GCAGA: A Gini Coefficient-Based Optimization Strategy for Computation Offloading in Multi-User-Multi-Edge MEC System

To support the explosive growth of Information and Communications Technology(ICT),Mobile Edge Comput-ing(MEC)provides users with low latency and high bandwidth service by offloading computational tasks to the network’s edge.However,resource-constrained mobile devices still suffer from a capacity mismatch when faced with latency-sensitive and compute-intensive emerging applications.To address the difficulty of running computationally intensive applications on resource-constrained clients,a model of the computation offloading problem in a network consisting of multiple mobile users and edge cloud servers is studied in this paper.Then a user benefit function EoU(Experience of Users)is proposed jointly considering energy consumption and time delay.The EoU maximization problem is decomposed into two steps,i.e.,resource allocation and offloading decision.The offloading decision is usually given by heuristic algorithms which are often faced with the challenge of slow convergence and poor stability.Thus,a combined offloading algorithm,i.e.,a Gini coefficient-based adaptive genetic algorithm(GCAGA),is proposed to alleviate the dilemma.The proposed algorithm optimizes the offloading decision by maximizing EoU and accelerates the convergence with the Gini coefficient.The simulation compares the proposed algorithm with the genetic algorithm(GA)and adaptive genetic algorithm(AGA).Experiment results show that the Gini coefficient and the adaptive heuristic operators can accelerate the convergence speed,and the proposed algorithm performs better in terms of convergence while obtaining higher EoU.The simulation code of the proposed algorithm is available:https://github.com/Grox888/Mobile_Edge_Computing/tree/GCAGA.

Joint Optimization of Resource Allocation and Trajectory Based on User Trajectory for UAV-Assisted Backscatter Communication System

The Backscatter communication has gained widespread attention from academia and industry in recent years. In this paper, A method of resource allocation and trajectory optimization is proposed for UAV-assisted backscatter communication based on user trajectory. This paper will establish an optimization problem of jointly optimizing the UAV trajectories, UAV transmission power and BD scheduling based on the large-scale channel state signals estimated in advance of the known user trajectories, taking into account the constraints of BD data and working energy consumption, to maximize the energy efficiency of the system. The problem is a non-convex optimization problem in fractional form, and there is nonlinear coupling between optimization variables.An iterative algorithm is proposed based on Dinkelbach algorithm, block coordinate descent method and continuous convex optimization technology. First, the objective function is converted into a non-fractional programming problem based on Dinkelbach method,and then the block coordinate descent method is used to decompose the original complex problem into three independent sub-problems. Finally, the successive convex approximation method is used to solve the trajectory optimization sub-problem. The simulation results show that the proposed scheme and algorithm have obvious energy efficiency gains compared with the comparison scheme.

Intelligent Reflecting Surface Assisted Transmission Optimization Strategies in Wireless Networks

Wireless Power Transfer(WPT)technology can provide real-time power for many terminal devices in Internet of Things(IoT)through millimeterWave(mmWave)to support applications with large capacity and low latency.Although the intelligent reflecting surface(IRS)can be adopted to create effective virtual links to address the mmWave blockage problem,the conventional solutions only adopt IRS in the downlink from the Base Station(BS)to the users to enhance the received signal strength.In practice,the reflection of IRS is also applicable to the uplink to improve the spectral efficiency.It is a challenging to jointly optimize IRS beamforming and system resource allocation for wireless energy acquisition and information transmission.In this paper,we first design a Low-Energy Adaptive Clustering Hierarchy(LEACH)clustering protocol for clustering and data collection.Then,the problem of maximizing the minimum system spectral efficiency is constructed by jointly optimizing the transmit power of sensor devices,the uplink and downlink transmission times,the active beamforming at the BS,and the IRS dynamic beamforming.To solve this non-convex optimization problem,we propose an alternating optimization(AO)-based joint solution algorithm.Simulation results show that the use of IRS dynamic beamforming can significantly improve the spectral efficiency of the system,and ensure the reliability of equipment communication and the sustainability of energy supply under NLOS link.

Specific Order and Bisection Search Aided Joint 3D Beamforming and RIS Reflecting Optimization

In this paper,a three-node transmission model is conceived,where the base station(BS)node leverages 3D beamforming,the reconfigurable intelligent surface(RIS)node can constructively reconfigure the wireless channel,the user node only has a single antenna due to a limited price.Maximization of its downlink spectral efficiency is a joint optimization problem of three variables,namely phase-shift matrixΦof RIS,tilt angleθand beamforming vector w used in BS 3D beamforming.We solve this problem by employing the alternating optimization(AO)algorithm.But,in each iteration,a specific optimization order of firstlyΦ,secondlyθand finally w is proposed,which facilitates the search of optimalθin the way of narrowing its trust region and enabling unimodal property over the narrowed trust region.It finally results in a better combination of{Φ,θ,w}.

Joint optimization for secure ambient backscatter communication in NOMA-enabled IoT networks

Non-Orthogonal Multiple Access(NOMA)has emerged as a novel air interface technology for massive connectivity in Sixth-Generation(6G)era.The recent integration of NOMA in Backscatter Communication(BC)has triggered significant research interest due to its applications in low-powered Internet of Things(IoT)networks.However,the link security aspect of these networks has not been well investigated.This article provides a new optimization framework for improving the physical layer security of the NOMA ambient BC system.Our system model takes into account the simultaneous operation of NOMA IoT users and the Backscatter Node(BN)in the presence of multiple EavesDroppers(EDs).The EDs in the surrounding area can overhear the communication of Base Station(BS)and BN due to the wireless broadcast transmission.Thus,the chief aim is to enhance link security by optimizing the BN reflection coefficient and BS transmit power.To gauge the performance of the proposed scheme,we also present the suboptimal NOMA and conventional orthogonal multiple access as benchmark schemes.Monte Carlo simulation results demonstrate the superiority of the NOMA BC scheme over the pure NOMA scheme without the BC and conventional orthogonal multiple access schemes in terms of system secrecy rate.

Joint Beamforming Optimization for Reconfigurable Intelligent Surface-Enabled MISO-OFDM Systems

In this paper,we investigate the reconfigurable intelligent surface(RIS)-enabled multiple-input-single-output orthogonal frequency division multiplexing(MISO-OFDM)system under frequency-selective channels,and propose a low-complexity alternating optimization(AO)based joint beamforming and RIS phase shifts optimization algorithm to maximize the achievable rate.First,with fixed RIS phase shifts,we devise the optimal closedform transmit beamforming vectors corresponding to different subcarriers.Then,with given active beamforming vectors,near-optimal RIS reflection coefficients can be determined efficiently leveraging fractional programming(FP)combined with manifold optimization(MO)or majorization-minimization(MM)framework.Additionally,we also propose a heuristic RIS phase shifts design approach based on the sum of subcarrier gain maximization(SSGM)criterion requiring lower complexity.Numerical results indicate that the proposed MO/MM algorithm can achieve almost the same rate as the upper bound achieved by the semidefinite relaxation(SDR)algorithm,and the proposed SSGM based scheme is only slightly inferior to the upper bound while has much lower complexity.These results demonstrate the effectiveness of the proposed algorithms.

An adaptive waveform-detection threshold joint optimization method for target tracking

The joint optimization of detection threshold and waveform parameters for target tracking which comes from the idea of cognitive radar is investigated for the modified probabilistic data association(MPDA)filter.The transmitted waveforms and detection threshold are adaptively selected to enhance the tracking performance.The modified Riccati equation is adopted to predict the error covariance which is used as the criterion function,while the optimization problem is solved through the genetic algorithm(GA).The detection probability,false alarm probability and measurement noise covariance are all considered together,which significantly improves the tracking performance of the joint detection and tracking system.Simulation results show that the proposed adaptive waveform-detection threshold joint optimization method outperforms the adaptive threshold method and the fixed parameters method,which will reduce the tracking error.The average reduction of range error between the adaptive joint method and the fixed parameters method is about 0.6 m,while that between the adaptive joint method and the adaptive threshold only method is about 0.3 m.Similar error reduction occurs for the velocity error and acceleration error.

Joint optimization of maintenance inspection and spare provisioning for aircraft deteriorating parts

With the wide application of condition based maintenance(CBM) in aircraft maintenance practice, the joint optimization of maintenance and inventory management, which can take full advantage of CBM and reduce the aircraft operational cost, is receiving increasing attention. In order to optimize the inspection interval, maintenance decision and spare provisioning together for aircraft deteriorating parts, firstly, a joint inventory management strategy is presented, then, a joint optimization of maintenance inspection and spare provisioning for aircraft parts subject to the Wiener degradation process is proposed based on the strategy.Secondly, a combination of the genetic algorithm(GA) and the Monte Carol method is developed to minimize the total cost rate.Finally, a case study is conducted and the proposed joint optimization model is compared with the existing optimization model and the airline real case. The results demonstrate that the proposed model is more beneficial and effective. In addition, the sensitivity analysis of the proposed model shows that the lead time has higher influence on the optimal results than the urgent order cost and the corrective maintenance cost, which is consistent with the actual situation of aircraft maintenance practices and inventory management.

Joint Power-Trajectory-Scheduling Optimization in A Mobile UAV-Enabled Network via Alternating Iteration

This work focuses on an unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) system based on device-to-device(D2D) communication. In this system, the UAV exhibits caching,computing and relaying capabilities to periodically provide specific service to cellular users and D2D receiver nodes in the appointed time slot. Besides, the D2D transmitter can provide additional caching services to D2D receiver to reduce the pressure of the UAV. Note that communication between multi-type nodes is mutually restricted and different links share spectrum resources. To achieve an improved balance between different types of node, we aim to maximize the overall energy efficiency while satisfying the quality-of-service requirements of the cellular nodes.To address this problem, we propose an alternating iteration algorithm to jointly optimize the scheduling strategies of the user, transmitting power of the UAV and D2D-TX nodes, and UAV trajectory. The successive convex approximation, penalty function, and Dinkelbach method are employed to transform the original problem into a group of solvable subproblems and the convergence of the method is proved. Simulation results show that the proposed scheme performs better than other benchmark algorithms, particularly in terms of balancing the tradeoff between minimizing UAV energy consumption and maximizing throughput.

Performance Analysis of Sparse Array based Massive MIMO via Joint Convex Optimization

Massive multiple-input multiple-output(MIMO)technology enables higher data rate transmission in the future mobile communications.However,exploiting a large number of antenna elements at base station(BS)makes effective implementation of massive MIMO challenging,due to the size and weight limits of the masssive MIMO that are located on each BS.Therefore,in order to miniaturize the massive MIMO,it is crucial to reduce the number of antenna elements via effective methods such as sparse array synthesis.In this paper,a multiple-pattern synthesis is considered towards convex optimization(CO).The joint convex optimization(JCO)based synthesis is proposed to construct a codebook for beamforming.Then,a criterion containing multiple constraints is developed,in which the sparse array is required to fullfill all constraints.Finally,extensive evaluations are performed under realistic simulation settings.The results show that with the same number of antenna elements,sparse array using the proposed JCO-based synthesis outperforms not only the uniform array,but also the sparse array with the existing CO-based synthesis method.Furthermore,with a half of the number of antenna elements that on the uniform array,the performance of the JCO-based sparse array approaches to that of the uniform array.

Faster-than-Nyquist signaling based on filter bank multicarrier modulation with joint optimization

Multi-carrier faster-than-Nyquist(MFTN)can improve the spectrum efficiency(SE).In this paper,we first analyze the benefit of time frequency packing MFTN(TFP-MFTN).Then,we propose an efficient digital implementation for TFP-MFTN based on filter bank multicarrier modulation.The time frequency packing ratio pair in our proposed implementation scheme is optimized with the SE criterion.Next,the joint optimization for the coded modulation MFTN based on extrinsic information transfer(EXIT)chart is performed.The Monte-Carlo simulations are carried out to verify performance gain of the joint inner and outer code optimization.Simulation results demonstrate that the TFPMFTN has a 0.8 dB and 0.9 dB gain comparing to time packing MFTN(TP-MFTN)and higher order Nyquist at same SE,respectively;the TFP-MFTN with optimized low density parity check(LDPC)code has a 2.9 dB gain comparing to that with digital video broadcasting(DVB)LDPC.Compared with previous work on TFP-MFTN(SE=1.55 bit/s/Hz),the SE of our work is improved by 29%and our work has a 4.1 dB gain at BER=1×10^(-5).

Joint Optimization Strategy for Video Transmission over Distributed Cognitive Radio Networks

A novel joint optimization strategy for the secondary user( SU) was proposed to consider the short-term and long-term video transmissions over distributed cognitive radio networks( DCRNs).Since the long-term video transmission consisted of a series of shortterm transmissions, the optimization problem in the video transmission was a composite optimization process. Firstly,considering some factors like primary user's( PU's) collision limitations,non-synchronization between SU and PU,and SU's limited buffer size, the short-term optimization problem was formulated as a mixed integer non-linear program( MINLP) to minimize the block probability of video packets. Secondly,combining the minimum packet block probability obtained in shortterm optimization and SU's constraint on hardware complexity,the partially observable Markov decision process( POMDP) framework was proposed to learn PU's statistic information over DCRNs.Moreover,based on the proposed framework,joint optimization strategy was designed to obtain the minimum packet loss rate in long-term video transmission. Numerical simulation results were provided to demonstrate validity of our strategies.

GREENSKY:A fair energy-aware optimization model for UAVs in next-generation wireless networks

Unmanned Aerial Vehicles(UAVs)offer a strategic solution to address the increasing demand for cellular connectivity in rural,remote,and disaster-hit regions lacking traditional infrastructure.However,UAVs’limited onboard energy storage necessitates optimized,energy-efficient communication strategies and intelligent energy expenditure to maximize productivity.This work proposes a novel joint optimization model to coordinate charging operations across multiple UAVs functioning as aerial base stations.The model optimizes charging station assignments and trajectories to maximize UAV flight time and minimize overall energy expenditure.By leveraging both static ground base stations and mobile supercharging stations for opportunistic charging while considering battery chemistry constraints,the mixed integer linear programming approach reduces energy usage by 9.1%versus conventional greedy heuristics.The key results provide insights into separating charging strategies based on UAV mobility patterns,fully utilizing all available infrastructure through balanced distribution,and strategically leveraging existing base stations before deploying dedicated charging assets.Compared to myopic localized decisions,the globally optimized solution extends battery life and enhances productivity.Overall,this work marks a significant advance in UAV energy management by consolidating multiple improvements within a unified coordination framework focused on joint charging optimization across UAV fleets.The model lays a critical foundation for energy-efficient aerial network deployments to serve the connectivity needs of the future.

Joint optimization of production,maintenance,and quality control considering the product quality variance of a degraded system

The joint optimization of production,maintenance,and quality control has shown effectiveness in reducing long-term operational costs in production systems.However,existing studies often assume that changes in the mean value of product quality characteristics in a deteriorating system follow a specific distribution while keeping variance constant.To address this limitation,we propose an innovative method based on the continuous ranking probability score(CRPS).This method enables the simultaneous detection of changes in mean and variance in nonconformities,thus removing the assumption of a specific distribution for quality characteristics.Our approach focuses on developing optimal strategies for production,maintenance,and quality control to minimize cost per unit of time.Additionally,we employ a stochastic model to optimize the production time allocated to the inventory buffer,resulting in significant cost reductions.The effectiveness of our proposed joint optimization method is demonstrated through comprehensive numerical experiments,sensitivity analysis,and a comparative study.The results show that our method can achieve cost reductions compared to several other related methods,highlighting its practical applicability for manufacturing companies aiming to reduce costs.

Throughput Maximization for Multi-UAV Enabled Millimeter Wave WPCN: Joint Time and Power Allocation

In this paper,we investigate the effective deployment of millimeter wave(mmWave)in unmanned aerial vehicle(UAV)-enabled wireless powered communication network(WPCN).In particular,a novel framework for optimizing the performance of such UAV-enabled WPCN in terms of system throughput is proposed.In the considered model,multiple UAVs monitor in the air along the scheduled flight trajectory and transmit monitoring data to micro base stations(mBSs)with the harvested energy via mmWave.In this case,we propose an algorithm for jointly optimizing transmit power and energy transfer time.To solve the non-convex optimization problem with tightly coupled variables,we decouple the problem into more tractable subproblems.By leveraging successive convex approximation(SCA)and block coordinate descent techniques,the optimal solution is obtained by designing a two-stage joint iteration optimization algorithm.Simulation results show that the proposed algorithm with joint transmit power and energy transfer time optimization achieves significant performance gains over Q-learning method and other benchmark schemes.

Compliant landing of a trotting quadruped robot based on hybrid motion/force robust control

A compliant landing strategy for a trotting quadruped robot on unknown rough terrains based on contact force control is presented. Firstly, in order to lower the disturbance caused by the landing impact force, a landing phase is added between the swing phase and the stance phase, where the desired contact force is set as a small positive constant. Secondly, the joint torque optimization of the stance legs is formulated as a quadratic programming(QP) problem subject to equality and inequality/bound constraints. And a primal-dual dynamical system solver based on linear variational inequalities(LVI) is applied to solve this QP problem. Furthermore, based on the optimization results, a hybrid motion/force robust controller is designed to realize the tracking of the contact force, while the constraints of the stance feet landing angles are fulfilled simultaneously. Finally, the experiments are performed to validate the proposed methods.

Bidirectional secondary transmissions with energy harvesting in cognitive wireless sensor networks

To the existing spectrum sharing schemes in wireless-powered cognitive wireless sensor networks,the protocols are limited to either separate the primary and the secondary transmission or allow the secondary user to transmit signals in a time slot when it forwards the primary signal.In order to address this limitation,a novel cooperative spectrum sharing scheme is proposed,where the secondary transmission is multiplexed with both the primary transmission and the relay transmission.Specifically,the process of transmission is on a three-phase time-switching relaying basis.In the first phase,a cognitive sensor node SU1 scavenges energy from the primary transmission.In the second phase,another sensor node SU2 and primary transmitter simultaneously transmit signals to the SU1.In the third phase,the node SU1 can assist the primary transmission to acquire the opportunity of spectrum sharing.Joint decoding and interference cancellation technique is adopted at the receivers to retrieve the desired signals.We further derive the closed-form expressions for the outage probabilities of both the primary and secondary systems.Moreover,we address optimization of energy harvesting duration and power allocation coefficient strategy under performance criteria.An effective algorithm is then presented to solve the optimization problem.Simulation results demonstrate that with the optimized solutions,the sensor nodes with the proposed cooperative spectrum sharing scheme can utilize the spectrum in a more efficient manner without deteriorating the performance of the primary transmission,as compared with the existing one-directional scheme in the literature.

BER Performance Analysis of Asynchronous NOMA with Arbitrary Phase Offset

An asynchronous transmission scenario for non-orthogonal multiple access(NOMA)user signals with arbitrary phase offset is investigated in this paper.To improve the system performance in the user power-balanced conditions,we adopt a synthetic detection method at the receiver,i.e.,the jointly optimal maximal likelihood detection aided triangular successive interference cancellation(JO ML-TSIC)method.Analytical bit error rate(BER)solutions are obtained for a two-user case with the optimal,intentional onehalf symbol period time delay implemented between the user signals.Furthermore,closed-form BER solutions for the case using the triangular successive interference cancellation(TSIC)detection method are also derived for comparisons.Numerical results show that the JO ML-TSIC receiver for the asynchronous system outperforms the TSIC receiver as well as the synchronous successive interference cancellation(SIC)receiver in all the conditions concerned.The results also show that the superiority of the JO ML-TSIC receiver is strengthened when the signals experience flat Rayleigh fading channels compared to the TSIC and the synchronous SIC receivers.

THE DESIGN OF AN INTEGRATED SYSTEM FOR PREDICTING AND ANALYZING SOLDER JOINT SHAPE AND RELIABILITY INSMT

Solder joint reliability is one of critical problems detemining whether Sirface Mount Technology (SMT) can be used in the prohotion of important electronic products. Optimizing solder joint shape is one of effective ways to improve SMT solder joint reliability.In this paper. based on the theorem of minimum potential energy, an energy model of 3 - D solder joint shape is established,and the forma- tion of solder joint is numerically simulated by Surface Ecolver program. On the basis of the prediction of SMT solder joint shape,the mechanical model of analyzing solder joint reliability is established. An elasto - plasto - creep mateial model and its mechanical constitutive equaton are established for SnPb solder alloy, and the stress/strain response of SMT solder joint under thermal cyclical loabing is ana- lyzed with nonlinear 3 - D FEM. The fatigue life of solder joint is predicted according to Coffin- Manson fatigue life model. An integrated system for Predicting and analyzing SMT solder joint shape and reliability(PSAR) is developed.The system can analyze efficiently SMT solder joint reliability with the variation of structural parameters in the joint and give the optimal structure.

Fair data scheduling in OFDM wireless networks based on maximizing utility

This paper proposes a joint layer scheme for fair downlink data scheduling in muhiuser OFDM wireless networks. Based on the optimization model formulated as the maximization of total utility function with respect to the mean waiting time of user queue, we present an algorithm with low complexity for dynamic subcarrier allocation （DSA）. The decision for subcarrier allocation was made according to delay utility function obtained by the algorithm that instantaneously estimated both channel condition and queue length using an exponentially weighted low-pass time window and pilot signals resPectively. The complexity of algorithm was reduced by varying the length of the time window to make use of time diversity, which provided higher throughput ratio. Simulation results demonstrate that compared with the conventional approach, the proposed scheme achieves better performance and can significantly improve fairness among users, with very limited delay performance degradation by using a decreasing concave utility function when the traffic load increases.