As a key component of the space-air-ground integrated network (SAGIN), low Earth orbit (LEO) satellites aim to provide global coverage and reliable services under high-speed mobile conditions, which are critically challenged by severe Doppler effect and inherent broadcast security threats. To address these issues, this paper investigates a multi-reconfigurable intelligent surface (RIS)-assisted orthogonal time frequency space (OTFS) downlink transmission system, where a LEO satellite serves multiple information receivers and potential eavesdroppers acting as energy receivers via simultaneous wireless information and power transfer (SWIPT). By jointly optimizing multi-dimensional resource variables, such as transmit beamforming and RIS reflection coefficients of the spatial domain, and the symbol scheduling matrix of the time-frequency domain, this paper aims to maximize the sum secrecy rate while satisfying constraints on satellite transmit power, the legitimate users’ quality of service, and energy-harvesting requirements. Given the high-dimensional, non-convex, and NP-hard nature of this problem, we develop an enhanced actor-critic deep reinforcement learning (DRL) framework. The core innovation lies in designing an episodic return-prioritized experience selection mechanism with online mixing, which significantly improves the sampling efficiency and policy stability by intelligently selecting training data. Simulation results demonstrate that the proposed approach outperforms existing schemes in achieving a higher sum secrecy rate, providing a practical and highly efficient resource scheduling solution for building secure and reliable next-generation SAGIN.

Massive multiple-input multiple-output (MIMO) systems with one-bit analog-to-digital converters (ADCs) face a severe trade-off between hardware efficiency and channel estimation accuracy. While generative adversarial networks (GANs) show promise for this challenge, their deployment is hindered by training instability and mode collapse. To address these issues, we propose ReRpGAN, a novel adversarial learning framework that integrates a regularized relativistic pairing GAN loss and anL1loss within a deep residual network. This architecture effectively stabilizes the training process and prevents mode collapse, enabling precise channel reconstruction from severely quantized signals. Extensive experiments on a realistic ray-tracing channel dataset validate our theoretical claims. Key findings demonstrate that ReRpGAN consistently outperforms conventional GAN-based and deep learning estimators, particularly in challenging scenarios with low signal-to-noise ratios and limited pilot overhead. Furthermore, unlike existing methods that suffer from divergence, ReRpGAN exhibits superior scalability, delivering improved estimation accuracy as the number of base station antennas increases. This work sets a new benchmark for robust, data-driven channel estimation in next-generation wireless systems.

Satellite communications (SatCom) face fundamental security challenges due to their inherent broadcast nature. To address this, we exploit beyond-diagonal reconfigurable intelligent surface (BD-RIS) to unleash its full-scattering matrix control for enhanced secure beamforming flexibility in SatCom with rate-splitting multiple access (RSMA), where the satellite attempts to convey private signals to legitimate users with blocked direct downlinks and multiple eavesdroppers. To maximize the worst-case secrecy rate among legitimate users, a max-min fairness (MMF) problem is formulated with imperfect wiretap channel state information (CSI) via joint precoding, RIS configuration, and rate splitting optimization. By using the block coordinate descent (BCD) method, these optimization variables are decoupled with different subproblems and solved by the penalty dual decomposition (PDD) method iteratively. Furthermore, we develop a computationally efficient suboptimal solution that employs diagonal RIS (D-RIS) with reduced hardware and computational complexity, where alternating optimization (AO) and successive convex approximation (SCA) methods are employed to solve the non-convex problem. Simulation results demonstrate that our proposed BD-RIS-RSMA scheme achieves significant performance improvements compared to baseline schemes, while the suboptimal diagonal RIS scheme offers a favorable performance-complexity tradeoff.

Multi-reconfigurable intelligent surfaces (RIS) has recently gained significant interest as emerging technology for exploiting Intelligent electromagnetic environment. Inspired by opportunistic communications, a low-complexity adaptive selection and training method for multi-RISs is proposed in this paper. Firstly appropriate number of the multi-RISs is selected to assist communication. The elements of the selected RIS are grouped, and the grouped elements share a common coefficient to reduce training overhead. Secondly, the communication performance is evaluated and other RIS will be selected to assist communication if the communication performance not meet user requirement. In this way, the system performance and the training complexity of multi-RISs can be trade-off efficiently. Simulation results show that the proposed scheme outperforms the state-of-the-art benchmarks in terms of training overhead and robustness in different channel condition, and the training overhead is 30% lower compared to the centralized deployment scheme proposed in [13].

In the context of future 6G networks, reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA) are emerging as pivotal technologies for enhancing signal quality and eliminating coverage blind spots. This paper addresses the issue of secure and resilient transmission in RISassisted NOMA systems. Specifically, the base station transmits private signals to multiple legitimate users while dealing with the threat of potential eavesdropping. To model this challenge, we optimize the beamforming vectors and the RIS phase-shift matrix to maximize the sum secrecy rate while satisfying the user quality of service (QoS) requirements and the power constraints of the base station. Since the problem involves high-dimensional variables and non-convex objective functions, it is difficult to be solved by traditional optimization methods. Therefore, the twin delayed deep deterministic policy gradient algorithm (TD3) based on deep reinforcement learning (DRL) is proposed in this paper to effectively address the complexity of the original problem. Numerical results show that the proposed scheme exhibits satisfactory performance in improving communication security, transmission efficiency, and resistance to channel errors.

Aiming to circumvent the low energy efficiency (EE) dilemma of multiple-input multiple-output (MIMO) systems induced by employing hundreds of antennas, this paper investigates the potentials of stacked metasurface (SM) and semantic communications (SemCom) for achieving energy-efficient holographic communications in MIMO systems. Specifically, SM enables hybrid beamforming with increased degrees of freedom (DoFs) and reduced energy consumption, while SemCom transmits dramatically compressed key informantion that comes with low power consumption and high EE. To this end, we formulate a worstcase semantic EE (Sem-EE) maximization problem in terms of the transmit beamformer and SM's phase shifts. By proposing a semantic majorization-minimization to handle the fractional and quasi-convex Sem-EE form, quadratically constrained quadratic programs and cyclic coordinate descent can be exploited to solve the optimization variables with low computational complexity. Numerical simulations demonstrate the enhanced EE performance of SMaided semantic beamforming scheme compared to the conventional MIMO systems.

Index modulation based frequency hopping spread spectrum (IM-FHSS) has shown the attractive anti-jamming capability. With the aid of jamming detection, IM-FHSS becomes efficient to defend various malicious jamming attacks, especially the reactive jamming. In this paper, we propose two jamming detection approaches for IM-FHSS to efficiently detect reactive jamming. Specifically, the pilot symbols for channel estimation are used in the first detection approach, where a single frame to calculate the optimal test statistic and make the decision. Second, the proposed approach II collects the pilot symbols from many data frames to detect the reactive jamming. Moreover, we theoretically derive the closed-form expressions of the optimal thresholds and the probabilities of detection, and provide the complexity comparison. Simulation results validate the correctness of our performance analysis and show that the proposed approaches outperform the prior detection schemes.

Integrated sensing and communication (ISAC), which can exploit the wireless spectrum for concurrent sensing and communication functions, is regarded as a promising technology for the future sixth generation (6G) wireless communication networks. This paper proposes a robust beam tracking method for maneuverable unmanned aerial vehicles (UAVs) with jittering effect within the ISAC framework. By utilizing reflected echoes, the kinematic parameters are measured and the interacting multiple model with extended Kalman filter (IMM-EKF) is designed for robust beam tracking of maneuverable UAVs. Furthermore, due to the jittering effect, the UAV may not point to the optimal alignment direction. To this end, we propose the coordinate descent particle swarm optimization (CDPSO) algorithm to balance the jittering effect by maximizing the received signal-to-noise ratio (SNR) of the UAV. The effectiveness of the proposed scheme is verified via simulation results.

Abstract Traffic prediction serves as a critical foundation for traffic balancing and resource management in Low Earth Orbit (LEO) satellite networks, ultimately enhancing the efficiency of data transmission. The self‐similarity of traffic sequences stands as a key indicator for accurate traffic prediction. In this article, the self‐similarity of satellite traffic data was first analyzed, followed by the construction of a satellite traffic prediction model based on an improved Long Short‐Term Memory (LSTM). An early stopping mechanism was incorporated to prevent overfitting during the model training process. Subsequently, the Diebold‐Mariano (DM) test method was applied to assess the significance of the prediction effect between the proposed model and the comparison model. The experimental results demonstrated that the improved LSTM satellite traffic prediction model achieved the best prediction performance, with Root Mean Squared Error values of 18.351 and 8.828 on the two traffic datasets, respectively. Furthermore, a significant difference was observed in the DM test compared to the other models, providing a solid basis for subsequent satellite traffic planning.

Malicious jamming attacks pose a significant threat to the integrity and performance of Internet of Things (IoT) networks. However, many jamming patterns are rare or infrequent, which makes them difficult to counter effectively. This article addresses the critical issue of anti-jamming (AJ) under few-shot sample conditions in IoT networks. Source separation is a key component of AJ communication. Although deep learning-based source separation has demonstrated significant advantages, it typically requires a large amount of labeled data, which can be impractical in certain environments. To overcome this challenge, we propose two novel schemes that leverage multitask learning (MTL) and meta-learning (ML) to enhance the model’s signal separation capabilities within the constraints of limited sample scenarios. MTL enhances robustness by leveraging shared representations across tasks, while ML allows for rapid adaptation to novel jamming signals with minimal samples. Specifically, we employ a modified separation model, SepFormer, as our baseline and integrate MTL and ML schemes to enable the separation of unknown or few-shot jamming signals. Additionally, we have constructed two datasets encompassing both simulated and real-world environmental data to test and evaluate the performance of the proposed methods. Simulation results demonstrate the superior AJ performance of our schemes, particularly when compared with a direct application of the separation model with few-shot samples. Furthermore, our evaluation of performance across various jamming scenarios and interference-to-signal ratios (ISRs) further confirms the effectiveness of our proposed scheme.

This article focuses on the serious threat to security of key-based physical-layer authentication (PLA) by an eavesdropper using an elaborate impersonation attack, which aims to pass the authentication process illegally. To prevent the eavesdropper from decreasing the authentication performance, we propose a robust channel-phase-based PLA scheme for multicarriers transmission, which contains a novel two-level decision. Specifically, the first level decision is used to protect legitimate users from high-receiver power caused by the elaborate attack of the eavesdropper, and the second one is used to further authenticate the user. The optimal threshold for accurately detecting the response signal with high-receiver power is derived. Moreover, we provide the theoretical performance analysis for the proposed scheme, and derive the closed-form expressions of the probability of detection and false alarm via the numerical statistic and the proper approximation. Simulation results show the robustness of our proposed scheme and verify the effectiveness of the theoretical analysis.

To provide services with various statistical delay quality-of-service (QoS) constraints and minimum performance limitations in future satellite networks, this paper proposes a hybrid access scheme for the satellite networks, where the satellite can transmit with users either in non-orthogonal multiple access (NOMA) or orthogonal multiple access (OMA) mode. Taking user’s statistical delay QoS constraints into consideration, the effective capacities of two modes are investigated and a maximization problem is formulated by jointly considering multiple access selection and power allocation strategy. Specifically, this jointly problem is decomposed into three steps: Firstly, with K-means based Gaussian mixture model algorithm, users are classified into different clusters according to their delay exponents. Based on which, a roughly multiple access suggestion and three user pairing strategies are proposed, by taking the achieved performance with OMA scheme as constraints. Then, users, who are potentially served in NOMA mode, are carefully decided whether the NOMA model is suitable by a deep neural network. Finally, to find the near optimal power allocation factor for each NOMA group, a deep reinforcement learning (DRL) with varying bounds is proposed, with which the lower and upper action space bounds can be flexible varied according to reward, to optimize the power allocation scheme in the considered system. Numerical simulations are provided to show the impacts of statistical delay QoS requirements on users’ achievable effective capacities, the minimum performance on the superiority of pure NOMA scheme, the user pairing strategy on achieved performance, and the proposed method on further enhancing convergence rate as well as achievable performance in satellite networks.

Physical-layer key generation (PKG) technology offers a lightweight encryption solution for device-to-device (D2D) communications. However, it faces significant challenges due to the high correlation between the eavesdropping and legitimate channels, as well as the imperfect estimated channel, which cause substantial degradation of secret key capacity. To address the challenges, we propose a novel PKG framework that leverages the reflective beamforming of reconfigurable intelligent surface (RIS) for D2D communications. Specifically, we first derive closed-form expression for the secret key capacity under correlated and imperfect estimated channels. Then, we propose to maximize the minimum secret key capacity by optimizing the reflection coefficient matrix (O-RCM) of RIS, which is a non-convex optimization problem. Next, a semi-definite relaxation and successive convex approximation-based method for O-RCM (SSO-RCM) is proposed to tackle the non-convex max-min-min problem. To further reduce the computational complexity, we propose a low-complexity method based on the path-following algorithm for O-RCM (PFO-RCM). Simulation results show that under correlated and imperfect channels, both proposed methods significantly improve the minimum secret key capacity compared to the existing RIS-assisted methods. Moreover, the PFO-RCM method, while exhibiting lower computational complexity than the SSO-RCM method, incurs a small performance loss.

To achieve stringent performance requirements in next generation wireless networks, such as ultra-high data rates, ubiquitous connectivity, and extremely high reliability, this paper proposes a radically novel rate splitting assisted dual-polarized stacked metasurface (RS-DPSM) transceiver architecture. In this architecture, a multi-layer dual-polarized metasurface is stacked at the active antennas and its two inherent polarizations are implemented to enable RS’s common and private messages in parallel. In sharp contrast to the conventional multiple-input multiple-output (MIMO) and metasurface-based transceiver designs, our proposed transceiver is capable of enhancing the channel capacity and introducing multi-dimensional degrees of freedom (DoFs) in the power, spatial, and polarization domains, thus enabling multi-functional, broad-spectrum, and all-time/domain/space communications without requiring massive radio-frequency (RF) chains. In addition, we derive new analytical expressions for the upper bounds of RS-DPSM transceiver’s channel capacity and ergodic sum rate, and provide some key insights. To highlight its potential benefits, we apply the proposed RS-DPSM transceiver to anti-jamming communications, and formulate a generalized sum rate maximization problem under the jammer’s imperfect angular channel state information and unknown cross-polarization discrimination. To enable an efficient resource management under the above practical conditions, we present a low-complexity optimization framework by leveraging the discretization method, properties of the quadratic function, reduced-majorization-minimization algorithm, and block successive upper-bound minimization, which admit the semi-closed-form solutions. Finally, our numerical simulations verify the superiority of our proposed transceiver architecture and optimization framework over key benchmarks.

Hostile jamming and eavesdropping threats bring severe challenges to reliable and secure communication demands of future networks. In light of the potentials of high-altitude platform (HAP) providing wide communication coverage with low cost and Ad-hoc network facilitating flexible access without support by hardware infrastructure, this paper proposes a multi-HAPs assisted air-terrestrial integrated Ad-hoc networks (HAIN) framework to defend against jamming and eavesdropping simultaneously. Specifically, the HAPs align the beamformer to the terrestrial users while nullifying the reception of eavesdropper. In addition, the Ad-hoc network enables cooperative anti-jamming transmission, where the cooperative users (CUs) provide communication assistance by forming anti-jamming coalition for blocked users (BUs). Building upon this framework, we aim to maximize the sum rate of BUs by jointly optimizing the beamforming and cooperative coalition formation with the imperfect channel state information (CSI). To handle the intractable problem, we first convert the imperfect CSI into the worst-case one, and then a sequential convex approximation combined with first order Taylor series expansion is proposed to optimize the beamforming. Furthermore, for the optimization of anti-jamming coalition formation, we reformulate it as the coalition formation game (CFG) and a partial best coalition preference order is put forward to enhance the sum rate of BUs. With the help of exact potential game (EPG), it’s proved that the CFG can converge to stable coalition formation by exploiting the proposed distributed anti-jamming coalition formation algorithm. Simulation results demonstrate that the proposed scheme has the superior secure transmission performance to benchmark schemes.

Due to the users' overlapping channels and the open nature of the wireless medium, inter-user interference and malicious jamming attacks deteriorate the performance of unmanned aerial vehicle (UAV) communications. With this focus, this paper proposes a novel integration of dual polarization, rate-splitting multiple access (RSMA), and stacked reconfigurable intelligent surface (RIS) transceiver into UAV networks, thus simultaneously mitigating the inter-user interference and malicious interference by fully exploiting their potentials in the power, space, and polarization domains. Building upon this architectural framework, a generalized sum rate maximization problem is formulated under the jammer's imperfect angular channel state information and unknown cross-polarization discrimination. To efficiently tackle the challenges posed by the intractable non-convex design problem with both high-dimensional variables and the multiple QoS constraints, a low-complexity optimization framework is presented, where a discretization method combined with quadratic property, a reduced-majorization-minimization algorithm, and two computationally efficient algorithms using block successive upper-bound minimization are developed to obtain the semi-closed-form solutions. Finally, numerical simulations verify the superiority and validity of our proposed architecture and optimization framework over benchmarks.

This paper investigates the pilot assignment and power control problems for secure UAV communications in cell-free massive MIMO network with the user-centric scheme, where numerous distributed access points (APs) simultaneously serve multiple UAVs and terminal users. Meanwhile, there exists one UAV acting as an eavesdropper which can perform pilot spoofing attack. Considering a mixture of Rayleigh and Ricean fading channels, the APs respectively perform MMSE estimation and distributed conjugate beamforming for uplink training and downlink data transmission. Using random matrix theory, the closed-form expression for a tight lower bound on the achievable secrecy rate is derived, which enables the impact analysis of key parameters, such as power, antenna configuration, UAV height, etc. Taking into account both performance and complexity, a novel pilot assignment scheme is proposed by combining weighted graphic framework and genetic algorithm, which can actualize global search with limited iterations. The max-min power control with security constraints is then studied in parallel, which can not only enhance the network fairness but also ensure the security. Accordingly, successive convex approximation and fractional optimization are jointly utilized to solve this non-convex problem. Simulation results numerically verify the analytical results and indicate the superiority of the proposed pilot assignment and power control schemes.

Satellite and high-altitude platform (HAP) are considered as the key parts of the next generation networks, and specifically for the Internet-of-Things networks, which are utilized to provide unobstructed connections and massive user access for terrestrial networks. In this article, we investigate the power allocation (PA) and system performance of nonorthogonal multiple access (NOMA)-enabled integrated satellite-HAP-terrestrial systems under practical limitations. Particularly, a practical system model is established by considering the channel estimation errors and imperfect successive interference cancelation at the receiver. To achieve the different quality of service requirements among multiple served users, we propose a novel NOMA-based PA scheme. In addition, the analytical and asymptotic expressions for the outage probability of NOMA users are obtained to verify the proposed scheme as well as the ergodic capacity. Finally, numerical results are corroborated with Monte Carlo simulations, which show the correctness of our analytical results, and the benefits of our proposed scheme. The proposed scheme indicates that the HAP relay link plays a significant role in the system performance.

Orthogonal time frequency space (OTFS) has attracted a lot of attention as a feasible waveform applied in joint communication and radar (JCR) systems in contrast to orthogonal frequency division multiplexing (OFDM) waveform. To explore the advantages of OTFS waveform, first, a unified matrix (UM) expression is summarized by utilizing discrete fractional Fourier transform (DFrFT), and then a novel OTFS waveform based on UM expression is investigated in this article. The fractional order parameters of the proposed UM-OTFS waveform is set to the same values during preprocessing and Heisenberg transformation stages, and the UM-OTFS waveform can be converted into other waveform forms by undergoing different fractional order parameters. In addition, a three-stage sensing parameter estimation algorithm is developed for target velocity and range estimation through grid partitioning, coarse and fine estimation. Meanwhile, a low-complexity fractional zero force (ZF) or minimum mean square error (MMSE) equalizer based on lower-upper (LU) decomposition (LU-ZF/MMSE) is presented, which results in a log-linear order of complexity without any performance degradation of bite error ratio (BER) by analyzing sparsity and quasi-banded structure of the equivalent matrix. The simulation results indicate the superiority of the proposed UM-OTFS waveform in terms of sensing parameter estimation and BER performance compared with several advanced waveforms.

The reconfigurable intelligent surface (RIS) has attracted significant research interests recently due to its abilities of dynamic channel reconstruction, flexible deployment and reduced power consumption. However, a malicious RIS can introduce serious signal degradation and even interception risk. This article investigates destructive beamforming design from the perspective of a malicious RIS, where the RIS is active and able to amplify the reflected signals from the base station (BS) to an Internet of Things Device (IoTD). We consider two scenarios where the BS is known and unknown to the identity of malicious RIS, and the objective is to minimize the received signal-to-noise ratio (SNR) at the IoTD with the constraints of total power budget and RIS signal amplification. To solve the above nonconvex optimization problem, we first propose a low-complexity scheme by integrating several classical beamforming methods with the Taylor expansion approach to solve the original problem for the case of known malicious RIS at BS. While for the unknown malicious RIS case, we propose an alternating optimization scheme by using the successive convex approximation method to obtain the beamforming vector and reflection coefficient matrix iteratively. Finally, numerical results verify that, through the proposed destructive beamforming design, the RIS only brings pain without gain for the signal reception.

Sparse code multiple access (SCMA) and mobile edge computing (MEC) are two promising technologies for future Internet of Things (IoT) networks. SCMA enables large-scale connections, while MEC brings computing resources closer to user devices, resulting in faster response time and improved user experiences through task offloading. In this article, we investigate a large-scale grant-free (GF) SCMA enhanced MEC network. First, we propose the offloading protocol for the GF-SCMA enhanced MEC framework and describe the task offloading process using GF-SCMA in detail. Then, we model and analyze the performance of this network, deriving closed-form solutions for the offloading probability and SCMA ergodic rate using stochastic geometry. Additionally, we apply queueing theory to examine the impact of GF-SCMA on task latency and energy consumption in the MEC network. The accuracy of the theoretical expressions is confirmed by simulation results, demonstrating that SCMA outperforms orthogonal multiple access (OMA) in terms of increasing offloading probability and ergodic rate, as well as reducing task delay and energy consumption. Furthermore, this advantage becomes more pronounced with higher user density and task generation rate. Through parameter comparison, it is seen that increasing the pilot and codebook number of GF-SCMA can improve the performance of the proposed scheme in practical implementations.

Due to the threat of various jamming attacks in wireless communications, the efficient and general anti-jamming schemes are required to guarantee communication the quality of wireless networks. To this end, this paper proposes the efficient index modulation based frequency hopping spread spectrum (IM-FHSS) scheme. Unlike the classical IM-FHSS which only considered the reactive jamming, the efficient IM-FHSS scheme extends the anti-jamming capacity to various jamming models. Specifically, we firstly derive the closed-form expressions of bit error rate (BER) performance of the IM-FHSS under three jamming models in an additional white gaussian noise (AWGN) channel and a Rayleigh fading channel, which enables the design of anti-jamming strategies. Then, the unified anti-jamming framework is provided for designing the efficient anti-jamming system. Based on the framework, we further provide the strategies of the efficient IM-FHSS for reactive jamming, constant and random jamming. Simulations show that the theoretical derivations match well with the simulated results, which validates the performance analysis. Moreover, the efficient IM-FHSS has demonstrated the superior anti-jamming performance for three typical jamming models, which is also energy-efficient and reliable for achieving targeted bit error rate (BER) performance.

Mobile edge computing-assisted integrated aerial-ground network (MEC-IAGN) emerges as a promising key component of the sixth-generation (6G) wireless networks due to its potential capabilities in providing ubiquitous connectivity for global coverage and computing services. However, the inevitable existences of computation-intensive tasks, uncontrollable propagation environment, and malicious jamming attacks pose three significant bottlenecks for enabling efficient MEC-IAGN. With these focuses, we propose a novel framework of multi-functional reconfigurable intelligent surface (MF-RIS) aided semantic anti-jamming communication and computing in MEC-IAGN. Under this framework, a semantic transceiver exhibits inherent robustness and data compression capability, and MF-RIS can customize the full-space wireless environment by leveraging its signal reflection, refraction, amplification, and energy harvesting functions, thereby achieving substantial global coverage, reliable connectivity, and high-rate computing. Based on our proposed framework, we formulate a semantic computation rate maximization problem considering the impacts of jammer’s channel state information (CSI) imperfection, while maintaining the energy partition constraint for computation offloading decision, semantic similarity requirement, semantic computation rate target, and MF-RIS’s self-sustainability. Then, by transforming the imperfect CSI into a worst-case one by exploiting a discretization method, we propose a fast-converging monotonic optimization algorithm that is combined with decoupling second-order cone programming to obtain a globally optimal solution with fewer feasibility evaluations. Furthermore, to strike a satisfactory tradeoff between performance and computational complexity, we develop a suboptimal generalized power iteration algorithm. Numerical simulations demonstrate the superiority of our proposed framework and algorithms compared to various benchmarks.

Aiming to circumvent the severe large-scale fading and the energy scarcity dilemma in high-altitude platform (HAP) networks, this paper investigates the benefits of the reconfigurable intelligent surface (RIS) and simultaneous wireless information and power transfer (SWIPT) on HAP communications. Specifically, we propose a concept of multi-layer refracting RIS-assisted receiver to achieve concurrent transmission of the information and energy, which is conducive to overcoming the severe fading effect induced by extreme long-distance HAP links and fully exploits RIS’s degrees-of-freedom (DoFs) for the SWIPT design. Based on the RIS-enhanced receiver, we then formulate a worst-case sum-rate maximization problem by considering the channel state information (CSI) error, the information rate requirements, and the energy harvesting constraint. To handle the intractable non-convex problem, a scalable robust optimization framework is proposed to obtain semi-closed-form solutions. Specifically, a discretization method is adopted to convert the imperfect CSI into a robust one. Then, by utilizing the LogSumExp inequality to smooth the objective and constraints, we develop a dual method to obtain the optimal solution for the HAP transmit precoder. In addition, a modified cyclic coordinate descent (M-CCD) is adopted to update the block-wise RIS coefficients. Moreover, closed-form solutions for power splitting (PS) ratios and the receive decoder are derived. Finally, the asymptotic performance of our proposed RIS-enhanced receiver is provided to reveal the substantial capacity gain for HAP communications. Numerical simulations demonstrate that the proposed architecture and optimization framework are capable of achieving superior performance with low complexity compared to state-of-the-art schemes in HAP networks.

Integrating sparse code multiple access (SCMA) and mobile edge computing (MEC) into the Internet of Things (IoT) networks can enable efficient connectivity and timely computation for resource-limited IoT users. This paper studies the computation rate maximization problem under task deadline constraints in dynamic SCMA-MEC networks. Specifically, we propose a predictive deep Q-network for SCMA resource allocation and computation offloading (PQ-RACO) algorithm for single-cell scenarios, where IoT devices use long short-term memory (LSTM) networks to predict the states and actions of other agents. However, the PQ-RACO algorithm is not scalable for increasing numbers of IoT devices. To address this issue, an improved multi-agent deep Q-network for SCMA resource allocation and computation offloading algorithm (MQ-RACO) is proposed for multi-cell scenarios. The algorithm is a centralized training and decentralized execution (CTDE) multi-agent reinforcement learning (MARL) algorithm with explicit rewards, which is tailored to the special structure of joint rewards. Simulation results demonstrate that the proposed algorithm outperforms several state-of-the-art MARL algorithms and other benchmark schemes in terms of convergence speed and computation rate.

Recently, symbol level jamming (SLJ) has been proposed and studied as an effective jamming technique. However, the detection performance estimation and efficient detection schemes for SLJ have not been investigated in the literature. This paper proposes a novel active detection scheme for SLJ to enhance the detection capability of the legitimate user, where the detection performance estimation and target detection performance can be actively obtained. Specifically, based on a preset tolerant threshold of the bit error rate (BER) and necessary inputs, the threatening jamming power level (TJPL) for SLJ is obtained for the legitimate user, and then the TJPL is used for active performance estimation and adaption. We first derive the theoretical detection performance for QPSK modulated SLJ and noise modulated SLJ. Moreover, the TJPL required for detection is derived based on BER expressions and analytical results. Thereafter, two modes of active detection scheme are discussed, aiming to actively estimate the detection performance and obtain expected detection performance, respectively. Simulation results show that the active detection scheme can accurately estimate the jamming detection performance. Additionally, it can achieve expected detection performance, which adapts well to different preset thresholds, SNR regions and the modulation types.

The utilization of a large-scale antenna array has led to substantial performance improvements in anti-jamming communications. However, due to the practical constraints of hardware cost and power consumption, deploying such a large-scale antenna array at the user side is impractical. Inspired by the remarkable advantages of reconfigurable intelligent surfaces (RIS), we propose an active-passive cascaded RIS-aided receiver architecture that facilitates the cost- and energy-efficient deployment of a large-scale antenna array at the user side, while also providing additional degrees-of-freedom for effective beamforming design. Building upon this architectural framework and taking into account the practical imperfections in the angular channel state information (CSI), we formulate a worst-case achievable rate maximization problem for anti-jamming communications. To address the challenges posed by the intractable non-convex design problem, we present a low-complexity optimization framework that obtains semi-closed-form solutions. Specifically, we first develop a Pareto-dual scheme to handle the general power constraints in devising the optimal precoder for the base station. Subsequently, by introducing a novel anti-jamming criterion and employing the discretization method to transform the imperfect CSI of jammers into a robust form, we derive two jamming-nulling feasibility conditions and a unified unit-modulus zero-forcing scheme to determine the coefficients of the passive RIS. To strike a satisfactory balance between complexity and performance, we further design three computationally-efficient algorithms based on alternating majorization-minimization (AMM) and conventional/modified cyclic coordinate descent (C/M-CCD) methods to obtain the coefficients of the active RIS. Finally, through comprehensive numerical simulations, we validate the effectiveness of the proposed architecture and optimization framework, demonstrating their capacity to achieve exceptional performance in a cost-effective manner.

To mitigate the severe large-scale fading and the energy scarcity problem in long-distance high-altitude platform (HAP) networks, in this paper, we investigate the potentials of a multi-layer refracting reconfigurable intelligent surface (RIS) -assisted receiver for enabling simultaneous wireless information and power transfer (SWIPT) in HAP networks. Unlike the existing RIS-aided reflector and transmitter, the multi-layer RIS-receiver can well overcome the severe “double fading” effect induced by the extreme long-distance HAP links and fully exploit RIS's degrees-of-freedom (DoFs) for SWIPT design. Building on the proposed RIS-receiver, this paper formulates a worst-case sum rate maximization problem under angular channel state information (CSI) imperfection, while satisfying the information rate requirements of the earth stations (ESs) and the harvested energy constraint. To handle the intractable non-convex problem, a scalable robust optimization framework utilizing the discretization method, LogSumExp-dual scheme, and modified cyclic coordinate descent (M-CCD) is proposed to obtain the semi-closed-form solutions. Numerical simulations demonstrate that the proposed architecture and optimization framework achieve superior performance with lower complexity compared with state-of-the-art schemes in HAP networks.

This paper investigates the covert millimeter wave (mmWave) communication in the finite block-length regime, where spatially random wardens attempt to determine the presence of transmission. First, we derive a novel expression of covertness constraint by using the tools of stochastic geometry, based on which the expression of average effective covert throughput (AECT) is also presented. Then, considering the constraint of maximal available block-length, the optimization problem for maximizing the AECT is formulated, and the optimal transmit power and block-length are analytically determined. Our results show the superiority of our optimization in terms of AECT in contrast to the fixed block-length case, and the improvement is more significant when the density of wardens becomes large. Furthermore, the performance of covert mmWave communication can indeed be improved via increasing the number of antennas even there exist random distributed wardens.

The use of a large-scale antenna array has achieved significant performance gains in anti-jamming communications. However, due to the hardware cost and power consumption constraints, it is impractical to deploy such large-scale antenna array at the user side. Inspired by the remarkable advantages of reconfigurable intelligent surface (RIS), we propose an active-passive cascaded RIS-aided receiver architecture, which facilitate the deployment of a large-scale antenna array at the user side in a cost- and energy-efficient way and provides additional degree-of-freedom for beamforming design. Building upon this architecture and considering the practical angular channel state information (CSI) imperfection, a worst-case achievable rate maximization problem is formulated for anti-jamming communications. To handle the non-convex problem, a low-complexity optimization framework is proposed, where the new anti-jamming criterion, Pareto-dual scheme, unified unit-modulus zero-forcing scheme, and conventional-cyclic coordinate descent algorithm are developed to obtain the semi-closed-form solutions. Finally, numerical simulations verify that the proposed architecture and optimization framework are capable of achieving excellent performance with low complexity.

Satellite-terrestrial integrated networks (STINs) are troubled with the serious jamming threats in the counterwork environment. Non-orthogonal multiple access (NOMA) approach can not only improve the resource utilization by resource sharing, but also has the potential advantages to be used for anti-jamming. In this paper, under the threat of smart jammer with adaptive jamming policies, we investigate the NOMA-based anti-jamming problem in multi-cell STINs by jointly considering the NOMA-based user grouping in each cell and the beam allocation among multiple cells. Specifically, for each cell, the users can enhance anti-jamming performance and improve the sum rate by NOMA-based users grouping, which is formulated as the anti-jamming Stackelberg game and grouping game to obtain the equilibrium solutions. Then, an adaptive beam allocation algorithm with a low complexity is proposed to avoid allocation conflicts and achieve fairness among multiple cells. Finally, simulation results prove the performance of the proposed scheme.

Satellite-enabled Internet of Things (IoT) (SatIoT) has drawn increasing attentions due to the ubiquitous coverage, high capacity and massive connectivity. The inherent openness and broadcast nature of the SatIoT are vulnerable to security threats, particularly the jamming attacks for interrupting transmissions. Nonorthogonal multiple access (NOMA) scheme has the potential to be applied in anti-jamming communication for SatIoT due to the characteristic of resource sharing. The severely jammed users can get more allocated power by forming NOMA groups with other users, and both parties can improve the spectrum efficiency by frequency sharing. In this article, we aim to improve the performance of sum rate for SatIoT under the jamming environments. An anti-jamming transmission scheme is developed by jointly considering the NOMA-based user grouping and the power allocation (PA) for each NOMA group. Specifically, the users can enhance anti-jamming performance and improve the sum rate by NOMA-based users grouping, which is formulated as an anti-jamming coalition formation game, and the equilibrium solution is proved by the exact potential game theory. Moreover, in order to further improve NOMA performance, we derive the PA solution for multiuser NOMA by considering the imperfect successive interference cancellation. Finally, simulation results briefly highlight some details of the proposed approaches.

With the increasing demand of high data rate and massive access in both ultra-dense and industrial Internet-of-things networks, spectral efficiency (SE) and energy efficiency (EE) are regarded as two important and inter-related performance metrics for future networks. In this paper, we investigate a novel integration of rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) into cellular systems to achieve a desirable tradeoff between SE and EE. Different from the commonly used passive RIS, we adopt reflection elements with active load to improve a newly defined metric, called resource efficiency (RE), which is capable of striking a balance between SE and EE. This paper focuses on the RE optimization by jointly designing the base station (BS) transmit precoding and RIS beamforming (BF) while guaranteeing the transmit and forward power budgets of the BS and RIS, respectively. To efficiently tackle the challenges for solving the RE maximization problem due to its fractional objective function, coupled optimization variables, and discrete coefficient constraint, the formulated nonconvex problem is solved by proposing a two-stage optimization framework. For the outer stage problem, a quadratic transformation is used to recast the fractional objective into a linear form, and a closed-form solution is obtained by using auxiliary variables. For the inner stage problem, the system sum rate is approximated into a linear function. Then, an alternating optimization (AO) algorithm is proposed to optimize the BS precoding and RIS BF iteratively, by utilizing the penalty dual decomposition (PDD) method. Simulation results demonstrate the superiority of the proposed design compared to other benchmarks.

Dual-function radar and communication (DFRC) has recently drawn significant attention due to its enormous potential. This letter deals with waveform design of DFRC to improve target detectability embedded in clutter environment while guaranteeing the service quality of communication users. Our design objective is to maximize the output signal-to-clutter-plus-noise ratio (SCNR) of multiple-input multiple-output (MIMO) radar, subject to worst-case received symbol errors at communication users. Coordinate descent (CD) as an efficient iteration algorithm is proposed to solve above optimization problem, which splits high-dimensional problem into multiple one-dimensional problem. Furthermore, we introduce Dinkelbach algorithm (DA) to increase rate of convergence, which is an efficient way to reduce complexity. Finally, simulation results are presented to illustrate the effectiveness of the proposed techniques.

This paper investigates a joint transmissive and reflective reconfigurable intelligent surfaces (RIS) -aided secure multiple-input multiple-output (MIMO) system, where both a RIS-assisted transmitter and a RIS-based reflector are deployed to defend against the simultaneous jamming attack and wiretapping threat. Our design focuses on maximizing the sum rate under the unknown jammer’s beamforming, joint RISs’ coupled phase shift errors (PSEs), and spatially-correlated angular channel uncertainties. Besides, we take into account the various quality-of-service (QoS) requirement constraints for guaranteeing the secure performance. Since the problem is non-convex and mathematically intractable, a new optimization framework is established to facilitate the solution development to the formulated problem. Specifically, armed with the Akaike information criterion, a novel diagonalization method is first proposed to estimate the unknown jamming covariance matrix. Then, a series of fractional-eliminated rate expressions is derived that facilitates the application of the proposed Double Deterministic Transformation (DDT) to tackle the coupled stochastic PSEs. Besides, regardless of the spatial correlation matrix, a general discretization method is proposed to convert the e spatially-correlatd angular uncertainties into a worst-case robust one. Subsequently, building upon the above transformations which transform the original problem into tractable one, a two-layer iterative Lagrange multiplier algorithm capitalizing a low-complexity dual method is proposed to obtain the globally optimal solution of the digital precoder, where the multiple QoS constraints are handled without iteration. Meanwhile, we develop a novel polyblock-based multiple penalty method to obtain the globally optimal solutions to RISs’ phase shifts which can simultaneously satisfy the multiple QoS constraints. Moreover, to address the narrow feasibility region induced by the multiple QoS constraints, a heuristic initial optimization method is proposed, which strengthens the existing result. Finally, theoretical analysis and numerical results demonstrate the optimality and the excellent performance of our proposed optimization framework.

This paper investigates the performance of non-orthogonal multiple access (NOMA) based hybrid satellite-unmanned aerial vehicle (UAV) systems, where a low Earth orbit (LEO) satellite communicates with the ground users via a decode and forward (DF) UAV relay. We investigate a two NOMA users system, where a far user (FU) and a near user (NU) are served by the UAV which is located at a certain height above the origin of the coverage circle. The channel between satellite and UAV is assumed to follow a Shadowed-Rician fading and the channels between UAV and users are assumed to follow a Nakagami-m fading. New closed-form expressions of the outage probabilities for the two users and the system are derived. Different from other work in literature, we take into consideration different parameters affecting the total link budget. Additionally, we propose an algorithm for minimizing the system outage probability. The mathematical analysis is verified by extensive representative Monte-Carlo (MC) simulations. Finally, simulations are provided to demonstrate the impact of important parameters on the considered system as well as the superiority of the NOMA scheme the over reference scheme.

This paper investigates the secure communication in reconfigurable intelligent surface aided cell-free massive MIMO system in the presence of active eavesdropping, where the access points (APs) are equipped with low resolution analogto-digital/digital-to-analog converters (ADCs/DACs). Specifically, the additive quantization noise model (AQNM) is explored to capture the impacts of coarse ADCs/DACs. To reduce the channel estimation overhead, all APs employ a minimum mean-squared-error channel estimator which estimates the aggregated channel including both the direct and indirect channels. With the available imperfect channel state information (CSI), conjugate beamforming and random beamforming are applied at the APs and the RISs for downlink data transmission. Moreover, the closed-form expression of the achievable ergodic secrecy rate is derived which can be used to evaluate the impact of the number of the APs, the number of the RISs and the ADCs/DACs resolution on the systems performance. Finally, numerical and simulation results are presented to verify the analytical results.

Nonorthogonal multiple access (NOMA) and cognitive integrated satellite terrestrial relay networks are the promising and key part for the next-generation wireless networks. This article researches the joint effects of channel estimation errors (CEEs) and hardware impairments on the secrecy performance of cognitive integrated satellite terrestrial relay networks. The noncolluding eavesdropping scheme is applied in the multiple eavesdroppers, where the eavesdropper with the highest eavesdropping capacity is selected to overhear the legitimate transmission signal. Moreover, the detailed analysis for the secrecy outage probability (SOP) is obtained based on the utilized partial terrestrial relay selection strategy. To obtain the insightful conclusions, the asymptotic analysis along with the secrecy coding gain and secrecy diversity order for the SOP are further derived, which gives the effective methods to valuate the impacts of CEEs and hardware impairments on the considered system with the NOMA scheme in high signal-to-noise ratio regime. Moreover, simulations are derived for the secrecy energy efficiency. Finally, Monte Carlo simulations are given to prove the correctness of the theoretical SOP analysis.

Sparse code multiple access (SCMA) is a kind of code-domain nonorthogonal multiple access (NOMA) scheme, which can support the increasing requirements for high spectral efficiency and massive connections. Meanwhile, multiaccess edge computing (MEC) is a promising technology for providing resource-constrained users with computing resources. In this article, we propose a novel optimization scheme in the SCMA-based MEC network from the perspective of energy and latency for the Internet of Things (IoT) devices. Specifically, a system utility is first used to calculate the weighted energy consumption and task execution latency. The initial utility minimization problem is nonconvex and then can be subdivided into two tractable subproblems by fixing task offloading decisions, namely, optimal local computing via CPU frequency scheduling and optimal edge computing via the SCMA codebook assignment, subcarrier power allocation, and MEC server computing resources distribution. Primarily, a joint SCMA codebook assignment based on the bidirectional matching principle and optimal power allocation algorithm is proposed. Moreover, we come up with CPU frequency scheduling strategies utilizing convex optimization to optimize the computing resources allocation (CRA) of local devices and the MEC server. Finally, a low-complexity task offloading policy based on simulated annealing is presented. Numerical results show that our proposed joint optimization algorithm for resource allocation and task offloading can achieve a good compromise between time delay and energy consumption for IoT devices. It is demonstrated that the proposed strategy has a remarkable advantage compared to the previous SCMA-MEC schemes.

Malicious jamming attacks have been regarded as a serious threat to Internet of Things (IoT) networks, which can significantly degrade the Quality of Service (QoS) of users. This article utilizes an intelligent reflecting surface (IRS) to enhance anti-jamming performance due to its capability in reconfiguring the wireless propagation environment via dynamically adjusting each IRS reflecting elements. To enhance the communication performance against jamming attacks, a robust beamforming optimization problem is formulated in a multiuser IRS-assisted anti-jamming communications scenario with or without imperfect jammer’s channel state information (CSI). In addition, we further consider the fact that the jammer’s transmit beamforming can not be known at BS. Specifically, with no knowledge of jammers transmit beamforming, the total transmit power minimization problems are formulated subject to the outage probability requirements of legitimate users with the jammer’s statistical CSI, and signal-to-interference-plus-noise ratio requirements of legitimate users without the jammer’s CSI, respectively. By applying the decomposition-based large deviation inequality, Bernstein-type inequality, Cauchy–Schwarz inequality, and penalty nonsmooth optimization method, we efficiently solve the initial intractable and nonconvex problems. Numerical simulations demonstrate that the proposed anti-jamming approaches achieve superior anti-jamming performance and lower power-consumption compared to the non-IRS scheme and reveal the impact of key parameters on the achievable system performance.

The integration of aerial platforms to provide ubiquitous coverage and connectivity for densely deployed terrestrial networks is expected to be a reality in the emerging sixth-generation networks. Energy-effificient and secure transmission designs are two important components for integrated terrestrial-aerial networks (ITAN). Inlight of the potential of reconfigurable intelligent surface (RIS) for significantly reducing the system power consumption and boosting information security, this paper proposes a multi-layer RIS-assisted secure ITAN architecture to defend against simultaneous jamming and eavesdropping attacks, and investigates energy-efficient hybrid beamforming for it. Specifically, with the availability of imperfect angular channel state information (CSI), we propose a block coordinate descent (BCD) framework for the joint optimization of the user’s received decoder, the terrestrial and aerial digital precoder, and the multi-layer RIS analog precoder to maximize the system energy efficiency (EE) performance. For the design of the received decoder, a heuristic beamforming scheme is proposed to convert the worst-case design problem into a min-max one and facilitate the developing a closed-form solution. For the design of the digital precoder, we propose an iterative sequential convex approximation approach via capitalizing the auxiliary variables and first-order Taylor series expansion. Finally, a monotonic vertex-update algorithm with a penalty convex-concave procedure (P-CCP) is proposed to obtain the analog precoder with satisfactory performance. Numerical results show the superiority and effectiveness of the proposed optimization framework and architecture over various benchmark schemes.

Wireless communications are increasingly vulnerable to simultaneous jamming and eavesdropping attacks due to the inherent broadcast nature of wireless channels. With this focus, due to the potential of reconfigurable intelligent surface (RIS) in substantially saving power consumption and boosting information security, this paper is the first work to investigate the effect of the RIS-assisted wireless transmitter in improving both the spectrum efficiency and the security of multi-user cellular network. Specifically, with the imperfect angular channel state information (CSI), we aim to address the worst-case sum rate maximization problem by jointly designing the receive decoder at the users, both the digital precoder and the artificial noise (AN) at the base station (BS), and the analog precoder at the RIS, while meeting the minimum achievable rate constraint, the maximum wiretap rate requirement, and the maximum power constraint. To address the non-convexity of the formulated problem, we first propose an alternative optimization (AO) method to obtain an efficient solution. In particular, a heuristic scheme is proposed to convert the imperfect angular CSI into a robust one and facilitate the developing a closed-form solution to the receive decoder. Then, after reformulating the original problem into a tractable one by exploiting the majorization-minimization (MM) method, the digital precoder and AN can be addressed by the quadratically constrained quadratic programming (QCQP), and the RIS-aided analog precoder is solved by the proposed price mechanism-based Riemannian manifold optimization (RMO). To further reduce the computational complexity of the proposed AO method and gain more insights, we develop a low-complexity monotonic optimization algorithm combined with the dual method (MO-dual) to identify the closed-form solution. Numerical simulations using realistic RIS and communication models demonstrate the superiority and validity of our proposed schemes over the existing benchmark schemes.

Due to the emergence of non-terrestrial platforms with extensive coverage, flexible deployment, and reconfigurable characteristics, the hybrid satellite-aerial-terrestrial networks (HSATNs) can accommodate a great variety of wireless access services in different applications. To effectively reduce the transmission latency and facilitate the frequent update of files with improved spectrum efficiency, we investigate the performance of cache-enabled HSATN, where the user retrieves the required content files from the cache-enabled aerial node (AN) or the satellite with the non-orthogonal multiple access (NOMA) scheme. If the required content files of the user are cached in the AN, the cache-enabled node would serve directly. Otherwise, the user would retrieve the content file from the satellite system, where the satellite system seeks opportunities for proactive content pushing to ANs during the user content delivery phase. Specifically, taking into account the uncertainty of the number and location of ANs, along with the channel fading of terrestrial users, the outage probability and hit probability of the considered network are, respectively, derived based on stochastic geometry. Numerical results unveil the effectiveness of the cache-enabled HSATN with the NOMA scheme and proclaim the influence of key factors on the system performance. The realistic, tractable, and expandable framework, as well as associated methodology, provide both useful guidance and a solid foundation for evolved networks with advanced configurations in the performance of cache-enabled HSATN.

Satellite-based Internet of Things (IoT) is recognized as a cost-effective approach for global access. In this article, we aim at improving the spectrum efficiency of satellite systems to serve a huge number of IoT devices. To this end, we present a cognitive satellite-terrestrial framework, where a multibeam satellite system with full frequency reuse shares the spectrum with terrestrial networks based on the underlay paradigm. Considering DVB-S2X recommendations, geometric configurations, and channel characteristics, we investigate a robust multigroup multicast beamforming design for the satellite-based IoT coexisting with terrestrial networks in the presence of a phase error on channel state information, and characterize the achievable rate region under the outage probability constraint for the terminal and the power consumption constraint for the satellite. Based on the concept of rate profile, an associated optimization problem is formulated to design robust beamformers and determine the Pareto boundary of the region. To solve the intractable problem, we propose a two-level iterative algorithm on the basis of joint bisection search and penalty function enabled nonsmooth optimization. In particular, we develop a Bernstein-type inequality aided method and a large deviation inequality aided method to obtain a tractable and conservative approximation for the probabilistic constraint, respectively. Numerical results are provided to confirm the validity and superiority of our proposed scheme over the existing approaches and reveal the impact of key parameters on the achievable system performance.

In this article, we investigate the multichannel cooperative spectrum sharing in hybrid satellite-terrestrial Internet of Things (IoT) networks with the auction mechanism, which is designed to reduce the operational expenditure of the satellite-based IoT (S-IoT) network while alleviating the spectrum scarcity issues of terrestrial-based IoT (T-IoT) network. The cluster heads of selected T-IoT networks assist the primary satellite users transmission through cooperative relaying techniques in exchange for spectrum access. We propose an auction-based optimization problem to maximize the sum transmission rate of all primary S-IoT receivers with the appropriate secondary network selection and corresponding radio resource allocation profile by the distributed implementation while meeting the minimum transmission rate of secondary receivers of each T-IoT network. Specifically, the one-shot Vickrey-Clarke-Groves (VCG) auction is introduced to obtain the maximum social welfare, where the winner determination problem is transformed into an assignment problem and solved by the Hungarian algorithm. To further reduce the primary satellite network decision complexity, the sequential Vickrey auction is implemented by sequential fashion until all channels are auctioned. Due to incentive compatibility with those two auction mechanisms, the secondary T-IoT cluster yields the true bids of each channel, where both the nonorthogonal multiple access (NOMA) and time division multiple access (TDMA) schemes are implemented in cooperative communication. Finally, simulation results validate the effectiveness and fairness of the proposed auction-based approach as well as the superiority of the NOMA scheme in secondary relays selection. Moreover, the influence of key factors on the performance of the proposed scheme is analyzed in detail.

With the help of a power-domain non-orthogonal multiple access (NOMA) scheme, satellite networks can simultaneously serve multiple users within limited time/spectrum resource block. However, the existence of channel estimation errors inevitably degrade the judgment on users' channel state information (CSI) accuracy, thus affecting the user pairing processing and suppressing the superiority of the NOMA scheme. Inspired by the advantages of machine learning (ML) algorithms, we propose an improved support vector machine (SVM) scheme to reduce the inappropriate user pairing risks and enhance the performance of NOMA based satellite networks with imperfect CSI. Particularly, a genetic algorithm (GA) is employed to optimize the regularization and kernel parameters of the SVM, which effectively improves the classification accuracy of the proposed scheme. Simulations are provided to demonstrate that the performance of the proposed method is better than that with random user paring strategy, especially in the scenario with a large number of users.

A multiuser large-scale MIMO system with antenna correlation and mutual coupling is investigated in this paper. Based on the maximum signal-to-interference-plus-noise ratio (SINR) criteria, the optimal beamforming (BF) vector at the base station (BS) for each user is first obtained using statistical channel state information (CSI). Then, a closed-form expression for the achievable sum rate is derived in terms of a finite number of generalized Meijer-G functions, which is applicable to an arbitrary number of array elements and/or users, and provides an efficient means of evaluating the system performance. Finally, numerical results are provided to confirm the validity of the theoretical analysis and show the impact of various channel parameters on the system performance.

This study investigates the secrecy performance of a hybrid satellite‐terrestrial relay network (HSTRN) in the presence of multiple eavesdroppers, where the satellite link undergoes Shadowed‐Rician fading, while the terrestrial link follows Rayleigh fading. The authors suppose the direct link between the satellite and the intended user is unavailable due to heavy shadowing and adopt a multi‐antenna relay using either a decode‐and‐forward (DF) or an amplify‐and‐forward (AF) protocol to assist the transmission. By employing perfect channel state information of each link at the relay, the authors first apply receive maximal ratio combining beamforming (BF) and transmit zero‐forcing BF schemes to obtain the output signal‐to‐noise ratios (SNRs) of the intended user and eavesdroppers. Then, based on the Meijer‐G function and the moment generating function, the authors derive the analytical expressions of the ergodic secrecy rate for the considered HSTRN for both DF and AF protocols. Finally, Monte‐Carlo simulations are conducted to validate the theoretical performance analysis and reveal the effects of certain representative parameters on the system secrecy performance.

This paper investigates the outage performance of cognitive broadband satellite systems and terrestrial cellular network in millimeter wave (nunWave) scenario. Considering the state-of-art standard recommendations and nun Wave propagation model, we first define a general framework for the coexistence of broadband satellite system and terrestrial cellular networks with distinct geometry, configuration, and channel characteristics. Then, by employing a interference temperature constraint at the fixed satellite service (FSS) receiver to limit the interference below an acceptable level, closed-form expression for the outage probability (OP) of the cognitive cellular user is derived, which is general and applicable to various channel parameters and geometric scenarios. Eventually, simulation results are carried out to verify the theoretical derivations, and shows the impact of key system parameters on the performance of the terrestrial cellular user with the coexistence of FSS.

The integration of cognitive radio (CR) into satellite networks is recognized as an effective strategy to enhance the efficiency of radio spectrum. This paper investigates the ergodic capacity of a multiple antenna cognitive satellite terrestrial network, where the secondary terrestrial system can coexist with the primary satellite system as long as the interference imposed from the secondary user (SU) to the primary user (PU) is below a predefined threshold. Specifically, the Meijer-G function based analytical expression for the ergodic capacity of the secondary network is derived, which not only provides an efficient means to evaluate the system performance but also characterize the impact of various channel parameters on the network. Finally, simulation results are provided to demonstrate the validity of the theoretical analysis.

This paper investigates the physical layer security of a satellite network, whose downlink spectral resource is shared with a terrestrial cellular network. We propose to employ a multi-antenna base station (BS) as a source of green interference to enhance secure transmission in the satellite network. By taking the mutual interference between these two networks into account, we first formulate a constrained optimization problem to maximize the instantaneous rate of the terrestrial user while satisfying the interference probability constraint of the satellite user. Then, with the assumption that imperfect channel state information (CSI) and statistical CSI of the link between the BS and satellite user are available at the BS, we present two beamforming (BF) schemes, namely, hybrid zero-forcing and partial zero-forcing to solve the optimization problem and obtain the BF weight vectors in a closed form. Moreover, we analyze the secrecy performance of primary satellite network by considering two practical scenarios, namely: Scenario I, the eavesdroppers CSI is unknown at the satellite and Scenario II, the eavesdroppers CSI is known at the satellite. Specifically, we derive the analytical expressions for the secrecy outage probability for Scenario I and the average secrecy rate for Scenario II. Finally, numerical results are provided to confirm the superiority of the proposed BF schemes and the validity of the performance analysis, as well as demonstrate the impacts of various parameters on the secrecy performance of the satellite network.

The integration of cooperative transmission into satellite networks is regarded as an effective strategy to increase the energy efficiency as well as the coverage of satellite communications. This paper investigates the performance of an amplify-and-forward (AF) hybrid satellite-terrestrial relay network (HSTRN), where the links of the two hops undergo Shadowed-Rician and Rayleigh fading distributions, respectively. By assuming that a single antenna relay is used to assist the signal transmission between the multi-antenna satellite and multi-antenna mobile terminal, and multiple interferers corrupt both the relay and destination, we first obtain the equivalent end-to-end signal-to-interference-plus-noise ratio (SINR) of the system. Then, an approximate yet very accurate closed-form expression for the ergodic capacity of the HSTRN is derived. The analytical lower bound expressions are also obtained to efficiently evaluate the outage probability (OP) and average symbol error rate (ASER) of the system. Furthermore, the asymptotic OP and ASER expressions are developed at high signal-to-noise ratio (SNR) to reveal the achievable diversity order and array gain of the considered HSTRN. Finally, simulation results are provided to validate of the analytical results, and show the impact of various parameters on the system performance.