The satellite-aerial-terrestrial network (SATN) is a promising architecture to achieve seamless global coverage and meet diverse quality-of-service (QoS) requirements in next-generation wireless communications. To support such multi-layered connectivity, we consider a mixed radio frequency (RF) and free-space optical (FSO) architecture, where heterogeneous users access a high-altitude platform (HAP) via RF links, and the HAP, acting as an aerial relay, forwards the aggregated traffic to a satellite through an FSO backhaul. Existing transmission schemes for such mixed RF-FSO SATNs, however, are not well suited to providing differentiated statistical QoS guarantees for heterogeneous users. To address this limitation, we propose a mixed RF-FSO QoS-aware uplink transmission (MRQ-UT) scheme. Specifically, we impose statistical delay-QoS constraints at both the user and HAP buffers, thereby explicitly capturing heterogeneous constraints on queueing delay and buffer overflow. On this basis, we derive the system effective capacity using a two-stage tandem queue model, which captures the sequential queuing behavior over the RF access and FSO backhaul links. Building upon this model, we develop a tractable effective-capacity-based optimization framework and propose a statistical channel-aware joint power and beamforming algorithm that enhances QoS provisioning under imperfect channel state information. Simulation results demonstrate that the proposed MRQ-UT scheme significantly outperforms benchmark schemes in terms of effective capacity and statistical QoS performance.

ABSTRACT In this paper, we investigate an unmanned aerial vehicle (UAV) enabled secure multicast communication system, where a UAV serves multiple legitimate ground users in the presence of multiple coordinated eavesdroppers. Taking into account the inherent jittering characteristics of UAVs caused by the airflow, we aim to maximise the worst‐case secrecy energy efficiency (SEE) under a constrained UAV transmission power budget. The formulated optimization problem is inherently non‐convex and challenging to solve due to the combined effects of jittering uncertainties and the max–min fractional structure of the SEE metric. To address these challenges, we first simplify the original SEE maximization problem by introducing auxiliary variables. Next, considering the impact of jittering on the antenna array response, we develop a novel second‐order Taylor series expansion‐based approach to approximate beamforming gains as quadratic functions of the angle‐of‐departure errors, which can be subsequently transformed into deterministic convex constraints by using S ‐Procedure. Based on these theoretical results, we design an iterative algorithm that combines the penalty function method with the successive convex approximation to efficiently obtain a suboptimal solution. Finally, the simulation results demonstrate the effectiveness and superiority of the proposed scheme compared to several benchmark schemes, highlighting its potential for practical implementation in UAV‐enabled secure multicast communication systems.

This paper investigates a highly spectrally efficient transmission scheme in an integrated satellite and aerial network (ISAN). Specifically, we first propose a novel uplink access framework, where the co-design of communication and over-the-air computation (AirComp) is implemented through layer division multiplexing (LDM) in the aerial network, while the cognitive radio-inspired non-orthogonal multiple access (CR-NOMA) technology is employed in the satellite network. Then, according to the proposed framework, we mathematically formulate a joint optimization problem that aims at maximizing the system achievable sum rate, subject to the constraints of minimal accuracy requirement of AirComp and minimal quality-of-service requirements of communication service. Next, by introducing the inter-network interference-related auxiliary variable, we divide the original optimization problem into two subproblems associated with the optimization of the satellite and aerial networks. To tackle the first subproblem, we propose a beamspace-inspired analog beamforming (BF) method, and derive closed-form expressions for BF vectors and transmit powers to implement the CR-NOMA scheme in the satellite network. Meanwhile, to address the second subproblem, we propose a beamspace-inspired digital BF together with successive convex approximation and alternating optimization approaches, to obtain the BF matrices, transmit power coefficients and AirComp scaling factor, so that the LDM-based communication and computation co-design (CCCD) can be realized in the aerial network. Moreover, for complexity reduction, we propose a beamspace-inspired zero-forcing BF method to calculate the communication BF matrices, and then leverage the orthogonal beam superposition approach to obtain the computation BF matrix, thereby presenting another CCCD scheme. Finally, our simulation results confirm that since the proposed schemes can realize spectrum multiplexing for communication and AirComp services, we achieve higher system spectral efficiency and lower computation error than the benchmarks.

This paper proposes a robust decentralized beam-forming (BF) scheme for downlink non-orthogonal multiple access (NOMA) transmission in an integrated satellite and aerial network (ISAN) to reduce both power consumption and signaling overhead. By employing the imperfect channel state information (CSI) and the imperfect successive interference cancellation (SIC), we formulate an optimization problem to minimize the total transmit power, subject to the rate requirements of both satellite and aerial terminals, and the transmit power budget of satellite and aerial platforms. To address this complex problem, we adopt S-procedure to transform the nonconvex constraints into convex ones and then propose a decentralized BF algorithm using Lagrange duality to obtain the satisfactory solutions in an efficient way. Finally, simulation results demonstrate that our proposed scheme can achieve a similar performance but at a lower signaling overhead as compared with the centralized BF method, and confirm the superiority of the proposed scheme in terms of power consumption over other existing works.

This paper investigates a location-based downlink transmission scheme to provide diverse services for different users in an integrated satellite-terrestrial network (ISTN). Specifically, the satellite network employs multicast communication to disseminate information to multiple satellite users, while the terrestrial network incorporates non-orthogonal multiple access (NOMA) with intelligent reflecting surface (IRS) technology to serve terrestrial users. Given that the location information-based channel state information (LoI-CSI) of each user is available, we formulate an optimization problem to minimize the outage probability (OP) of the terrestrial network by optimizing the transmit power and beamforming (BF) weight vector at the base station, the IRS phase shift vector, and the power allocation factor, while meeting the quality-of-service (QoS) requirement of the satellite network. To make the optimization problem tractable, we first propose a low-complexity BF algorithm based on the LoI-CSI, which simplifies the optimization problem while guaranteeing the QoS requirement of the satellite network. Then, assuming that terrestrial links experience Rician fading, we derive an approximate yet accurate OP of the terrestrial network, which is explored to calculate the phase shift vector. Furthermore, we propose a novel power allocation method that employs an exponential-type approximation of the first-order Marcum Q-function, to obtain the power allocation coefficient. Finally, simulation results confirm the theoretical formulas’ validity and reveal the proposed algorithms’ superiority in system performance.

In this paper, satellite-terrestrial assisted multi-tier computing networks (STMTCN) are proposed to satisfy the growing computation demands of user terminals (UTs) in next generation wireless networks. In the STMTCN, UT’s computation task can be processed at different computing entities and a multi-tier computation model named computing depth is proposed to better reflect the multi-tier computing process. Then, we formulate a weighted sum energy consumption minimization problem via jointly optimizing UT-satellite association, computing depth, multiple-input multiple-out (MIMO) precoding, and computation resource allocation. The non-convex optimization problem is decomposed into four subproblems, each of which is solved iteratively. Specifically, the UT-satellite association subproblem is solved by quadratic transform based fractional programming and Lagrangian dual method and a closed-form expression is obtained. The computing depth for local tier and the satellite tier is solved respectively with first-order Taylor expansion. Then, MIMO precoding subproblem for UT and satellite offloading is solved by quadratic transform and interior point method (IPM). Finally, the computation resource allocation for UT and satellite is obtained in a closed-form expression and the GW computation resource allocation is solved by using IPM. Simulation results show that the proposed STMTCN and algorithms can fulfill the UT’s computing demands with low energy consumption.

Driven by the ubiquitous sensing demands, integrated sensing and communication (ISAC) is viewed as an essential technology in future networks. In this paper, we investigate a multiple ISAC-enabled user terminal (UT) system that multi-antenna UTs perform radar sensing and communicate with the BS at the same time. Then, we formulate a multi-UT sum rate maximization problem by jointly considering UT's maximum transmit power and minimum radar signal-to-clutter plus interference and noise ratio (SCINR) requirements. To solve the transmit precoding optimization problem, we first handle the non-convex rate function with weighted minimum mean-squared error method. Then, we use first-order Taylor expansion to deal with the minimum radar SCINR constraints. At last, we propose an iterative optimization algorithm to solve the problem. Simulation results verify the effectiveness of our proposed design.

This paper investigates a semi-grant-free (SGF) access scheme for satellite communication systems, where one earth station (ES) and many mobile terminals (MTs) desire to access the satellite network simultaneously via uplink non-orthogonal multiple access (NOMA). We first propose two NOMA assisted SGF transmission schemes according to the availability of different channel state information (CSI). When perfect CSI is available, the satellite broadcasts an instantaneous access threshold to all MTs to enable dynamic power control (DPC), thus guaranteeing the quality of service of the ES. Otherwise, with the introduction of a statistical access threshold, a novel DPC with probability constraint is proposed to fulfill a satisfactory transmission. Next, we propose a new probability density function to characterize the shadowed-Rician distribution with estimation error, with which closed-form system throughput expressions under the proposed schemes are derived. Finally, computer simulations validate the effectiveness and superiority of our analysis and reveal the impact of CSI errors on the system performance.

This paper investigates a low-complexity downlink transmission to provide diverse services for different users in an integrated satellite-terrestrial network (ISTN). Specifically, given that the location information-based channel state information (LoI-CSI) of each user is available, we formulate an optimization problem to minimize the outage probability (OP) of the terrestrial network by jointly optimizing the transmit power and beamforming (BF) weight vector at the base station (BS), and the phase shift vector at the intelligent reflecting surface (IRS), while meeting the quality-of-service (QoS) requirement of the satellite network. To make the optimization problem tractable, we first use the LoI-CSI and propose a low-complexity BF algorithm to obtain the transmit power and BF weight vector. Then, employing IRS and non-orthogonal multiple access (NOMA) in the terrestrial network, we derive its closed-form expression for the OP of the terrestrial network, which is explored to calculate the IRS phase shift vector. Finally, simulation results confirm the validity of the theoretical formulas and reveal the proposed algorithms superiority in system performance.

This paper proposes a downlink transmission scheme for intelligent reflecting surface (IRS) enhanced cognitive-satellite-aerial-network to support massive access of Internet-of-Things devices (IoTDs). By sharing the same frequency band with satellite network, the aerial network offers services for IoTDs having line-of-sight links through space division multiple access, and for IoTDs locating in blocked area via IRS-enhanced non-orthogonal multiple access. Assuming that only the imperfect channel state information is available, we formulate a transmit power minimization problem subject to the probabilistic constraints of the quality-of-service requirements for IoTDs, the co-channel interference power limitation, and unit-modulus requirement for IRS. To tackle this mathematically intractable problem, we propose a generalized zero-forcing based low-complexity robust transmission algorithm, integrating the second-order Taylor expansion and Bernstein-type inequality, to obtain a satisfactory performance while reducing the computational load. Finally, simulation results validate the effectiveness and superiority of the proposed robust algorithms compared to existing algorithms.

Unmanned aerial vehicles (UAVs) have improved the capacity and coverage of wireless networks. Mobile edge computing (MEC) has provided substantial computation capability to user equipment (UEs). The integration of UAV and MEC can take advantages of both to provide flexible computation service. In UAV assisted MEC networks, delay and energy consumption are two main concerns, which are conflicting to a certain extent. This paper investigates delay and energy consumption jointly in a multiple UAVs assisted MEC network. A cost function is defined to balance the delay and the energy consumption. The user access, task offloading, and computational resource allocation are jointly considered to minimize the long-term cost. To tackle this difficult problem, we formulate the long-term problem into sequential decision problem and treat all UEs as intelligent agents. Each UE decides its access UAV, task offloading proportion, and required edge computation resource to minimize the its own cost. Moreover, the optimal task offloading proportion and required computation resource can be obtained in closed-form given user access so that the action space can be significantly reduced. Then, an adversarial multi-armed bandit based algorithm is employed at each UE and a distributed scheme is proposed to solve the joint optimization problem. Simulation results validate the effectiveness and robustness of the distributed scheme and show its superiority to benchmarks.

The integrated sensing and communication has emerged as a key technology for future wireless systems. This article considers a multislot integrated visible light communication and sensing (IVLCS) system. In the IVLCS system, the primary purpose is sensing, while the second purpose is communication. We formulate a joint slot selection and power allocation problem by minimizing the total transmitted power under the echo-to-noise ratio constraint, communication sum rate constraint, sensing slot number constraint, and power constraint. Such a problem is shown to be nonconvex. After convex relaxation reformulation, the original problem is divided into a sensing subproblem and a communication subproblem. We propose a sensing priority and power minimization-based joint slot selection and power allocation (SPPM-JSSPA) algorithm to solve the two subproblems. To further reduce the complexity, a low-complexity fixed slot selection and power allocation (FSSPA) algorithm is also proposed. The convergence and complexity analysis indicates that both the proposed SPPM-JSSPA algorithm and the FSSPA algorithm are convergent and efficient. Numerical results show that the proposed SPPM-JSSPA algorithm can obtain the best performance compared to the existing algorithms, and the low-complexity FSSPA algorithm can achieve a comparable performance to the SPPM-JSSPA algorithm.

This paper investigates a semi-grant-free (SGF) based transmission strategy to provide a flexible connectivity for various kinds of users in an integrated satellite-aerial-terrestrial network (ISATN). Herein, a high-altitude platform (HAP) termed as a grant-based user (GBU), which serves multiple mobile terminals (MTs) through space division multiple access (SDMA), wants to access a satellite network with multiple earth stations (ESs) termed as grant-free users (GFUs) simultaneously via non-orthogonal multiple access (NOMA) assisted SGF. To this end, we first propose two SGF-based uplink transmission schemes for both perfect channel state information (CSI) and imperfect CSI cases. When perfect CSI is available, a zero-forcing based beamforming (BF) scheme is used in HAP network while an adaptive transmit power allocation (ATPA) approach is adopted for SGF transmission. When only imperfect CSI is available, BF scheme employing the derived channel correlation matrix of HAP-MT link is proposed to achieve SDMA, and a novel ATPA strategy with rate probability constraint is proposed to guarantee quality-of-service of the GBU. Next, we derive the closed-form throughput expressions to evaluate the performance of the considered ISATN with the proposed two SGF-based schemes. Finally, computer simulations are conducted to validate the theoretical performance analysis and show the superiority of the proposed schemes over the related works. Moreover, our numerical results not only demonstrate a satisfactory performance of the proposed SGF-based scheme using imperfect CSI, but also reveal the impact of CSI errors on the system performance.

Cognitive satellite and terrestrial network (CSTN) is considered as a promising technology to provide ubiquitous connectivity for various users within wide-coverage. This paper proposes a robust downlink transmission scheme for multiple intelligent reflecting surfaces (IRSs) assisted CSTN. Here, the satellite network adopts multigroup multicast transmission scheme to serve many earth stations, while the terrestrial network exploits space division multiple access and multi-IRS-enhanced non-orthogonal multiple access technology to communicate with many terrestrial users. By assuming that these two networks share the same frequency band having only the angular information based imperfect channel state information of each user, we formulate an optimization problem to minimize the total transmit power subject to the constraints of quality-of-service requirement for each user, per-antenna transmit power budgets of satellite and BS, and unit-modulus requirement for each reflecting element. To tackle this mathematically intractable problem, we then employ angular discretization together with the successive convex approximation method to obtain the active beamforming (BF) vectors of satellite and BS, the passive BF vector of IRS, and the power allocation coefficients. Moreover, we propose a generalized zero forcing BF and alternative optimization to obtain the suboptimal solutions of the optimization problem with low computational complexity. Finally, simulation results are given to demonstrate the effectiveness and superiority of the proposed two schemes over the benchmarks.

A dense satellite-terrestrial integrated mobile-edge computing network (SATIMECN) architecture is developed to meet the computing demands for next generation networks. We formulate an average weighted sum energy consumption minimization problem by jointly considering task ratio allocation of computing or offloading at local and the gateway (GW), ground user terminal (GUT)-satellite association relation, GUT multiple-input and multiple-output (MIMO) precoding, and computation resource allocation at local and the GW. Due to the stochastic property of the optimization problem, we adopt Lyapunov optimization theory to transform it into a deterministic one. Then, we decompose the optimization problem into four subproblems and solve each one iteratively. Specifically, task ratio allocation of computing or offloading at local and the GW is obtained in a closed-form expression using the delay constraint. Then, the binary GUT-satellite association subproblem is solved by the weighted minimum mean-squared error and quadratic transform based fractional programming (QTFP) methods. Moreover, the MIMO precoding subproblem is solved by QTFP and interior point methods. Finally, the computation resource allocation subproblem for local and edge computing is derived in closed-form expressions. Simulation results demonstrate that the tradeoff between the average weighted sum energy consumption and the average queue length can be realized by adjusting the Lyapunov control parameter. Moreover, the proposed MIMO communication and frequency reuse schemes for dense satellite network can realize efficient computation offloading with relative low cost.

Due to the flexible deployment, unmanned surface vessels (USVs) have attracted much interest recently. To solve the resource scarcity problem at sea, USV needs to leverage existing terrestrial and satellite systems for efficient backhaul and spectrum sharing. In this case, the multiple input multiple output (MIMO) technology can be applied for diversity gain improvement and interference coordination. However, how to adopt MIMO technology into maritime networks with a sparse scattering environment is still an open issue. In this paper, we employ a multi-antenna USV to support on-demand communications. Utilizing the two-ray channel, we aim to maximize the sum throughput over all USV intended users, by jointly optimizing the cooperative beamforming and trajectory, subject to several practical constraints, including the USV kinetics, quality of service requirement and backhaul capacity. Different from existing whole period designs, we decompose the problem into sequential one-slot problems. Within each slot, the non-convex problem is solved iteratively by using problem decomposition and successive convex optimization methods. Then, channel estimation errors are considered to investigate a robust beamforming scheme. Numerical simulations validate that the USV coexists well with the satellite network and show that the beamforming scheme and trajectory design complement each other for performance improvement.

An integrated satellite and aerial network (ISAN) is considered a promising candidate to provide seamless connectivity for future wireless communication systems. In this paper, we propose a multi-objective based robust beamforming (BF) scheme for an ISAN to support heterogeneous services with high flexibility, where the satellite network serves various heterogeneous satellite terminals through multicast non-orthogonal multiple access (MC-NOMA), while the aerial network offers services to many internet of things devices using layered division multiplexing (LDM). Specifically, we first formulate a multi-objective optimization problem (MOOP) to achieve a good trade-off between sum rate maximization and total transmit power minimization. To tackle this mathematically intractable problem, we exploit the weighted Tchebycheff approach to transform the MOOP into a single-objective problem. Since only the angular information based channel state information is available, we exploit the angular discretization method and sequential convex approximation to design a robust BF algorithm to obtain the Pareto optimal solutions. Finally, simulation results demonstrated that our proposed scheme can achieve a optimal trade-off between multiple performance metrics with high spectrum and energy efficiency, so as to support heterogeneous services in the ISAN and fill the gap of only single type of serivce in the existing ISAN works.

In physical-layer security, secrecy capacity is an important performance metric. This work aims to determine the secrecy capacity for an indoor visible light communication system consisting of a transmitter, a legitimate receiver and an eavesdropping receiver. In such a system, both signal-independent noise and signal-dependent noise are considered. Under nonnegativity and average optical intensity constraints, lower and upper bounds on secrecy capacity are derived by the variational method, the dual expression of the secrecy capacity, and the concept of “the optimal input distribution that escapes to infinity”. By an asymptotic analysis at large optical intensity, there is a small gap between the asymptotic upper and lower bounds. Then, by adding a peak optical intensity constraint, we further analyze the exact and asymptotic secrecy-capacity bounds. For practical considerations, the effects of imperfect channel state information, multi-photodiode eavesdropper, and artificial noise on secrecy performance are also discussed. Finally, the derived secrecy-capacity bounds are verified by numerical results.

This paper proposes a novel bi-directional non-orthogonal multiple access (NOMA)-based cooperative underlay satellite-terrestrial network (CUSTN), in which two secondary satellite users can serve as a potential relay for each user, thus gaining higher cooperative diversity order (DO). We analyze the exact and asymptotic outage probabilities (OPs) of both users under a multi-antenna setup and a practical residual hardware impairment (RHI) consideration. Numerical results are provided to validate the analysis, reveal the impacts of key parameters on the system performance, and demonstrate the advantages of our proposed scheme over other benchmarks.

Unmanned surface vehicles (USVs) have recently found increasing applications in marine scenarios. In this paper, we investigate the cooperative communication of the hybrid terrestrial-maritime wireless system coexisting with a satellite network, where a multi-antenna USV is used as the relay to assist the communication between the terrestrial base station (TBS) and marine users (MUs). Considering the shortage of communication resources, the USV shares the same frequency spectrum with the satellite network. Using the composite maritime two-ray channel, we aim to maximize the throughput over all MUs by optimizing the cooperative beamforming scheme and association jointly with the USV trąjectory, subject to the constraints of USV kinematics, power consumption, quality-of-service requirements, and information-causality. Since the formulated optimization problem is non-convex, we propose an efficient iterative algorithm by applying the block coordinate descent and successive convex optimization methods. Simulation results confirm the significant performance gains of the proposed design as compared to other benchmark methods.

Satellite and unmanned aerial vehicle (UAV) integrated networks (SUINs) are considered as a promising method to offer various Internet of Remote Things (IoRT) applications. In this article, we investigate the downlink transmission of SUINs where the satellite-to-UAV link uses the free-space optical (FSO) technology with an equal gain combining (EGC) scheme while the links from UAV to IoRT devices exploit radio frequency (RF) with the space-division multiple access (SDMA) technique. Specifically, considering that only statistical channel state information (CSI) is available, we first formulate an optimization problem to maximize the ergodic sum rate (ESR) of the system, which is constrained by the total transmit power budget and IoRT devices’ rate requirements. Then, a beamforming (BF) scheme based on the alternating direction method of multipliers (ADMM) is proposed to solve the nonconvex problem. Furthermore, a zero-forcing (ZF)-based suboptimal approach is also presented to reduce the implementation complexity. Finally, by assuming that the FSO link and RF links are subject to Gamma–Gamma fading and Nakagami-$m$fading, respectively, we derive closed-form ESR expressions for the considered network with the proposed BF schemes. Simulation results are provided to confirm the accuracy of the theoretical analysis. Moreover, it is revealed that our proposed EGC scheme for FSO communication and BF schemes for RF transmission can both achieve better performance than the existing works.

Employing unmanned surface vehicles (USVs) as marine data collectors is promising for large-scale environment sensing in remote ocean monitoring network. In this article, we consider a USV-aided marine data collection network, where a USV collects data from multiple monitoring terminals while avoiding collisions with monitoring terminals and obstacles. Aiming at minimizing energy consumption and data loss, we formulate a trajectory optimization problem with practical constraints, including collision avoidance, steering angle, and velocity limitation. The problem is intractable due to the stochastic arrived data and the random emergence and movement of dynamic obstacles. To efficiently solve it, we transform it as a constrained Markov decision process (MDP) problem and address it using a target-oriented double deep${Q}$-learning network (D2QN)-based collision avoidance and trajectory planning algorithm. In the proposed algorithm, the USV acts as an agent to explore and learn its trajectory planning policy by utilizing the causal knowledge. Numerical results demonstrate that the performance of the proposed algorithm is superior in terms of successful probability, energy consumption, and data loss.

In this paper, an integrated communication, radar sensing, and mobile-edge computing (CRMEC) architecture is developed, where user terminals (UTs) perform radar sensing and computation offloading simultaneously at the same spectrum by using multiple-input and multiple-output (MIMO) arrays and dual-function radar-communication techniques. We formulate a multi-objective optimization problem to jointly consider the performance of multi-UT MIMO radar beampattern design and computation offloading energy consumption while jointly optimizing individual transmit precoding for radar and communication and computation resource allocation. To address the optimization problem, we first decompose the it into three subproblems and adopt an iterative optimization algorithm. Specifically, quadratic transform based fractional programming methods are used to minimize the offloading energy consumption. The design objective of MIMO radar beampattern is handled by the first-order Taylor expansion. Transmit precoding is designed to optimize radar sensing and computation task offloading. The local and edge computation resource allocation are obtained in closed-form. Numerical results verify the effectiveness of the proposed algorithms. The proposed CRMEC architecture can generate the desired multi-UT MIMO radar beampattern and perform computation offloading simultaneously.

In this paper, we investigate the outage performance of an uplink (UL) non-orthogonal multiple access (NOMA)-based hybrid satellite-terrestrial relay network (HSTRN), in which two users communicate with the satellite through a decode-and-forward (DF) relay due to the lack of direct link. To provide a comprehensive yet hitherto unexplored outage analysis framework, we consider a more generalized channel model, i.e., the terrestrial and satellite links, respectively, undergo$\alpha -\mu $and$\kappa -\mu $shadowed fadings. Under fixed power allocation (FPA) for both multiple access phase and relaying phase, we firstly study three successive interference cancellation (SIC) decoding schemes, of which the first two are refined from existing schemes, while the third one, named asextended SIC (ESIC), is proposed in this paper to satisfy the quality of service (QoS) decoding criterion, which is shown to offer better performances for both users as compared to the former two SIC schemes. We also propose a novel dynamic power allocation (DPA) scheme for the multiple access phase, termed asenhanced DPA (EDPA), to overcome both users’ error floor (EF) issue yet provide better user fairness than the conventional DPA in the literature. We then analyze the exact and asymptotic outage performance for three SIC and theEDPAschemes under the generalized channel setting. It is shown that both the proposedESICandEDPAcan circumvent the EF issue and moreover, each has its own advantage in terms of the diversity order (DO). Our results also reveal that there exists a trade-off betweenESICandEDPA, since the former requires a premise on users’ targets rates to overcome the EF and once this premise is satisfied, no DO degradation will occur, while the latter does not entail such a premise but may face potential DO degradation. Finally, we present numerical results to verify the theoretical analysis, manifest the impacts of key parameters on the system performance, and demonstrate the advantages of our proposed network over other benchmarks.

To reduce the computational complexity and channel estimation overhead for multi-intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) multiple-input multiple-output (MIMO) communication, we consider a joint design of the hybrid precoders at the base station and the passive precoders at the IRSs to maximize the ergodic spectral efficiency by exploiting the twin-timescale channel state information (CSI). Specifically, the digital precoder is designed according to the instantaneous CSI of a reduced-dimensional assist channel matrix, while the IRS passive reflection coefficient matrices and the analog precoder are optimized using the statistical CSI of all links. However, such a design problem is challenging to solve due to the non-convexity and the twin timescale. This work proposes efficient algorithms to jointly design the precoders, where the update of the IRS reflection coefficient matrices is independent of the hybrid precoders and the design of the analog precoder is independent of the digital precoder. Simulation results demonstrate the effectiveness of the proposed algorithms and provide the application scenes of the fully-connected and subarray-connected architectures. The results also show that the ergodic spectral efficiency for the fully-connected architecture using the twin-timescale CSI can approach that using the existing CSI schemes with less channel estimation overhead and computational complexity.

A key challenge for millimeter wave (mmWave) multiple-input multiple-output (MIMO) communication is that the signals at mmWave band are highly susceptible to blockage. To address this challenge, we introduce intelligent reflecting surface (IRS) to increase coverage area and improve communication performance. This paper considers a joint design of hybrid precoders at the base station and the passive precoder at the IRS to maximize the average spectral efficiency in an IRS-assisted mmWave MIMO system by exploiting the mixed timescale channel state information (CSI). Specifically, the hybrid precoders are designed according to the instantaneous CSI of the overall channel, while the IRS reflection coefficient matrix is optimized using the statistical CSI of all links. However, such a design problem is challenging to solve due to the non-convexity and the mixed timescale. This work proposes efficient algorithms to design jointly the hybrid precoders and the IRS reflection coefficient matrix where the update of the IRS reflection coefficient matrix is independent of the hybrid precoders. Simulation results demonstrate the effectiveness of the proposed algorithms. More interestingly, the results also show that adding low-cost reflector elements at the IRS can reduce the number of required high-cost radio frequency chains.

In this paper, we investigate a satellite-aerial integrated edge computing network (SAIECN) to combine a low-earth-orbit (LEO) satellite and aerial high altitude platforms (HAPs) to provide edge computing services for ground user equipment (GUE). In the SAIECN, GUE’s computing tasks can be offloaded to HAP(s) or LEO satellite. In this paper, we minimize the weighted sum energy consumption of SAIECN via joint GUE association, multi-user multiple input and multiple output (MU-MIMO) transmit precoding, computation task assignment, and resource allocation. To solve the nonconvex problem, we decompose the optimization problem into four subproblems and solve each one iteratively. For the GUE association subproblem, quadratic transform based fractional programming (QTFP) and difference of convex function are utilized. The MU-MIMO transmit precoding subproblem is solved via QTFP and the weighted minimum mean-squared method. The computation task assignment is addressed using the classic interior point method while the computation resource allocation is derived in closed form. The numerical results show that the proposed SAIECN and the corresponding algorithm can solve the satellite based edge computing quite well and the energy cost is maintained at a relative low level.

In this paper, we investigate a low earth orbit satellite (LEO SAT) and high altitude platform (HAP) integrated edge computing network to provide computing services for ground mobile devices (GMDs). We propose to minimize the weighted sum energy consumption via jointly optimizing the GMD association, precoding design, computation task assignment and computation resource allocation. To solve the nonconvex problem, we propose an algorithm that decomposes the optimization problem into four subproblems and solves each sub-problem iteratively. Specially, the GMD association subproblem is solved by quadratic transform based fractional programming (QTFP) and difference of convex function; the precoding design subproblem is obtained via QTFP and weighted minimum mean square (WMMSE) method; the computation task assignment is solved by the interior point method and the computation resource allocation is derived in closed form. The numerical results show that the proposed algorithms can solve the problems quite well and the energy consumption is maintained at a relative low level.

This paper investigates the outage performance of a novel two-user non-orthogonal multiple access (NOMA)-based hybrid satellite-terrestrial relay network (HSTRN), where one user has direct link to the satellite (termed as direct-link user), while the other user needs to seek the help of the direct-link user or a dedicated relay to acquire its desired signal. An adaptive switch-and-stay combining relaying (ASSCR) scheme is proposed for boosting the system performance without excessive use of relaying resources of the network. Exact and asymptotic expressions of the outage probabilities (OPs) of both users are derived under the fixed-gain (FG) amplify-and-forward (AF) protocol. Finally, numerical results are presented to verify the theoretical analysis, manifest the impacts of key parameters on the system performance, and demonstrate the advantages of our proposed network over other benchmarks.

To satisfy the explosive access demands of Internet-of-Things (IoT) devices, various kinds of multiple access techniques have received much attention. In this article, we investigate the multicast communication of a satellite and aerial-integrated network (SAIN) with rate-splitting multiple access (RSMA), where both satellite and unmanned aerial vehicle (UAV) components are controlled by network management center and operate in the same frequency band. Considering a content delivery scenario, the UAV subnetwork adopts the RSMA to support massive access of IoT devices (IoTDs) and achieve desired performances of interference suppression, spectral efficiency, and hardware complexity. We first formulate an optimization problem to maximize the sum rate of the considered system subject to the signal-interference-plus-noise-ratio requirements of IoTDs and per-antenna power constraints at the UAV and satellite. To solve this nonconvex optimization problem, we exploit the sequential convex approximation and the first-order Taylor expansion to convert the original optimization problem into a solvable one with the rank-one constraint, and then propose an iterative penalty function-based algorithm to solve it. Finally, simulation results verify that the proposed method can effectively suppress the mutual interference and improve the system sum rate compared to the benchmark schemes.

Relay-assisted free-space optical (FSO) communication systems are exploited as a means to mitigate the limiting effects of the turbulence induced atmospheric scintillation. However, conventional ground relays are stationary, and their optimal placement is not always feasible. Due to their mobility and flexibility, unmanned aerial vehicles (UAVs) provide new opportunities for FSO relaying systems. In this paper, a hovering UAV-based serial FSO decode-and-forward relaying system is investigated. In the channel modelling for such a system, four types of impairments (i.e., atmospheric loss, atmospheric turbulence, pointing error, and link interruption due to angle-of-arrival fluctuation) are considered. Based on the proposed channel model, a tractable expression for the probability density function of the total channel gain is obtained. Closed-form expressions of the link outage probability and end-to-end outage probability are derived. Asymptotic outage performance bounds for each link and the overall system are also presented to reveal insights into the impacts of different impairments. To improve system performance, we optimize the beam width, field-of-view and UAVs' locations. Numerical results show that the derived theoretical expressions are accurate to evaluate the outage performance of the system. Moreover, the proposed optimization schemes are efficient and can improve performance significantly.

High-throughput satellites (HTSs) play an important role in future millimeter-wave (mmWave) aeronautical communication to meet high speed and broad bandwidth requirements. This paper investigates the outage performance of an aeronautical broadband satellite communication system’s forward link, where the feeder link from the gateway to the HTS uses free-space optical (FSO) transmission and the user link from the HTS to aircraft operates at the mmWave band. In the user link, spot beam technology is exploited at the HTS and a massive antenna array is deployed at the aircraft. We first present a location-based beamforming (BF) scheme to maximize the expected output signal-to-noise ratio (SNR) of the forward link with the amplify-and-forward (AF) protocol, which turns out to be a phased array. Then, by supposing that the FSO feeder link follows Gamma-Gamma fading whereas the mmWave user link experiences shadowed Rician fading, we take the influence of the phase error into account, and derive the closed-form expression of the outage probability (OP) for the considered system. To gain further insight, a simple asymptotic OP expression at a high SNR is provided to show the diversity order and coding gain. Finally, numerical simulations are conducted to confirm the validity of the theoretical analysis and reveal the effects of phase errors on the system outage performance.

This paper investigates the massive access for a satellite-aerial-terrestrial network (SATN), where a high-altitude platform (HAP) is deployed as a relay to assist the uplink transmission from terrestrial user equipment (UE) to satellite. Unlike previous works, we adopt radio frequency (RF) and free space optical for the aerial-terrestrial and satellite-aerial links, respectively. Specifically, by assuming that imperfect angular information (IAI) of each UE is known at the HAP, we develop a space division multiple access (SDMA) scheme to maximize the ergodic sum rate (ESR). To this end, we first exploit the IAI to calculate the analytical expression of channel correlation matrix. Then, by considering the limitation of array freedom, we propose a subspace-based UE grouping and scheduling scheme to cluster all UEs into groups. Next, we present a computationally effective beamforming (BF) scheme for each UE at HAP to efficiently implement SDMA in the RF link. Furthermore, a closed-form expression for the ESR of the SATN is derived to validate the proposed BF and SDMA schemes. Finally, simulation results corroborate the derived theoretical formulas and reveal the impacts of array size, angular estimation error, the number of UEs and scheduling threshold on the system performance.

In this paper, we investigate secrecy-energy efficient hybrid beamforming (BF) schemes for a satellite-terrestrial integrated network, wherein a multibeam satellite system shares the millimeter wave spectrum with a cellular system. Under the assumption of imperfect angles of departure for the wiretap channels, the hybrid beamformer at the base station and digital beamformers at the satellite are jointly designed to maximize the achievable secrecy-energy efficiency, while satisfying signal-to-interference-plus-noise ratio constraints of both the earth stations (ESs) and cellular users. Since the formulated optimization problem is nonconvex and mathematically intractable, we propose two robust BF schemes to obtain approximate solutions with low complexity. Specifically, for the case of a single ES, we integrate the Charnes-Cooper approach with an iterative search algorithm to convert the original nonconvex problem into a solvable one and obtain the BF weight vectors. In the case of multiple ESs, by exploiting the sequential convex approximation method, we convert the original problem into a linear one with multiple matrix inequalities and second-order cone constraints, for which we obtain a solution with satisfactory performance. The effectiveness and superiority of the proposed robust BF design schemes are validated via simulations using realistic satellite and terrestrial downlink channel models.

In maritime wireless communications, unmanned surface vehicles (USVs) can improve coverage and transmission performance due to their agile maneuverability and flexible deployment. This paper considers a USV-enabled maritime wireless network, where a USV is employed to assist the communication between the terrestrial base station and ships. Considering the maritime environment characteristics and earth curvature, we establish the systematic USV kinetics and information transmission models. To guarantee fairness, we aim to maximize the minimum expected throughput overall ships by jointly optimizing the trajectory and communication resource allocation, subject to the constraints of the USV kinetics, safe sailing, breakpoint distances, line-of-sight links, resource allocation, and information-causality. Due to the complexity of the maritime two-ray signal propagation model, we propose a channel approximation method to find an upper bound of the throughput for the original problem. By the problem decomposition, two sub-problems are derived and solved iteratively using successive convex approximation and interior-point methods. Simulation results confirm the effectiveness of the proposed method and show that USV can significantly improve transmission performance in maritime wireless networks.

Mobile edge computing is considered as a promising method to release the computation burden of mobile devices (MDs) by transferring the computation tasks to the nearby edge server. In this paper, we address the computation offloading problem by jointly optimizing offloading-decision making, multi-user multiple input and multiple output (MU-MIMO) precoding and computation resource allocation. The optimization problem is formulated as the minimization of the weighted sum of energy consumption and time delay of MDs, which is a mixed-integer non-linear programming problem. Due to the complexity of offloading time delay, we consider two special cases namely, the lower bound and upper bound of offloading time delay for the original problem, and exploit semidefinite relaxation and rounding methods to obtain the offloading decisions. Specially, we adopt the quadratic transform based fractional programming and the weighted minimum mean square error methods to solve the MU-MIMO precoding design problem for the two cases of offloading time delay, respectively. Simulation results confirm the effectiveness of the proposed method, and show that the application of multi-antenna MU-MIMO communication into MEC can sufficently reduce the energy consumption and time delay during computation offloading.

Channel capacity bounds are derived for a point-to-point indoor visible light communications (VLC) system with signal-dependent Gaussian noise. Considering both illumination and communication, the non-negative input of VLC is constrained by peak and average optical intensity constraints. Two scenarios are taken into account: one scenario has both average and peak optical intensity constraints, and the other scenario has only average optical intensity constraint. For both two scenarios, we derive closed-from expressions of capacity lower and upper bounds. Specifically, the capacity lower bound is derived by using the variational method and the property that the output entropy is invariably larger than the input entropy. The capacity upper bound is obtained by utilizing the dual expression of capacity and the principle of “capacity-achieving source distributions that escape to infinity”. Moreover, the asymptotic analysis shows that the asymptotic performance gap between the capacity lower and upper bounds approaches zero. Finally, all derived capacity bounds are confirmed using numerical results.

This paper investigates the uplink space division multiple access in a satellite-aerial-terrestrial network, where multiple users access to satellite with the aid of a unmanned aerial vehicle, and the two hops adopt radio frequency (RF) and free space optical (FSO), respectively. We first formulate an optimization problem to maximize the ergodic sum rate (ESR) of the considered system. Then a statistical channel state information (CSI)-based beamforming (BF) scheme is proposed to solve the non-convex ESR maximization problem. The main advantage of the proposed method is that only statistical CSI is used to obtain BF vectors so that a low implementation complexity can be achieved. Then, by assuming that the RF and FSO links undergo Nakagami-m and Gamma-Gamma fading, respectively, closed-form expression for ESR of the considered system is derived. Simulation results validate the derived expression and confirm the effectiveness of the proposed BF scheme.

In this paper, we propose a novel robust downlink beamforming (BF) design for satellite-terrestrial integrated networks. Under a realistic assumption that the angular information of eavesdroppers is not perfectly known, we establish an optimization framework for hybrid BF at the terrestrial base station and digital BF at the satellite to maximize the secrecy-energy efficiency of the system, while satisfying the quality-of-service constraints of both earth station and cellular user. Since the formulated optimization problem is mathematically intractable, we present an iterative algorithm based on the Charnes-Cooper approach to optimize the BF weight vectors. The effectiveness and superiority of the proposed robust hybrid BF scheme are validated via computer simulations.

In this paper, a hovering unmanned aerial vehicle (UAV)-based free-space optical (FSO) serial multi-hop decode-and-forward relaying system is investigated. Considering the joint effect of atmospheric loss, atmospheric turbulence, pointing error and angle-of-arrival (AOA) fluctuation, the novel closed-form expressions of the link outage probabilities for ground-to-UAV, UAV-to-UAV, and UAV-to-ground links are derived, and the expression of the end-to-end outage probability for the UAV-based relaying system is also obtained. The asymptotic outage performance bounds for each link and the considered system are presented to reveal insights into the impact of AOA fluctuations. Based on the derived theoretical results, an optimization problem of receiver's field-of-view (FOV) is formulated to alleviate the impairment of AOA fluctuation. Numerical results show that the derived theoretical expressions are accurate to evaluate the outage performance of UAV-based FSO system. Moreover, the derived FOV can improve performance significantly.

Hybrid architecture is a promising candidate precoding scheme to balance the achievable spectral efficiency and power consumption in millimeter wave (mmWave) multiple input multiple output (MIMO) systems. A practical partially dynamic subarray-connected architecture is developed to improve the transmission performance. In this proposed architecture, the set of antennas in each subarray is fixed, but the subarrays connected to each radio frequency chain are dynamic. Moreover, we study how to optimize jointly the partially dynamic subarray structure and the hybrid precoders under the constraints of total transmit power and hardware limitation. This joint optimization problem is divided into two sub-problems. For the first sub-problem, a low complexity algorithm is proposed to determine the partition of subarrays using the long-term spatial channel covariance. Then, the penalty decomposition method is adopted to design the hybrid precoders. Numerical results verify that the partially dynamic subarray design algorithm offers one or two orders of computation time saving compared with the existing algorithms. Moreover, the proposed structure achieves spectral efficiency gain using less hardware, compared with the fully dynamic subarray structure adopted in the existing algorithms.

In this paper, the outage performance of nonorthogonal multiple access (NOMA) based coordinated direct and relay transmission (CDRT) system is analyzed. Exact and asymptotic outage probabilities of both cell-center user (CCU) and celledge user (CEU) are derived. Our analysis is conducted based on the independent but not identically distributed (i.n.i.d) Nakagami-m fading model and the fixed-gain amplify-and-forward relaying protocol, and hence the result is applicable to general relay-aided communications, where only statistical channel state information is available. Monte Carlo simulation results are provided to verify the accuracy of the derived analytical expressions with comparison to conventional orthogonal multiple access (OMA) counterpart as well as the decode-and-forward NOMA-based CDRT, demonstrating the superiority of the proposed scheme.

In this paper, we investigate energy efficient transmission for a satellite-aerial-terrestrial network (SATN), where a multi-antenna unmanned aerial vehicle (UAV) is employed as a relay to assist the satellite signal delivery. By considering total power constraint (TPC) or per-antenna power constraint (PPC) at the UAV, we first formulate an optimization problem to maximize the energy efficiency of the SATN, which is defined as ratio of the ergodic capacity to the total power consumption for communication at UAV. Then, by jointly exploiting array signal processing with the Dinkelbach's method, two new beamforming (BF) schemes, namely, TPC-BF and PPC-BF are proposed to solve the non-convex energy efficiency maximization problem. The main advantage of our method is that only angular information-based channel state information is used to obtain BF weight vectors so that a low implementation complexity is achieved. Furthermore, by assuming that the satellite-UAV link undergoes correlated Shadowed-Rician fading while the UAV-terminal link experiences correlated Rician fading, closed-form expressions for the statistics of the equivalent output signal-to-noise ratio are derived and, thus energy efficiency for the considered SATN with BF schemes is analytically presented. Finally, simulation results corroborate the derived expressions and confirm the effectiveness of the proposed BF schemes.

Hybrid architecture is a promising candidate precoding scheme to balance the achievable spectral efficiency and power consumption in millimeter wave multiple input multiple output systems. A practical partially dynamic subarray-connected architecture is developed to improve the transmission performance. In this proposed architecture, the set of antennas in each subarray is fixed, but the subarrays connected to each radio frequency chain are dynamic. Moreover, we study how to optimize jointly the partially dynamic subarray structure and the hybrid precoders under the constraints of total transmit power and hardware limitation. This joint optimization problem is divided into two sub-problems. For the first sub-problem, a low-complexity algorithm is proposed to determine the partition of subarrays using the long-term spatial channel covariance. Then, the penalty decomposition method is adopted to design the hybrid precoders. Numerical results verify that the partially dynamic subarray design algorithm offers one or two orders of computation time saving compared with the existing algorithms, and the hybrid precoding algorithm outperforms the existing algorithms in terms of spectral efficiency. Moreover, compared with the fully dynamic subarray structure adopted in the existing algorithms, the proposed structure achieves spectral efficiency gain and energy efficiency gain using less hardware.

Mobile edge computing (MEC) is considered as an efficient method to relieve the computation burden of mobile devices. In order to reduce the energy consumption and time delay of mobile devices (MDs) in MEC, multiple users multiple input and multiple output (MU-MIMO) communications is considered to be applied to the MEC system. The purpose of this paper is to minimize the weighted sum of energy consumption and time delay of MDs by jointly considering the offloading decision and MU-MIMO beamforming problems. And the resulting optimization problem is a mixed-integer non- linear programming problem, which is NP-hard. To solve the optimization problem, a semidefinite relaxation based algorithm is proposed to solve the offloading decision problem. Then, the MU-MIMO beamforming design problem is handled with a newly proposed fractional programming method. Simulation results show that the proposed algorithms can effectively reduce the energy consumption and time delay of the computation offloading.

In this paper, we propose a novel zero-forcing (ZF)- based beamforming (BF) scheme for a wireless powered cognitive satellite-terrestrial network (CSTN) operated in the millimeter wave band. Assuming that the satellite and base station are equipped with multiple antennas, we aim at maximizing the sum rate of the CSTN while satisfying the signal-to-interference-plus-noise- ratio requirements for both the information receivers (IRs) and earth stations, the energy harvesting requirements of the energy receivers (ERs), and the secrecy constraints at the ERs. Since the resulting optimization problem is mathematically intractable, we propose a novel multi-beam-based ZF BF scheme to generate beamforming vectors to serve the IRs and ERs. Specifically, the original nonconvex problem is decomposed into two independent subproblems. The first subproblem, which features beam orthogonality constraints, leads to closed form solutions for the beamforming vectors. The second subproblem, aiming at finding the optimal power allocation, is solved via the S-procedure. Finally, the effectiveness of the proposed scheme is demonstrated by simulation results.

With the development of Internet of Things, the mobile edge computing has become a promising technology for real-time communications. This paper investigates a mobile edge computing system that consists of an access point integrated with a mobile edge computing server, multiple mobile stations, and a malicious eavesdropper. By offloading part of the computing tasks to the mobile edge computing server, the energy consumption of mobile stations can be reduced significantly and the lifetime is prolonged as well. Moreover, the physical layer security is an effective technique to guarantee the secure transmission of the offloading data. Based on the proposed system model, we formulate an optimization problem to minimize the energy consumption of the system by jointly optimizing the allocations of local computing tasks, local central processor's frequency, offloading power, and offloading timeslots. A difference of convex algorithm based scheme is proposed to solve the problem. The performance of the proposed scheme is superior to the benchmark schemes, which is demonstrated by simulation results.

The grayscale-thermal tracking has attracted increasing attention due to the fact that it can make thermal information complement with grayscale information. Since there exists a large gap between the grayscale and the thermal video sequences, how to exploit the intrinsic relation between the grayscale and the thermal targets has become the key point. To address this issue, in this paper, we propose an inverse sparse representation based framework for the grayscale-thermal tracking, in which a canonical correlation analysis based inverse sparse representation model is adopted to jointly encode the target candidates in the grayscale and the thermal video sequences. The target coding process can explore the similarity between the grayscale and the thermal appearance in a common subspace, which can highlight the useful and discriminative information in both grayscale and thermal targets. The experiments on OSU-CT dataset can illustrate the promising performance of our tracking framework.

High-speed trains are being widely deployed around the world. To meet the high data rate transmission requirements, millimeter wave high-speed train communication systems with large antenna arrays have drawn increasingly attentions. Since channel conditions vary rapidly in high-speed train communication scenarios, frequent channel estimation is required. Moreover, due to the limit period of each transmission time interval, the key challenge in channel estimation is to design an efficient beam searching scheme to allow more time for data transmission. This paper formulates the beam searching problem into a multi-armed bandit problem, and proposes a bandit inspired beam searching scheme to reduce the number of measurements. The performance of the proposed scheme is evaluated in terms of regret, and simulation results show that the proposed scheme can approach the theocratical limit quickly.

This paper proposes a beamforming (BF) scheme with non-orthogonal multiple access (NOMA) for a cognitive satellite-terrestrial network (CSTN), where the satellite network shares the radio frequency bandwidth with the terrestrial network. By assuming that the satellite adopts multicast technology to serve several satellite terminals (STs), while the base station (BS) employs the combination of BF and NOMA to significantly enhance the spectrum efficiency, we aim at maximizing the sumrate of the considered CSTN under the constraints of per-antenna power budget and the quality of service (QoS) requirements for desired cellular users (CUs) and STs. Then, based on the S-procedure and Taylor approximation approach, we present a method to convert the nonconvex problem to a solvable one with linear constraints, and obtain the optimal BF weight vectors through iterative procedure. Finally, numerical results demonstrate the validity and superiority of our proposed scheme.

In this paper, we investigate the physical-layer security for a spatial modulation (SM)-based indoor visible light communication (VLC) system, which includes multiple transmitters, a legitimate receiver, and a passive eavesdropper (Eve). At the transmitters, the SM scheme is employed, i.e., only one transmitter is active at each time instant. To choose the active transmitter, a uniform selection (US) scheme is utilized. Two scenarios are considered: one is with non-negativity and average optical intensity constraints and the other is with non-negativity, average optical intensity, and peak optical intensity constraints. Then, lower and upper bounds on the secrecy rate are derived for these two scenarios. Besides, the asymptotic behaviors for the derived secrecy rate bounds at high signal-to-noise ratio (SNR) are analyzed. To further improve the secrecy performance, a channel adaptive selection (CAS) scheme and a greedy selection (GS) scheme are proposed to select the active transmitter. Numerical results show that the lower and upper bounds of the secrecy rate are tight. At high SNR, small asymptotic performance gaps exist between the derived lower and the upper bounds. Moreover, the proposed GS scheme has the best performance, followed by the CAS scheme and the US scheme.

Pruning the parameters of basis filters can effectively eliminate the negative effect of redundant deep features in discriminative correlation filter based trackers. However, traditional methods often treat feature maps in Convolutional Neural Networks (CNN) as isolate observations, ignore the intrinsic correlation between partially attentional feature maps in multiple convolutional layers, when basis filter pruning is pursued. In this letter, we propose a multi-layer factorized discriminant correlation filter (MLF-DCF) for visual tracking. By integrating the multi-view discriminant learning and the discriminative correlation filter into a unified optimization problem, we can explore the correlation between different target sub-regions from multi-layer viewpoint, thus can effectively prune multi-layer basis filters. To enhance the efficiency of MLF-DCF in terms of speed and accuracy, we not only adopt alternating direction method of multipliers (ADMM) to solve the unified optimization, but also employ a mask estimation strategy to eliminate the background noise in deep features. A large number of experiments on challenging video sequences are given to illustrate the superiority of our tracking method.

This paper investigates the performance of a dual-hop decode-and-forward (DF) relaying-aided land mobile satellite communication over Shadowed-Rician (SR) fading channels. A practical model for the satellite relaying system is first developed, where the impacts of satellite multi-beam antenna, radio propagation loss, and random shadowing are taken into account. Next, by assuming that the multi-beam satellite suffers from hardware impairments (HIs) and is perturbed by interference signals, we derive an equivalent end-to-end signal-to-interference-plus-noise-and-distortion-ratio of the system, and justify that the maximum ratio transmission at the source and the maximum ratio combining at the destination are the optimal transmit-receive beamforming schemes on the proposed HIs model. Then, closed-form expressions for the probability density function (PDF) of the sum of independent and identically distributed (i.i.d) squared SR random variables in the case of integer and rational Nakagami-m fading parameters are derived. Based on the derived PDF, new analytical expressions for the outage probability (OP) and average throughput are obtained in the presence of HIs and interference. Moreover, the asymptotic OP and average throughput at high signal-to-noise ratio are investigated to reveal the achievable diversity order of the system. Finally, Monte Carlo simulation results are provided to corroborate the analytical results.

In this paper, we investigate the secure communication of a satellite-terrestrial integrated network (STIN). By supposing that the satellite employs multi-beam antenna while the base station (BS) is equipped with a uniform planar array (UPA), we first formulate a joint constrained optimization problem to maximize the sum rate of STIN while satisfying both the quality-of- service (QoS) requirement of the information receivers and earth stations (ESs), the energy harvest (EH) requirement of the energy receivers (ERs), the secrecy constraint at ERs. Since the formulated optimization problem is non-convex and mathematically intractable, we then propose a joint beamforming (BF) scheme to obtain the optimal solutions through an iterative algorithm, which exploits the sequential convex approximation (SCA) and Taylor expansion to convert the original non-convex problem into a solvable one. Finally, simulation results are given to demonstrate the effectiveness of the proposed joint BF schemes.

Channel estimation is essential for precoding/combining in millimeter wave (mmWave) communications. However, accurate estimation is usually difficult because the receiver can only observe the low-dimensional projection of the received signals due to the hybrid architecture. We take the high speed scenario into consideration where the Doppler effect caused by fast-moving users can seriously deteriorate the channel estimation accuracy. In this paper, we propose to incorporate the nested array into analog array architecture by using RF switch networks with an objective of reducing the complexity and power consumption of the system. Based on the covariance fitting criterion, a joint Doppler and channel estimation method is proposed without need of discretizing the angle space, and thus the model mismatch effect can be totally eliminated. We also present an algorithmic implementation by solving the dual problem of the original one in order to reduce the computational complexity. Numerical simulations are provided to demonstrate the effectiveness and superiority of our proposed method.

As a novel statistical model, Málaga distribution is proposed recently, which covers many commonly used fading models as special cases. The secure transmission of information over the Málaga fading channels has not been discussed in literature. In this paper, the secrecy outage probability (SOP) over the Málaga fading channels is investigated. Initially, an exact expression of the SOP is derived, which is with an integral term and can not be easily used in practice. To reduce its computational complexity, a closed-from expression for the lower bound of the SOP is then obtained. Numerical results that illustrate the effect of the Málaga fading on system performance are provided. The accuracy of the performance analysis is verified by simulations.

This paper investigates a force-position controller using neural network model free control with time-delay estimation(TDE-MFNNC) for the motion of the upper limb exoskeleton. The system establishes the muscle force prediction model based on the scaling principle of Hill model to estimate the torque provided by the upper limb. Then the upper limb exoskeleton is built in Solidworks as a virtual prototype which is used to instead the real exoskeleton model. We performed control stability to verify the effectiveness of the controller by comparing with TDE-iPD controller and with compensation the simulation result has a more satisfactory tracking 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 studies the robust secure beamforming (BF) issue of fifth generation (5G) cellular system operating at millimeter wave frequency and coexisting with a satellite network. By employing an uniform planar array at the base station (BS) and assuming known imperfect angle-of-arrival-based channel state informations of multiple eavesdroppers (Eves), a constrained optimization problem is first formulated to maximize the worst-case achievable secrecy rate of the cellular user under the constraints of the transmit power of BS and the interference threshold of satellite earth station. Then, we propose two robust BF methods to solve the complex optimization problem for both coordinated and uncoordinated Eves. For the case of coordinated Eves, we propose a heuristic BF scheme, which transfers the worst-case problem into a min-max one such that the BF weight vectors can be obtained analytically. For uncoordinated Eves, we convert the non-convex problem into a convex one, and further propose an iterative penalty function-based algorithm to obtain the optimal BF weight vectors. Finally, simulation results are provided to confirm the effectiveness and superiority of the proposed robust BF schemes.

This paper investigates the secure communication of a cognitive satellite terrestrial network with software-defined architecture, where a gateway is acting as a control center to offer the resource allocation for the wireless systems. Specifically, we propose beamforming (BF) schemes to utilize the interference from the terrestrial network as a green source to enhance the physical-layer security for the satellite network, provided that the two networks share the portion of millimeter-wave frequencies. Supposing that the satellite employs multibeam antenna while the base station is equipped with a uniform planar array, we first formulate a constrained joint optimization problem to minimize the total transmit power while satisfying both the quality-of-service requirement of the terrestrial user and the secrecy rate (SR) requirements of the satellite users. Since the formulated optimization problem is nonconvex and mathematically intractable, we then propose two BF schemes to obtain the optimal solutions with high computational efficiency. For the case of one eavesdropper (Eve), we present a method to convert the nonconvex SR constraint to a second-order cone one and then adopt a penalty function approach to obtain the BF weight vectors. In the case of multiple Eves, by introducing a list of auxiliary variables, we propose a two-layer iterative BF scheme using penalty function approach together with gradient-based method to calculate the BF weight vectors. Finally, simulation results are given to demonstrate the effectiveness and superiority of the proposed BF schemes.

This letter proposes a beamforming (BF) scheme to enhance wireless information and power transfer in terrestrial cellular networks coexisting with satellite networks. By assuming that the energy receivers are the potential eavesdroppers overhearing signals intended for information receivers (IRs), we first formulate a constrained optimization problem to maximize the minimal achievable secrecy rate of the IRs subject to the constraints of energy harvest requirement, interference threshold, and transmit power budget. Through exploiting the sequential convex approximation method, we convert the original problem into a linear one with a series of linear matrix inequality and second-order cone constraints. An iterative algorithm is then proposed to obtain the BF weight vectors. Finally, simulation results demonstrate the effectiveness and superiority of the proposed scheme.

This paper investigates the physical-layer security for an indoor visible light communication network consisting of a transmitter, a legitimate receiver, and an eavesdropper. Both the main channel and the wiretapping channel have non-negative inputs, which are corrupted by additive white Gaussian noises. Considering the illumination requirement and the physical characteristics of lighting source, the input is also constrained in both its average and peak optical intensities. Two scenarios are investigated: one is only with an average optical intensity constraint and the other is with both average and peak optical intensity constraints. Based on the information theory, closed-form expressions of the upper and lower bounds on secrecy capacity for the two scenarios are derived. Numerical results show that the upper and lower bounds on secrecy capacity are tight, which validates the derived closed-form expressions. Moreover, the asymptotic behaviors in the high signal-to-noise ratio (SNR) regime are analyzed from the theoretical aspects. At high SNR, when only considering the average optical intensity constraint, a small performance gap exists between the asymptotic upper and lower bounds on secrecy capacity. When considering both average and peak optical intensity constraints, the asymptotic upper and lower bounds on secrecy capacity coincide with each other. These conclusions are also confirmed by numerical results.

Recently, optical spatial modulation (OSM) has been proposed for indoor optical wireless communications (OWC), which is considered as a feasible complementary solution for high data rate transmission. This paper investigates the constellation optimization problem for OSM based OWC. An OWC system with multiple transmitters and multiple receivers is considered. By using OSM, only a single transmitter is active at each time instance. Considering the non-negativity and the peak optical intensity constraints, the constellation space for OSM based OWC system is established. By exploiting the channel state information at the transmitter, this paper designs the multi-dimensional constellations by maximizing the minimum Euclidean distance between the received constellation points. Two feasible algorithms are proposed to solve the optimization problem. To evaluate the performance of the proposed algorithms, the symbol error rate is also derived. Simulation results show that the derived constellations using the proposed two algorithms outperform the existing uniformly distributed constellations.

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.

In this paper, an indoor visible light communication (VLC) system with the input-dependent noise is considered. In the system, the main noise is caused by Gaussian noise, however, with a noise variance depending on the current input signal strength. In the presence of the input-dependent noise, the theoretical expression of the bit error rate (BER) for the VLC using on-off keying is derived. Based on the derived BER, an optimization problem is formulated to improve the BER performance by tilting the receiver plane. The proposed optimization problem is proven to be a convex optimization problem, which can be efficiently solved by using the specialized solver such as the CVX toolbox for MATLAB. To verify the accuracy of the derived expression of the BER, all theoretical results are thoroughly confirmed by using the Monte-Carlo simulations. Moreover, simulation results show that the larger the variance of the input-dependent noise is, the worse the BER performance becomes. Additionally, the BER performance can be dramatically improved by tilting the receiver plane properly.

In this paper, we present a robust beamforming (BF) scheme to improve the physical layer security (PLS) of a cognitive satellite terrestrial network (CSTN) at millimeter wave (mmWave) frequency. By employing the standard recommendations and the mmWave propagation model, a PLS framework is first defined for the CSTN in the presence of multiple eavesdroppers (Eves). A constrained optimization problem is then formulated to maximize the worst-case achievable secrecy rate of the cellular user subject to an allowable interference level for the satellite user. By expressing the imperfect Eve''s channel state information (CSI) as a combination of many given angle-of-arrival (AOA) based discrete sets, we propose a method to transform the worst-case optimization problem into a min-max problem and then develop an iterative BF scheme to yield an analytical solution for the weight vectors. Finally, simulation results confirming the effectiveness and superiority of the proposed BF scheme are provided.

In this paper, we study the energy efficiency of secure communication in an underlay cognitive radio network (CRN). We first formulate an optimization problem to maximize the secrecy energy efficiency (SEE) while meeting the quality-of-service (QoS) requirement for the primary user and the transmit power constraint at each base station. Since the problem is non-convex and very difficult to solve, we then convert the original fractional form into a subtractive one, and adopt the difference of two-convex functions (D.C.) approximation method to obtain an equivalent convex problem. Furthermore, a two-layer iterative algorithm is presented to solve the problem and obtain the optimal beamforming (BF) weight vectors. Finally, numerical results are provided to demonstrate the superiority of the proposed scheme.

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 is concerned with the energy efficiency of secure transmission in an underlay cognitive relay network (CRN), where a secondary source communicates with a secondary destination via a multi-antenna relay in the presence of an eavesdropper. We first establish an optimization problem to maximize the secrecy energy efficiency (SEE) under the constraints of data rate and transmit power of the cognitive transmission as well as the interference limitation to the primary user. Then, we recast the original non-convex problem in fractional form into an equivalent subtractive one with an additional rank-one constraint. Moreover, we incorporate the rank-one constraint into the objective function as the penalty term and apply the difference of two-convex functions (D.C.) approach to obtain an equivalent convex problem. Finally, we present an iterative algorithm to obtain the optimal solution for the SEE maximization problem in the CRN. Numerical results are provided to demonstrate the effectiveness of the proposed scheme.

This paper presents a joint beamforming scheme for secure communication in cognitive hybrid satellite- terrestrial network (HSTN). In this network, a multibeam satellite communication network termed as the primary network under the intercept of an eavesdropper shares spectrum with a terrestrial network termed as the secondary network. Specifically by considering that the powers of both the primary and secondary transmitters are limited to the certain values, we aim to maximize the secrecy rate for the primary user (PU), while the quality- of-service (QoS) of the secondary user (SU) is satisfied, and set up a constrained optimization problem. Since the optimization problem is nonconvex and its solution is very difficult to be obtained, we propose a reformulation technique to convert the objective function into a second order cone constrain. Thus, standard numerical packages and randomization techniques can be used to calculate the beamforming (BF) vectors of the satellite and base station (BS). Simulation results are provided to verify that with joint BF algorithm, the network can maintain a sufficient QoS of the SU, while providing high secrecy rate of the PU.

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.

In this paper, we address the issue of joint beamforming (BF) and power control for a device-to-device (D2D) communication underlaying cellular network, where the wireless channels of the D2D link and the base station to user equipment link experience Rician and correlated Rayleigh fading, respectively. Based on the property of the integral network, we first formulate a constrained optimization problem to minimize the total transmit power of the devices in the network, while meeting the quality-of-service requirement of both the D2D and cellular users and suppressing the mutual interference to a certain level. Then, by adopting the available statistical channel state information and proposing an approximation method to relax the constraints, a support-vector-machine-based algorithm is presented to solve the optimization problem for the transmit powers and BF weight vectors of each user. Furthermore, we derive the analytical expressions for the cumulative density function and the generalized moments of the output signal-to-interference-plus-noise ratios, thereby developing some novel theoretical formulas for the ergodic capacity and the average symbol error rate of each user in the network. Finally, computer simulation results are provided to demonstrate the validity and efficiency of the proposed scheme and its performance analysis.

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.

This paper addresses robust beamforming (BF) design for the uplink transmission of wireless networks, where the base station (BS) equipped with a very large number of antennas communicates with multiple users on the same frequency band simultaneously. Based on the assumption that the wireless channels undergo correlated Rayleigh fading, we first formulate an optimization problem to maximize the output signal-to-interference-plus-noise ratio (SINR) of the intended users. Then, by using the fact that channel uncertainty is norm-bounded and imperfect channel state information (CSI) is available at the BS, we transform the optimization problem to a support vector machine (SVM) regression one, and obtain the robust solution for the BF weight vectors by means of quadratic programming (QP) technique or iterative reweighted least squares (IRWLS) procedure. The computational cost of the proposed robust BF scheme depends on the number of channel vector samples rather than that of the antennas, thus it is suitable for the wireless systems with large-scale antennas. Finally, the efficiency and superiority of the proposed new scheme are confirmed through computer simulation.

In this paper, we address the problem of beamforming (BF) design in a cognitive relay network (CRN), where the cognitive network not only shares the spectrum with the primary one but also acts as a relay to assist the primary signal transmission. Considering that all of the wireless channels are subject to the correlated Rayleigh fading distribution, we first formulate a constrained optimization problem to minimize the total transmit power of the cognitive base station (CBS) with multiple antennas, while guaranteeing the quality-of-service (QoS) of the primary and secondary users and keeping the mutual interference below an acceptable level. Then, by adopting the available partial channel state information (CSI), and proposing an approximation method to relax the constraints, a support vector machine (SVM) based algorithm is presented to solve the optimization problem for the BF weight vectors. The benefit of the new approach is that the slow change of the partial CSI can be taken into account. Finally, computer simulation results are provided to confirm the superiority of the proposed BF design strategy.

In this paper, a dual-hop multiple-input multiple-output (MIMO) amplify-and-forward (AF) relay network, where the source, relay and destination are each equipped with multiple antennas, is studied. By deriving and maximizing the receive signal-to-noise ratio (SNR) at the destination, we first obtain the optimal beamforming (BF) weights for the relay network. In order to evaluate the performance of the relay network, we then investigate the outage probability (OP), probability density function (PDF) and moments of the receive SNR as well as the ergodic capacity of the system in a closed-form. Furthermore, the average symbol error rate (ASER) expression of the relay network with the optimal transmit-receive BF is derived for three commonly used modulation formats, namely, M-ary pulse amplitude modulation (M-PAM), M-ary phase shift keying (M-PSK), and M-ary quadrature amplitude modulation (M-QAM). Finally, computer simulations are conducted to demonstrate the validity and efficacy of the designed MIMO relay network and its performance analysis.

In this paper, a new transmit scheme combining beamforming (BF) with space-time block coding (STBC) is proposed for correlated fading channels. Based on maximizing the output mean signal-to-noise ratio (SNR) at the receiver, the transmitter BF weight vectors are first derived. Then, utilizing the minimal bit error rate (BER) upper bound as the design criterion, a simple power allocation algorithm is developed. Next, the BER performance of the system with the proposed transmit scheme is analyzed by considering an M-QAM constellation. Finally, computer simulation results are given to verify the effectiveness of the proposed scheme.

In this paper, we present an open-loop transmit scheme for MIMO communications. We first use the uplink channel correlation matrix (UCCM) to calculate the array weight vectors for downlink beamforming (DLBF). Then based on the symbol error rate (SER) upper bound as design criterion, we derive an algorithm for adaptive power allocation among multiple beams, and thus develop the transmit scheme joint adaptive beamforming (ABF) with space-time block coding (STBC). The main benefit of the scheme is that it can almost achieve the optimal performance with low complexity. Next, using the moment generation function (MGF) approach and the Gauss-Chebyshev integration, we derive a simple and accurate numerical analysis method for the proposed scheme under three widely used modulations. Finally, computer simulation results demonstrate the superiority of the open-loop transmit scheme.