We construct a one-dimensional quasiperiodic quantum walk to investigate the localization–delocalization transition.The inverse participation ratio and Lyapunov exponent are employed as two indexes to determine the m...We construct a one-dimensional quasiperiodic quantum walk to investigate the localization–delocalization transition.The inverse participation ratio and Lyapunov exponent are employed as two indexes to determine the mobility edge, a critical energy to distinguish the energy regions of extended and localized states. The analytical solution of mobility edge is obtained by the Lyapunov exponents in global theory, and the consistency of the two indexes is confirmed. We further study the dynamic characteristics of the quantum walk and show that the probabilities are localized to some specific lattice sites with time evolution. This phenomenon is explained by the effective potential of the Hamiltonian which corresponds to the phase in the coin operator of the quantum walk.展开更多
A one-dimensional non-Hermitian quasiperiodic p-wave superconductor without PT-symmetry is studied.By analyzing the spectrum,we discovered that there still exists real-complex energy transition even if the inexistence...A one-dimensional non-Hermitian quasiperiodic p-wave superconductor without PT-symmetry is studied.By analyzing the spectrum,we discovered that there still exists real-complex energy transition even if the inexistence of PT-symmetry breaking.By the inverse participation ratio,we constructed such a correspondence that pure real energies correspond to the extended states and complex energies correspond to the localized states,and this correspondence is precise and effective to detect the mobility edges.After investigating the topological properties,we arrived at a fact that the Majorana zero modes in this system are immune to the non-Hermiticity.展开更多
The mobility edges and reentrant localization transitions are studied in one-dimensional dimerized lattice with non-Hermitian either uniform or staggered quasiperiodic potentials.We find that the non-Hermitian uniform...The mobility edges and reentrant localization transitions are studied in one-dimensional dimerized lattice with non-Hermitian either uniform or staggered quasiperiodic potentials.We find that the non-Hermitian uniform quasiperiodic disorder can induce an intermediate phase where the extended states coexist with the localized ones,which implies that the system has mobility edges.The localization transition is accompanied by the PT symmetry breaking transition.While if the non-Hermitian quasiperiodic disorder is staggered,we demonstrate the existence of multiple intermediate phases and multiple reentrant localization transitions based on the finite size scaling analysis.Interestingly,some already localized states will become extended states and can also be localized again for certain non-Hermitian parameters.The reentrant localization transitions are associated with the intermediate phases hosting mobility edges.Besides,we also find that the non-Hermiticity can break the reentrant localization transition where only one intermediate phase survives.More detailed information about the mobility edges and reentrant localization transitions are presented by analyzing the eigenenergy spectrum,inverse participation ratio,and normalized participation ratio.展开更多
We analytically and numerically study a 1 D tight-binding model with tunable incommensurate potentials.We utilize the self-dual relation to obtain the critical energy,namely,the mobility edge.Interestingly,we analytic...We analytically and numerically study a 1 D tight-binding model with tunable incommensurate potentials.We utilize the self-dual relation to obtain the critical energy,namely,the mobility edge.Interestingly,we analytically demonstrate that this critical energy is a constant independent of strength of potentials.Then we numerically verify the analytical results by analyzing the spatial distributions of wave functions,the inverse participation rate and the multifractal theory.All numerical results are in excellent agreement with the analytical results.Finally,we give a brief discussion on the possible experimental observation of the invariable mobility edge in the system of ultracold atoms in optical lattices.展开更多
We establish a general mapping from one-dimensional non-Hermitian mosaic models to their non-mosaic counterparts.This mapping can give rise to mobility edges and even Lyapunov exponents in the mosaic models if critica...We establish a general mapping from one-dimensional non-Hermitian mosaic models to their non-mosaic counterparts.This mapping can give rise to mobility edges and even Lyapunov exponents in the mosaic models if critical points of localization or Lyapunov exponents of localized states in the corresponding non-mosaic models have already been analytically solved.To demonstrate the validity of this mapping,we apply it to two non-Hermitian localization models:an Aubry-Andre-like model with nonreciprocal hopping and complex quasiperiodic potentials,and the Ganeshan-Pixley-Das Sarma model with nonreciprocal hopping.We successfully obtain the mobility edges and Lyapunov exponents in their mosaic models.This general mapping may catalyze further studies on mobility edges,Lyapunov exponents,and other significant quantities pertaining to localization in non-Hermitian mosaic models.展开更多
Recently, an interesting family of quasiperiodic models with exact mobility edges（MEs） has been proposed（Phys.Rev. Lett. 114 146601（2015））. It is self-dual under a generalized duality transformation. However, su...Recently, an interesting family of quasiperiodic models with exact mobility edges（MEs） has been proposed（Phys.Rev. Lett. 114 146601（2015））. It is self-dual under a generalized duality transformation. However, such transformation is not obvious to map extended（localized） states in the real space to localized（extended） ones in the Fourier space. Therefore,it needs more convictive evidences to confirm the existence of MEs. We use the second moment of wave functions, Shannon information entropies, and Lypanunov exponents to characterize the localization properties of the eigenstates, respectively.Furthermore, we obtain the phase diagram of the model. Our numerical results support the existing analytical findings.展开更多
We study the cross-stitch flatband lattice subject to the quasiperiodic complex potential exp(ix). We firstly identify the exact expression of quadratic mobility edges through analytical calculation, then verify the t...We study the cross-stitch flatband lattice subject to the quasiperiodic complex potential exp(ix). We firstly identify the exact expression of quadratic mobility edges through analytical calculation, then verify the theoretical predictions by numerically calculating the inverse participation ratio. Further more, we study the relationship between the real–complex spectrum transition and the localization–delocalization transition, and demonstrate that mobility edges in this non-Hermitian model not only separate localized from extended states but also indicate the coexistence of complex and real spectrum.展开更多
We study the one-dimensional tight-binding model with quasi-periodic disorders,where the quasi-period is tuned to be large compared to the system size.It is found that this type of model with large quasi-periodic diso...We study the one-dimensional tight-binding model with quasi-periodic disorders,where the quasi-period is tuned to be large compared to the system size.It is found that this type of model with large quasi-periodic disorders can also support the mobility edges,which is very similar to the models with slowly varying quasi-periodic disorders.The energy-matching method is employed to determine the locations of mobility edges in both types of models.These results of mobility edges are verified by numerical calculations in various examples.We also provide qualitative arguments to support the fact that large quasi-periodic disorders will lead to the existence of mobility edges.展开更多
The mobility edge(ME)is a crucial concept in understanding localization physics,marking the critical transition between extended and localized states in the energy spectrum.Anderson localization scaling theory predict...The mobility edge(ME)is a crucial concept in understanding localization physics,marking the critical transition between extended and localized states in the energy spectrum.Anderson localization scaling theory predicts the absence of ME in lower dimensional systems.Hence,the search for exact MEs,particularly for single particles in lower dimensions,has recently garnered significant interest in both theoretical and experimental studies,resulting in notable progress.However,several open questions remain,including the possibility of a single system exhibiting multiple MEs and the continual existence of extended states,even within the strong disorder domain.Here,we provide experimental evidence to address these questions by utilizing a quasiperiodic mosaic lattice with meticulously designed nanophotonic circuits.Our observations demonstrate the coexistence of both extended and localized states in lattices with broken duality symmetry and varying modulation periods.By single-site injection and scanning the disorder level,we could approximately probe the ME of the modulated lattice.These results corroborate recent theoretical predictions,introduce a new avenue for investigating ME physics,and offer inspiration for further exploration of ME physics in the quantum regime using hybrid integrated photonic devices.展开更多
In dynamic 5G network environments,user mobility and heterogeneous network topologies pose dual challenges to the effort of improving performance of mobile edge caching.Existing studies often overlook the dynamic natu...In dynamic 5G network environments,user mobility and heterogeneous network topologies pose dual challenges to the effort of improving performance of mobile edge caching.Existing studies often overlook the dynamic nature of user locations and the potential of device-to-device(D2D)cooperative caching,limiting the reduction of transmission latency.To address this issue,this paper proposes a joint optimization scheme for edge caching that integrates user mobility prediction with deep reinforcement learning.First,a Transformer-based geolocation prediction model is designed,leveraging multi-head attention mechanisms to capture correlations in historical user trajectories for accurate future location prediction.Then,within a three-tier heterogeneous network,we formulate a latency minimization problem under a D2D cooperative caching architecture and develop a mobility-aware Deep Q-Network(DQN)caching strategy.This strategy takes predicted location information as state input and dynamically adjusts the content distribution across small base stations(SBSs)andmobile users(MUs)to reduce end-to-end delay inmulti-hop content retrieval.Simulation results show that the proposed DQN-based method outperforms other baseline strategies across variousmetrics,achieving a 17.2%reduction in transmission delay compared to DQNmethods withoutmobility integration,thus validating the effectiveness of the joint optimization of location prediction and caching decisions.展开更多
Mobile Edge Computing(MEC)is a technology designed for the on-demand provisioning of computing and storage services,strategically positioned close to users.In the MEC environment,frequently accessed content can be dep...Mobile Edge Computing(MEC)is a technology designed for the on-demand provisioning of computing and storage services,strategically positioned close to users.In the MEC environment,frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery,ultimately enhancing the quality of the user experience.However,due to the typical placement of edge devices and nodes at the network’s periphery,these components may face various potential fault tolerance challenges,including network instability,device failures,and resource constraints.Considering the dynamic nature ofMEC,making high-quality content caching decisions for real-time mobile applications,especially those sensitive to latency,by effectively utilizing mobility information,continues to be a significant challenge.In response to this challenge,this paper introduces FT-MAACC,a mobility-aware caching solution grounded in multi-agent deep reinforcement learning and equipped with fault tolerance mechanisms.This approach comprehensively integrates content adaptivity algorithms to evaluate the priority of highly user-adaptive cached content.Furthermore,it relies on collaborative caching strategies based onmulti-agent deep reinforcement learningmodels and establishes a fault-tolerancemodel to ensure the system’s reliability,availability,and persistence.Empirical results unequivocally demonstrate that FTMAACC outperforms its peer methods in cache hit rates and transmission latency.展开更多
The unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC)has been deemed a promising solution for energy-constrained devices to run smart applications with computationintensive and latency-sensitive require...The unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC)has been deemed a promising solution for energy-constrained devices to run smart applications with computationintensive and latency-sensitive requirements,especially in some infrastructure-limited areas or some emergency scenarios.However,the multi-UAVassisted MEC network remains largely unexplored.In this paper,the dynamic trajectory optimization and computation offloading are studied in a multi-UAVassisted MEC system where multiple UAVs fly over a target area with different trajectories to serve ground users.By considering the dynamic channel condition and random task arrival and jointly optimizing UAVs'trajectories,user association,and subchannel assignment,the average long-term sum of the user energy consumption minimization problem is formulated.To address the problem involving both discrete and continuous variables,a hybrid decision deep reinforcement learning(DRL)-based intelligent energyefficient resource allocation and trajectory optimization algorithm is proposed,named HDRT algorithm,where deep Q network(DQN)and deep deterministic policy gradient(DDPG)are invoked to process discrete and continuous variables,respectively.Simulation results show that the proposed HDRT algorithm converges fast and outperforms other benchmarks in the aspect of user energy consumption and latency.展开更多
As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational ...As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational capability of the vehicle which reduces task processing latency and power con-sumption effectively and meets the quality of service requirements of vehicle users.However,there are still some problems in the MEC-assisted IoV system such as poor connectivity and high cost.Unmanned aerial vehicles(UAVs)equipped with MEC servers have become a promising approach for providing com-munication and computing services to mobile vehi-cles.Hence,in this article,an optimal framework for the UAV-assisted MEC system for IoV to minimize the average system cost is presented.Through joint consideration of computational offloading decisions and computational resource allocation,the optimiza-tion problem of our proposed architecture is presented to reduce system energy consumption and delay.For purpose of tackling this issue,the original non-convex issue is converted into a convex issue and the alternat-ing direction method of multipliers-based distributed optimal scheme is developed.The simulation results illustrate that the presented scheme can enhance the system performance dramatically with regard to other schemes,and the convergence of the proposed scheme is also significant.展开更多
Low earth orbit(LEO)satellites with wide coverage can carry the mobile edge computing(MEC)servers with powerful computing capabilities to form the LEO satellite edge computing system,providing computing services for t...Low earth orbit(LEO)satellites with wide coverage can carry the mobile edge computing(MEC)servers with powerful computing capabilities to form the LEO satellite edge computing system,providing computing services for the global ground users.In this paper,the computation offloading problem and resource allocation problem are formulated as a mixed integer nonlinear program(MINLP)problem.This paper proposes a computation offloading algorithm based on deep deterministic policy gradient(DDPG)to obtain the user offloading decisions and user uplink transmission power.This paper uses the convex optimization algorithm based on Lagrange multiplier method to obtain the optimal MEC server resource allocation scheme.In addition,the expression of suboptimal user local CPU cycles is derived by relaxation method.Simulation results show that the proposed algorithm can achieve excellent convergence effect,and the proposed algorithm significantly reduces the system utility values at considerable time cost compared with other algorithms.展开更多
This paper presents an algorithm named the dependency-aware offloading framework(DeAOff),which is designed to optimize the deployment of Gen-AI decoder models in mobile edge computing(MEC)environments.These models,suc...This paper presents an algorithm named the dependency-aware offloading framework(DeAOff),which is designed to optimize the deployment of Gen-AI decoder models in mobile edge computing(MEC)environments.These models,such as decoders,pose significant challenges due to their interlayer dependencies and high computational demands,especially under edge resource constraints.To address these challenges,we propose a two-phase optimization algorithm that first handles dependencyaware task allocation and subsequently optimizes energy consumption.By modeling the inference process using directed acyclic graphs(DAGs)and applying constraint relaxation techniques,our approach effectively reduces execution latency and energy usage.Experimental results demonstrate that our method achieves a reduction of up to 20%in task completion time and approximately 30%savings in energy consumption compared to traditional methods.These outcomes underscore our solution’s robustness in managing complex sequential dependencies and dynamic MEC conditions,enhancing quality of service.Thus,our work presents a practical and efficient resource optimization strategy for deploying models in resourceconstrained MEC scenarios.展开更多
With the rapid development of intelligent transportation systems,vehicular networks(VANETs)have become an essential part of the intelligent transportation infrastructure.Due to the high dynamics of efficient vehicular...With the rapid development of intelligent transportation systems,vehicular networks(VANETs)have become an essential part of the intelligent transportation infrastructure.Due to the high dynamics of efficient vehicular network nodes and the low latency requirement of data interaction,the traditional cloud computing model makes it difficult to meet the real-time and performance criteria,and the storage optimization strategy based on edge computing can effectively improve the data access efficiency and system response.This paper aims to explore how to optimize the data caching mechanism of intelligent telematics using edge computing to reduce network latency,improve data availability,and enhance overall system performance.展开更多
The emergence of the mobility edge(ME)has been recognized as an important characteristic of Anderson localization.The difficulty in understanding the physics of the MEs in three-dimensional(3 D)systems from a microsco...The emergence of the mobility edge(ME)has been recognized as an important characteristic of Anderson localization.The difficulty in understanding the physics of the MEs in three-dimensional(3 D)systems from a microscopic image encourages the development of models in lower-dimensional systems that have exact MEs.While most of the previous studies are concerned with one-dimensional(1 D)quasiperiodic systems,the analytic results that allow for an accurate understanding of two-dimensional(2 D)cases are rare.In this work,we disclose an exactly solvable 2 D quasicrystal model with parity-time(PT)symmetry displaying exact MEs.In the thermodynamic limit,we unveil that the extended-localized transition point,observed at the PT symmetry breaking point,is topologically characterized by a hidden winding number defined in the dual space.The coupling waveguide platform can be used to realize the 2 D non-Hermitian quasicrystal model,and the excitation dynamics can be used to detect the localization features.展开更多
With the rapid growth of the Industrial Internet of Things(IIoT), the Mobile Edge Computing(MEC) has coming widely used in many emerging scenarios. In MEC, each workflow task can be executed locally or offloaded to ed...With the rapid growth of the Industrial Internet of Things(IIoT), the Mobile Edge Computing(MEC) has coming widely used in many emerging scenarios. In MEC, each workflow task can be executed locally or offloaded to edge to help improve Quality of Service(QoS) and reduce energy consumption. However, most of the existing offloading strategies focus on independent applications, which cannot be applied efficiently to workflow applications with a series of dependent tasks. To address the issue,this paper proposes an energy-efficient task offloading strategy for large-scale workflow applications in MEC. First, we formulate the task offloading problem into an optimization problem with the goal of minimizing the utility cost, which is the trade-off between energy consumption and the total execution time. Then, a novel heuristic algorithm named Green DVFS-GA is proposed, which includes a task offloading step based on the genetic algorithm and a further step to reduce the energy consumption using Dynamic Voltage and Frequency Scaling(DVFS) technique. Experimental results show that our proposed strategy can significantly reduce the energy consumption and achieve the best trade-off compared with other strategies.展开更多
ultra-Dense Network(UDN)has been envisioned as a promising technology to provide high-quality wireless connectivity in dense urban areas,in which the density of Access Points(APs)is increased up to the point where it ...ultra-Dense Network(UDN)has been envisioned as a promising technology to provide high-quality wireless connectivity in dense urban areas,in which the density of Access Points(APs)is increased up to the point where it is comparable with or surpasses the density of active mobile users.In order to mitigate inter-AP interference and improve spectrum efficiency,APs in UDNs are usually clustered into multiple groups to serve different mobile users,respectively.However,as the number of APs increases,the computational capability within an AP group has become the bottleneck of AP clustering.In this paper,we first propose a novel UDN architecture based on Mobile Edge Computing(MEC),in which each MEC server is associated with a user-centric AP cluster to act as a mobile agent.In addition,in the context of MEC-based UDN,we leverage mobility prediction techniques to achieve a dynamic AP clustering scheme,in which the cluster structure can automatically adapt to the dynamic distribution of user traffic in a specific area.Simulation results show that the proposed scheme can highly increase the average user throughput compared with the baseline algorithm using max-SINR user association and equal bandwidth allocation,while it guarantees at the same time low transmission delay.展开更多
By pushing computation,cache,and network control to the edge,mobile edge computing(MEC)is expected to play a leading role in fifth generation(5G)and future sixth generation(6G).Nevertheless,facing ubiquitous fast-grow...By pushing computation,cache,and network control to the edge,mobile edge computing(MEC)is expected to play a leading role in fifth generation(5G)and future sixth generation(6G).Nevertheless,facing ubiquitous fast-growing computational demands,it is impossible for a single MEC paradigm to effectively support high-quality intelligent services at end user equipments(UEs).To address this issue,we propose an air-ground collaborative MEC(AGCMEC)architecture in this article.The proposed AGCMEC integrates all potentially available MEC servers within air and ground in the envisioned 6G,by a variety of collaborative ways to provide computation services at their best for UEs.Firstly,we introduce the AGC-MEC architecture and elaborate three typical use cases.Then,we discuss four main challenges in the AGC-MEC as well as their potential solutions.Next,we conduct a case study of collaborative service placement for AGC-MEC to validate the effectiveness of the proposed collaborative service placement strategy.Finally,we highlight several potential research directions of the AGC-MEC.展开更多
文摘We construct a one-dimensional quasiperiodic quantum walk to investigate the localization–delocalization transition.The inverse participation ratio and Lyapunov exponent are employed as two indexes to determine the mobility edge, a critical energy to distinguish the energy regions of extended and localized states. The analytical solution of mobility edge is obtained by the Lyapunov exponents in global theory, and the consistency of the two indexes is confirmed. We further study the dynamic characteristics of the quantum walk and show that the probabilities are localized to some specific lattice sites with time evolution. This phenomenon is explained by the effective potential of the Hamiltonian which corresponds to the phase in the coin operator of the quantum walk.
基金the National Natural Science Foundation of China(Grant Nos.11835011 and 12174346).
文摘A one-dimensional non-Hermitian quasiperiodic p-wave superconductor without PT-symmetry is studied.By analyzing the spectrum,we discovered that there still exists real-complex energy transition even if the inexistence of PT-symmetry breaking.By the inverse participation ratio,we constructed such a correspondence that pure real energies correspond to the extended states and complex energies correspond to the localized states,and this correspondence is precise and effective to detect the mobility edges.After investigating the topological properties,we arrived at a fact that the Majorana zero modes in this system are immune to the non-Hermiticity.
基金Project supported by the National Key Research and Development Program of China(Grant Nos.2016YFA0300600 and 2016YFA0302104)the National Natural Science Foundation of China(Grant Nos.12074410,12047502,11934015,11947301,and 11774397)+1 种基金the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDB33000000)the Fellowship of China Postdoctoral Science Foundation(Grant No.2020M680724).
文摘The mobility edges and reentrant localization transitions are studied in one-dimensional dimerized lattice with non-Hermitian either uniform or staggered quasiperiodic potentials.We find that the non-Hermitian uniform quasiperiodic disorder can induce an intermediate phase where the extended states coexist with the localized ones,which implies that the system has mobility edges.The localization transition is accompanied by the PT symmetry breaking transition.While if the non-Hermitian quasiperiodic disorder is staggered,we demonstrate the existence of multiple intermediate phases and multiple reentrant localization transitions based on the finite size scaling analysis.Interestingly,some already localized states will become extended states and can also be localized again for certain non-Hermitian parameters.The reentrant localization transitions are associated with the intermediate phases hosting mobility edges.Besides,we also find that the non-Hermiticity can break the reentrant localization transition where only one intermediate phase survives.More detailed information about the mobility edges and reentrant localization transitions are presented by analyzing the eigenenergy spectrum,inverse participation ratio,and normalized participation ratio.
基金supported by the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20200737)NUPTSF(Grants Nos.NY220090 and NY220208)+2 种基金the National Natural Science Foundation of China(Grant No.12074064)the Innovation Research Project of Jiangsu Province,China(Grant No.JSSCBS20210521)NJUPT-STITP(Grant No.XYB2021294)。
文摘We analytically and numerically study a 1 D tight-binding model with tunable incommensurate potentials.We utilize the self-dual relation to obtain the critical energy,namely,the mobility edge.Interestingly,we analytically demonstrate that this critical energy is a constant independent of strength of potentials.Then we numerically verify the analytical results by analyzing the spatial distributions of wave functions,the inverse participation rate and the multifractal theory.All numerical results are in excellent agreement with the analytical results.Finally,we give a brief discussion on the possible experimental observation of the invariable mobility edge in the system of ultracold atoms in optical lattices.
基金the National Natural Science Foundation of China(Grant No.12204406)the National Key Research and Development Program of China(Grant No.2022YFA1405304)the Guangdong Provincial Key Laboratory(Grant No.2020B1212060066)。
文摘We establish a general mapping from one-dimensional non-Hermitian mosaic models to their non-mosaic counterparts.This mapping can give rise to mobility edges and even Lyapunov exponents in the mosaic models if critical points of localization or Lyapunov exponents of localized states in the corresponding non-mosaic models have already been analytically solved.To demonstrate the validity of this mapping,we apply it to two non-Hermitian localization models:an Aubry-Andre-like model with nonreciprocal hopping and complex quasiperiodic potentials,and the Ganeshan-Pixley-Das Sarma model with nonreciprocal hopping.We successfully obtain the mobility edges and Lyapunov exponents in their mosaic models.This general mapping may catalyze further studies on mobility edges,Lyapunov exponents,and other significant quantities pertaining to localization in non-Hermitian mosaic models.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61475075 and 61170321)
文摘Recently, an interesting family of quasiperiodic models with exact mobility edges（MEs） has been proposed（Phys.Rev. Lett. 114 146601（2015））. It is self-dual under a generalized duality transformation. However, such transformation is not obvious to map extended（localized） states in the real space to localized（extended） ones in the Fourier space. Therefore,it needs more convictive evidences to confirm the existence of MEs. We use the second moment of wave functions, Shannon information entropies, and Lypanunov exponents to characterize the localization properties of the eigenstates, respectively.Furthermore, we obtain the phase diagram of the model. Our numerical results support the existing analytical findings.
基金supported by the Natural Science Foundation of Jiangsu Province, China (Grant No. BK20200737)NUPTSF (Grant Nos. NY220090 and NY220208)+2 种基金the National Natural Science Foundation of China (Grant No. 12074064)the Innovation Research Project of Jiangsu Province, China (Grant No. JSSCBS20210521)China Postdoctoral Science Foundation (Grant No. 2022M721693)。
文摘We study the cross-stitch flatband lattice subject to the quasiperiodic complex potential exp(ix). We firstly identify the exact expression of quadratic mobility edges through analytical calculation, then verify the theoretical predictions by numerically calculating the inverse participation ratio. Further more, we study the relationship between the real–complex spectrum transition and the localization–delocalization transition, and demonstrate that mobility edges in this non-Hermitian model not only separate localized from extended states but also indicate the coexistence of complex and real spectrum.
基金Project supported by the National Natural Science Foundation of China (Grant No.11874272)Science Specialty Program of Sichuan University (Grant No.2020SCUNL210)。
文摘We study the one-dimensional tight-binding model with quasi-periodic disorders,where the quasi-period is tuned to be large compared to the system size.It is found that this type of model with large quasi-periodic disorders can also support the mobility edges,which is very similar to the models with slowly varying quasi-periodic disorders.The energy-matching method is employed to determine the locations of mobility edges in both types of models.These results of mobility edges are verified by numerical calculations in various examples.We also provide qualitative arguments to support the fact that large quasi-periodic disorders will lead to the existence of mobility edges.
基金support from Swedish Research Council(2023-06671 and 2023-05288)Vinnova project(2024-00466)+6 种基金the G?ran Gustafsson Foundationsupport from Knut and Alice Wallenberg(KAW)Foundation through the Wallenberg Centre for Quantum Technology(WACQT)Swedish Research Council(VR)Starting Grant(2016-03905)Vinnova quantum kick-start project 2021support from the KAW and VRsupported by European Research Council under the European Union Seventh Framework ERS-2018-SYG HERO,KAW 2019.0068the University of Connecticut。
文摘The mobility edge(ME)is a crucial concept in understanding localization physics,marking the critical transition between extended and localized states in the energy spectrum.Anderson localization scaling theory predicts the absence of ME in lower dimensional systems.Hence,the search for exact MEs,particularly for single particles in lower dimensions,has recently garnered significant interest in both theoretical and experimental studies,resulting in notable progress.However,several open questions remain,including the possibility of a single system exhibiting multiple MEs and the continual existence of extended states,even within the strong disorder domain.Here,we provide experimental evidence to address these questions by utilizing a quasiperiodic mosaic lattice with meticulously designed nanophotonic circuits.Our observations demonstrate the coexistence of both extended and localized states in lattices with broken duality symmetry and varying modulation periods.By single-site injection and scanning the disorder level,we could approximately probe the ME of the modulated lattice.These results corroborate recent theoretical predictions,introduce a new avenue for investigating ME physics,and offer inspiration for further exploration of ME physics in the quantum regime using hybrid integrated photonic devices.
基金supported by the Liaoning Provincial Education Department Fund,grant number JYTZD2023083.
文摘In dynamic 5G network environments,user mobility and heterogeneous network topologies pose dual challenges to the effort of improving performance of mobile edge caching.Existing studies often overlook the dynamic nature of user locations and the potential of device-to-device(D2D)cooperative caching,limiting the reduction of transmission latency.To address this issue,this paper proposes a joint optimization scheme for edge caching that integrates user mobility prediction with deep reinforcement learning.First,a Transformer-based geolocation prediction model is designed,leveraging multi-head attention mechanisms to capture correlations in historical user trajectories for accurate future location prediction.Then,within a three-tier heterogeneous network,we formulate a latency minimization problem under a D2D cooperative caching architecture and develop a mobility-aware Deep Q-Network(DQN)caching strategy.This strategy takes predicted location information as state input and dynamically adjusts the content distribution across small base stations(SBSs)andmobile users(MUs)to reduce end-to-end delay inmulti-hop content retrieval.Simulation results show that the proposed DQN-based method outperforms other baseline strategies across variousmetrics,achieving a 17.2%reduction in transmission delay compared to DQNmethods withoutmobility integration,thus validating the effectiveness of the joint optimization of location prediction and caching decisions.
基金supported by the Innovation Fund Project of Jiangxi Normal University(YJS2022065)the Domestic Visiting Program of Jiangxi Normal University.
文摘Mobile Edge Computing(MEC)is a technology designed for the on-demand provisioning of computing and storage services,strategically positioned close to users.In the MEC environment,frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery,ultimately enhancing the quality of the user experience.However,due to the typical placement of edge devices and nodes at the network’s periphery,these components may face various potential fault tolerance challenges,including network instability,device failures,and resource constraints.Considering the dynamic nature ofMEC,making high-quality content caching decisions for real-time mobile applications,especially those sensitive to latency,by effectively utilizing mobility information,continues to be a significant challenge.In response to this challenge,this paper introduces FT-MAACC,a mobility-aware caching solution grounded in multi-agent deep reinforcement learning and equipped with fault tolerance mechanisms.This approach comprehensively integrates content adaptivity algorithms to evaluate the priority of highly user-adaptive cached content.Furthermore,it relies on collaborative caching strategies based onmulti-agent deep reinforcement learningmodels and establishes a fault-tolerancemodel to ensure the system’s reliability,availability,and persistence.Empirical results unequivocally demonstrate that FTMAACC outperforms its peer methods in cache hit rates and transmission latency.
基金supported by National Natural Science Foundation of China(No.62471254)National Natural Science Foundation of China(No.92367302)。
文摘The unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC)has been deemed a promising solution for energy-constrained devices to run smart applications with computationintensive and latency-sensitive requirements,especially in some infrastructure-limited areas or some emergency scenarios.However,the multi-UAVassisted MEC network remains largely unexplored.In this paper,the dynamic trajectory optimization and computation offloading are studied in a multi-UAVassisted MEC system where multiple UAVs fly over a target area with different trajectories to serve ground users.By considering the dynamic channel condition and random task arrival and jointly optimizing UAVs'trajectories,user association,and subchannel assignment,the average long-term sum of the user energy consumption minimization problem is formulated.To address the problem involving both discrete and continuous variables,a hybrid decision deep reinforcement learning(DRL)-based intelligent energyefficient resource allocation and trajectory optimization algorithm is proposed,named HDRT algorithm,where deep Q network(DQN)and deep deterministic policy gradient(DDPG)are invoked to process discrete and continuous variables,respectively.Simulation results show that the proposed HDRT algorithm converges fast and outperforms other benchmarks in the aspect of user energy consumption and latency.
基金in part by the National Natural Science Foundation of China(NSFC)under Grant 62371012in part by the Beijing Natural Science Foundation under Grant 4252001.
文摘As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational capability of the vehicle which reduces task processing latency and power con-sumption effectively and meets the quality of service requirements of vehicle users.However,there are still some problems in the MEC-assisted IoV system such as poor connectivity and high cost.Unmanned aerial vehicles(UAVs)equipped with MEC servers have become a promising approach for providing com-munication and computing services to mobile vehi-cles.Hence,in this article,an optimal framework for the UAV-assisted MEC system for IoV to minimize the average system cost is presented.Through joint consideration of computational offloading decisions and computational resource allocation,the optimiza-tion problem of our proposed architecture is presented to reduce system energy consumption and delay.For purpose of tackling this issue,the original non-convex issue is converted into a convex issue and the alternat-ing direction method of multipliers-based distributed optimal scheme is developed.The simulation results illustrate that the presented scheme can enhance the system performance dramatically with regard to other schemes,and the convergence of the proposed scheme is also significant.
基金supported by National Natural Science Foundation of China No.62231012Natural Science Foundation for Outstanding Young Scholars of Heilongjiang Province under Grant YQ2020F001Heilongjiang Province Postdoctoral General Foundation under Grant AUGA4110004923.
文摘Low earth orbit(LEO)satellites with wide coverage can carry the mobile edge computing(MEC)servers with powerful computing capabilities to form the LEO satellite edge computing system,providing computing services for the global ground users.In this paper,the computation offloading problem and resource allocation problem are formulated as a mixed integer nonlinear program(MINLP)problem.This paper proposes a computation offloading algorithm based on deep deterministic policy gradient(DDPG)to obtain the user offloading decisions and user uplink transmission power.This paper uses the convex optimization algorithm based on Lagrange multiplier method to obtain the optimal MEC server resource allocation scheme.In addition,the expression of suboptimal user local CPU cycles is derived by relaxation method.Simulation results show that the proposed algorithm can achieve excellent convergence effect,and the proposed algorithm significantly reduces the system utility values at considerable time cost compared with other algorithms.
文摘This paper presents an algorithm named the dependency-aware offloading framework(DeAOff),which is designed to optimize the deployment of Gen-AI decoder models in mobile edge computing(MEC)environments.These models,such as decoders,pose significant challenges due to their interlayer dependencies and high computational demands,especially under edge resource constraints.To address these challenges,we propose a two-phase optimization algorithm that first handles dependencyaware task allocation and subsequently optimizes energy consumption.By modeling the inference process using directed acyclic graphs(DAGs)and applying constraint relaxation techniques,our approach effectively reduces execution latency and energy usage.Experimental results demonstrate that our method achieves a reduction of up to 20%in task completion time and approximately 30%savings in energy consumption compared to traditional methods.These outcomes underscore our solution’s robustness in managing complex sequential dependencies and dynamic MEC conditions,enhancing quality of service.Thus,our work presents a practical and efficient resource optimization strategy for deploying models in resourceconstrained MEC scenarios.
文摘With the rapid development of intelligent transportation systems,vehicular networks(VANETs)have become an essential part of the intelligent transportation infrastructure.Due to the high dynamics of efficient vehicular network nodes and the low latency requirement of data interaction,the traditional cloud computing model makes it difficult to meet the real-time and performance criteria,and the storage optimization strategy based on edge computing can effectively improve the data access efficiency and system response.This paper aims to explore how to optimize the data caching mechanism of intelligent telematics using edge computing to reduce network latency,improve data availability,and enhance overall system performance.
基金supported by the National Natural Science Foundation of China(Grant Nos.11604188,12047571)Beijing National Laboratory for Condensed Matter Physics+4 种基金STIP of Higher Education Institutions in Shanxi(Grant No.2019L0097)supported by the Nankai Zhide Foundationsupported by the National Natural Science Foundation of China(Grant No.11974413)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB33000000)supported by the Natural Science Foundation for Shanxi Province(Grant No.1331KSC)。
文摘The emergence of the mobility edge(ME)has been recognized as an important characteristic of Anderson localization.The difficulty in understanding the physics of the MEs in three-dimensional(3 D)systems from a microscopic image encourages the development of models in lower-dimensional systems that have exact MEs.While most of the previous studies are concerned with one-dimensional(1 D)quasiperiodic systems,the analytic results that allow for an accurate understanding of two-dimensional(2 D)cases are rare.In this work,we disclose an exactly solvable 2 D quasicrystal model with parity-time(PT)symmetry displaying exact MEs.In the thermodynamic limit,we unveil that the extended-localized transition point,observed at the PT symmetry breaking point,is topologically characterized by a hidden winding number defined in the dual space.The coupling waveguide platform can be used to realize the 2 D non-Hermitian quasicrystal model,and the excitation dynamics can be used to detect the localization features.
基金Supported by the National Natural Science Foundation of China(62102292)the Hubei Key Laboratory of Intelligent Robot(Wuhan Institute of Technology) of China(HBIRL202103,HBIRL202204)+1 种基金Science Foundation Research Project of Wuhan Institute of Technology of China(K202035)Graduate Innovative Fund of Wuhan Institute of Technology of China(CX2021265)。
文摘With the rapid growth of the Industrial Internet of Things(IIoT), the Mobile Edge Computing(MEC) has coming widely used in many emerging scenarios. In MEC, each workflow task can be executed locally or offloaded to edge to help improve Quality of Service(QoS) and reduce energy consumption. However, most of the existing offloading strategies focus on independent applications, which cannot be applied efficiently to workflow applications with a series of dependent tasks. To address the issue,this paper proposes an energy-efficient task offloading strategy for large-scale workflow applications in MEC. First, we formulate the task offloading problem into an optimization problem with the goal of minimizing the utility cost, which is the trade-off between energy consumption and the total execution time. Then, a novel heuristic algorithm named Green DVFS-GA is proposed, which includes a task offloading step based on the genetic algorithm and a further step to reduce the energy consumption using Dynamic Voltage and Frequency Scaling(DVFS) technique. Experimental results show that our proposed strategy can significantly reduce the energy consumption and achieve the best trade-off compared with other strategies.
基金This work was partially supported by the National Natural Science Foundation of China(61801208,61671233,61931023)the Jiangsu Science Foundation(BK20170650)+2 种基金the Postdoctoral Science Foundation of China(BX201700118,2017M621712)the Jiangsu Postdoctoral Science Foundation(1701118B)the open research fund of National Mobile Communications Research Laboratory(2019D02).
文摘ultra-Dense Network(UDN)has been envisioned as a promising technology to provide high-quality wireless connectivity in dense urban areas,in which the density of Access Points(APs)is increased up to the point where it is comparable with or surpasses the density of active mobile users.In order to mitigate inter-AP interference and improve spectrum efficiency,APs in UDNs are usually clustered into multiple groups to serve different mobile users,respectively.However,as the number of APs increases,the computational capability within an AP group has become the bottleneck of AP clustering.In this paper,we first propose a novel UDN architecture based on Mobile Edge Computing(MEC),in which each MEC server is associated with a user-centric AP cluster to act as a mobile agent.In addition,in the context of MEC-based UDN,we leverage mobility prediction techniques to achieve a dynamic AP clustering scheme,in which the cluster structure can automatically adapt to the dynamic distribution of user traffic in a specific area.Simulation results show that the proposed scheme can highly increase the average user throughput compared with the baseline algorithm using max-SINR user association and equal bandwidth allocation,while it guarantees at the same time low transmission delay.
基金supported in part by the National Natural Science Foundation of China under Grant 62171465,62072303,62272223,U22A2031。
文摘By pushing computation,cache,and network control to the edge,mobile edge computing(MEC)is expected to play a leading role in fifth generation(5G)and future sixth generation(6G).Nevertheless,facing ubiquitous fast-growing computational demands,it is impossible for a single MEC paradigm to effectively support high-quality intelligent services at end user equipments(UEs).To address this issue,we propose an air-ground collaborative MEC(AGCMEC)architecture in this article.The proposed AGCMEC integrates all potentially available MEC servers within air and ground in the envisioned 6G,by a variety of collaborative ways to provide computation services at their best for UEs.Firstly,we introduce the AGC-MEC architecture and elaborate three typical use cases.Then,we discuss four main challenges in the AGC-MEC as well as their potential solutions.Next,we conduct a case study of collaborative service placement for AGC-MEC to validate the effectiveness of the proposed collaborative service placement strategy.Finally,we highlight several potential research directions of the AGC-MEC.
