In recent years, uncrewed aerial vehicles (UAVs) have become increasingly prevalent for collecting environmental data from various wireless sensors. However, existing research on employing UAVs to collect data from wireless sensors has often ignored the heterogeneous requirements of sensors. In this paper, we investigate joint deployment, user association, and power allocation for data collection in the UAV-assisted wireless sensor network to accommodate the heterogeneous requirements of sensors, where a novel satisfaction function is designed for three types of sensors, including sensors with delay requirements, sensors with energy consumption requirements, and sensors with both delay and energy consumption requirements. Leveraging the satisfaction function, we formulate the optimization problem aimed at jointly optimizing the positions of UAVs, the association between sensors and UAVs, and the power allocation of sensors to maximize overall satisfaction of sensors. In order to effectively address the considered problem, we decompose it into two subproblems, i.e., joint UAV deployment and user association subproblem, and transmission power allocation subproblem. An enhanced human evolutionary algorithm is developed to tackle the joint UAV deployment and user association subproblem, and the Lagrange dual method and gradient descent method are employed to solve the transmission power allocation subproblem. The suboptimal solution is achieved by iteratively addressing the two subproblems until convergence of the proposed enhanced Lagrange and gradient descent-based human evolutionary optimization algorithm is attained. Extensive simulations demonstrate the effectiveness of the proposed algorithm in enhancing overall satisfaction of sensors, underscoring its significant advantages in managing heterogeneous network environments.

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.

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.

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.

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.

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 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.

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.

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.