Visual Object Tracking (VOT) remains challenging under occlusion scenarios, where traditional trackers often suffer from feature degradation and target loss. To address this issue, we propose OASAMT, an occlusion-aware tracking framework that equips SAM2 with explicit temporal occlusion reasoning via two Temporal Convolutional Networks (TCNs) and a Dual-Memory Bank (DMB). Specifically, two TCN-based modules are designed to model temporal occlusion dynamics: the Temporal Occlusion Classifier (TOC) for inferring target occlusion states using confidence scores, mask IoU, and area ratio; and the Temporal Occlusion Predictor (TOP) for forecasting target bounding boxes during occlusion. The proposed DMB consists of a Non-Occlusion Memory Bank (N-OMB) and an Occlusion Memory Bank (OMB), explicitly decoupling reliable and occluded representations to prevent memory contamination and improve re-detection after occlusion. Additionally, to facilitate systematic evaluation under occlusion scenarios, we construct OccTrack, a dedicated occlusion-oriented dataset derived from four UAV-view benchmarks. Extensive experiments were conducted on the OccTrack, LaSOT, LaSOT ext , and GOT-10k datasets. The results demonstrate that OASAMT consistently outperforms SAM2.1 and other advanced trackers in both occlusion-specific and general tracking scenarios. The code and the dataset are available at https://github.com/ChaseFalcon99/OASAMT .

The deployment of extremely large antenna arrays (ELAAs) in extremely large-scale multiple-input multiple-output (XL-MIMO) systems introduces significant near-field effects, such as spherical wavefront propagation and spatially non-stationary (SnS) properties. When combined with the dual-wideband effects inherent to wideband systems, these phenomena fundamentally alter the channel’s sparsity patterns in the angular-delay domain, rendering existing estimation methods insufficient. To address these challenges, this paper reconsiders the channel estimation problem for wideband XL-MIMO systems. Leveraging the spatial-chirp property of array responses, we first quantitatively characterize the angular-delay domain sparsity of wideband XL-MIMO channels, revealing both global block sparsity and local common-delay sparsity. To effectively capture this structured sparsity, we then propose a novel column-wise hierarchical prior model that integrates a precision sharing mechanism and a Markov random field (MRF) structure. Building on this prior model, the channel estimation task is formulated as a multiple measurement vector (MMV)-based Bayesian inference problem. Tailored to the complex factor graph induced by this hierarchical prior, we develop a MMV-based hybrid message passing (MMV-HMP) algorithm. This algorithm performs message updates along the edges of the factor graph, and selectively applies either the variational message passing (VMP) or sum-product (SP) rules, depending on the factor-node structure and message tractability. Simulation results validate the effectiveness of the proposed column-wise hierarchical prior model through ablation studies and demonstrate that the MMV-HMP algorithm, while maintaining moderate computational complexity, consistently outperforms existing baselines which fail to capture the structured sparsity of wideband XL-MIMO channels.

This paper analyzes the spatial multiplexing capability in the line-of-sight (LoS) extremely large-scale multiple-input multiple-output (XL-MIMO) systems, where the impacts of array geometry (such as the shape, size, position, and orientation) on spatial degrees of freedom (DoF) is presented, resulting the validation of promising performance gain of the fluid antenna system (FAS). To this end, we first provide an exact closed-form expression for the local spatial bandwidth at the center of the receive array. Then, we analyze the maximum local spatial bandwidth at different spatial positions. An approximate closed-form expression for the achievable spatial DoF is obtained based on the derived local spatial bandwidth. Simulation results are presented for validation.

Existing channel estimation algorithms for ex-tremely large-scale multiple-input multiple-output (XL-MIMO) systems are predominantly designed for single-user scenarios and often overlook inter-user correlations. To address this limitation, this paper reformulates the joint multiuser channel estimation problem as a multiple-measurement vector (MMV)-based sparse signal recovery task. To solve this, we propose a novel row-wise hierarchical prior model that captures the structured sparsity of the joint multiuser channel in the angular-delay domain. Specifically, shared precision parameters for each row of the angular-delay domain channel are introduced to model common-row sparsity, while a Markov random field (MRF) is employed to encourage cluster sparsity. Building on this structured prior, we develop a computationally effi-cient channel estimation algorithm using variational message passing. Simulation results demonstrate that the proposed method significantly outperforms existing single-user-based approaches.

This paper investigates the spatial bandwidth of line-of-sight (LoS) channels in extra-large MIMO (XL-MIMO) systems. For linear large-scale antenna arrays (LSAAs) with transceivers randomly positioned in 3D space, a simple but accurate closed-form expression is derived to characterize the local spatial bandwidth. Based on this analysis, we examine the properties of local spatial bandwidth and further derive expressions for the effective spatial bandwidth and the achievable degrees of freedom (i.e., theKnumber) for LSAAs. We also conduct case studies for both coplanar and non-coplanar transmitting and receiving arrays, providing more concise and intuitive expressions for local spatial bandwidth and achievable spatial degrees of freedom. Finally, the impact of array geometry on LoS XL-MIMO channel capacity is explored. When the transmitting and receiving arrays are coplanar and perpendicular to the line connecting their centers, the effective degree of freedom of the LoS channel is found to be approximately maximized. This orientation also maximizes the channel capacity in near-field high-SNR scenarios.

With the growing demand for virtual reality (VR) applications, mobile wireless networks should support the connections of a massive number of VR users. To support ultra-high data rates of multiple simultaneously transmitted VR streamings, we propose a mobile edge computing (MEC)-assisted rate splitting (RS) VR streaming transmission system. In the proposed system, RS technology exploits the shared interests of multiple VR users and MEC offloads the partial rendering tasks to achieve a better quality of experience (QoE) for VR users. Aiming to minimize the total weighted energy consumption, we formulate a joint communication and computing resource optimization problem while ensuring the required distortion and latency of VR users. To deal with the formulated intractable problem, we propose a joint rendering offloading and resource allocation algorithm that alternately solves the subproblems of quantization parameters selection, rendering offloading decision, transmit precoding design, rate allocation of RS transmission, and computing resource allocation. The simulation results demonstrate the effectiveness of the proposed algorithm in saving energy consumption. Specially, the performance of the proposed algorithm is 22.1% higher than that of the multicast-unicast scheme and can achieve 95.9% of the exhaustive search based algorithm.

In this paper, we investigate the channel estimation problem for extremely large-scale multiple-input multiple-output (XL-MIMO) systems with a hybrid analog-digital architecture, implemented within a decentralized baseband processing (DBP) framework with a star topology. Existing centralized and fully decentralized channel estimation methods face limitations due to excessive computational complexity or degraded performance. To overcome these challenges, we propose a novel two-stage channel estimation scheme that integrates local sparse reconstruction with global fusion and refinement. Specifically, in the first stage, by exploiting the sparsity of channels in the angular-delay domain, the local reconstruction task is formulated as a sparse signal recovery problem. To solve it, we develop a graph neural networks-enhanced sparse Bayesian learning (SBL-GNNs) algorithm, which effectively captures dependencies among channel coefficients, significantly improving estimation accuracy. In the second stage, the local estimates from the local processing units (LPUs) are aligned into a global angular domain for fusion at the central processing unit (CPU). Based on the aggregated observations, the channel refinement is modeled as a Bayesian denoising problem. To efficiently solve it, we devise a variational message passing algorithm that incorporates a Markov chain-based hierarchical sparse prior, effectively leveraging both the sparsity and the correlations of the channels in the global angular-delay domain. Simulation results show the effectiveness and superiority of the proposed SBL-GNNs algorithm over existing methods, demonstrating improved estimation performance and reduced computational complexity.

To cope with the unendurable delay of maritime wireless networks (MWNs), this paper proposes a collaborative transmission framework utilizing a multi-antenna uncrewed surface vessel (USV) and multiple cache-aided buoys to satisfy the on-demand file requirements for remote users (RUs). Specifically, a direct transmission scheme is adopted for hit-requested files and a multi-hop transmission scheme is devised to handle cache misses. To fully exploit the local cache and signal processing capabilities, we integrate two schemes into a collaborative transmission framework, where the USV dynamically supports buoys in uncached file fetching, and buoys collaborate to forward both cached and fetched files to RUs through a cooperative beamforming policy. We aim to minimize the overall transmission completion time by jointly optimizing the USV trajectory, cooperative beamforming, and transmission duration under the constraints of USV kinetic, transmit power, and file requirements. By leveraging the completion condition analysis, the original problem is transformed into a sequence of one-slot problems and a finite-horizon problem, where the closed-form solution for the local caching beamforming at each buoy is derived. Due to the complexity of the multivariable coupling, we propose an equivalent rate transformation method for transmission strategy design. Numerical results validate the effectiveness of the proposed scheme and algorithm.

With the combination of extra-large arrays and high frequencies, near-field transmissions have become prevalent, challenging the validity of classical channel representations typically derived under the plane wavefront assumption. In this paper, we investigate the angular-domain representation of line-of-sight (LoS) extra-large MIMO (XL-MIMO) channels, considering the impact of spherical wavefront effects. First, we demonstrate the structured sparsity of LoS XL-MIMO channels in the angular domain. Leveraging this sparsity, we propose an effective spatial bandwidth channel representation method, which characterizes near-field LoS XL-MIMO channels as a superposition of multiple plane wave components, enabling us to capture the spherical wavefront effect in a low-dimensional angular channel. Subsequently, we introduce an angular-domain transceiver architecture based on this low-dimensional channel representation. This architecture could significantly facilitate the implementation of LoS XL-MIMO systems. Finally, simulation results confirm the effectiveness of the effective spatial bandwidth identification method and analyze the impact of various array geometries on the effective spatial bandwidth. Additionally, the availability of the angular-domain processing architecture is validated.

In this work, we investigate the joint visibility region (VR) detection and channel estimation (CE) problem for extremely large-scale multiple-input-multiple-output (XL-MIMO) systems considering both the spherical wavefront effect and spatial non-stationary (SnS) property. Unlike existing SnS CE methods that rely on the statistical characteristics of channels in the spatial or delay domain, we propose an approach that simultaneously exploits the antenna-domain spatial correlation and the wavenumber-domain sparsity of SnS channels. To this end, we introduce a two-stage VR detection and CE scheme. In the first stage, the belief regarding the visibility of antennas is obtained through a VR detection-oriented message passing (VRDO-MP) scheme, which fully exploits the spatial correlation among adjacent antenna elements. In the second stage, leveraging the VR information and wavenumber-domain sparsity, we accurately estimate the SnS channel employing the belief-based orthogonal matching pursuit (BB-OMP) method. Simulations show that the proposed algorithms lead to a significant enhancement in VR detection and CE accuracy as compared to existing methods, especially in low signal-to-noise ratio (SNR) scenarios.

Due to long-term reliability, wireless control systems (WCSs) have attracted significant interest recently. However, mission-critical control requires stringent ultra-reliability and low-latency communication (URLLC) with massive data delivery, which are major challenges for conventional wireless networks. This paper investigates downlink URLLC in WCS, where the semantic communication is adopted at the control center to extract task-oriented semantic information from original large-sized data. To efficiency, the control center utilizes the rate splitting policy to deliver semantic information through private messages, while the semantic knowledge is transmitted through one common message. We aim to maximize the weighted sum semantic information transmission rate by jointly optimizing the semantic information extraction, delivery duration, rate splitting, and transmit beamforming, subject to several practical constraints, including recovery accuracy, quality of service requirements, communication latency and computation delay. By the problem decomposition, two sub-problems are obtained, where the closed-form solution for the semantic information extraction is derived at each step. Due to the complexity of the multivariable coupling in the channel dispersion, we propose fractional transformation methods for rate splitting design. Numerical results confirm that the RSMA and semantic communication design can complement each other for multiplexing gains enhancement and latency reduction to achieve overloaded connections.

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 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.

With the increasing demand of virtual reality (VR) applications, wireless systems need to provide ultra-high data rate to support VR streaming for multiple users simultaneously. In this paper, we propose a mobile edge computing-assisted rate splitting (RS) VR streaming transmission scheme to pursue better quality of experience (QoE) and alleviate the computing burden of VR users (VUs). We formulate an optimization problem to minimize the weighted energy consumption while ensuring the required QoE. The quantization parameters selection, rendering offloading decision, transmit precoding, rate allocation, and computing resource allocation are optimized and a joint Wrendering offloading and resource allocation algorithm is proposed. Simulation results validate the efficiency of the proposed algorithm and reveal the performance gain obtained from RS.

With the combination of extra-large arrays and high frequencies, near-field transmissions have become increasingly prevalent. In this paper, we investigate the angular-domain representation of near-field line-of-sight (LoS) extra-large multiple-input-multiple-output (XL-MIMO) channels. Specifically, we first demonstrate the structured sparsity of the near-field LoS channel in the angular domain. By leveraging this sparsity property, we propose an effective spatial bandwidth channel representation method. This method characterizes near-field LoS XL-MIMO channels as a superposition of multiple plane wave components within the effective spatial band between transceiver arrays. Finally, simulation results validate the equivalence between the proposed representation and the existing antenna domain channel model and demonstrate the effects of array geometries on the effective spatial bandwidth.

In this work, we aim to minimize the deployment cost of active simultaneously transmitting and reflecting RISs (STAR-RISs) with the constraints of users’ communication quality requirements. To address this problem, we decouple the optimization problem into a transmission optimization subproblem and a deployment optimization subproblem. The transmission scheme is obtained by leveraging fractional programming (FP). In addition, we propose two approaches to efficiently obtain the deployment scheme of active STAR-RIS, namely a penalty-majorization-minimization (MM) method and a heuristic binary search method. Simulation results validated the effectiveness of the proposed algorithm in terms of deployment cost.

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.

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.

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.

The fifth-generation mobile communication technology (5G) requires the ability to support a variety of different types of services in parallel. This paper considers the problem of enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communication (URLLC) services being jointly precoded under a multiple-input single-output (MISO) system, where the optimization objective is to minimize the precoding power at the base station (BS). However, the problem is difficult to be solved directly due to the complexity caused by interference between URRLC and eMBB users. Thus, we first transform it into a quadratic constraint quadratic programming (QCQP) problem, and then relax it into a convex problem through semidefinite relaxation (SDR). After that, an SDR-Precoding Power Minimization (SDR-PPM) algorithm is proposed to obtain the optimal solution iteratively. Meanwhile, a low-complexity (LC) comparison algorithm is also proposed. Simulation results verify the effectiveness of the proposed algorithms and show the performance of the algorithms as well as the impact of the parameters on precoding power.

In the power grid networks, mobile edge computing (MEC) is a critical technology to improve processing capacity and real-time business processing while Intelligent Reconfigurable Surface (IRS) is a promising approach which can effectively improve the propagation environment. This paper considers an IRS-assisted MEC system, which minimizes the transmission energy consumption of Base Station (BS) and mobile devices (MDs) by jointly optimizing the transmission power of MDs, the receiving beamforming vector of BS, computing resource allocation, and the phase shift of IRS. The computation resource allocation and phase shift are optimized by using quadratic transformation and Lagrange dual transformation while the transmitted power of MDs is optimized by using Difference of Convex function Algorithm (DCA). Simulation results verify the effectiveness of the optimization method and the IRS-assisted MEC system.

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.

Traditional Canny edge detection algorithm has been widely used for the extraction of hyperbolic edges in ground penetrating radar (GPR). However, the Canny edge detection algorithm cannot adaptively determine the segmentation thresholds. Moreover, multiple hyperbolic edges are usually detected for each target, which increases the complexity of subsequent processing. In this letter, we propose a novel method that obtains only one hyperbolic edge for each target and automatically determines the segmentation threshold. Both simulation and real data results show that the proposed method is able to extract the target hyperbolic edges precisely and significantly improves the efficiency of target detection combined with the Hough transform in impulse GPR systems.

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.

Intelligent reflecting surface (IRS) is a revolutionizing technology for improving the spectrum and energy efficiency in wireless communications. In this paper, a new communication framework enabled by multiple unmanned aerial vehicle (UAV)-carried intelligent reflecting surfaces (IRSs) is proposed to enhance multiuser downlink transmissions. To take full advantage of multi-UAV-IRS assisted system, we formulate the problem as maximizing the downlink sum rate by jointly optimizing the placement of UAVs, active beamforming and power allocation at the BS, and passive beamforming at the IRSs. An efficient algorithm is proposed by invoking successive convex approximation technique to decompose the joint optimization problem into several subproblems, which can be solved in an iterative manner. Simulation results show that the proposed scheme achieves considerable sum rate gain, which outperforms other benchmark schemes.

Integrating non-orthogonal multiple access (NOMA) in intelligent reflecting surface (IRS) is expectedly an effective solution to enhance system's spectrum efficiency. In this paper, we investigate joint beamforming and power allocation for uplink NOMA transmission in an IRS-assisted cognitive satellite and terrestrial network operating at millimeter wave frequency band. Specifically, based only on imperfect channel state information in terms of the angular information of both primary users (PUs) and secondary users, we formulate an optimization problem to maximize the sum rate of the PUs in terrestrial network. To handle the resulting intractable optimization problem, we first transform the uncertainty channel vectors into a deterministic form with the aid of angular discretization. Then, by combining successive convex approximation with Taylor expansion and S-procedure methods, we propose an optimization scheme to jointly optimize the beamforming weight vector and power coefficients. Finally, simulation results show that the proposed scheme can achieve outstanding sum rate performance compared to state-of-the-art schemes.

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.

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.

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.

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.

Intelligent reflecting surface (IRS) is a new technique to improve the ergodic capacity in wireless networks. IRS consists of a large number of passive elements which digitally manipulate electromagnetic waves, and thus can act as a passive precoder in the communication. This paper introduces the IRS to millimeter wave (mmWave) multiple-input-multiple-output (MIMO) systems. Specifically, we consider a joint design of hybrid precoders at the base station (BS) and the passive precoder at the IRS to maximize the ergodic capacity of the system. In particular, since the instantaneous channel state information (CSI) of the BS-IRS link and the IRS-user link is challenging to obtain in practice, the statistical CSI is exploited for the joint hybrid and passive precoder design. However, such a design problem is challenging to solve due to the non-convexity. Thus, the block-coordinate-descent based algorithms are proposed to solve the problem efficiently. Simulation results demonstrate that, compared with the traditional systems without IRSs, the joint design of hybrid and passive precoding improves the ergodic capacity significantly. The results also show that adding some low-cost reflector elements at the IRS can help reduce the number of high-cost RF chains in the BS of the IRS-assisted mmWave MIMO systems.

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, 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.

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.

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.

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.

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 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 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.

This paper considers the problem of transmission scheduling of delay-sensitive data over a point-to-point correlated Rayleigh fading channel with channel estimation errors. According to the imperfect channel state information (CSI) and the buffer state, the transmit power and the modulation and coding scheme (MCS) are determined to jointly maximize the energy efficiency, and minimize transmission delay and overflow probability. To account of the effects of the channel estimation errors, the CSI imperfection is modeled as uncertain sets using the ellipsoidal approximation. Then the joint optimization problem is formulated using the weighted sum method. Using the idea of online learning, two algorithms are proposed to schedule the delay-sensitive data for the situations with and without the uncertainty bound of channel estimation, respectively. The numerical results indicate that the proposed online learning based scheduling algorithms can tackle the imperfect CSI issue and improve the system performance in terms of the energy efficiency, transmission delay and overflow probability. Moreover, the convergence times are very short, which highlights the feasibility of the proposed online learning based scheduling for practical systems.

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.

In this paper, we investigate secure transmission over the large-scale multiple-antenna wiretap channel with finite alphabet inputs. First, we show analytically that a generalized singular value decomposition (GSVD) based design, which is optimal for Gaussian inputs, may exhibit a severe performance loss for finite alphabet inputs in the high signal-to-noise ratio (SNR) regime. In light of this, we propose a novel Per-Group-GSVD (PG-GSVD) design which can effectively compensate the performance loss caused by the GSVD design. More importantly, the computational complexity of the PG-GSVD design is by orders of magnitude lower than that of the existing design for finite alphabet inputs in \cite{Wu2012TVT} while the resulting performance loss is minimal. Numerical results indicate that the proposed PG-GSVD design can be efficiently implemented in large-scale multiple-antenna systems and achieves significant performance gains compared to the GSVD design.

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.

Distributed antenna systems (DAS) is a promising approach to cope with challenges of next generation mobile communications. This paper studies the downlink ergodic capacity of a (N, K), (K ≤ N), virtual cell based DAS, in which each mobile station (MS) selects the N closest antenna ports (APs) to form its virtual cell and the K closest APs will serve the target MS cooperatively with a total transmit power constraint. Using the stochastic geometry, the locations of APs and MSs are modeled as two independent Poisson point processes, respectively. Then, a computationally tractable integral expression is derived for the downlink ergodic capacity of the (N, K) virtual cell based DAS. Numerical results indicate that the cooperation among multiple APs within each virtual cell can improve the downlink ergodic capacity significantly.

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 investigate secure transmission over the large-scale multiple-antenna wiretap channel with finite alphabet inputs. First, we investigate the case where instantaneous channel state information (CSI) of the eavesdropper is known at the transmitter. We show analytically that a generalized singular value decomposition (GSVD)-based design, which is optimal for Gaussian inputs, may exhibit a severe performance loss for finite alphabet inputs in the high signal-to-noise ratio regime. In light of this, we propose a novel Per-Group-GSVD (PG-GSVD) design, which can effectively compensate the performance loss caused by the GSVD design. More importantly, the computational complexity of the PG-GSVD design is by orders of magnitude lower than that of the existing design for finite alphabet inputs while the resulting performance loss is minimal. Then, we extend the PG-GSVD design to the case where only statistical CSI of the eavesdropper is available at the transmitter. Numerical results indicate that the proposed PG-GSVD design can be efficiently implemented in large-scale multiple-antenna systems and achieves significant performance gains compared with the GSVD design.

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.

In this paper, we focus on a dimmable visible light communication (VLC) system using PIN photodiodes. In such a system, the main distortion is caused by additive Gaussian noise, however, with a noise variance depending on the current signal strength. Under the non-negativity, peak power and dimmable average power constraints, the lower and upper bounds on the channel capacity are derived, respectively. Specifically, the derivation of the lower bound is based on the fact that the entropy of the output is always larger than the entropy of the input, while the derivation of the upper bound relies on the dual expression of the channel capacity and the notion of capacity-achieving input distribution that escape to infinity. Numerical results show that the gap between the lower and upper bounds is very small in the VLC application zone.

This paper aims to derive the upper and lower bounds for the channel capacity of dimmable visible light communications (VLC) systems. Because the information is modulated into the instantaneous optical intensity of light emitting diodes (LEDs), the transmitted optical intensity signal is represented by a nonnegative input, which is corrupted by an additive white Gaussian noise. Considering the illumination support and LED device ability in VLC systems, the transmitted optical intensity signal must satisfy the illumination and the allowed peak optical intensity constraints. With these constraints, a lower bound on the channel capacity is derived through the maximum mutual information between the channel input and output, which is determined by the source entropy maximization. By applying the dual expression of capacity, an upper bound on channel capacity is derived. Both the upper and lower bounds are presented as the closed forms. The numerical results show that the presented bounds are very tight. Moreover, the peak optical intensity constraints will result in the loss of channel capacity. However, such capacity loss is so small that it can be negligible when the allowed peak optical intensity is twice of the nominal optical intensity of LED devices.

In the last 2 years, all‐optical relaying techniques for free‐space optical (FSO) communications have drawn considerable attention. In this study, the outage performance of an FSO communication system with parallel all‐optical relays is investigated. A composite channel is established, which includes atmospheric loss, pointing error and atmospheric turbulence. To show the impact of the channel state information (CSI) on system performance, either full CSI or semi‐blind CSI at the relay nodes is considered. After that, the theoretical expressions of the outage probabilities for different scenarios are derived. Numerical results show that the derived expressions are quite accurate to evaluate the system performance. Moreover, the derived results demonstrate that the systems with semi‐blind CSI relaying can provide comparable performance to systems with full CSI relaying, and also indicate that the performance over strong turbulence channels with clear weather outperforms that over weak turbulence channels with light fog.

The transmitted optical intensity in visible light communications (VLC) system is represented by a nonnegative input, which is corrupted by an additive white Gaussian noise. In this paper, we consider that the transmitted optical intensity signal must satisfy the illumination constraint to fulfill the illumination support in VLC system. So the average transmitted optical intensity is constrained by a target illumination intensity which is determined by the nominal optical intensity of light source devices and the dimming target. The derivation of the upper bound is based on signal space geometry via a sphere-packing argument. The lower bound is derived through the maximum mutual information between the channel input and output, which is determined by the source entropy maximization. Both the bounds are presented in terms of the closed forms. The numerical results show that the presented bounds are very tight at the application zone of dimmable VLC links.

This paper studies the system uplink outage probability in distributed antenna systems (DAS). Firstly, owing to the complexity of actual wireless environments, this paper establishes a composite channel model which takes path loss, lognormal shadowing and Rayleigh fading into account. Then, the probability density function (PDF) of the output signal-to-noise ratio (SNR) is derived. After that, an analytical expression of the uplink outage probability for the MS over a given position is obtained after employing the selective transmission (ST) scheme. Further, considering the distribution of MSs in the cell, a closed-form expression of the system outage probability is derived. Numerical results show that the closed-form expression of the system outage probability can provide sufficient precision for evaluating the outage performance of DAS.

This paper analyzes the spectral efficiency of the distributed multi-input multi-output (D-MIMO) system with antenna cooperation over the rayleigh-log-normal fading channel. Under the cooperative transmission scheme (CTS), analytical expressions of the ergodic normalized capacity and outage capacity are derived. Moreover, to optimize the system performance, an adaptive CTS (ACTS) is proposed by the criterion of minimizing the outage capacity, and a theoretical expression is derived to operate the ACTS. Simulation results are presented to verify the theoretical outcomes.

This paper studies the problem of subcarrier grouping in the downlink of multi-carrier code division multiple access (MC-CDMA) systems with linear minimum mean square error (MMSE) receiver. Further, the problem is considered as combinatorial optimization problem. By theoretical analysis, an optimal subcarrier grouping scheme is proposed. Moreover, the proposed scheme is efficient in computational time. According to the instantaneous channel conditions, the proposed optimal scheme can be applied adaptively subcarrier grouping to maximize the system capacity. The simulation results show that the proposed scheme outperforms the conventional static schemes and is efficient in improving the system performance.

In this paper, an channel and queue aware fair (CQAF) packet scheduling scheme is proposed for the downlink packet transmission in multiuser orthogonal frequency division multiplexing (OFDM) systems. By making use of the information on the channel conditions and the queue lengths, the proposed CQAF packet scheduling scheme efficiently allocates the subcarriers, transmission power and modulation level to users under the constraints of total transmission power, the number of subcarriers, bit-error-rate (BER) requirement and generalized processor sharing (GPS)-based fairness requirement. The numerical results show that the proposed CQAF packet scheduling scheme can reduce the transmission delay and queue length significantly while maximizing system throughput and maintaining fairness among users.

Motivated by the increasing popularity of multicarrier code division multiple access (MC-CDMA) systems, this letter studies the throughput maximization of MC-CDMA as a downlink multi-carrier multiple-access scheme and presents the theoretical analysis for the optimal solution to throughput optimization problem of downlink MC-CDMA systems. Then, a low-complexity allocation method is proposed with negligible throughput loss. Finally, several resource allocation principles are presented.