Joint Transceiver Designs for Full-Duplex MIMO SWIPT Systems Based on MSE Criterion

For the simultaneous wireless information and power transfer(SWIPT), the full-duplex MIMO system can achieve simultaneous transmission of information and energy more efficiently than the half-duplex. Based on the mean-square-error(MSE) criterion, the optimization problem of joint transceiver design with transmitting power constraint and energy harvesting constraint is formulated. Next, by semidefinite relaxation(SDR) and randomization method, the SDRbased scheme is proposed. In order to reduce the complexity, the closed-form scheme is presented with some simplified measures. Robust beamforming is then studied considering the practical condition. The simulation results such as MSE versus signal-noise-ratio(SNR), MSE versus the iteration number, well prove the performance of the proposed schemes for the system model.

Towards the Deep Convergence of Communication and Computing in RAN: Scenarios, Architecture, Key Technologies, Challenges and Future Trends

To accommodate the diversified emerging use cases in 5G,radio access networks(RAN)is required to be more flexible,open,and versatile.It is evolving towards cloudification,intelligence and openness.By embedding computing capabilities within RAN,it helps to transform RAN into a natural cost effective radio edge computing platform,offering great opportunity to further enhance RAN agility for diversified services and improve users’quality of experience(Qo E).In this article,a logical architecture enabling deep convergence of communication and computing in RAN is proposed based on O-RAN.The scenarios and potential benefits of sharing RAN computing resources are first analyzed.Then,the requirements,design principles and logical architecture are introduced.Involved key technologies are also discussed,including heterogeneous computing infrastructure,unified computing and communication task modeling,joint communication and computing orchestration and RAN computing data routing.Followed by that,a VR use case is studied to illustrate the superiority of the joint communication and computing optimization.Finally,challenges and future trends are highlighted to provide some insights on the potential future work for researchers in this field.

Joint Return Value Estimation of Significant Wave Heights and Wind Speeds with Bivariate Copulas

The joint design criteria of significant wave heights and wind speeds are quite important for the structural reliability of fixed offshore platforms.However,the design method that regards different ocean environmental variables as independent is conservative.In the present study,we introduce a bivariate sample consisting of the maximum wave heights and concomitant wind speeds of the threshold by using the peak-over-threshold and declustering methods.After selecting the appropriate bivariate copulas and univariate distributions and blocking the sample into years,the bivariate compound distribution of annual extreme wave heights and concomitant wind speeds is constructed.Two joint design criteria,namely,the joint probability density method and the conditional probability method,are applied to obtain the joint return values of significant wave heights and wind speeds.Results show that(28.5±0.5)m s^(-1)is the frequently obtained wind speed based on the Atlantic dataset,and these joint design values are more appropriate than those calculated by univariate analysis in the fatigue design.

Optimal Design of the Modular Joint Drive Train for Enhancing Cobot Load Capacity and Dynamic Performance

Automation advancements prompts the extensive integration of collaborative robot(cobot)across a range of industries.Compared to the commonly used design approach of increasing the payload-to-weight ratio of cobot to enhance load capacity,equal attention should be paid to the dynamic response characteristics of cobot during the design process to make the cobot more flexible.In this paper,a new method for designing the drive train parameters of cobot is proposed.Firstly,based on the analysis of factors influencing the load capacity and dynamic response characteristics,design criteria for both aspects are established for cobot with all optimization design criteria normalized within the design domain.Secondly,with the cobot in the horizontal pose,the motor design scheme is discretized and it takes the joint motor diameter and gearbox speed ratio as optimization design variables.Finally,all the discrete values of the optimization objectives are obtained through the enumeration method and the Pareto front is used to select the optimal solution through multi-objective optimization.Base on the cobot design method proposed in this paper,a six-axis cobot is designed and compared with the commercial cobot.The result shows that the load capacity of the designed cobot in this paper reaches 8.4 kg,surpassing the 5 kg load capacity commercial cobot which is used as a benchmark.The minimum resonance frequency of the joints is 42.70 Hz.

Conceptual design of compliant translational joints for high-precision applications

Compliant translational joints （CTJs） have been extensively used in precision engineering and microelectromechanical systems （MEMS）. There is an increasing need for designing higher-performance CTJs. This paper deals with the conceptual design of CTJs via three approaches： parallelogram based method, straight- line motion mechanism based method and combination based method. Typical emerging CTJ designs are reviewed by explaining their design principles and qualitatively analyzing their characteristics. New CTJs are proposed using three approaches, including an asymmetric double parallelogram mechanism with slaving mechanism, several compact and symmetric double parallelogram mechanisms with slaving mechanisms and a general CTJ using the center drift compensation and a CTJ using Roberts linkage and several combination designs. This paper provides an overview of the current advances/progresses of CTJ designs and lays the foundation for further optimization, quantitative analysis and characteristic comparisons.

Energy-Efficient Federated Edge Learning with Joint Communication and Computation Design

This paper studies a federated edge learning system,in which an edge server coordinates a set of edge devices to train a shared machine learning(ML)model based on their locally distributed data samples.During the distributed training,we exploit the joint communication and computation design for improving the system energy efficiency,in which both the communication resource allocation for global ML-parameters aggregation and the computation resource allocation for locally updating ML-parameters are jointly optimized.In particular,we consider two transmission protocols for edge devices to upload ML-parameters to edge server,based on the non-orthogonal multiple access(NOMA)and time division multiple access(TDMA),respectively.Under both protocols,we minimize the total energy consumption at all edge devices over a particular finite training duration subject to a given training accuracy,by jointly optimizing the transmission power and rates at edge devices for uploading ML-parameters and their central processing unit(CPU)frequencies for local update.We propose efficient algorithms to solve the formulated energy minimization problems by using the techniques from convex optimization.Numerical results show that as compared to other benchmark schemes,our proposed joint communication and computation design significantly can improve the energy efficiency of the federated edge learning system,by properly balancing the energy tradeoff between communication and computation.

Design of Energy Modulation Massive SIMO Transceivers via Machine Learning

This paper considers a massive single-input multiple-output(SIMO)system,where multiple singleantenna transmitters simultaneously communicate with a receiver equipped with a large number of antennas.Different from the conventional noncoherent transceivers which require a certain level of the statistical information on the channel fading,we propose a joint transceiver design method based on machine learning,requiring a limited number of channel realizations.In the proposed method,the multiple transmitters,the channel,and the receiver are represented with a deep neural network(NN),and an autoencoder is adopted to minimize the end-to-end transmission error probability.Besides,the relationship between the number of training samples and the transmission error probability is analyzed based on the confidence interval method.Simulation results show that the proposed NN-based transceiver achieves lower transmission error probability in typical scenarios,and is more robust against the channel parameters variation compared with the existing methods.

MMSE based joint Tomlinson-Harashima source and linear relay precoder design in non-regenerative MIMO relay system

This paper proposes a joint nonlinear transceiver design scheme based on minimum mean square error （MMSE） criterion for non-regenerative multiple input multiple output （MIMO） relay system. The proposed scheme decomposes the error covariance matrix, reformulates the original joint design problem as two separate optimization problems, and then provides a closed-form solution with only local channel state information （CSI） available at the source and destination. Performance evaluation shows that the proposed scheme significantly outperforms linear schemes, and has a competitive performance compared with existing global CSI based nonlinear schemes, both iterative and non-iterative.

Design of a grating by a joint optimization method for a phase-shifting point diffraction interferometer

A grating is an important element of a phase-shifting point diffraction interferometer, and the grating constant and duty cycle have a great impact on the interferometer, so the design of a grating becomes significant. In order to measure the projection objective with a numerical aperture of 0.2, we present a joint optimization method of a pinhole and grating based on scalar diffraction and the finite difference time domain method. The grating constant and the film thickness are selected, and the duty cycle of the grating is optimized. The results show that in the grating processing the material chromium is adopted, the thickness is 200 nm, and the grating constant is 15 μm. When the duty cycle is 55%, the interference fringe contrast is the greatest. The feasibility of the design result is further verified by experiment.

A modular cable-driven humanoid arm with anti-parallelogram mechanisms and Bowden cables

This paper proposes a novel modular cable-driven humanoid arm with anti-parallelogram mechanisms(APMs)and Bowden cables.The lightweight arm realizes the advantage of joint independence and the rational layout of the driving units on the base.First,this paper analyzes the kinematic performance of the APM and uses the rolling motion between two ellipses to approximate a pure-circular-rolling motion.Then,a novel type of one-degree-of-freedom(1-DOF)elbow joint is proposed based on this principle,which is also applied to design the 3-DOF wrist and shoulder joints.Next,Bowden cables are used to connect the joints and their driving units to obtain a modular cable-driven arm with excellent joint independence.After that,both the forward and inverse kinematics of the entire arm are analyzed.Last,a humanoid arm prototype was developed,and the assembly velocity,joint motion performance,joint stiffness,load carrying,typical humanoid arm movements,and repeatability were tested to verify the arm performance.