Earticle

Beyond 6G services and applications demand high and efficient processing capacity due to the massive connectivity of users equipment (UEs). However, the high computational capability and energy consumption of UEs are limited, which becomes a main challenge to overcome. Multi-access edge computing (MEC) has recently been studied widely as it can potentially assist complex tasks executed at UEs. Furthermore, several techniques have been proposed to optimize task offloading among users. Thus, another challenge in MEC is emerging due to the fact that mobile users do not always have a line-of-sight (LoS) to the base station (BS) due to the blocking object. Therefore, it can affect users data rate and result in incremental energy consumption. This research introduces the concept of reconfigurable intelligence surfaces (RIS) to support multiple-input-single-output (MISO) base stations (BS) in both uplink (UL) and downlink (DL) using BCD algorithms. While previous studies concentrate on enhancing task offloading and neglecting inter-user interference, this study suggests an optimization approach for UL and DL data rates, as well as minimizing task offloading delays. The results indicate that optimizing task placement, phase shift, and precoding can reduce the duration of task offloading.

This paper proposes a Machine Learning-enhanced Gradient-Method Based Linear Optimization (ML-GMBLO) framework for secure beamforming in intelligent reflecting surface (IRS)-assisted 6G wireless networks. The method jointly optimizes beamforming vectors, artificial noise, IRS phase shifts, and user associations through an iterative process. A hybrid offline?online architecture is employed, where a deep neural network (DNN) trained on representative channel features provides near-optimal initializations. This reduces convergence iterations by approximately 40% compared to Alternating Optimization with Semi-Definite Programming (AO-SDP) under identical 4-bit phase quantization. Under the same channel estimation errors and Doppler conditions, ML-GMBLO achieves around 30% higher secrecy rate (5.2 vs. 4.0 bps/Hz), 35% lower latency, and near-linear scalability with respect to IRS size. This integrated approach enhances physical layer security while maintaining compatibility with hardware constraints.

Reconfigurable intelligent Surface (RIS) can effectively improve the communication quality and extend the signal coverage range by reconfiguring the wireless communication transmission environment, which is very suitable for millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. In this paper, a product manifold optimization-based beamforming design (PMO-BD) algorithm for a RIS-assisted multi-user MIMO system is proposed to enhance fairness among users. We establish a lower bound for the minimum signal-to-interference-plus-noise ratio (SINR) among users as the optimization objective, which is subjected to multiple constraints. By maximizing the lower bound to ensure the quality of service (QoS) for each user, fairness among users get improved. The PMO-BD algorithm exploits the internal structure of the product manifold to deal with the high dimension and nonlinear characteristics of the problem effectively, thereby reducing algorithm complexity and enhancing convergence rates. Simulation results verify the advantages of the proposed PMO-BD algorithm in comparison to other algorithms.

With the surge in mobile traffic and the demand for wide coverage, Low-Earth Orbit (LEO) satellite internet has garnered significant attention. However, the limited battery capacity of LEO satellites necessitates efficient energy management. Meanwhile, content caching presents a promising solution to reduce transmission delays and energy consumption of LEO satellites by caching popular files nearby end users. Unlike terrestrial networks, content caching in LEO satellites requires joint optimization with multi-hop routing due to inherent structural differences in ISL (Inter-Satellite Link) networks. In this paper, we formulate an optimization problem aimed at minimizing the average transmission power of LEO satellites while ensuring timely content delivery. To this end, we apply Lyapunov optimization framework to obtain a Hierarchical Caching and Routing (HCR) algorithm where content caching decisions are made over longer periods while routing decisions are made over shorter periods. Finally, via extensive simulations, we demonstrate that the HCR algorithm excels existing algorithms, showing up to an average of 54% and 67% reduction in average queue backlog and average power consumption, respectively.

Improving the ability of an unmanned aerial vehicle (UAV) to perform tasks is of great significance in the future battlefield. This paper investigates the detection, tracking, and communication tasks performed by a radar-communication UAV under time and energy constraints. To balance the constraints and task performance, we formulate a joint detection, tracking, and communication optimization problem in a UAV-assisted radar and communication (RC) system by jointly optimizing dwell time, scheduling interval, and UAV trajectory. Due to the non-convex problem, we propose an alternative iterative optimization (AIO) algorithm to find the suboptimal solution. Simulation results demonstrate that the proposed algorithm can obtain higher joint performance than baseline schemes while reaching convergence.

This study proposes an energy-efficient framework for non-terrestrial networks (NTNs) integrating a low Earth orbit (LEO) satellite, an unmanned aerial vehicle (UAV)-mounted reconfigurable intelligent surface (RIS), and a terrestrial user. The framework jointly optimizes the UAV’s 3D trajectory, satellite beamforming vectors, and RIS reflection coefficients to maximize energy efficiency (EE), accounting for UAV propulsion energy consumption and Quality of Service (QoS) constraints. The resulting non-convex fractional problem is solved using a low-complexity iterative algorithm combining successive convex approximation (SCA) and second-order cone programming (SOCP). Simulation results reveal up to 35% EE improvement over baseline schemes, highlighting the framework’s scalability and practicality for sustainable NTN systems.

A new method is proposed to identify the joint structural parameters of complex systems using a frequency response function (FRF)-based substructuring method and an optimization technique. The FRF method is used to estimate the joint parameters indirectly by minimizing the difference between the reference and calculated responses using a gradient-based optimization technique with analytical gradient information. To assess the robustness of the identification method with respect to noisy input data, FRFs contaminated by uniformly distributed random noise were tested in a numerical example. The effects of the random noise and the magnitude of the connection stiffness values on the accuracy of the method were investigated while identifying the joint parameters. When the FRFs were contaminated with random noise, the proposed procedure performed well when used to identify the stiffness values, but the accuracy of identification is deteriorative when used to identify the damping coefficients. The joint parameters of a real bolted structure were also identified by the proposed method. The results show that it can be applied successfully to real structures, and that a hybrid approach using both calculated and measured FRFs in the substructure model can enhance the quality of the identification results.

In this paper, the 5 joint toggle link of an injection molding machine is optimized. A model considering toggle link kinematics, frictions at the pin joints, and stiffness is verified by a finite element analysis(FEA) and a measured result. The FEA was carried out by the commercial FEA software ANSYS and the clamping force was calculated by measuring the tie-bar deformation of the molding machine. The influence on the maximum clamping force and stroke ratio are analyzed for the length variations of links. Based on the mathematical model and design parameter, the clamping force, the stroke ratio, and the maximum stroke of moving plate are optimized by using the response surface method(RSM) for the length of the toggle link, where the lager-the-better, the norminal-is-best, and the smaller-the-better characteristics are applied for the clamping force, the stroke ratio, and the maximum stroke of moving plate.

This paper proposes the joint optimization of power allocation, subcarrier assignment and relay selection strategy for cooperative OFDM system to maximize the total capacity. We use Lagrangeandual approach for relaxing some available constraints and also make use of the simple greedy algorithm to perform lesser iterations to converge. Extensive experiments with realistic data prove that the subgradient dual method is approriate to dynamic resource allocation.

A two-bar plane truss is builtin MATLAB based on mathematical model. Then the authors use genetic algorithm toolbox to solve this problem. The parametric truss model is set up in the finite element analysis software ANSYS. It is analyzed by first-order algorithm. The comparison of two kinds results show the pure genetic algorithm doesn’t always have an advantage over other algorithms. In the end, a joint optimization method is put forward on the basis of genetic algorithm. It combines genetic algorithm based on MATLAB toolbox and numerical algorithm based on quasi-Newton method. This method is illustrated by the numerical example of the two-bar plane truss. The results show this joint optimization method can get the global optimal solution of this problem every time.

Aiming at the grid-connected problems of the double-fed induction generator (DFIG), this paper researches the control technology of the rotor excitation and the pitch angle and proposes a grid-connected DFIG joint optimization control strategy of the rotor excitation and the pitch angle based on the structure decentralized theory, and establishes the joint optimization model of the grid-connected DFIG. On the basis of the model, the rotor excitation current adaptive terminal sliding mode controller and the pitch angle controller are designed. The simulation results show that the proposed control strategy can achieve an effective control of the DFIG’s power angle and the DFIG’s rotor flux and reduce the adverse effects of the power angle when the double-fed wind generator and synchronous generator (SG) are in parallel operation. The algorithm given by this article not only improves the system's ability to resist external disturbances, but also enhances the transient stability of the power system. It has important actual significance and practical application value.

This article aims to apply a friction stir spot welding for producing a lap joint of AA1100 aluminum alloy and SGACD zinc coated steel. The experiment was designed by MINITAB and then investigated the relation among the friction spot joint parameters. The experimental results are as follows. The friction spot joining could successively produce the lap joint of AA1100 aluminum alloy and SGACD zinc coated steel. Interaction between the rotate speed, the hold time and the tool insert speed affected to vary the tensile shear strength of the lap joint. The prediction of the optimized welding parameters that indicated the tensile shear strength of 1966 N was the rotated speed of 4000 rpm, the pin hold time of 6 sec, the pin insert rate of 6 mm/min with the S/N ratio of 66.56 that was higher than that of the total mean S/N ratio. The practical experiment of the predicted welding parameters indicated the tensile shear strength of 2165 N and had the S/N ratio of 66.70 that was higher than the predicted tensile shear strength.

Destination-assisted jamming (DAJ) is usually used to protect confidential information against untrusted relays and eavesdroppers in wireless networks. In this paper, a DAJ-based untrusted relay network with multiple antennas installed is presented. To increase the secrecy, a joint optimization of beamforming and power allocation at the source and destination is studied. A matched-filter precoder is introduced to maximize the cooperative jamming signal by directing cooperative jamming signals toward untrusted relays. Then, based on generalized singular-value decomposition (GSVD), a novel transmitted precoder for confidential signals is devised to align the signal into the subspace corresponding to the confidential transmission channel. To decouple the precoder design and optimal power allocation, an iterative algorithm is proposed to jointly optimize the above parameters. Numerical results validate the effectiveness of the proposed scheme. Compared with other schemes, the proposed scheme shows significant improvement in terms of security performance.

Due to limited spectrum resources and differences in link loads, network congestion is one of the key issues in cognitive radio wireless mesh networks. In this letter, a congestion avoidance model with power control, channel allocation, and routing under the signal-to-interference-and-noise ratio is presented. As a contribution, a nested optimization scheme combined with a genetic algorithm and linear programming solver is proposed. Extensive simulation results are presented to demonstrate the effectiveness of our algorithm.

Joint optimization of preventive age replacement and inventory policy is considered in this paper. There are three decision variables in the problem: (i) preventive replacement age of the operating unit, (ii) order quantity per order and (iii) reorder point for spare replenishment. Preventive replacement age and order quantity are jointly determined so as to minimize the expected cost rate, and then the reorder point for meeting a desired service level is found. A numerical example is included to explain the joint optimization model.

When the number of users is finite, the performance improvement of the orthogonal random beamforming (ORBF) scheme is limited in high signal-to-noise ratio regions. In this paper, to improve the performance of the ORBF scheme, the user set and transmit power allocation are jointly determined to maximize sum rate under the total transmit power constraint. First, the transmit power allocation problem is expressed as a function of a given user set. Based on this expression, the optimal user set with the maximum sum rate is determined. The suboptimal procedure is also presented to reduce the computational complexity, which separates the user set selection procedure and transmit power allocation procedure.

Systems reliability can be improved by using redundancy and/or developing more reliable components. This papaer considers a joint optimization of both alternatives for a series system. It is shown that the n-stage optimization problem can be decomposed into n single stage subproblem. Each subproblem is further transformed into a univariate optimization problem for which a simple and efficient solution method is developed.

High-speed I/O channels require adaptive techniques to optimize the settings for filter tap weights at decision feedback equalization (DFE) read channels to compensate for channel inter-symbol interference (ISI) and crosstalk from multiple adjacent channels. Both ISI and crosstalk tend to vary with channel length, process, and temperature variations. Individually optimizing parameters such as those just mentioned leads to suboptimal solutions. We propose a joint optimization technique for crosstalk cancellation (XTC) at DFE to compensate for both ISI and XTC in high-speed I/O channels. The technique is used to compensate for between 15.7 dB and 19.7 dB of channel loss combined with a variety of crosstalk strengths from $60mV_{p-p}$ to $180mV_{p-p}$ adaptively, where the transmit non-return-to-zero signal amplitude is a constant $500mV_{p-p}$.