generic and applicable to excitations under diverse WiFi standards (e.g., 802.11b/g/n).

This paper investigates space-aerial assisted mixed cloud-edge computing services for space-aerial integrated networks, where unmanned aerial vehicles (UAVs) provide edge computing services and one satellite (SAT) provides ubiquitous cloud computing services. To effectively and efficiently schedule such services under constraints on the available resources of computational capacity, energy, and communications of UAVs and the SAT, a problem for minimizing the total computing and offloading delay is formulated. A learning algorithm for handling the reformulated problem is proposed that alternatively performs convex optimization based computation capacity allocation (involving real variables) and double deep Q-learning (DDQL) based task assignment (involving binary variables) among all UAVs and the SAT. Extensive simulation results are presented to demonstrate that the efficacy of the proposed algorithm is significantly superior over some state-of-the-art reinforcement learning-based methods in terms of the algorithm running time and system scalability in the training stage and total computing and offloading delay in the testing stage.

With the expanding demands for the space-air-ground integrated Internet of Things (IoT), the low-Earth orbit (LEO) satellite can be regarded as an important complement to IoT networks. Due to the transparency of satellite orbit information and the exposing nature of transmitting links, satellite-ground communication is extremely vulnerable to eavesdropping and jamming attacks. To establish a transmission link, the most crucial procedure was signal acquisition. Existing frequency hopping/direct sequence signal acquisition algorithms were either too time costing for the short visibility window of the LEO satellite or too resource costing for the LEO satellite devices. To alleviate this issue, we propose a novel differential coherent accumulation acquisition strategy for the LEO satellite-enabled IoT network to strike a balance between performance and complexity. It is also demonstrated that the proposed acquisition strategy is capable of achieving high-performance signal acquisition in the low-carrier-to-noise ratio and large dynamic regions. Moreover, we derive and simulate false alarm probability, detection probability, computational complexity, and mean square error of both the delay and Doppler factors in the additive white Gaussian noise channel. Numerical simulation results show that the proposed acquisition strategy improves the performance by 1.6 dB over the noncoherent accumulation strategy but at the expense of 0.43% complexity increase.

With the development of the sixth-generation wireless communication technology, the difficulty of covert communication in the satellite communications system (SCS) is further increased. In this paper, a satellite covert communication system based on reconfigurable intelligent surface (RIS) is studied, which consists of a satellite, a terrestrial receiver, RIS, and a warden. Specifically, the ground terminal simultaneously receives the signal transmitted by the satellite and the signal reflected by RIS, while the warden wants to overhear the transmitted signal from the satellite and that reflected by the RIS. The main target of this wort is to maximize the minimum covert rate to improve signal quality while considering the limited onboard resources, the hardware constraints of RIS, and the requirement to implement covert communication. To address the non-convex problem, we propose an effective alternating optimization scheme by jointly optimizing transmitter precoding and RIS to improve the covert communication performance of the considered system. In addition to ideal RIS, we also consider the non-ideal RIS, the angle of which is discrete in the actual case. In addition, several simplified methods are proposed to decrease the computational complexity of the covert communication optimal problem under multiple terminals. Finally, simulation results are provided to verify the superiority of the proposed scheme compared with the benchmark scheme, which suggests that the considered RIS-based satellite-terrestrial covert communication system can significantly improve the achievable masking.

With the emerging concept of satellite mega-constellations (SatCons), connections are about to become faster and broader, building an engine to boost innovation and productivity for every industry. In the following decades, over a hundred of SatCons with enormous application potentials are on the cusp of deployment, and their long-term success hinges on reliable information exchanges. We identify the renascent multicarrier spread spectrum (MCSS) paradigm of sufficient spectral flexibility as the most effective and reasonable candidate for such issues. This article first sheds light on possible use cases for MCSS in SatCons, such as navigation-communication-telecontrol integration, physical-layer security, and intelligent anti-jamming. It also enlightens opportunities by virtue of the many benefits that MCSS may offer, and highlights major challenges encountered during its utilization. Finally, insights into future trends are provided. This article could support system designers in finding the right optimization priorities for future SatCons.

In this work, a reconfigurable intelligent surface (RIS)-based multi-input–multi-output (MIMO) covert communication system is considered and studied for Internet of Things (IoT) networks, while the joint optimization of precoder and RIS reflection phase is carried out to improve the covert communication performance. To solve this problem, we first derive the optimal signal-interference-to-noise ratio under covert communication and transmit power constraints. Then, we simplify the optimized function and use an iterative optimization algorithm to determine the optimal phase shift and precoding in continuous and discrete cases. Simulation results show that the RIS-aided MIMO covert communication system proposed in this article can significantly improve the invisibility of the implementation from Willie. In addition, for covert communication, the performance of the continuous phase shift case outperforms that of the discrete phase shift and fixed phase.

The Internet of Remote Things (IoRT) supported by low-Earth orbit (LEO) satellites is becoming indispensable for remote sensing and it will play an important role in the forthcoming sixth-generation (6G) communication network. In exploring its applications in remote mining and smart grid, etc., it is found that the implementation of IoRT faces challenges, including limited energy supplies, high-mobility, and security vulnerabilities. To address these challenges, we propose employing collaborative LEO satellites to enable the implementation of secure and green IoRT. By combining the uplink signals received at collaborative LEO satellites, the signal-to-noise ratio (SNR) can be significantly improved, so that relieving the transmit power requirement of the energy-limited terminal. Aiming at constructing a collaborative LEO satellite-based IoRT network, this article introduces the system design principles regarding to frequency planning, waveform selection, collaboration strategies, and terminal design. In order to obtain optimal collaboration performance, we propose a signal coherent combining scheme to compensate Doppler shift, propagation delay, and phase differences. Furthermore, we propose a modified SUMPLE algorithm to estimate and compensate phase differences among satellites, which is applicable to direct-sequence spread spectrum (DSSS) signal scheme. Simulation results demonstrate that our proposed algorithm outperforms the traditional SUMPLE algorithm in combining gain.

Nowadays, satellite and aerial platforms are playing an important role in realizing global seamless wireless coverage. In this paper, a cooperative satellite-aerial-terrestrial network (SATN) is considered, in which two kinds of relaying links, satellite and aerial relaying links, are used to assist a group of aerial terminals to forward their information to a remote terrestrial destination (D). Specifically, we model these aerial platforms sharing the same frequency band as a Matérn hard-core point process type-II. Also, a group of aerial jammers at D’s side is modeled as a Poisson point process. To demonstrate the end-to-end (e2e) performance of the two relaying links, the statistical characteristics of the received signal-to-interference are characterized and then a closed-form expression for the outage probability (OP) over the uplink from the aerial source to the satellite/the aerial relay, the downlink from the satellite/the aerial relay to D, and the inter-aerial relay link are derived. Numerical results are presented to verify the proposed analysis models and compare the outage performance of the considered cooperative SATN with the two relay links under numerous scenarios.

In this work, we study the covert communications in ambient backscatter systems (ABS) with uncontrollable RF source on AWGN channels. In contrast to the prior works impractically assuming the existence of acontrollableRF excitation source, our work arms the receiver of covert information with the full-duplex ability. The covert receiver can emit artificial noise (AN) with variable power to cover up the modulation action of a tag on the excitation signals while receiving the backscattered information. Specifically, we first derive the warden’s optimum power-detection threshold that minimizes the detection error probability. To against the optimal warden, we design the covert backscatter communication policy that determines the feasible region of the AN power at the covert receiver depending on the transmission power of the RF source and the reflection coefficient of the tag and guarantees the covertness constraint. We analyze the performance of the policy and provide the closed-form maximum covert rate and maximum detection error probability at the warden, revealing their tradeoff. The numerical analysis shows the increasing transmission power of the RF source and tag’s reflection coefficient would degrade the covertness of ABS when the covert receiver has to increase the AN power.

Satellite-aerial communication (SAC) is the most necessary and reliable way to bridge the aerial terminals with their remote command and control center when terrestrial relay infrastructure is unavailable. However, as an inescapable obstacle, adjacent space interference from neighboring satellites unavoidably leads to the loss of the performance of SACs. To uncover the impacts of space interference, in this paper, a SAC system including a satellite (S), an aerial platform (D), and a space interference (I) is considered. Specifically, S operates in a circular orbit and I, which is randomly distributed in the neighboring space around S, produces a jamming signal to interfere with the information delivery between S and D. Considering the randomness of S and I and employing geometric probability theory, an approximate and asymptotic outage probabilities of the considered SAC system are studied. Moreover, the multi-interference scenario is investigated accordingly. Finally, numerical results are presented to validate our proposed analysis and some insightful conclusions are achieved to reveal the effects of spatially random space interference on SAC.

Due to the considerable fading arising from the long-distance transmission between satellites and terrestrial terminals as well as the complex geographic environments on the earth, aerial relays have been designed to facilitate the information delivery between the satellites and terrestrial terminals when reliable direct data transmissions cannot be achieved. In this paper, a cooperative satellite-aerial-terrestrial network (CSATN) system including a satellite (S), an aerial relay (R), and a terrestrial receiver (D) is considered, while decode-and-forward relay scheme is adopted. Specifically, inspired by the beam shape of the transmission signal from S, we model the operation space of R as a circular truncated cone to accurately reflect the practical large-scale transmission scenarios, the height of which is decided by the upper and lower limitations of the operation height of R. Thus, considering the randomness of R and D and employing geometric probability theory, the exact and asymptotic outage probability (OP) of S-R, S-D, and R-D links is studied, and then the end-to-end outage performance of CSATN is investigated accordingly by using approximation method. Finally, numerical results are presented to validate our proposed analysis.

Due to the proliferation of mobile devices, provisioning of massive connectivity has become a major challenge for future networks. The combination of millimeter wave (mmWave) with non-orthogonal multiple access (NOMA) provides a promising solution to massive connectivity. However, the security issue therein cannot be ignored due to the openness of wireless channels. To overcome the security challenge in mmWave-NOMA based networks, the nonorthogonal interference can be exploited to improve the security. In this paper, we propose a novel mmWave-NOMA framework where the users are classified as secure users (SUs) and common users (CUs), to satisfy their heterogeneous security service needs with the presence of randomly located eavesdroppers. According to their channel disparity, the NOMA users with stronger channel gains are deemed as SUs for better secrecy performance, while the remaining ones are served as CUs. To further enhance the security, hybrid precoding for SUs is designed to strengthen the desired signal and reduce interference. In addition, to reduce the complexity and satisfy the diverse demands, user grouping and power allocation are jointly optimized to maximize the sum rate of CUs subject to the SUs’ requirements. To solve the intractable non-convex problem, we decompose it into two subproblems, i.e., user grouping and power optimization, and a hybrid SU-CU grouping algorithm and a successive convex approximation based algorithm are proposed to solve them, respectively. Finally, simulation results are provided to show the advantages of the proposed scheme.

Considering the security issue in mmWave-NOMA based networks, the nonorthogonal interference can be exploited to improve the security. In this paper, we propose a novel mmWave-NOMA framework where the users are classified as secure users (SUs) and common users (CUs), to satisfy their heterogeneous security service needs with the presence of ran-domly located eavesdroppers. For better secrecy performance, the NOMA users with stronger channel gains are deemed as SUs, and the hybrid precoding for SUs is designed to strengthen the desired signal and reduce interference. In addition, to reduce the complexity and satisfy the diverse demands, user grouping and power allocation are jointly optimized to maximize the sum rate of CUs subject to the SUs' requirements. The non-convex problem is decomposed into two subproblems, i.e., user grouping and power optimization, and a hybrid SU-CU grouping algorithm and a successive convex approximation based algorithm are proposed to solve them, respectively. Finally, simulation results are provided to show the advantages of the proposed scheme.

Caching popular contents at a large number of access points and edge-clouds is a promising solution to alleviate the increasing backhaul congestion in beyond fifth-generation (B5G) networks. By integrating with cell-free massive multiple-input multiple-output (CF mMIMO), wireless caching can harness their combined virtues, i.e., almost uniform service quality, strong macro-diversity, and reduction of the data traffic from the core network. In this paper, we consider an offline cache-aided scenario with two caching strategies to minimize the total energy consumption (TEC), which are evaluated from the cache hit probability (CHP). The TEC minimization is showed to be NP-complete and, hence, dealt with a proposed greedy algorithm. An adaptive power control policy is proposed to reduce the TEC. We compare CF mMIMO with small cells in terms of the successful content delivery probability (SCDP) and TEC, respectively. The numerical results show that CF mMIMO can offer a much more uniform service, significantly higher SCDP, and lower average TEC when compared to than SC.

In this article, we investigate the analog–digital hybrid transceiver optimization for distributed Internet-of-Things (IoT) sensing networks consisting of a multiantenna fusion center (FC) and several multiantenna sensor nodes. Analog–digital hybrid transceiver is an economic way to realize tradeoffs between hardware cost and performance for multiantenna communications. Under the nonconvex unit modulus constraints and transmit power constraint at each sensor, two synchronization schemes are considered for the hybrid linear minimum mean-square error (LMMSE) transceiver optimization. First, a centralized algorithm is proposed, in which the hybrid transceivers are computed at the FC. Based on the framework of alternating direction method of multipliers (ADMMs), the unit modulus constraints can be satisfied by projecting the elements of analog transceivers onto the unit modulus circle. However, the centralized algorithm usually suffers from strict synchronous requirements and high communication overhead. In order to accommodate the inevitable computing and communication delays in distributed IoT sensing networks, an asynchronous distributed ADMM (AD-ADMM) algorithm is proposed. By using the aged information, the hybrid transceivers are computed at the sensors without the coordination of the FC. Thus, the AD-ADMM algorithm can greatly reduce the computation overhead of the FC and improve the scalability of IoT sensing networks. Simulation results are presented to show that both the centralized ADMM and AD-ADMM algorithms perform closely to the fully digital counterpart.

A popular technique of designing multiple-input multiple-output (MIMO) communication systems relies on optimizing the positive semidefinite covariance matrix at the source. In this paper, a unified MIMO optimization framework based on the Karush-Kuhn-Tucker (KKT) conditions is proposed. In this framework, with the aid of matrix optimization theory,Theorem 1presents a generic optimal transmit covariance matrix for MIMO systems with diverse objective functions subject to various power constraints and different levels of channel state information (CSI). Specifically,Theorem 1fundamentally reveals that for a diverse family of MIMO systems, the optimal transmit covariance matrices associated with different objective functions under various power constraints can be derived in a unified generic water-filling-like form. When applyingTheorem 1to the case of multiple general power constraints, we firstly equivalently transform multiple power constraints into a single counterpart by introducing multiple weighting factors based on Pareto optimization theory. The optimal weighting factors can be found by the proposed modified subgradient method. On the other hand, for the imperfect MIMO system with statistical CSI errors, we firstly address the non-convexity of the robust optimization problem by following the idea of alternating optimization. Finally, our numerical results verify the optimal solution structure inTheorem 1and the global optimality of the proposed modified subgradient method, as well as demonstrate the performance advantages of the proposed alternating optimization algorithm.

Data aggregation is a promising technology in the Internet-of-Things (IoT) network for a wide range of applications, e.g., environmental monitoring, traffic control, and real-time surveillance. In order to meet the high requirement of transmission rate for data aggregation, we investigate the transceiver optimization to improve the spectral efficiency. As a tradeoff between the system complexity and performance, hybrid transceivers are adopted for data aggregation in the IoT network. We first present the optimal structures of digital precoders and unconstrained analog transceivers to maximize the spectral efficiency. Then, we propose two different kinds of iterative algorithms to optimize the analog transceivers under nonconvex unit-modulus constraints. The first algorithm is based on the framework of the alternating direction method of multipliers (ADMM). The second one is the steepest descent (SD) algorithm based on the Riemannian geometry, which has lower computational complexity than the first one. For both algorithms, closed-form solutions are derived in each iteration. Finally, numerical results demonstrate that the performance of the proposed algorithms in the hybrid transceiver design is very close to the fully digital solution but with less hardware complexity and power consumption.

Multi-hop communication with the aid of large-scale antenna arrays will play a vital role in future emergence communication systems. In this paper, we investigate amplify-and-forward based and multiple-input multiple-output assisted multi-hop communication, in which all nodes employ hybrid transceivers. Moreover, channel errors are taken into account in our hybrid transceiver design. Based on the matrix-monotonic optimization framework, the optimal structures of the robust hybrid transceivers are derived. By utilizing these optimal structures, the optimizations of analog transceivers and digital transceivers can be separated without loss of optimality. This fact greatly simplifies the joint optimization of analog and digital transceivers. Since the optimization of analog transceivers under unit-modulus constraints is nonconvex, a projection type algorithm is proposed for analog transceiver optimization to overcome this difficulty. Based on the derived analog transceivers, the optimal digital transceivers can then be derived using matrix-monotonic optimization. Numerical results obtained demonstrate the performance advantages of the proposed hybrid transceiver designs over other existing solutions.

In this work, we investigate in depth the two timescale robust system energy efficiency (EE) precoding design for the multiuser dual-polarized multiple-input multiple-output (MIMO) system. To achieve good performance, low feedback overhead as well as low implementation complexity, the dual-structured linear precoding scheme is adopted, which is based on the two timescale channel state information (CSI) and the dual-polarized antenna structure. The subgrouping technique, which is based on polarization, is also utilized to divide spatially grouped users into co-polarized subgroups to further reduce channel feedback overhead. The proposed robust system EE precoding design can achieve the maximization of the worst-case system EE, with the norm-bounded channel errors of all users. This robust EE optimization is naturally decomposed into two parts. In the first part, based on the polarized spatial correlation information, the block diagonalization is utilized to design the preprocessing matrix. In the second part, based on the relationship between the mean square error and the signal-to-interference-plus-noise ratio, the linear precoding matrix can be optimized by utilizing the sign-definiteness lemma and the fractional programming technique. Specifically, the corresponding nonconvex EE fractional optimization problem is converted to a series of semidefinite programming problems, which are solved by the convex optimization method efficiently. Simulation results indicate that the proposed two timescale based dual-structured precoding has many advantages on the robust system EE performance in the dual-polarized multiuser MIMO system.

In this paper, we propose a superimposed training based two-phase robust channel estimation scheme for the unmanned aerial vehicle (UAV) assisted cellular communication system, in which various unitarily-invariant channel statistics errors are considered. Specifically, in the first phase, mobile station (MS) estimates the UAV-MS channel via the UAV training sequence, of which the robust design can be solved based on convex-concave theory. While in the second phase, the superimposed training scheme is considered at the ground base station (GBS) to improve spectrum efficiency. Then the robust GBS training sequence, the information signal power and the UAV amplifying factor are jointly optimized for the partially cascaded GBS-UAV-MS channel estimation subject to GBS and UAV transmit power constraints as well as the required information signal strength at the MS. To tackle this NP-hard problem, the optimal structures of involved variables are firstly derived, based on which the robust superimposed training design is simplified and proved to be quasi-convex in the UAV amplifying factor. Particularly, for Spectral norm and Nuclear norm bounded errors, the optimal training sequence can be obtained via convex-concave theory and Golden section searchWhile for Frobenius norm bounded error, a tractable upper-bounding scheme is proposed for the robust superimposed training design. Furthermore, we extend our work into the more general probabilistic path loss scenario of UAV-ground channels, and analyze the impacts of the probabilistic path loss and RicianK-factor on channel estimation performance. Numerical results illustrate the excellent performance of the proposed superimposed training based two-phase channel estimation scheme.

We investigate the energy efficiency (EE) of multiple-input-multiple-output (MIMO) amplify-and-forward relaying networks relying on the realistic imperfect channel state information (CSI). Specifically, the relay jointly optimizes the source covariance and relay beamforming matrices by maximizing the EE under additive or multiplicative relay-destination CSI errors. The optimal channel-diagonalizing structure is derived for the source covariance and relay beamforming matrices under the spectral-norm constrained additive or multiplicative CSI error. Then, the existence of a saddle point is proved, which shows that the channel-diagonalizing transmission strategy is optimal in the robust EE maximization under these two types of CSI errors, and the original matrix-valued fractional robust EE problem is transformed into a scalar fractional problem. We propose the Dinkelbach method-based alternating optimization scheme for this transformed robust EE problem, which is capable of finding a locally optimal solution of the original robust EE problem efficiently, and show that the semi-closed-form solution to each of the two associated subproblems can be obtained. We then prove that the channel-diagonalizing transmission strategy remains optimal when the statistically imperfect source-relay channel is additionally imposed. We also extend our work into multi-hop MIMO relaying scenarios and prove that the channel-diagonalizing structure is optimal for the source covariance matrix and the multiple relay beamforming matrices.

The number of array elements significantly influences the computational complexity of space-borne phased array antenna systems. When the satellite is on-orbit, the performances of space-borne phased array antennas will be easily influenced by the environment and devices aging. Therefore, the space-borne antennas need to be regularly calibrated after satellite is launched. However, the complexity of calibration system increases in direct ratio to the radio frequency (RF) chain number of phased array antenna system. To solve the problem, a low complexity calibration method is proposed, whose system complexity is irrelevant to the number of array elements and calibration time is short. This method is especially suitable for large-scale space-borne phased array antennas. Simulation and experimental results of relative amplitude/phase inaccuracy probability under different SNR illustrate that when the value of SNR is higher than 16dB, the measurement accuracy of relative power (amplitude) and phase can reach ±0.1dB and ±1°, respectively. The simulation and experiment results also provide a significant basis to the link budget of the space-borne phased array antenna systems.

Joint Timing and Channel Estimation (JTCE) for bandlimited long-code-aided Multi-Carrier Direct-Sequence Code Division Multiple Access (MC-DS-CDMA) systems is investigated. We establish the optimal multiuser timing and channel estimates for the uplink MC-DS-CDMA receiver by minimising a weighted least squares cost function with respect to K independent parameters, where K is the number of active users. A guided random search procedure known as Repeated Weighted Boosting Search (RWBS) is invoked for numerically solving this challenging multivariate optimisation problem, and thereby for producing near-optimal timing and channel estimates. The Cramer-Rao Lower Bound (CRLB) for the JTCE problem of interest is derived to benchmark the performance of the proposed RWBS based estimator. Quantitatively, for the scenario of K=10 users, E_b/N_0≥3 dB where E_b is the energy per bit and N_0 the single-sided noise power spectral density, and for a near-far ratio of 10 dB, the RWBS based estimator using an observation window of 20 symbols is shown to approach the CRLB at a complexity 10 orders of magnitude lower in comparison to its full maximum likelihood search based counterpart. The proposed algorithm does not require the transmission of known pilots, yet it is capable of handling time-variant channel states.

This paper considers pilot-based parameter estimation for bandlimited MC-DS-CDMA systems relying on long spreading codes. Three different schemes are proposed and compared. The two so-called unstructured algorithms, namely the Least Squares Estimator (LS-E) and the Least Absolute Shrinkage and Selection Operator Estimator (LASSOE) first estimate the composite channel impulse response, and then extract the propagation delay, amplitude and phase. By contrast, the third algorithm namely the Structured LS Search Estimator (SLSS-E) exploits the a priori knowledge of the chip waveform and directly estimates the channel parameters. Parallel interference cancelation (PIC) is incorporated in the SLSS-E for the sake of mitigating the effect of multiple access interference and hence to further improve the performance. The complexity of PIC assisted SLSS-E and LS-E only increases linearly with the number of users, with the number of subcarriers and with the length of the pilot sequence. Simulation results indicate that the PIC assisted structured estimator outperforms its unstructured counterparts.

The code timing acquisition problem of bandlimited long-code based DS-CDMA systems operating in doubly-selective time-varying multipath channels is considered. We extend the unstructured schemes proposed by Buzzi to long-code spreading, which first estimates the Symbol Weighted Composite Channel Impulse Response (SW-CCIR) vectors and then extracts the timing informations. We also propose novel structured acquisition schemes, which directly estimate the multipath propagation delays by exploiting the a priori knowledge of the chip waveform. Both the structured and unstructured schemes are blind, since they require no other a priori information but the aperiodic spreading code of the desired user. Our numerical results demonstrate that the structured acquisition significantly outperforms both its unstructured counterpart and the classic correlator-based acquisition at a reasonable complexity increase.

We present a robust threshold setting strategy with modest computational complexity for frequency domain interference excision in direct sequence spread-spectrum (DSSS) commu-nication systems. The proposed strategy calculates the threshold by multiplying the minimum value of the averaged squared magnitude with a predefined scaling factor. An analytical framework for choosing the scaling factor is developed based on the principle of constant false-alarm rate (CFAR). Numerical results indicate that the new strategy outperforms existing ones in a wide range of partial-band jamming scenarios.