The unmanned/uncrewed aerial vehicle (UAV) assisted mobile edge computing (MEC) technology has become a viable and flexible solution for providing computation offloading and energy charging services for ground users, especially in scenarios with terrible direct links. Therefore, latency has become one of the crucial design issues subject to the energy limitations of the UAV and users. Motivated by this, we study a latency-aware air ground hybrid MEC system with an assistant UAV and a ground base station (GBS) to serve and charge multiple users under both the time-division multiple access (TDMA) and non-orthogonal multiple access (NOMA) protocols. The task completion latency minimization problems are formulated by jointly optimizing the time slot scheduling, CPU frequency allocation, UAV's three dimensional (3D) trajectory design, transmit power allocation, as well as the number of required time slots. To address the formulated mixed integer non-convex optimization problems, we introduce an efficient alternating optimization algorithm with a double-loop structure. In the outer loop, we constantly adjust the number of time slots by employing the bisection search method and determine the search range via feasibility check. In the inner loop, we first transform the original subproblem into an equivalent problem that maximizes the minimum computation completion ratio of the users. Then we further deconmpose this transformed problem into four subproblems, which can be solved by a proposed iterative algorithm. Extensive experiments are con ducted to illustrate the efficacy and superiority of the proposed algorithm over the other benchmark schemes in minimizing the task completion latency, particularly in scenarios where the computing resource is limited or the density of users is high.

Integrated sensing, communications, and computation (ISCC) is becoming increasingly critical, particularly for enabling advanced intelligent applications. This paper proposes an ISCC framework for edge-intelligent networks, where edge intelligent devices (EIDs) cooperatively sense multiple mobile targets and simultaneously offload radar sensing data to a base station (BS) equipped with an edge server for processing. To address the time-varying nature of the network, we develop an online resource management strategy that maximizes the long-term average weighted sum rate (AWSR), subject to queue stability, average power constraints, and quality-of-service (QoS) requirements. Using the Lyapunov drift-plus-penalty framework, the original stochastic optimization problem is decomposed into a sequence of deterministic subproblems across time slots. At each time slot, sensing scheduling, transmit beamforming for both sensing and communications, receive beamforming for radar echoes, and computing resource allocation at the BS are jointly optimized through an efficient alternating optimization algorithm based on the current system state. Simulation results validate the effectiveness of the proposed online strategy, showing superior performance over baseline methods and revealing the influence of key parameters. In particular, a trade-off is observed between the AWSR and queue backlogs, which can be flexibly tuned via control parameters.

This paper investigates a full-space covert communication (CC) scheme aided by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS), which can resist the position randomness of the eavesdropper (PRE). In contrast to prevailing STAR-RIS assisted CC schemes assuming an eavesdropper with fixed position, the proposed CC scheme considers an eavesdropper which randomly locate at both sides of the STAR-RIS, leading to 360° eavesdropping risks. To restrict the eavesdropper’s ability to detect the CCs, the covert user is designed with two antennas working in full-duplex mode. One of the antennas is employed to receive the desired covert messages, while the other produces jamming signals at various power levels to impede the eavesdropper’s detection. Except the covert user, the base station (BS) also needs to serve a public user, which has certain quality of service (QoS) requirement. In order to construct a robust covert constraint, we analyze and derive a closed-form formula for the eavesdropper’s minimum detection error probability (DEP) in the worst-case situation. Subsequently, an optimization problem is established to maximize the covert rate of the system, through joint optimizing the bandwidth allocation, the active beamforming of the BS, and the passive beamforming of STAR-RIS, while adhering to the covert constraint and QoS requirement for the public user. An iterative algorithm is provided to tackle this non-convex optimization problem, utilizing the semi-definite relaxation (SDR) method and the augmented Lagrange technique. The simulation results demonstrate that the proposed STAR-RIS assisted CC scheme exhibits superior performance in resisting the full-space eavesdropping compared to other benchmark schemes, thereby validating the efficacy of the proposed scheme.

In this paper, we investigate the precoding design for multi-input multi-output (MIMO) integrated sensing and communication (ISAC) systems based on the concept of exploiting constructive interference (CI). Considering low-resolution digital-to-analog converter (DAC) and low-resolution phase shifter (PS) as two efficient hardware options, we propose corresponding finite-alphabet precoding schemes. The formulated optimization problem aims at maximizing a weighted objective function consisting of two parts: the minimum CI scaling factor for communications and target illumination power for radar sensing. The cross-entropy optimization (CEO) framework is employed to effectively solve this discrete non-convex optimization problem. Moreover, an “indirect power scaling” method is proposed for the precoding design based on DAC quantization to enhance the ISAC performance. From the simulation results, we can observe that the proposed precoding schemes can achieve satisfactory ISAC performance with low complexity. In the considered ISAC systems, increasing the quantization bits for DAC and PS quantizations can improve the ISAC performance, and the gain for DAC quantization is more pronounced.

In response to the “security blind zone” challenges faced by traditional reconfigurable intelligent surface (RIS)-aided covert communication (CC) systems, the joint distance-angle beamforming capability of frequency diverse RIS (FD-RIS) shows significant potential for addressing these limitations. Therefore, this paper initially incorporates the FD-RIS into the CC systems and proposes the corresponding CC transmission scheme. Specifically, we first develop the signal processing model of the FD-RIS, which considers effective control of harmonic signals by leveraging the time-delay techniques. The joint distance-angle beamforming capability is then validated through its normalized beampattern. Based on this model, we then construct an FD-RIS-assisted CC system under a multi-warden scenario and derive an approximate closed-form expression for the covert constraints by considering the worst-case eavesdropping conditions and utilizing the logarithmic moment-generating function. An optimization problem is formulated which aims at maximizing the covert user’s achievable rate under covert constrains by jointly designing the time delays and modulation frequencies. To tackle this non-convex problem, an iterative algorithm with assured convergence is proposed to effectively solve the time-delay and modulation frequency variables. To evaluate the performance of the proposed scheme, we consider three communication scenarios with varying spatial correlations between the covert user and wardens. Simulation results demonstrate that FD-RIS can significantly improve covert performance, particularly in angular-overlap scenarios where traditional RIS experiences severe degradation. These findings further highlight the effectiveness of FD-RIS in enhancing CC robustness under challenging spatial environments.

This paper proposes a novel pattern-reconfigurable fluid reconfigurable intelligent surface (FRIS) framework, where each fluid element can dynamically adjust its radiation pattern based on instantaneous channel conditions. To evaluate its potential, we first conduct a comparative analysis of the received signal power in point-to-point communication systems assisted by three types of surfaces: (1) the proposed pattern-reconfigurable FRIS, (2) a position-reconfigurable FRIS, and (3) a conventional RIS. Theoretical results demonstrate that the pattern-reconfigurable FRIS provides a significant advantage in modulating transmission signals compared to the other two configurations. To further study its capabilities, we extend the framework to a multiuser communication scenario. In this context, the spherical harmonics orthogonal decomposition (SHOD) method is employed to accurately model the radiation patterns of individual fluid elements, making the pattern design process more tractable. An optimization problem is then formulated with the objective of maximizing the weighted sum rate among users by jointly designing the active beamforming vectors and the spherical harmonics coefficients, subject to both transmit power and pattern energy constraints. To tackle the resulting non-convex optimization problem, we propose an iterative algorithm that alternates between a minimum mean-square error (MMSE) approach for active beamforming and a Riemannian conjugate gradient (RCG) method for updating the spherical harmonics coefficients. Simulation results show that the proposed pattern-reconfigurable FRIS significantly outperforms traditional RIS architectures based on the 3GPP 38.901 and isotropic radiation models, achieving average performance gains of 161.5% and 176.2%, respectively. Additionally, it reduces the required number of antennas and RIS elements by over 300%, offering substantial improvements in hardware efficiency.

In this paper, we investigate the precoding design for multi-input multi-output (MIMO) integrated sensing and communication (ISAC) systems with the assistance of constructive interference (CI). Considering low-resolution digital-to-analog converter (DAC) and low-resolution phase shifter (PS) as two efficient hardware options, we propose corresponding finite-alphabet precoding schemes based on them. The formulated optimization problem aims at maximizing a weighted objective function consisting of two parts: the minimum CI scaling factor for communications and target illumination power for radar sensing. The cross-entropy optimization (CEO) framework is employed to effectively solve this discrete non-convex optimization problem. Simulation results have been implemented to validate the superiority of the proposed algorithms. When the number of elements in the quantization sets is fixed, the ISAC performance of the precoding scheme based on DAC quantization is superior to that of the precoding scheme based on PS quantization, thanks to the more dispersed level distribution of DAC quantization.

Reconfigurable antennas (RAs) can actively adjust their radiation patterns to meet the demands of the communication network. In this paper, we investigate precoding and radiation pattern design in a multi-user multi-input single-output (MUMISO) downlink system. To fully exploit RAs, we introduce pattern sample vectors corresponding to each user's sub channel to assess the impact of pattern reconfiguration across all scattering paths. Based on this, we formulate the design problem by adopting closed-form precoding to optimize the radiation patterns for maximizing the sum rate. Specifically, we propose two methods: a heuristic approach using singular value optimization (SVO) and a successive convex approximation (SCA)-based minimization strategy to achieve desired patterns. These methods effectively address the non-convexity in the objective function. Numerical results demonstrate that the reconfigurable radiation pattern can strategically manipulate the wireless channel, leading to significant enhancements in system performance.

To address the increasing demand for transmission systems with enhanced efficiency and flexibility, two-way (TW) full-duplex (FD) systems have become a vital research focus in wireless communications. In this paper, a novel reconfigurable intelligent surfaces (RISs) assisted TW-FD spatial modulation (SM) model is proposed to improve the transmission reliability performance. With this proposal, we introduce the transmit-side SM to activate single antenna for fulfilling the transmission of amplitude and phase modulated symbols. The transmission is then reflected by the corresponding RIS to the antenna at the receiver end. Moreover, the prewired phase-offset (PPO) is included in the transmission process for attaining enhanced detecting reliability. To evaluate the performance boundaries of the proposed system, we consider the maximum likelihood detection based analytical derivations. More specifically, upon utilizing the moment generating function method, we derive the closed-form results for the system average bit error probability. Finally, with the simulation based comparative analysis, we verify the reliability and efficiency of the proposed TW-FD-RIS-PPOSM system.

In this paper, we investigate symbol-level precoding (SLP) and efficient decoding techniques for downlink transmission, where we focus on scenarios where the base station (BS) transmits multiple quadrature amplitude modulation (QAM) constellation streams to users equipped with multiple receive antennas. We begin by formulating a symbol-level joint design scheme aimed at collaboratively optimizing the transmit precoding and receive combining matrices. This coupled problem is addressed by employing the alternating optimization (AO) method, and closed-form solutions are derived by analyzing the obtained two subproblems. Furthermore, to address the dependence of the receive combining matrix on the transmit signals, we switch to maximum likelihood detection (MLD) method for decoding. Notably, we have demonstrated that the smallest singular value of the precoding matrix significantly impacts the performance of MLD method. Specifically, a lower value of the smallest singular value results in degraded detection performance. Additionally, we show that the traditional SLP matrix is rank-one, making it infeasible to directly apply MLD at the receiver end. To circumvent this limitation, we propose a novel symbol-level smallest singular value maximization problem, termed SSVMP, to enable SLP in systems where users employ the MLD decoding approach. Moreover, to reduce the number of variables to be optimized, we further derive a more generic semidefinite programming (SDP)-based optimization problem. Numerical results validate the effectiveness of our proposed schemes and demonstrate that they significantly outperform the traditional block diagonalization (BD)-based method.

We study constructive interference based block-level precoding (CI-BLP) in the downlink of multi-user multiple-input single-output (MU-MISO) systems. Specifically, our aim is to extend the analysis on CI-BLP to the case when the number of symbol slots in a given transmission block is smaller than the number of users. To this end, we mathematically prove the feasibility of using the pseudo-inverse to obtain a closed-form structure of the optimal CI-BLP precoding matrix. Similar to the case when the number of symbol slots in a given transmission block is not smaller than the number of users, we show that a quadratic programming (QP) optimization on simplex can be constructed. We also design a low-complexity algorithm based on the alternating direction method of multipliers (ADMM) framework, which can achieve a flexible trade-off between communication performance and execution time by modifying the maximum number of iterations. We further analyze the convergence and complexity of the proposed algorithm. Numerical results validate our analysis and the optimality of the QP optimization, and further show that the proposed ADMM algorithm can provide satisfactory results in dozens of iterations, which motivates the use of CI-BLP in practical wireless systems.

This paper explores a simultaneous tasks offloading and communications (STOC) scheme in mobile edge computing (MEC) networks, supported by the combination of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and the unmanned aerial vehicle (UAV). Different from the traditional MEC schemes, the proposed scheme concurrently considers the computation and communication capabilities of the MEC networks, which is actually more practical in reality. Specifically, an optimization problem is devised to maximize the weighted sum of the minimum computed task data and communication data, while ensuring the quality of service (QoS) constraints for STOC through joint design of time scheduling, resource allocation, active and passive beamforming, alongside with the UAV trajectory planning. This non-convex problem with strong couplings among variables is challenging to solve directly. Then, a novel alternating optimization method is proposed, leveraging the successive convex approximation (SCA) and semi-definite relaxation (SDR) techniques. We provide sufficient numerical results to validate the effectiveness of the proposed STOC scheme, which demonstrate that the proposed scheme supported by STAR-RIS and UAV outperforms five benchmark schemes in terms of performance gain. It is important to note that the proposed scheme offers a feasible and realistic way for the implementations of STOC in practical MEC networks.

Constructive interference (CI) precoding, which converts the harmful multi-user interference into beneficial signals, is a promising and efficient interference management scheme in multi-antenna communication systems. However, CI-based symbol-level precoding (SLP) experiences high computational complexity as the number of symbol slots increases within a transmission block, rendering it unaffordable in practical communication systems. In this paper, we propose a symbol-level extrapolation (SLE) strategy to extrapolate the precoding matrix by leveraging the relationship between different symbol slots within in a transmission block, during which the channel state information (CSI) remains constant, where we design a closed-form iterative algorithm based on SLE for both PSK and QAM modulation. In order to further reduce the computational complexity, a sub-optimal closed-form solution based on SLE is further developed for PSK and QAM, respectively. Moreover, we design an unsupervised SLE-based neural network (SLE-Net) to unfold the proposed iterative algorithm, which helps enhance the interpretability of the neural network. By carefully designing the loss function of the SLE-Net, the time-complexity of the network can be reduced effectively. Extensive simulation results illustrate that the proposed algorithms can dramatically reduce the computational complexity and time complexity with only marginal performance loss, compared with the conventional SLP design methods.

In this paper, we investigate the constructive interference (CI) based symbol-level precoding (SLP) design for integrated sensing and communication (ISAC) systems, where a multi-antenna base station (BS) serves multiple single-antenna communication users while simultaneously detecting targets of interest. Specifically, the minimum communication CI scaling factor among the users is maximized under radar performance constraint and power constraint. In order to solve the proposed optimization problem, two groups of approximate feasible domains are adopted to transform the optimization problem into convex. In order to improve the efficiency of the proposed precoding scheme, we adopt a modified Hooke-Jeeves pattern search algorithm for the convex subproblems. We further propose a weighted optimization scheme which considers the tradeoff between radar performance and communication performance as the objective function. By analyzing the Lagrangian function and Karush-Kuhn-Tucker (KKT) condition of the weighted optimization problem, we formulate the corresponding dual problem, which is a simple quadratic programming (QP) problem and can be easily solved. In addition, we further extend the proposed CI precoding scheme from symbol level to block level, in order to be more consistent with the currently used communication systems and achieve better ISAC performance. Extensive simulation results are provided to demonstrate the advantages and the effectiveness of the proposed symbol-scaling based CI-SLP design and CI-based block-level precoding (CI-BLP) design in ISAC systems.

A robust full-space physical layer security (PLS) transmission scheme is proposed in this paper considering the full-space wiretapping challenge of wireless networks supported by simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). Different from the existing schemes, the proposed PLS scheme takes account of the uncertainty on the eavesdropper’s position within the 360◦ service area offered by the STAR-RIS. Specifically, the large system analytical method is utilized to derive the asymptotic expression of the average security rate achieved by the security user, considering that the base station (BS) only has the statistical information of the eavesdropper’s channel state information (CSI) and the uncertainty of its location. To evaluate the effectiveness of the proposed PLS scheme, we first formulate an optimization problem aimed at maximizing the weighted sum rate of the security user and the public user. This optimization is conducted under the power allocation constraint, and some practical limitations for STAR-RIS implementation, through jointly designing the active and passive beamforming variables. A novel iterative algorithm based on the minimum mean-square error (MMSE) and cross-entropy optimization (CEO) methods is proposed to effectively address the established non-convex optimization problem with discrete variables. Simulation results indicate that the proposed robust PLS scheme can effectively mitigate the information leakage across the entire coverage area of the STAR-RIS-assisted system, leading to superior performance gain when compared to benchmark schemes encompassing traditional RIS-aided scheme.

This paper introduces a novel multi-user mobile edge computing (MEC) scheme facilitated by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and a unmanned aerial vehicle (UAV). Unlike existing MEC approaches, the proposed scheme enables bi-directional offloading, allowing users to concurrently offload tasks to the MEC servers located at ground base station (BS) and UAV with the support of the STAR-RIS. To evaluate the effectiveness of the proposed MEC scheme, we first formulate an optimization problem aiming at maximizing the energy efficiency of the system while ensuring the quality of service (QoS) constraints by jointly optimizing the resource allocation, user scheduling, passive beamforming of the STAR-RIS, and the UAV trajectory. A block coordinate descent (BCD) iterative algorithm designed with the Dinkelbach’s algorithm and the successive convex approximation (SCA) technique is proposed to effectively handle the formulated non-convex optimization problem characterized by significant coupling among variables. Simulation results indicate that the proposed STAR-RIS enhanced UAV-enabled MEC scheme possesses significant advantages in enhancing the system energy efficiency over other baseline schemes including the conventional RIS-aided scheme.

Despite the enhanced spectral efficiency brought by the integrated radar and communication technique, it poses significant risks to communication security when confronted with malicious radar targets. To address this issue, a reconfigurable intelligent surface (RIS)-aided transmission scheme is proposed to improve secure communication in two systems, i.e., the radar and communication co-existing (RCCE) system, where a single transmitter is utilized for both radar sensing and communication, and the dual-functional radar and communication (DFRC) system. At the design stage, optimization problems are formulated to maximize the secrecy rate while satisfying the radar detection constraint via joint active beamforming at the base station and passive beamforming of RIS in both systems. Particularly, a zero-forcing-based block coordinate descent (BCD) algorithm is developed for the RCCE system. Besides, the Dinkelbach method combined with semidefinite relaxation is employed for the DFRC system, and to further reduce the computational complexity, a Riemannian conjugate gradient-based alternating optimization algorithm is proposed. Moreover, the RIS-aided robust secure communication in the DFRC system is investigated by considering the eavesdropper’s imperfect channel state information (CSI), where a bounded uncertainty model is adopted to capture the angle error and fading channel error of the eavesdropper, and a tractable bound for their joint uncertainty is derived. Simulation results confirm the effectiveness of the developed RIS-aided transmission scheme to improve the secrecy rate even with the eavesdropper’s imperfect CSI, and comparisons between both systems reveal that the RCCE system can provide a higher secrecy rate than the DFRC system.

We study constructive interference based block-level beamforming (CI-BLB) in the downlink of multi-user multiple-input single-output (MU-MISO) systems. CI-BLB achieves im-proved performance over the traditional CI-based symbol-level beamforming (CI-SLB) method, because of its more intelligent power allocation scheme over the considered block of symbol slots. In this paper, we design a low-complexity algorithm based on the alternating direction method of multipliers (ADMM) framework, which can efficiently solve QP problems. We analyze the convergence and complexity of the proposed algorithm. Nu-merical results validate the optimality of the proposed algorithm, and further show that the proposed algorithm offers a flexible performance-complexity tradeoff by limiting the maximum num-ber of iterations, which motivates the use of CI - BLB in practical wireless systems.

In this paper, we investigate a hybrid reconfigurable intelligent surface (RIS) enabled integrated sensing and commu-nication (ISAC) system, in which a hybrid RIS is employed to assist a base station (BS) to sense a specified target, while interacting with multiple communication users (CUs) simultaneously. In particular, a hybrid RIS is introduced such that each of its surface module is able to switch between active and passive modes, reducing the system's power consumption. Subsequently, an optimization problem is formulated with the aim of maximizing the radar output signal-to-noise ratio while satisfying communication requirement for each CU, transmit power constraint for BS and the active RIS elements, by jointly optimizing radar recieve filter, BS's transmit beamforming matrix, RIS reflection coefficients, and the selection matrix that determines the working modes for each unite of the hybrid RIS. Since this design problem is not convex, we propose an alternating optimization based method to solve this problem. Eventually, upon the simulation analysis, we demonstrate that the performance achievable by the proposed scheme is significantly better than the counterparts assisted solely by the active or passive RIS.

This paper investigates the utilization of simultaneously transmitting and reflecting RIS (STAR-RIS) in supporting joint physical layer security (PLS) and covert communications (CCs) in a multi-antenna millimeter wave (mmWave) system, where the base station (BS) communicates with both covert and security users while defeating eavesdropping by wardens with the help of a STAR-RIS. Specifically, analytical derivations are performed to obtain the closed-form expression of warden’s minimum detection error probability (DEP). Furthermore, the asymptotic result of the minimum DEP and the lower bound of the secure rates are derived, considering the practical assumption that BS only knows the statistical channel state information (CSI) between STAR-RIS and the wardens. Subsequently, an optimization problem is formulated with the aim of maximizing the average sum of the covert rate and the minimum secure rate while ensuring the covert requirement and quality of service (QoS) for legal users by jointly optimizing the active and passive beamformers. Due to the strong coupling among variables, an iterative algorithm based on the alternating strategy and the semi-definite relaxation (SDR) method is proposed to solve the non-convex optimization problem. Simulation results indicate that the performance of the proposed STAR-RIS-assisted scheme greatly surpasses that of the conventional RIS scheme, which validates the superiority of STAR-RIS in simultaneously implementing PLS and CCs.

This paper investigates the multi-antenna covert communications assisted by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). In particular, to shelter the existence of covert communications between a multi-antenna transmitter and a single-antenna receiver from a warden, a friendly full-duplex receiver with two antennas is leveraged to make contributions where one antenna is responsible for receiving the transmitted signals and the other one transmits the jamming signals with a varying power to confuse the warden. Considering the worst case, the closed-form expression of the minimum detection error probability (DEP) at the warden is derived and utilized in a covert constraint to guarantee the system performance. Then, we formulate an optimization problem maximizing the covert rate of the system under the covertness constraint and quality of service (QoS) constraint with communication outage analysis. To jointly design the active and passive beamforming of the transmitter and STAR-RIS, an iterative algorithm based on semi-definite relaxation (SDR) method and Dinkelbach’s algorithm is proposed to effectively solve the non-convex optimization problem. Simulation results show that the proposed STAR-RIS-assisted scheme highly outperforms the case with conventional RIS, which validates the effectiveness of the proposed algorithm as well as the superiority of STAR-RIS in guaranteeing the covertness of wireless communications.

This paper investigates the multi-antenna covert communications assisted by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). In particular, to shelter the existence of communications between transmitter and receiver from a warden, a friendly full-duplex receiver with two antennas is leveraged to make contributions to confuse the warden. Considering the worst case, the closed-form expression of the minimum detection error probability (DEP) at the warden is derived and utilized as a covert constraint. Then, we formulate an optimization problem maximizing the covert rate of the system under the covertness constraint and quality of service (QoS) constraint with communication outage analysis. To jointly design the active and passive beamforming of the transmitter and STAR- RIS, an iterative algorithm based on globally convergent version of method of moving asymptotes (GCMMA) is proposed to effectively solve the non-convex optimization problem. Simu-lation results show that the proposed STAR-RIS-assisted scheme highly outperforms the case with conventional RIS.

With the explosive increase in computing demands and the rise of portable wearable devices, the concept of mobile-edge computing (MEC) has emerged and attracted a lot of attention from both academia and industry. Unmanned Aerial Vehicle (UAV) as flexible moving platform has been wide adopted as an edge computing server to help ground users compute their intensive tasks. Although UAV-assisted edge computing is capable to enhance the computing performance, there are still many challenges in this system, including UAV 3D trajectory design, the allocation of UAV computational resources and the communication time allocation between users and UAV. In this article, we try to solve these challenges in a UAV-assisted edge computing system, aiming at minimizing the completion time of computing users’ tasks. Specially, we propose a combination algorithm of the alternating optimization method and the bisection search method to minimize the delay of the whole system. The whole algorithm can be described in two iterative steps. In the first step, with given total number of time slot N assuming each slot with fixed length, we check whether the current N can satisfy the computational demands of the whole system through the alternating optimization algorithm to obtain the computational and time allocation. In the second step, we use the resource allocation results obtained in the first step to choose whether to increase or decrease N via the bisection search method. Then we repeat the first and second steps until we find the the smallest N that best fits the current computational demand. Extensive experimental results demonstrate that our proposed algorithm greatly reduces the users’ task completion time in comparison with traditional benchmarks. In addition, the convergence of the proposed algorithm can be guaranteed.

This paper investigates the utilization of simultaneously transmitting and reflecting RIS (STAR-RIS) in supporting joint physical layer security (PLS) and covert communications (CCs) in a multi-antenna millimeter-wave (mmWave) system. Specifically, analytical derivations are performed to obtain the closed-form expression of the warden’s minimum detection error probability (DEP) considering the practical assumption. Subsequently, an optimization problem is formulated with the aim of maximizing the average sum of the covert rate and the secure rate while ensuring the covert requirement and quality of service (QoS) for legal users by jointly optimizing the active and passive beamformers. Due to the strong coupling among variables, an iterative algorithm based on the alternating strategy and the semi-definite relaxation (SDR) method is proposed to solve the non-convex optimization problem. Simulation results indicate the superiority of STAR-RIS in simultaneously implementing PLS and CCs.

In this paper, we explore a multiple-input multiple- output (MIMO) system with orthogonal frequency division multiplexing (OFDM) transmissions and study the low peak- to-average power ratio (PAPR) MIMO-OFDM waveform design for integrated sensing and communications (ISAC). This is done by leveraging a weighted objective function on both communication and radar performance metrics under power and PAPR constraints. The formulated optimization problem can be equivalently transformed into several sub-problems which can be parallelly solved by the semi-definite relaxation (SDR) method and the optimal rank-1 solution can be obtained in general. The feasibility, effectiveness, and flexibility of the proposed low-PAPR MIMO-OFDM waveform design method are demonstrated by a range of simulations on communication sum rate, symbol error rate as well as radar beampattern and detection probability.

In this paper, we investigate a deep learning architecture for lightweight online implementation of a reconfigurable intelligent surface (RIS)-aided multi-user mobile edge computing (MEC) system, where the optimized performance can be achieved based on user equipment’s (UEs’) location-only information. Assuming that each UE is endowed with a limited energy budget, we aim at maximizing the total completed task-input bits (TCTB) of all UEs within a given time slot, through jointly optimizing the RIS reflecting coefficients, the receive beamforming vectors, and UEs’ energy partition strategies for local computing and computation offloading. Due to the coupled optimization variables, a three-step block coordinate descending (BCD) algorithm is first proposed to effectively solve the formulated TCTB maximization problem iteratively with guaranteed convergence. The location-only deep learning architecture is then constructed to emulate the proposed BCD optimization algorithm, through which the pilot channel estimation and feedback can be removed for online implementation with low complexity. The simulation results reveal a close match between the performance of the BCD optimization algorithm and the location-only data-driven architecture, all with superior performance to existing benchmarks.

In this paper, we explore optimization-based and data-driven solutions in a reconfigurable intelligent surface (RIS)-aided multi-user mobile edge computing (MEC) system, where the user equipment (UEs) can partially offload their computation tasks to the access point (AP). We aim at maximizing the total completed task-input bits (TCTB) of all UEs with limited energy budgets during a given time slot, through jointly optimizing the RIS reflecting coefficients, the AP's receive beamforming vectors, and the UEs' energy partition strategies for local computing and offloading. A three-step block coordinate descending (BCD) algorithm is first proposed to effectively solve the non-convex TCTB maximization problem with guaranteed convergence. In order to reduce the computational complexity and facilitate lightweight online implementation of the optimization algorithm, we further construct two deep learning architectures. The first one takes channel state information (CSI) as input, while the second one exploits the UEs' locations only for online inference. The two data-driven approaches are trained using data samples generated by the BCD algorithm via supervised learning. Our simulation results reveal a close match between the performance of the optimization-based BCD algorithm and the low-complexity learning-based architectures, all with superior performance to existing schemes in both cases with perfect and imperfect input features. Importantly, the location-only deep learning method is shown to offer a particularly practical and robust solution alleviating the need for CSI estimation and feedback when line-of-sight (LoS) direct links exist between UEs and the AP.

In this paper, we study the coexistence and synergy between edge and central cloud computing in a heterogeneous cellular network (HetNet), which contains a multi-antenna macro base station (MBS), multiple multi-antenna small base stations (SBSs) and multiple single-antenna user equipment (UEs). The SBSs are empowered by edge clouds offering limited computing services for UEs, whereas the MBS provides high-performance central cloud computing services to UEs via a restricted multiple-input multiple-output (MIMO) backhaul to their associated SBSs. With processing latency constraints at the central and the edge networks, we aim to minimize the system energy consumption used for task offloading and computation. The problem is formulated by jointly optimizing the cloud selection, the UEs' transmit powers, the SBSs' receive beamformers, and the SBSs' transmit covariance matrices, which is a mixed-integer and non-convex optimization problem. Based on the methods such as decomposition approach and successive pseudoconvex approach, a tractable solution is proposed via an iterative algorithm. The simulation results show that our proposed solution can achieve great performance gain over conventional schemes using edge or central cloud alone. Also, with large-scale antennas at the MBS, the massive MIMO backhaul can significantly reduce the complexity of the proposed algorithm and obtain even better performance.

A wireless-powered mobile edge computing (MEC) architecture with the cooperation between an access point (AP) and an unmanned aerial vehicle (UAV) is studied in this article. The AP, powered by the grid, is integrated with a high-performance processing server to help compute the user equipment's (UEs') offloaded tasks while also performing high-power laser-like energy charging for the UAV. The UAV serves as (1) an information relay to help the UEs offload/download their computation tasks/results, (2) an energy relay to broadcast energy from the AP to the UEs, as well as (3) an MEC server to help the UEs compute their tasks. We aim at maximizing the weighted sum completed task-input bits (WSCTB) of UEs under the task and time allocation, information-causality, energy-causality, and the UAV's trajectory constraints, by jointly optimizing the task and time allocation as well as the UAV's energy transmit power and trajectory. The formulated WSCTB maximization problem is non-convex, and we propose a three-step block coordinate descending algorithm to address three sub-problems iteratively for obtaining a proper solution. Simulation results show that the UAV's trajectories highly depend on the AP's location and the UEs' weight values. In addition, significant performance improvement is achieved by the proposed algorithm compared to some practical benchmarks.

In this paper, the synergy of combining the edge and central cloud computing is studied in heterogeneous cellular networks (HetNets). Multi-antenna small base stations (SBSs) equipped with edge cloud servers offer computing services for user equipment (UEs) proximally, whereas a macro base station (MBS) provides central cloud computing services for UEs via wireless multiple-input multiple-output (MIMO) backhaul allocated to their associated SBSs. With task processing latency constraints for UEs, the network energy consumption is minimized through jointly optimizing the cloud selection, the UEs' transmit powers, the SBSs' receive beamformers, and the SBSs' transmit covariance matrices. A mixed integer and non-convex optimization problem is formulated, and a decomposition algorithm is proposed to obtain a tractable solution iteratively. The simulation results confirm that great performance improvement can be achieved compared with the traditional scheme with central cloud computing only.

In this paper, we investigate a mobile edge computing (MEC) architecture with the assistance of an unmanned aerial vehicle (UAV). The UAV acts as a computing server to help the user equipment (UEs) compute their tasks as well as a relay to further offload the UEs' tasks to the access point (AP) for computing. The total energy consumption of the UAV and UEs is minimized by jointly optimizing the task allocation, the bandwidth allocation and the UAV's trajectory, subject to the task constraints, the information-causality constraints, the bandwidth allocation constraints, and the UAV's trajectory constraints. The formulated optimization problem is nonconvex, and we propose an alternating algorithm to optimize the parameters iteratively. The effectiveness of the algorithm is verified by the simulation results, where great performance gain is achieved in comparison with some practical baselines, especially in handling the computation- intensive and latency-critical tasks.

In this paper, we study an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) architecture, in which a UAV roaming around the area may serve as a computing server to help user equipment (UEs) compute their tasks or act as a relay for further offloading their computation tasks to the access point (AP). We aim to minimize the weighted sum energy consumption of the UAV and UEs subject to the task constraints, the information-causality constraints, the bandwidth allocation constraints and the UAV's trajectory constraints. The required optimization is nonconvex, and an alternating optimization algorithm is proposed to jointly optimize the computation resource scheduling, bandwidth allocation, and the UAV's trajectory in an iterative fashion. The numerical results demonstrate that significant performance gain is obtained over conventional methods. Also, the advantages of the proposed algorithm are more prominent when handling computation-intensive latency-critical tasks.

This paper studies the power-efficient joint radio and computational resource allocation for two near-far mobile devices in a wireless powered mobile edge computing system. To overcome the double-near-far effect for the farther device, cooperative communications in the form of relaying via the nearer device is considered for offloading. The access point (AP)'s total transmit power minimization problem is formulated under the constraints of the computation tasks, which is equivalent to a min-max problem and can be optimally solved by a two-phase method. Numerical results not only show the significant performance improvement of the proposed scheme, but also demonstrate its effectiveness in handling computation-intensive latency-critical (CILC) tasks and resisting the double-near-far effect.

This paper studies a mobile edge computing (MEC) system in which two mobile devices are energized by the wireless power transfer (WPT) from an access point (AP) and they can offload part or all of their computation-intensive latency-critical tasks to the AP connected with an MEC server or an edge cloud. This harvest-then-offload protocol operates in an optimized time-division manner. To overcome the doubly near-far effect for the farther mobile device, cooperative communications in the form of relaying via the nearer mobile device is considered for offloading. Our aim is to minimize the AP's total transmit energy subject to the constraints of the computational tasks. We illustrate that the optimization is equivalent to a min-max problem, which can be optimally solved by a two-phase method. The first phase obtains the optimal offloading decisions by solving a sum-energy-saving maximization problem for given an energy transmit power. In the second phase, the optimal minimum energy transmit power is obtained by a bisection search method. Numerical results demonstrate that the optimized MEC system utilizing cooperation has significant performance improvement over systems without cooperation.

A practical uncoordinated cooperative jamming (UCJ) scheme with multiple single-antenna helpers is proposed in this paper to enhance the physical layer security of single-input-multiple-output (SIMO) wiretap channel. In the scheme, both the intended receiver and the eavesdropper are equipped with multiple antennas and apply the minimum mean-square error (MMSE) combiner. The multiple uncoordinated single-antenna helpers transmit jamming signals independently to confound the eavesdropper, and we focus on power allocation for the helpers to solve the secrecy rate maximization (SRM) problem. Assuming that the statistical channel state information (CSI) concerning the eavesdropper is available, a convex conservative secrecy outage probability (SOP) constraint is derived and then used to solve the SRM problem with DC (difference of convex function) programming method. Furthermore, a bisection-like refinement method is provided to find a quality solution of the SRM problem with the original SOP constraint. Numerical results show that the proposed scheme has good secrecy performance especially when the number of helpers is large.