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Shan Zhang

Shan Zhang appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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Networking and Internet Architecturecond-mat.supr-conArtificial Intelligencecond-mat.mtrl-sciInformation Theorymath.ITComputer Visioncond-mat.dis-nncs.CYHuman-Computer InteractionMachine LearningApplicationsComputation and Languagecond-mat.softeess.IVeess.SPeess.SYGenomicsmath.OASystems and Control

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Published work

26 published item(s)

preprint2026arXiv

How to Assess AI Literacy: Misalignment Between Self-Reported and Objective-Based Measures

The widespread adoption of Artificial Intelligence (AI) in K-12 education highlights the need for psychometrically-tested measures of teachers' AI literacy. Existing work has primarily relied on either self-report (SR) or objective-based (OB) assessments, with few studies aligning the two within a shared framework to compare perceived versus demonstrated competencies or examine how prior AI literacy experience shapes this relationship. This gap limits the scalability of learning analytics and the development of learner profile-driven instructional design. In this study, we developed and evaluated SR and OB measures of teacher AI literacy within the established framework of Concept, Use, Evaluate, and Ethics. Confirmatory factor analyses support construct validity with good reliability and acceptable fit. Results reveal a low correlation between SR and OB factors. Latent profile analysis identified six distinct profiles, including overestimation (SR > OB), underestimation (SR < OB), alignment (SR close to OB), and a unique low-SR/low-OB profile among teachers without AI literacy experience. Theoretically, this work extends existing AI literacy frameworks by validating SR and OB measures on shared dimensions. Practically, the instruments function as diagnostic tools for professional development, supporting AI-informed decisions (e.g., growth monitoring, needs profiling) and enabling scalable learning analytics interventions tailored to teacher subgroups.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Let Me Try Again: Examining Replay Behavior by Tracing Students' Latent Problem-Solving Pathways

Prior research has shown that students' problem-solving pathways in game-based learning environments reflect their conceptual understanding, procedural knowledge, and flexibility. Replay behaviors, in particular, may indicate productive struggle or broader exploration, which in turn foster deeper learning. However, little is known about how these pathways unfold sequentially across problems or how the timing of replays and other problem-solving strategies relates to proximal and distal learning outcomes. This study addresses these gaps using Markov Chains and Hidden Markov Models (HMMs) on log data from 777 seventh graders playing the game-based learning platform of From Here to There!. Results show that within problem sequences, students often persisted in states or engaged in immediate replay after successful completions, while across problems, strong self-transitions indicated stable strategic pathways. Four latent states emerged from HMMs: Incomplete-dominant, Optimal-ending, Replay, and Mixed. Regression analyses revealed that engagement in replay-dominant and optimal-ending states predicted higher conceptual knowledge, flexibility, and performance compared with the Incomplete-dominant state. Immediate replay consistently supported learning outcomes, whereas delayed replay was weakly or negatively associated in relation to Non-Replay. These findings suggest that replay in digital learning is not uniformly beneficial but depends on timing, with immediate replay supporting flexibility and more productive exploration.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

MAESTRO: Multi-Agent Evaluation Suite for Testing, Reliability, and Observability

We present MAESTRO, an evaluation suite for the testing, reliability, and observability of LLM-based MAS. MAESTRO standardizes MAS configuration and execution through a unified interface, supports integrating both native and third-party MAS via a repository of examples and lightweight adapters, and exports framework-agnostic execution traces together with system-level signals (e.g., latency, cost, and failures). We instantiate MAESTRO with 12 representative MAS spanning popular agentic frameworks and interaction patterns, and conduct controlled experiments across repeated runs, backend models, and tool configurations. Our case studies show that MAS executions can be structurally stable yet temporally variable, leading to substantial run-to-run variance in performance and reliability. We further find that MAS architecture is the dominant driver of resource profiles, reproducibility, and cost-latency-accuracy trade-off, often outweighing changes in backend models or tool settings. Overall, MAESTRO enables systematic evaluation and provides empirical guidance for designing and optimizing agentic systems.

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preprint2026arXiv

Small, Private Language Models as Teammates for Educational Assessment Design

Generative AI increasingly supports educational design tasks, e.g., through Large Language Models (LLMs), demonstrating the capability to design assessment questions that are aligned with pedagogical frameworks (e.g., Bloom's taxonomy). However, they often rely on subjective or limited evaluation methods; focus primarily on proprietary models; or rarely systematically examine generation, evaluation, or deployment constraints in real educational settings. Meanwhile, Small Language Models (SLMs) have emerged as local alternatives that better address privacy and resource limitations; yet their effectiveness for assessment tasks remains underexplored. To address this gap, we systematically compare LLMs and SLMs for assessment question design; evaluate generation quality across Bloom's taxonomy levels using reproducible, pedagogically grounded metrics; and further assess model-based judging against expert-informed evaluation by analyzing reliability and agreement patterns. Results show that SLMs achieve competitive performance across key pedagogically motivated quality dimensions while enabling local, privacy-sensitive deployment. However, model-based evaluations also exhibit systematic inconsistencies and bias relative to expert ratings. These findings provide evidence to posit language models as bounded assistants in assessment workflows; underscore the necessity of Human-in-the-Loop; and advance the automated educational question generation field by examining quality, reliability, and deployment-aware trade-offs.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Neural Network Based Method with Transfer Learning for Genetic Data Analysis

Transfer learning has emerged as a powerful technique in many application problems, such as computer vision and natural language processing. However, this technique is largely ignored in application to genetic data analysis. In this paper, we combine transfer learning technique with a neural network based method(expectile neural networks). With transfer learning, instead of starting the learning process from scratch, we start from one task that have been learned when solving a different task. We leverage previous learnings and avoid starting from scratch to improve the model performance by passing information gained in different but related task. To demonstrate the performance, we run two real data sets. By using transfer learning algorithm, the performance of expectile neural networks is improved compared to expectile neural network without using transfer learning technique.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Atomic Origin of Annealing Embrittlement in Metallic Glasses

An atomistic understanding of annealing embrittlement is a longstanding issue for metallic glasses, which is still lacking due to the insurmountable gap between the thermal history of atomic models and laboratory-made samples. Here, based on a thermal-cycling annealing method that can vary the effective quenching rate over ten orders of magnitude, we perform an atomistic study of the ductile-brittle transition in a ternary model metallic glass, which can be keyed to the annealing embrittlement in bulk metallic glasses. We reveal that thermal annealing can effectively obliterate thermally active-able "defects", which are abundant in the hyper-quenched and ductile glass but gives rise to strain-created shear events in the well-annealed and brittle glass. While the activation of the strain-created events eventually causes single shear banding, other local structural disruptions can be "healed" by the same type of events upon stress reversal, thereby hindering shear band broadening or multiplication, and resulting in annealing embrittlement.

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preprint2022arXiv

CATrans: Context and Affinity Transformer for Few-Shot Segmentation

Few-shot segmentation (FSS) aims to segment novel categories given scarce annotated support images. The crux of FSS is how to aggregate dense correlations between support and query images for query segmentation while being robust to the large variations in appearance and context. To this end, previous Transformer-based methods explore global consensus either on context similarity or affinity map between support-query pairs. In this work, we effectively integrate the context and affinity information via the proposed novel Context and Affinity Transformer (CATrans) in a hierarchical architecture. Specifically, the Relation-guided Context Transformer (RCT) propagates context information from support to query images conditioned on more informative support features. Based on the observation that a huge feature distinction between support and query pairs brings barriers for context knowledge transfer, the Relation-guided Affinity Transformer (RAT) measures attention-aware affinity as auxiliary information for FSS, in which the self-affinity is responsible for more reliable cross-affinity. We conduct experiments to demonstrate the effectiveness of the proposed model, outperforming the state-of-the-art methods.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Change Detection from Synthetic Aperture Radar Images via Graph-Based Knowledge Supplement Network

Synthetic aperture radar (SAR) image change detection is a vital yet challenging task in the field of remote sensing image analysis. Most previous works adopt a self-supervised method which uses pseudo-labeled samples to guide subsequent training and testing. However, deep networks commonly require many high-quality samples for parameter optimization. The noise in pseudo-labels inevitably affects the final change detection performance. To solve the problem, we propose a Graph-based Knowledge Supplement Network (GKSNet). To be more specific, we extract discriminative information from the existing labeled dataset as additional knowledge, to suppress the adverse effects of noisy samples to some extent. Afterwards, we design a graph transfer module to distill contextual information attentively from the labeled dataset to the target dataset, which bridges feature correlation between datasets. To validate the proposed method, we conducted extensive experiments on four SAR datasets, which demonstrated the superiority of the proposed GKSNet as compared to several state-of-the-art baselines. Our codes are available at https://github.com/summitgao/SAR_CD_GKSNet.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Distributed Estimation in Large Scale Wireless Sensor Networks via a Two Step Group-based Approach

We consider the problem of collaborative distributed estimation in a large scale sensor network with statistically dependent sensor observations. In collaborative setup, the aim is to maximize the overall estimation performance by modeling the underlying statistical dependence and efficiently utilizing the deployed sensors. To achieve greater sensor transmission and estimation efficiency, we propose a two step group-based collaborative distributed estimation scheme, where in the first step, sensors form dependence driven groups such that sensors in the same group are highly dependent, while sensors from different groups are independent, and perform a copula-based maximum a posteriori probability (MAP) estimation via intragroup collaboration. In the second step, the estimates generated in the first step are shared via inter-group collaboration to reach an average consensus. A merge based K-medoid dependence driven grouping algorithm is proposed. Moreover, we further propose a group-based sensor selection scheme using mutual information prior to the estimation. The aim is to select sensors with maximum relevance and minimum redundancy regarding the parameter of interest under certain pre-specified energy constraint. Also, the proposed group-based sensor selection scheme is shown to be equivalent to the global/non-group based selection scheme with high probability, but computationally more efficient. Numerical experiments are conducted to demonstrate the effectiveness of our approach.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Non-unital tracially approximated ${\rm C^*}$-algebras

In this paper, we introduce a class of non-unital tracial approximation ${\rm C^*}$-algebras. Consider the class of ${\rm C^*}$-algebras which are tracially $\mathcal{Z}$-absorbing (in the sense of Amint, Golestani, Jamali, Phillips's simple tracially $\mathcal{Z}$-absorbing or Castillejos, Li, Szabvo's tracial $\mathcal{Z}$-stability). Then $A$ is tracially $\mathcal{Z}$-absorbing for any simple ${\rm C^*}$-algebra $A$ in the corresponding class of non-unital tracial approximation ${\rm C^*}$-algebras.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Observation of three superconducting transitions in the pressurized CDW-bearing compound TaTe2

Transition metal dichalcogenides host a wide variety of lattice and electronic structures, as well as corresponding exotic physical properties, especially under certain tuning conditions. Here, we are the first to report the observation of pressure-induced three superconducting transitions in TaTe2, a charge density wave (CDW) - bearing layered transition-metal dichalcogenide that is metallic but not superconducting at ambient pressure. We find that its CDW state can be easily suppressed upon increasing pressure up to ~ 1 GPa. A superconducting state then emerges from the suppressed CDW state and persists to the pressure about 7 GPa. Unexpectedly, another superconducting state appears at ~ 11 GPa within the same monoclinic (M) structure of its ambient-pressure one. Upon further compression to 21 GPa, a third superconducting state with higher Tc appears from a high-pressure (HP) phase. Our experimental results suggest that the pressure-induced three superconducting transitions in TaTe2 are respectively driven by the suppression of the CDW state, the change of the angle in the M phase and the transition of M-to-HP phase. These results demonstrate not only the versatile nature of this correlated electron system, but also the first experimental example that shows the pressure-induced evolution from a CDW state to three superconducting states driven by different mechanisms.

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preprint2022arXiv

Visual Behaviors and Mobile Information Acquisition

It is common for people to engage in information acquisition tasks while on the move. To understand how users' visual behaviors influence microlearning, a form of mobile information acquisition, we conducted a shadowing study with 8 participants and identified three common visual behaviors: 'glance', 'inspect', and 'drift'. We found that 'drift' best supports mobile information acquisition. We also identified four user-related factors that can influence the utilization of mobile information acquisition opportunities: situational awareness, switching costs, ongoing cognitive processes, and awareness of opportunities. We further examined how these user-related factors interplay with device-related factors through a technology probe with 20 participants using mobile phones and optical head-mounted displays (OHMDs). Results indicate that different device platforms significantly influence how mobile information acquisition opportunities are used: OHMDs can better support mobile information acquisition when visual attention is fragmented. OHMDs facilitate shorter visual switch-times between the task and surroundings, which reduces the mental barrier of task transition. Mobile phones, on the other hand, provide a more focused experience in more stable surroundings. Based on these findings, we discuss trade-offs and design implications for supporting information acquisition tasks on the move.

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preprint2020arXiv

AoI-Delay Tradeoff in Mobile Edge Caching with Freshness-Aware Content Refreshing

Mobile edge caching can effectively reduce service delay but may introduce information staleness, calling for timely content refreshing. However, content refreshing consumes additional transmission resources and may degrade the delay performance of mobile systems. In this work, we propose a freshness-aware refreshing scheme to balance the service delay and content freshness measured by Age of Information (AoI). Specifically, the cached content items will be refreshed to the up-to-date version upon user requests if the AoI exceeds a certain threshold (named as refreshing window). The average AoI and service delay are derived in closed forms approximately, which reveals an AoI-delay tradeoff relationship with respect to the refreshing window. In addition, the refreshing window is optimized to minimize the average delay while meeting the AoI requirements, and the results indicate to set a smaller refreshing window for the popular content items. Extensive simulations are conducted on the OMNeT++ platform to validate the analytical results. The results indicate that the proposed scheme can restrain frequent refreshing as the request arrival rate increases, whereby the average delay can be reduced by around 80% while maintaining the AoI below one second in heavily-loaded scenarios.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Cooperative Service Caching and Workload Scheduling in Mobile Edge Computing

Mobile edge computing is beneficial to reduce service response time and core network traffic by pushing cloud functionalities to network edge. Equipped with storage and computation capacities, edge nodes can cache services of resource-intensive and delay-sensitive mobile applications and process the corresponding computation tasks without outsourcing to central clouds. However, the heterogeneity of edge resource capacities and inconsistence of edge storage and computation capacities make it difficult to jointly fully utilize the storage and computation capacities when there is no cooperation among edge nodes. To address this issue, we consider cooperation among edge nodes and investigate cooperative service caching and workload scheduling in mobile edge computing. This problem can be formulated as a mixed integer nonlinear programming problem, which has non-polynomial computation complexity. To overcome the challenges of subproblem coupling, computation-communication tradeoff, and edge node heterogeneity, we develop an iterative algorithm called ICE. This algorithm is designed based on Gibbs sampling, which has provably near-optimal results, and the idea of water filling, which has polynomial computation complexity. Simulations are conducted and the results demonstrate that our algorithm can jointly reduce the service response time and the outsourcing traffic compared with the benchmark algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

In Situ Epitaxy of Pure Phase Ultra-Thin InAs-Al Nanowires for Quantum Devices

Hybrid semiconductor-superconductor InAs-Al nanowires with uniform and defect-free crystal interfaces are one of the most promising candidates used in the quest for Majorana zero modes (MZMs). However, InAs nanowires often exhibit a high density of randomly distributed twin defects and stacking faults, which result in an uncontrolled and non-uniform InAs-Al interface. Furthermore, this type of disorder can create potential inhomogeneity in the wire, destroy the topological gap, and form trivial sub-gap states mimicking MZM in transport experiments. Further study shows that reducing the InAs nanowire diameter from growth can significantly suppress the formation of these defects and stacking faults. Here, we demonstrate the in situ growth of ultra-thin InAs nanowires with epitaxial Al film by molecular-beam epitaxy. Our InAs diameter (~ 30 nm) is only one-third of the diameters (~ 100 nm) commonly used in literatures. The ultra-thin InAs nanowires are pure phase crystals for various different growth directions, suggesting a low level of disorder. Transmission electron microscopy confirms an atomically sharp and uniform interface between the Al shell and the InAs wire. Quantum transport study on these devices resolves a hard induced superconducting gap and $2e^-$ periodic Coulomb blockade at zero magnetic field, a necessary step for future MZM experiments. A large zero bias conductance peak with a peak height reaching 80% of $2e^2/h$ is observed.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Low-Latency and Fresh Content Provision in Information-Centric Vehicular Networks

In this paper, the content service provision of information-centric vehicular networks (ICVNs) is investigated from the aspect of mobile edge caching, considering the dynamic driving-related context information. To provide up-to-date information with low latency, two schemes are designed for cache update and content delivery at the roadside units (RSUs). The roadside unit centric (RSUC) scheme decouples cache update and content delivery through bandwidth splitting, where the cached content items are updated regularly in a round-robin manner. The request adaptive (ReA) scheme updates the cached content items upon user requests with certain probabilities. The performance of both proposed schemes are analyzed, whereby the average age of information (AoI) and service latency are derived in closed forms. Surprisingly, the AoI-latency trade-off does not always exist, and frequent cache update can degrade both performances. Thus, the RSUC and ReA schemes are further optimized to balance the AoI and latency. Extensive simulations are conducted on SUMO and OMNeT++ simulators, and the results show that the proposed schemes can reduce service latency by up to 80\% while guaranteeing content freshness in heavily loaded ICVNs.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Soft mode parameter as an indicator for the activation energy spectra in metallic glass

The activation energy (E_A) spectra of potential energy landscape (PEL) provides a convenient perspective for interpreting complex phenomena in amorphous materials; however, the link between the E_A spectra and other physical properties in metallic glasses is still mysterious. By systematically probing the E_A spectra for numerous metallic glass samples with distinct local geometric ordering, which correspond to broad processing histories, it is found that the shear modulus of the samples are strongly correlated with the arithmetic mean of the E_A spectra rather than with the local geometrical ordering. Furthermore, we studied the correlation of the obtained E_A spectra and various well-established physical parameters. The outcome of our research clearly demonstrates that the soft mode parameter Ψ and the E_A spectrum are correlated; therefore, it could be a good indicator of metallic glass properties and sheds important light on the structure-property relationship in metallic glass through the medium of PEL.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

The Design of Dynamic Probabilistic Caching with Time-Varying Content Popularity

In this paper, we design dynamic probabilistic caching for the scenario when the instantaneous content popularity may vary with time while it is possible to predict the average content popularity over a time window. Based on the average content popularity, optimal content caching probabilities can be found, e.g., from solving optimization problems, and existing results in the literature can implement the optimal caching probabilities via static content placement. The objective of this work is to design dynamic probabilistic caching that: i) converge (in distribution) to the optimal content caching probabilities under time-invariant content popularity, and ii) adapt to the time-varying instantaneous content popularity under time-varying content popularity. Achieving the above objective requires a novel design of dynamic content replacement because static caching cannot adapt to varying content popularity while classic dynamic replacement policies, such as LRU, cannot converge to target caching probabilities (as they do not exploit any content popularity information). We model the design of dynamic probabilistic replacement policy as the problem of finding the state transition probability matrix of a Markov chain and propose a method to generate and refine the transition probability matrix. Extensive numerical results are provided to validate the effectiveness of the proposed design.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Correlation between non-centrosymmetry and superconductivity in quasi-one-dimensional compounds A2Cr3As3 (A=K, Rb)

Non-centrosymmetric superconductors, whose crystal structure is absent of inversion symmetry, have recently received special attentions due to the expectation of unconventional pairings and exotic physics associated with such pairings. The newly discovered superconductors A2Cr3As3 (A=K, Rb), featured by the quasi-one dimensional structure with conducting CrAs chains, belongs to such kind of superconductor. In this study, we are the first to report the finding that the superconductivity of A2Cr3As3 (A=K, Rb) has a positive correlation with the extent of non-centrosymmetry. Our in-situ high pressure ac susceptibility and synchrotron x-ray diffraction measurements reveal that the larger bond angle of As-Cr-As in the CrAs chains can be taken as a key factor controlling superconductivity. While the smaller bond angle and the distance between the CrAs chains also affect the superconductivity due to their structural connections with the angle. We find that the larger value of the difference between the larger and samller angles, which is associated with the extent of the non-centrosymmetry of the lattice structure, is in favor of superconductivity. These results are expected to shed a new light on the underlying mechanism of the superconductivity in these Q1D superconductors and also to provide new perspective in understanding other non-centrosymmetric superconductors.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Energy-Aware Traffic Offloading for Green Heterogeneous Networks

With small cell base stations (SBSs) densely deployed in addition to conventional macro base stations (MBSs), the heterogeneous cellular network (HCN) architecture can effectively boost network capacity. To support the huge power demand of HCNs, renewable energy harvesting technologies can be leveraged. In this paper, we aim to make efficient use of the harvested energy for on-grid power saving while satisfying the quality of service (QoS) requirement. To this end, energy-aware traffic offloading schemes are proposed, whereby user associations, ON-OFF states of SBSs, and power control are jointly optimized according to the statistical information of energy arrival and traffic load. Specifically, for the single SBS case, the power saving gain achieved by activating the SBS is derived in closed form, based on which the SBS activation condition and optimal traffic offloading amount are obtained. Furthermore, a two-stage energy-aware traffic offloading (TEATO) scheme is proposed for the multiple-SBS case, considering various operating characteristics of SBSs with different power sources. Simulation results demonstrate that the proposed scheme can achieve more than 50% power saving gain for typical daily traffic and solar energy profiles, compared with the conventional traffic offloading schemes.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Reversible tuning of superconductivity in pressurized qausi-one-dimensional A2Cr3As3 (A=K and Rb)

In-situ hydrostatic and uniaxial high pressure studies were performed on recently discovered CrAs-based qausi-one-dimensional superconductors A2Cr3As3 (A=K and Rb). The established Pressure-Temperature phase diagram in this study clearly demonstrates that either hydrostatic pressure or uniaxial pressure globally suppresses the superconducting transition temperature (Tc), and the latter is more effective than the former. Interestingly, in the same hydrostatic pressure environment, the suppressing rate of Tc in Rb2Cr3As3 is nearly twice as that of K2Cr3As3. Significantly, the reduced Tc in these superconductors can fully recover to its ambient-pressure value after the applied pressure is entirely released. Our results suggest that the bonding distance and angle between Cr-Cr in the Cr3As3 chains are the key factor in determining Tc and that the optimal lattice for superconductivity is hosted in the pristine K2Cr3As3.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Breakdown of Three-dimensional Dirac Semimetal State in pressurized Cd3As2

We report the first observation of a pressure-induced breakdown of the 3D-DSM state in Cd3As2, evidenced by a series of in-situ high-pressure synchrotron X-ray diffraction (XRD) and single crystal transport measurements. We find that Cd3As2 undergoes a structural phase transition from a metallic tetragonal (T) phase in space group I41/acd to a semiconducting monoclinic (M) phase in space group P21/c at critical pressure 2.57 GPa, above this pressure, an activation energy gap appears, accompanied by distinct switches in Hall resistivity slope and electron mobility. These changes of crystal symmetry and corresponding transport properties manifest the breakdown of the 3D-DSM state in pressurized Cd3As2.

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preprint2015arXiv

How Many Small Cells Can Be Turned off via Vertical Offloading under a Separation Architecture?

To further improve the energy efficiency of heterogeneous networks, a separation architecture called hyper-cellular network (HCN) has been proposed, which decouples the control signaling and data transmission functions. Specifically, the control coverage is guaranteed by macro base stations (MBSs), whereas small cells (SCs) are only utilized for data transmission. Under HCN, SCs can be dynamically turned off when traffic load decreases for energy saving. A fundamental problem then arises: how many SCs can be turned off as traffic varies? In this paper, we address this problem in a theoretical way, where two sleeping schemes (i.e., random and repulsive schemes) with vertical inter-layer offloading are considered. Analytical results indicate the following facts: (1) Under the random scheme where SCs are turned off with certain probability, the expected ratio of sleeping SCs is inversely proportional to the traffic load of SC-layer and decreases linearly with the traffic load of MBS-layer; (2) The repulsive scheme, which only turns off the SCs close to MBSs, is less sensitive to the traffic variations; (3) deploying denser MBSs enables turning off more SCs, which may help to improve network energy-efficiency. Numerical results show that about 50% SCs can be turned off on average under the predefined daily traffic profiles, and 10% more SCs can be further turned off with inter-layer channel borrowing.

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preprint2015arXiv

Spatial Traffic Shaping in Heterogeneous Cellular Networks with Energy Harvesting

Energy harvesting (EH), which explores renewable energy as a supplementary power source, is a promising 5G technology to support the huge energy demand of heterogeneous cellular networks (HCN). However, the random arrival of renewable energy brings great challenges to network management. By adjusting the distribution of traffic load in spatial domain, traffic shaping helps to balance the cell-level power demand and supply, and thus improves the utilization of renewable energy. In this paper, we investigate the power saving performance of traffic shaping in an analytical way, based on the statistic information of energy arrival and traffic load. Specifically, an energy-optimal traffic shaping scheme (EOTS) is devised for HCNs with EH, whereby the on-off state of the off-grid small cell and the amount of offloading traffic are adjusted dynamically with the energy variation, to minimize the on-grid power consumption. Numerical results are given to demonstrate that for the daily traffic and solar energy profiles, EOTS scheme can significantly reduce the energy consumption, compared with the greedy method where users are always offloaded to the off-grid small cell with priority.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Connection between ambient-pressure and pressure-induced superconducting phases in alkaline iron selenide superconductors

A unique platform for investigating the correlation between the antiferromagnetic (AFM) and superconducting (SC) states in high temperature superconductors is created by the discovery of alkaline iron selenide superconductors which are composed of an AFM insulating phase and a SC phase separated spatially. Our previous studies showed that pressure can fully suppress the superconductivity of ambient-pressure superconducting phase (SC-I) and AFM order simultaneously, then induce another superconducting phase (SC-II) at higher pressure. Consequently, the connection between the two superconducting phases becomes an intriguing issue. In this study, on the basis of observing pressure-induced reemergence of superconductivity in Rb0.8Fe2-ySe2-xTex (x=0, 0.19 and 0.28) superconductors, we find that the superconductivity of the SC-I and SC-II phases as well as the AFM ordered state can be synchronously tuned by Te doping and disappear together at the doping level of x=0.4. We propose that the two superconducting phases are connected by the AFM phase, in other words, the state of long-ranged AFM order plays a role in giving rise to superconductivity of the SC-I phase, while the fluctuation state of the suppressed AFM phase drives the emergence of SC-II phase. These results comprehensively demonstrate the versatile roles of AFM states in stabilizing and developing superconductivity in the alkaline iron selenide superconductors.

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preprint2014arXiv

Robust antiferromagnetism preventing superconductivity in pressurized Ba0.61K0.39Mn2Bi2

Here, we report that K-doped BaMn2Bi2 shows no experimental evidence of superconductivity down to 1.5 K under pressures up to 35.6 GPa, however, a tetragonal to an orthorhombic phase transition is observed at pressure of 20 GPa. Theoretical calculations for the tetragonal and orthorhombic phases, on basis of our high-pressure XRD data, find that the AFM order is robust in both of the phases in pressurized Ba0.61K0.39Mn2Bi2. Our experimental and theoretical results suggest that the K-doped BaMn2Bi2 belongs to a strong Hunds AFM metal with a hybridization of localized spin electrons and itinerant electrons, and that its robust AFM order essentially prevents the emergence of superconductivity.

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