In this article, a prescribed-time output feedback controller is proposed for a class of uncertain nonlinear systems with unknown control coefficients and mismatched nonvanishing disturbances. Both unknown control coefficients and mismatched disturbances are tricky to address by the existing prescribed-time output feedback control frameworks. Differently, a novel prescribed-time control criterion in conjunction with Nussbaum functions is proposed, and prescribed-time stability is achieved. Furthermore, design methods for a state observer and a prescribed-time output feedback controller are developed. With the proposed control design, both the system output and observer errors are rigorously proved to converge to zero within a prescribed time. Moreover, the unified prescribed performance (UPP) of the system output and the satisfaction of output constraints are simultaneously achieved. Numerical simulations and experiments are provided to illustrate the effectiveness of the proposed control design.

In this article, we propose a self-triggered distributionally robust model predictive control algorithm for linear discrete systems with state chance constraints and unbounded stochastic disturbances. Assuming that only the first and second moments of the disturbance are accessible, we transform the objective function into a compact quadratic form and reformulate the state chance constraints into linear inequalities, which is more tractable when solving. In order to reduce communication and sampling times of the system, we propose a self-triggered update scheme, in which the state sampling and the control input sequence are updated when the control performance predicted based on the current sampling exceeds that of the periodic sampling scheme. We demonstrate that the optimization problem in the proposed self-triggered model predictive control (MPC) method is recursively feasible and stable. Numerical simulation results verify the effectiveness of the proposed algorithm.

This article studies the global asymptotic neural network (NN) tracking problem for full-state error constrained spacecraft attitude systems with actuator faults, inertia uncertainties, and external disturbances. In the literature, most existing NN control schemes can only achieve semiglobally bounded stability since the approximation capability of NNs is confined to a compact domain called the approximation domain. Differently, an attitude tracking control strategy in conjunction with a modified smooth switching mechanism is proposed to ensure the global asymptotic stability. Specifically, an adaptive NN controller is developed within the approximation domain to address unknown nonlinearities, and a robust controller is activated outside the approximation domain to drive back the system states. With the proposed design, both attitude and angular velocity errors (collectively defined as the full-state errors) are rigorously proven to globally asymptotically converge to zero. Moreover, the full-state errors are preserved within the unified prescribed performance constraints, which are uniform with respect to any initial conditions, thereby eliminating the requirement for offline computation of the performance boundary. In addition, the undesirable feasibility conditions on virtual control laws are completely eliminated. Theoretical analysis and numerical simulations validate the effectiveness of the proposed method.

We study a control-based design of beamforming and trajectory that incorporates the dynamic model, focusing on a scenario where a multi-antenna unmanned aerial vehicle (UAV) simultaneously performs radar sensing of multiple targets in a specific region and communication with multiple ground users. Two optimization problems are formulated for the three-degree-of-freedom (3-DoF) and six-degree-of-freedom (6-DoF) dynamic models of UAV, which are often overlooked in existing designs. These problems aim to maximize the average weighted communication rate while maintaining the dynamic constraints and the sensing service requirements by designing the UAV trajectory and the communication and sensing beamforming vectors. To deal with the challenges posed by the UAV dynamic constraints, we decompose the original problem into two subproblems: the communication and sensing beamforming design subproblem, and the UAV trajectory optimization subproblem. Given the UAV trajectory, we employ the sequential convex approximation (SCA) and semi-definite relaxation (SDR) methods to transform the beamforming design subproblem into a convex problem. Given the communication and sensing beamforming vectors, we propose a control-based approach with piecewise parameterization and exact penalty function strategies to transform the UAV trajectory optimization subproblem into a static nonlinear program, which can be efficiently solved by sequential quadratic programming (SQP). Numerical simulations indicate that the proposed scheme is more feasible in terms of the UAV control than the existing scheme in practical systems, with less performance loss or even no performance degradation.

Unmanned aerial vehicle (UAV) is regarded as a key enabling platform for low-altitude economy, due to its advantages such as three-dimensional (3D) maneuverability, flexible deployment, and line-of-sight (LoS) air-to-air/ground communication links. In particular, the intrinsic high mobility renders UAV especially suitable for operating as a movable antenna (MA) from the sky. In this paper, by exploiting the flexible mobility of UAV swarm and antenna position adjustment of MA, we propose a novel UAV swarm enabled two-level MA system, where UAVs not only individually deploy a local MA array, but also form a larger-scale MA system with their individual MA arrays via swarm coordination. We formulate a general optimization problem to maximize the minimum achievable rate over all ground user equipments (UEs), by jointly optimizing the 3D UAV swarm placement positions, their individual MAs’ positions (or local positions), and receive beamforming for different UEs. To gain useful insights, we first consider the special case where each UAV has only one antenna, under different scenarios of one single UE, two UEs, and arbitrary number of UEs. In particular, for the two-UE case, we derive the optimal UAV swarm placement positions in closed-form that achieves inter-UE interference (IUI)-free communication when the uniform plane wave (UPW) model holds, where the UAV swarm forms a uniform sparse array (USA) satisfying minimum safe distance constraint. While for the general case with arbitrary number of UEs, we propose an efficient alternating optimization algorithm to solve the formulated non-convex optimization problem. Then, we extend the results to the case where each UAV is equipped with multiple antennas. Numerical results verify that the proposed low-altitude UAV swarm enabled MA system significantly outperforms various benchmark schemes, thanks to the exploitation of two-level mobility to create more favorable channel conditions for multi-UE communications.

In this paper, we propose a time-division integrated sensing, communication and control (ISCC) scheme designed to dynamically enhance communication and sensing capabilities on the UAV platform. The UAV is dispatched to track a randomly moving target for capturing and transmitting sensing data to the base station via wireless communication. The goal is to leverage the ISCC framework for maximizing the cumulative sensing mutual information while guaranteeing successful data transmission by optimizing the allocation of the communication and sensing time slots together with the UAV’s control scheme. The formulated problem cannot be straightforwardly solved by off-the-shelf optimization algorithms due to the time-varying environment. To tackle this challenge, a constrained soft actor-critic (C-SAC) algorithm is developed, which dynamically switches between maximizing rewards and minimizing constraint violations to ensure robust performance in changing environments while maintaining the simplicity and efficiency of unconstrained policy optimization. Simulation results demonstrate that the proposed C-SAC algorithm outperforms dual-variable-based methods in handling the constrained problems, while extensive Monte Carlo tests confirm the robustness of the ISCC policy trained by the proposed algorithm, which adapts to varying target speeds and achieves higher cumulative mutual information compared to the point-mass UAV models.

This article presents an adaptive neural network-based sliding mode control (SMC) strategy aimed at achieving predefined-time and predefined-accuracy (PTPA) convergence of tracking errors. Notably, the proposed approach does not require prior knowledge of the upper bounds of uncertainties or the control direction. To ensure PTPA convergence and prevent singularity, a novel piecewise PTPA sliding mode manifold incorporating a nonlinear compensating term is introduced. This compensating term is specifically designed to address the bounded sliding-mode errors induced by model uncertainties and external disturbances. Furthermore, by enforcing a PTPA constraint on the sliding mode function and utilizing the Nussbaum function, the system states can converge to a predefined neighborhood of the sliding mode surface within a predefined time. An adaptive neural network-based PTPA SMC law is then developed, eliminating discontinuous terms and effectively mitigating the chattering issue. The proposed control scheme is rigorously proven to achieve PTPA convergence and asymptotic convergence. The efficacy of the designed control strategy is validated through two numerical examples, demonstrating its superior performance.

For quaternion-based spacecraft attitude tracking control, most existing prescribed performance control (PPC) schemes require that the scalar part of the error quaternion remain non-zero during the attitude maneuvering, meaning that only local operational range is allowed, which is too restrictive from practical point of view. Differently, by imposing a novel performance constraint, a piecewise virtual control law without singular term is designed, which is singular in most of the existing control schemes if the scalar part of the error quaternion is equal to zero, thus naturally obviating the classical assumption and resting in a global solution. Moreover, the unified prescribed performance constraints are imposed on both the attitude error and virtual angular velocity error. With such design, the performance boundary is uniform with respect to initial error, which implies that off-line computation of performance boundary for initial error can be avoided. Particularly, the initial value of the virtual angular velocity error is difficult to obtain off-line. In addition, by utilizing neural network approximation method, the Nussbaum gain technique and a positive integrable function, the proposed control is able to achieve asymptotic attitude tracking in the presence of inertia uncertainties, external disturbances and actuators fault, as rigorously authenticated by Lyapunov stability theory. A numerical example is provided to verify the effectiveness of proposed control scheme.

In this paper, we focus on the proximate orbital pursuit-evasion game of two spacecraft with magnitude-bounded continuous controls. Two scenarios are considered depending on whether the pursuer can access the control magnitude of the evader initially. When the pursuer accesses such information, we propose a fast numerical method for computing a sub-optimal control of the pursuer that guarantees the capture of the evader. The key to accelerating the solving is using a polynomial to approximate an important integration in the control computation, whose direct computing involves repeated calculations of matrices’ singular values. When the control magnitude of the evader is unavailable, we first propose a simple estimator for the evader’s control magnitude, by which the pursuer can estimate the maximal control effort of the evader disclosed over the history based on measured states. Based on the estimate, another fast method for computing the sub-optimal pursuing control is proposed based again on polynomial approximation. Then, considering practical measurements, we analyze how measurement noises influence the estimation of the control magnitude and the pursuing control computation. Finally, we present numerical examples to test the proposed computing methods and discuss the influence of noises.

The unmanned aerial vehicle (UAV)-enabled data collection system with a rotary-wing UAV and multiple ground nodes (GNs) is investigated in this paper. The average transmission data rate is maximized through the coordinated optimization of the GNs’ transmit power and the UAV’s trajectory. In particular, the UAV dynamic constraints and physical constraints are imposed. The UAV dynamics, which are governed by a group of differential equations, are usually ignored in existing works. As a consequence, the planned trajectory cannot be fully tracked by the controller in real world applications, which could lead to severe performance degradation. Thus, a control-based method is devised to address this issue. Specifically, by adopting the state-space model from control theory, the data collection problem is established as a dynamic optimization problem subject to state constraints, in which both of the decision variables and constraints are infinite-dimensional in nature. The key idea of the solution method is to convert the infinite-dimensional dynamic program into a finite-dimensional static nonlinear problem. This is achieved by deriving the required gradients of the dynamic optimization problem based on the control parametrization scheme and an exact penalty function method. The effectiveness and superiority of the proposed design are validated via numerical experiments.

The attitude tracking control with unwinding-free performance for rigid spacecraft is studied in this article. A full-state feedback control law based on a hyperbolic sine function is developed such that the resulted closed-loop system can achieve two stable equilibria. By Lyapunov stability theory and Barbalat’s Lemma, it is proven that the obtained closed-loop system is almost globally asymptotically stable, and achieves unwinding-free performance. Further, by constructing a strict Lyapunov function, it is demonstrated that the two stable equilibria are exponentially stable. Moreover, subsets of attraction regions corresponding to each stable equilibrium are characterized. The simulation results illustrate that the proposed attitude control scheme can effectively avoid the unwinding problem during attitude tracking.

Most existing barrier Lyapunov function (BLF)-based control schemes are only able to handle box-type constraints. However, many physical constraints are ellipsoidal constraints in real-world applications. Therefore, an asymptotic tracking control scheme embedded with an improved command filter is proposed for MIMO nonlinear systems under irregular ellipsoidal output constraints. A novel transformation function, explicitly depending on original constraints, is constructed. With such a design, not only ellipsoidal constraints but also partial ellipsoidal constraints, box-type constraints, and their combination-type constraints can be handled. Moreover, an innovative adaptive nonlinear filter is designed to resolve the complexity explosion problem caused by the repeated differentiations of virtual controllers. Different from the existing filters, the boundary layer errors of the proposed adaptive filter are fully compensated. Furthermore, tracking error is proved to be asymptotically converged to zero with the existence of model uncertainties and external disturbances. In addition, all signals within the closed-loop system are rigorously proved to be bounded. A numerical example is presented to verify the effectiveness of the designed control strategy.

In this paper, an adaptive finite-time bipartite consensus tracking control strategy is presented for a class of heterogeneous nonlinear nonstrict-feedback multi-agent systems (MASs) with output constraints. Firstly, to deal with the time-varying output constraints problem, an improved tan-type nonlinear mapping (NM) function is presented for the first time. And based on the improved NM function, a novel tracking error is constructed to design controller for each agent, which guarantees the bipartite consensus tracking is achieved while constraints requirement is not violated. Then, a state observer is designed to estimate the unmeasurable states of each agent. Moreover, in the case of unbalanced directed topological graph, a partition algorithm (PA) is employed to implement bipartite consensus tracking control. The developed distributed adaptive finite-time control strategy ensures that all the signals in the closed-loop system are bounded and the bipartite consensus tracking control is achieved in finite time. Finally, the validity of the designed control strategy is demonstrated by a simulation experiment.Note to Practitioners—At present, nonlinear MASs are widely used in practice, such as robots formation control, vehicular platoon systems control, etc. This paper investigated the adaptive finite-time bipartite consensus tracking control problem for a class of heterogeneous nonlinear nonstrict-feedback MASs with output constraints. In the scenarios of practical application, these two situations are common: 1) The communication topology graph of nonlinear MASs is unbalanced. 2) The output of each agent is constrained. Therefore, this paper presents an improved tan-type NM method to deal with the time-varying output constraints problem, and a partition algorithm is employed to implement bipartite consensus tracking control based on the unbalanced communication topology graph. Meanwhile, the nonsingular finite-time control strategy effectively improves the convergence of the studied nonlinear MASs. In addition, the system model and backstepping technology used in this paper are general and practical.

In this article, a trajectory tracking control strategy is proposed for robot manipulators via a fully actuated system (FAS) approach, which has shown its simplicity and flexibility for most of the nonlinear controller design. However, the motion control for robot manipulators is more complicated since unknown dynamical model, external disturbances, friction forces, and various physical constraints are required to be considered. Therefore, the FAS approach cannot be straightforwardly applied. To address these challenges, the dynamic model of robot manipulators is established via model identification methods. Furthermore, based on the identified model, an FAS composite control strategy with simple structure is designed, which is achieved by integrating a high-order disturbance observer (HODO) in the inner loop, with an FAS trajectory tracking controller in the outer loop. Specifically, the HODO is utilized for handling the uncertain dynamics and external disturbances. Moreover, the controller gains are optimized using a gradient-based optimal parameter tuning method (OPTM). By imposing joint angle constraints, joint angular velocity constraints, and input torque limits into the formulation, the OPTM also ensures the satisfaction of these physical constraints. Numerical simulations and experiments are provided to validate the performance of the proposed controller.

This article explores a novel singularity-free command-filtered adaptive fuzzy finite-time tracking control algorithm for the flexible robotic manipulator with dead-zone input. First, by considering the influence of the residual term in two cases, a novel practical finite-time stability criterion is presented, and the settling time is more accurately characterized, which is applied to the design of the flexible robotic manipulator. The unknown nonlinear function in the robotic manipulator is handled by employing an intelligent estimation technique based on fuzzy logic systems. Unlike the related finite-time work, a new adaptive command-filtered finite-time controller is constructed by fusing a piecewise continuous function in each step of the backstepping process, such that the singularity of the control signal of the finite-time results is completely circumvented. Further, the mathematical model of the dead zone is delicately reconstructed such that the dead-zone input can be rigorously designed using effective mathematical techniques. Finally, the robotic manipulator system demonstrates the validity of the designed finite-time algorithm.

In this article, a joint design of communication sensing and control (JDCSC) scheme is developed which focuses on a scenario where a cellular-connected unmanned aerial vehicles (UAV) senses a moving target. The goal is to maximize the sensing mutual information via jointly optimizing the transmit power, the trajectory of the UAV and the task completion time, while meeting the onboard energy, the communication service quality, and the UAV flight safety constraints. In particular, UAV dynamics are considered, which are usually ignored in the existing design and inferior communication and sensing quality of service might be resulted. The formulated problem is dynamic optimization problem, which is difficult to be solved. The control parameterization method and exact penalty function scheme are utilized to transform the problem into a static nonlinear program which can be solved by gradient-based methods. The effectiveness of the JDCSC approach is verified by carrying out some numerical examples.

This paper studies the three-dimensional (3D) trajectory optimization problem for unmanned aerial vehicle (UAV) aided wireless communication. Existing works mainly rely on the kinematic equations for UAV’s mobility modeling, while its dynamic equations are usually missing. As a result, the planned UAV trajectories are piece-wise line segments in general, which may be difficult to implement in practice. By leveraging the concept of state-space model, a control-based UAV trajectory design is proposed in this paper, which takes into account both of the UAV’s kinematic equations and the dynamic equations. Consequently, smooth trajectories that are amenable to practical implementation can be obtained. Moreover, the UAV’s controller design is achieved along with the trajectory optimization, where practical roll angle and pitch angle constraints are considered. Furthermore, a new energy consumption model is derived for quad-rotor UAVs, which is based on the voltage and current flows of the electric motors and thus captures both the consumed energy for motion and the energy conversion efficiency of the motors. Numerical results are provided to validate the derived energy consumption model and show the effectiveness of our proposed algorithms.

Wireless powered communication (WPC) has been considered as one of the key technologies in the Internet of Things (IoT) applications. In this paper, we study a wireless powered time-division duplex (TDD) multiuser multiple-input multiple-output (MU-MIMO) system, where the base station (BS) has its own power supply and all users can harvest radio frequency (RF) energy from the BS. We aim to maximize the users’ information rates by jointly optimizing the duration of users’ time slots and the signal covariance matrices of the BS and users. Different to the commonly used sum rate and max-min rate criteria, the proportional fairness of users’ rates is considered in the objective function. We first study the ideal case with the perfect channel state information (CSI), and show that the non-convex proportionally fair rate optimization problem can be transformed into an equivalent convex optimization problem. Then we consider practical systems with imperfect CSI, where the CSI mismatch follows a Gaussian distribution. A chance-constrained robust system design is proposed for this scenario, where the Bernstein inequality is applied to convert the chance constraints into the convex constraints. Finally, we consider a more general case where only partial knowledge of the CSI mismatch is available. In this case, the conditional value-at-risk (CVaR) method is applied to solve the distributionally robust system rate optimization problem. Simulation results are presented to show the effectiveness of the proposed algorithms.

In this article, a new relay-aided secure communication system is investigated, where a transmitter sends signals to a destination via an amplify-and-forward (AF) relay in the presence of an eavesdropper. We consider a general system configuration, where the source, relay, destination, and eavesdropper are all equipped with multiple antennas. In the practical scenarios of unknown eavesdropper's channel state information (CSI) and uncertainty of the eavesdropper's location, we aim to maximize the expected value of the system secrecy rate over the presumed distribution of the eavesdropper's channels, by exploiting the artificial noise (AN) transmitted by the source and relay nodes. The system design issue is formulated as a nonconvex stochastic optimization problem with a source transmission power constraint and a nonconvex relay transmission power constraint. A novel computational method is proposed to solve this challenging problem. The new method is developed based on an exact penalty function method together with a parallel stochastic decomposition algorithm. Numerical simulations are performed to study the effectiveness of the proposed scheme at various locations of the eavesdropper. Simulation results show that for most cases, secure communication can be achieved without the CSI knowledge of eavesdropper's channels, and the achievable secrecy rate follows the trend of a benchmark system where the eavesdropper's full CSI is available. In particular, the achievable system secrecy rate increases with the number of antennas at the legitimate users. Moreover, the optimal power allocated for the transmission of the AN increases with the system signal-to-noise ratio. The proposed computational method achieves a higher system secrecy rate than a conventional penalty function based approach.

Computation offloading over fog computing has the potential to improve reliability and reduce latency in future networks. This paper considers a scenario where roadside units (RSUs) are installed for offloading tasks to the computation nodes including nearby fog nodes and a cloud center. To guarantee the reliable communication, we formulate the first subproblem of power allocation, and leverage the conditional value-at-risk approach to analyze the successful transmission probability in the worse-case channel condition. To complete computation tasks with low latency, we formulate the second subproblem of task allocation into a multi-period generalized assignment problem (MPGAP), which aims at minimizing the total delay by offloading tasks to the `right' fog nodes at `right' period. Then, we propose a modified branch-and-bound algorithm to derive the optimal solution and a heuristic greedy algorithm to obtain approximate performance. In addition, the master problem is proposed as a non-convex optimization problem, which considers both the reliability-guaranteed and delay-sensitive requirements. We design the Lagreedy algorithm by combining the subgradient algorithm and the heuristic algorithm. Comprehensive evaluations demonstrate that the Lagreedy is able to obtain the shortest delay with a high power consumption, while the branch-and-bound algorithm can achieve both shorter delay and lower power consumption with reliability guarantees.

Backscatter communications have been considered as one of the key technologies in the Internet of Things (IoT) applications. In this paper, we consider a multitag ambient backscatter system, where the multiple tags can harvest radio frequency (RF) energy from the power station and backscatter the RF signals to the reader for data transmission. In order to guarantee the throughput requirements, we aim to maximize the minimum user rate among all the tags by jointly optimizing the backscatter time allocation and power reflection coefficient. Channel state information (CSI) mismatch is taken into account in our proposed optimization problem, which leads to a robust chance-constrained optimization problem. To deal with the nonconvex chance constraints, we propose two safe approximation methods: 1) Bernstein-type-in-equality and 2) conditional value-at-risk (CVaR), applying to the Gaussian distribution and arbitrary distribution of channel estimation errors, respectively. In addition, we develop an alternating optimization algorithm to obtain the optimal value of minimum throughput. Finally, simulation results reveal that the CVaR-based method outperforms the Bernstein-type-inequality-based method for the non-Gaussian channel estimation error.

The emerging distributed satellite cluster network (DSCN) holds great promise in various practical fields, including earth observation, disaster rescue, and tracking of forest fires. In the DSCN environment, it is essential to achieve the best data delivery performance by coordinating multi-dimensional heterogeneous and dynamic resources. However, in real-world applications, the distribution of long-term data arrival is not often fully known. Motivated by this fact, we propose a distributionally robust two-stage stochastic optimization framework with considering the dynamic network resources and the partially known distribution information of long-term data arrival. Aiming at maximizing the total network reward, we formulate a two-stage stochastic flow optimization problem based on the extended time expanded graph. Then, we introduce an ambiguity set for the uncertain distribution of the long-term random data arrival inspired by the idea from the distributionally robust optimization. On the basis of the proposed ambiguity set, we further propose a data arrival distribution robust two-stage recourse (DADR-TR) algorithm by converting the original stochastic optimization problem into a deterministic cone optimization problem, which is computationally tractable. The extensive simulations have been conducted to evaluate the impact of various network parameters on the algorithm performance and further validate that the proposed DADR-TR algorithm can achieve high data delivery performance without full distribution information of the long-term data arrival.

This letter is concerned with a robust minimum variance beamformer design. To hedge the mismatch between the true and the assumed steering vectors, a distributionally robust beamformer (DR-beamformer) is proposed. The tractable reformulation of this beamformer is developed. Compared with the existing robust beamformers (e.g., worst-case robust beamformer and Gaussian robust beamformer), the proposed robust beamformer does not assume full knowledge of the channel mismatch. Therefore, it is more flexible in practice and more general in formulation. In addition, the relationships of the proposed robust beamformer to the existing ones are investigated. The performance gain of the DR-beamformer over the other robust beamformers is highlighted through numerical simulations.

A two-hop amplify-and-forward multiple-input multiple-output relay system with direct link is considered in this paper. The relay node has no self-power supply and relies on harvesting the radio frequency energy transferred from the source node to forward information from source to destination. In particular, we consider the time switching (TS) protocol between wireless information and energy transfer. We study the joint optimization of the source and relay precoding matrices and the TS factor to maximize the achievable source-destination rate when a single data stream is transmitted from the source node. The optimal structure of the source and relay precoding matrices is derived, which reduces the original problem to a simpler optimization problem. The simplified problem is then solved efficiently by a two-step method. Numerical simulations show that the proposed algorithm yields a higher rate and better rate-energy tradeoff than suboptimal approaches.

In this paper, we study a two-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay system, where the relay node has no self-power supply, and relies on harvesting the radio frequency energy transferred from the source node to forward information from source to destination. We apply the time switching (TS) protocol between wireless information and energy transfer. As a novel contribution of this paper, we propose a more general energy consumption constraint at the source node during the information and energy transfer, which includes the constant power constraints used in existing works as special cases. We study the joint optimization of the source precoding matrices, the relay amplifying matrix, and the TS factor to maximize the source-destination mutual information (MI). The optimal structure of the source and relay matrices is derived, which reduces the original transceiver optimization problem to a simpler power allocation problem. Numerical simulations show that the proposed algorithm yield higher system MI and better rate-energy tradeoff than an existing approach.

A receiver design problem for multi-access space-time block coded multiple-input multiple-output systems is considered. To hedge the mismatch between the true and the estimated channel state information (CSI), several robust receivers have been developed in the past decades. Among these receivers, the Gaussian robust receiver has been shown to be superior in performance. This receiver is designed based on the assumption that the CSI mismatch has Gaussian distribution. However, in real-world applications, the assumption of Guassianity might not hold. Motivated by this fact, a more general distributionally robust receiver is proposed in this paper, where only the mean and the variance of the CSI mismatch distribution are required in the receiver design. A tractable semi-definite programming (SDP) reformulation of the robust receiver design is developed. To suppress the self-interferences, a more advanced distributionally robust receiver is proposed. A tight convex approximation is given and the corresponding tractable SDP reformulation is developed. Moreover, for the sake of easy implementation, we present a simplified distributionally robust receiver. Simulations results are provided to show the effectiveness of our design by comparing with some existing well-known receivers.

Zero-forcing beamforming (ZFBF) is a popular pre-coding scheme for MIMO systems. Most of the studies in the literature are under total power constraints. However, the per-antenna power constraints (PAPC) are more realistic. The state-of-the-art method is interior point method which is expensive to realize in practice due to the high computational complexity. Hence, a low complexity zero-forcing precoding scheme under the per-antenna power constraints is proposed in this paper. This is achieved by introducing a regularized dual method. Simulations are carried out to show the effectiveness of the proposed method, which has a low computational complexity. In addition, the algorithm can be implemented in parallel to further reduce the computational complexity.

Based on a ring, a disk and an elliptical scattering models, the power spectrum densities (PSDs) are derived and investigated for fixed-to-fixed (F2F) propagation scenarios where a local scatterer is moving in any direction with random velocity at the predefined geometries of the models. The velocity distributions of the scatterers are assumed to follow uniform, exponential and mixed Gaussian, and the transceiver vehicles are moving either with low- and high-speed. The results show that the one-ring, disk scattering model and the model in [15] are very close in describing the PSDs for the F2F radio channels. Moreover, different shape factors have little effect on the PSDs in the disk model. As the shape factor is large enough, the disk model tends to be the same as the one-ring model. The PSDs derived from the elliptical model are different from the one-ring and disk models because the scatterers are distributed not only close to the transceiver ends, but also between the transceiver.

This letter proposes a new algorithm for solving the optimal zero-forcing beamforming problem that maximizes the user achievable rate with restriction on the per-antenna element power constraints. An accelerated gradient method with step size search is proposed for solving the problem. For each iteration of the gradient approach, a quick one dimensional search is employed to obtain the step size. The advantage of the step-size search is that it is relatively fast and requires only a few calculations of the objective function. Design examples show that the proposed algorithm converges faster than the gradient approach and the accelerated gradient approach with a constant step size while achieving a low computational complexity.