IEEE INFOCOM 2024

Program at a Glance

IEEE INFOCOM 2024

Session Break-2-1: Coffee Break Session Break-2-2: Coffee Break

Session A-4: A-4: Software Defined Networking and Virtualization Session A-7: A-7: Consensus Protocols

Session B-4: B-4: Encryption and Payment Channel Networks Session B-7: B-7: Vehicular Networks

Session C-4: C-4: Routing Session C-7: C-7: Congestion Management

Session D-4: D-4: Acoustic and Multimodal Sensing Session D-7: D-7: Edge Computing

Session E-4: E-4: Federated Learning 3 Session E-5: E-5: Machine Learning with Transformers Session E-6: E-6: Federated Learning 4 Session E-7: E-7: Machine Learning 1

Session F-4: F-4: Caching Session F-5: F-5: Remote Direct Memory Access (RDMA) Session F-6: F-6: Video Delivery and Analytics Session F-7: F-7: Data Center Networking

Session Lunch-2: Conference Lunch (for Registered Attendees)

Session G-4: G-4: Energy Efficiency Session G-5: G-5: Localization Session G-6: G-6: Wireless Sensing Session G-7: G-7: Quantum Networking

Session Panel: Panel: AI+Network: Look Back, Look Forward

Session Reflection: A Reflection with INFOCOM Achievement Award Winner

In-Orbit Processing or Not? Sunlight-Aware Task Scheduling for Energy-Efficient Space Edge Computing Networks

Weisen Liu, Zeqi Lai, Qian Wu and Hewu Li (Tsinghua University, China); Qi Zhang (Zhongguancun Laboratory, China); Zonglun Li (Beijing Jiaotong University, China); Yuanjie Li and Jun Liu (Tsinghua University, China)

With the rapid evolution of space-borne capabilities, space edge computing (SEC) is becoming a new computation paradigm for future integrated space and terrestrial networks. Satellite edges adopt advanced on-board hardware, which not only enables new opportunities to perform complex intelligent space tasks, but also involves new challenges due to additional energy consumption in power-constrained space environment. In this paper, we present Phoenix, an energy-efficient task scheduling framework for futuristic SEC networks. Phoenix exploits a key insight that in a SEC network, there dynamically exists a number of sunlit edges which are illuminated during their orbital period and have sufficient energy supplement from the sun. Phoenix accomplishes energy-efficient in-orbit computing by judiciously offloading space tasks to "sunlight-sufficient" edges or to the ground. Specifically, Phoenix first formulates the SEC battery energy optimizing (SBEO) problem with the goal of minimizing average battery energy consumption while satisfying various task completion constraints. Then Phoenix incorporates a sunlight-aware SEC task scheduling mechanism to make scheduling decisions effectively and efficiently. We implement a prototype and build a hardware-in-the-loop SEC experimental environment. Extensive data-driven evaluations demonstrate that as compared to other state-of-the-art solutions, Phoenix can effectively prolong battery lifetime to 1.7× while still completing tasks on time.

Speaker Weisen Liu (Tsinghua University)

ScalO-RAN: Energy-aware Network Intelligence Scaling in Open RAN

Stefano Maxenti, Salvatore D'Oro, Leonardo Bonati and Michele Polese (Northeastern University, USA); Antonio Capone (Politecnico di Milano, Italy); Tommaso Melodia (Northeastern University, USA)

Virtualization, software, and orchestration are pivotal elements in contemporary networks. In this context, the O-RAN architecture bypasses vendor lock-in, enables network programmability, and facilitates integrated artificial intelligence (AI) support. Moreover, container orchestration frameworks (e.g., Kubernetes, OpenShift) simplify how cellular networks and the newly introduced RAN Intelligent Controllers (RICs) are deployed, managed, and orchestrated. While this enables cost reduction via infrastructure sharing, it also makes meeting O-RAN control latency requirements more challenging, especially during peak resource utilization. For instance, the Near-real-time RIC executes applications (xApps) that take control decisions within 1 s, and we show that container platforms available today fail in guaranteeing such timing. To address this, we propose ScalO-RAN, a control framework rooted in optimization and designed as an O-RAN rApp that allocates and scales AI-based applications (xApps, rApps and dApps) to: (i) abide by application-specific latency requirements, and (ii) monetize the shared infrastructure while reducing energy consumption. We prototype ScalO-RAN on OpenShift with base stations, RIC, and a set of AI-based xApps. ScalO-RAN optimally allocates and distributes O-RAN applications to accommodate stringent latency requirements. More importantly, scaling O-RAN applications is primarily a time-constrained rather than resource-constrained, where scaling policies must account for stringent inference latency of AI applications.

Speaker

Speaker biography is not available.

Competitive Online Age-of-Information Optimization for Energy Harvesting Systems

Qiulin Lin (City University of Hong Kong, China); Junyan Su and Minghua Chen (City University of Hong Kong, Hong Kong)

We consider the scenario where an energy harvesting source sends its updates to a receiver. The source optimizes its energy allocation over a decision period to maximize a sum of time-varying functions of the age of information (AoI), representing the value of providing timely information. In a practical online setting, we need to make irrevocable energy allocation decisions at each time while the time-varying functions and the energy arrivals are only revealed sequentially. The problem is then challenging as 1) we are facing uncertain energy harvesting arrivals and time-varying functions, and 2) the energy allocation decisions and the energy harvesting process are coupled due to the capacity-limited battery. In this paper, we develop an optimal online algorithm \textsf{CR-Reserve} and show it achieves $\ln\theta+1)$-competitive, where $\theta$ is a parameter representing the level of uncertainty of the time-varying functions. It is the optimal competitive ratio among all deterministic and randomized online algorithms. We conduct simulations based on real-world traces and compare our algorithms with conceivable alternatives. The results show that our algorithms achieve 12$\%$ performance improvement as compared to the state-of-the-art baseline.

Speaker

Speaker biography is not available.

Mean-Field Multi-Agent Contextual Bandit for Energy-Efficient Resource Allocation in vRANs

Jose A. Ayala-Romero (NEC Laboratories Europe GmbH, Germany); Leonardo Lo Schiavo (Universidad Carlos III de Madrid & IMDEA Networks Institute, Spain); Andres Garcia-Saavedra (NEC Labs Europe, Germany); Xavier Costa-Perez (ICREA and i2cat & NEC Laboratories Europe, Spain)

Radio Access Network (RAN) virtualization, key for new-generation mobile networks, requires Hardware Accelerators (HAs) that swiftly process wireless signal from Base Stations (BSs) to meet stringent reliability targets. However, HAs are expensive and energy-hungry, which increases costs and has serious environmental implications. To address this problem, we gather data from our experimental platform and compare the performance and energy consumption of a HA (NVIDIA GPU V100) vs. a CPU (Intel Xeon Gold 6240R, 16 cores) for energy-friendly software processing. Based on the insights obtained from this data, we devise a strategy to offload workloads to HAs opportunistically to save energy while preserving reliability. This offloading strategy, however, needs to be configured in near-real-time for every BS sharing common computational resources. This renders a challenging multi-agent collaborative problem in which the number of involved agents (BSs) can be arbitrarily large and can change over time. Thus, we propose an efficient multi-agent contextual bandit algorithm called ECORAN, which applies concepts from mean field theory to be fully scalable. Using a real platform and traces from a production mobile network, we show that ECORAN can provide up to 40% energy savings with respect to the approach used today by the industry.

Speaker

Speaker biography is not available.

Session Chair

Falko Dressler (TU Berlin, Germany)

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LoBaCa: Super-Resolution LoRa Backscatter Localization for Low Cost Tags

Boxin Hou and Jiliang Wang (Tsinghua University, China)

Long Range LoRa backscatter localization has shown great potential in many applications. However, the narrow bandwidth of LoRa and the defects of backscatter tag make localization challenging in practice. This paper presents LoBaCa, the first super-resolution LoRa backscatter localization system for low-cost tags. To increase the overall bandwidth, LoBaCa first utilizes frequency hopping technique and exploits the phase slope to synchronize multiple frequency bands. We further show that the low-cost backscatter tag causes additional phase distortion in the weak backscatter signal and thus introduces significant localization error. Therefore, LoBaCa leverages the upper and lower sideband signals to improve the SNR and correct the phase distortion. Finally, LoBaCa adopts a super-resolution ESPRIT algorithm to solve the complex multipath effect, estimate the angle of arrival (AoA) and localize the backscatter tag. We prototype LoBaCa and conduct extensive experiments to evaluate LoBaCa in both indoor and outdoor scenarios. Our results show that the localization error of LoBaCa is 5.0cm and 71cm when the LoBaCa tag is 5m and 40m away, which is 4.3times and 1.7times better than the state-of-the-arts.

Speaker Boxin Hou (Tsinghua University)

a phd candidate from Tsinghua University

Multi-Node Concurrent Localization in LoRa Networks: Optimizing Accuracy and Efficiency

Jingkai Lin, Runqun Xiong, Zhuqing Xu, Wei Tian, Ciyuan Chen, Xirui Dong and Luo Junzhou (Southeast University, China)

LoRa Localization, a fundamental service in LoRa networks, has garnered significant attention due to its long-range capabilities and low power consumption. However, existing approaches for LoRa localization are either incompatible with commercial devices or highly susceptible to environmental factors. To tackle this challenge, we propose SyncLoc, a TDoA-based LoRa localization framework that integrates a dedicated node for multi-dimensional time-drift correction. Our proposal is built on two key observations: firstly, the nanosecond-precise measurement of time differences between gateways, and secondly, the substantial impact of SNR on gateway time drift. To accomplish our objective, we present three progressively enhanced versions of SyncLoc, each intended to comprehensively analyze the factors influencing LoRa time synchronization accuracy across different deployment scenarios involving nodes, carrier frequencies, and spreading factors. In addition to improving accuracy, we identify inefficiencies in LoRa's multi-node concurrent localization, and introduce SyncLoc-4, a multi-node localization scheduling mechanism that optimizes efficiency with a 2-approximation ratio. Through extensive experiments utilizing commercial LoRa devices in real-world environments, we demonstrate a 2.44× improvement in accuracy achieved by SyncLoc compared to LoRaWAN. Furthermore, simulations of large-scale networks exhibit a 2.47× boost in localization scalability (i.e., the number of concurrently located nodes) when employing SyncLoc instead of LoRaWAN.

Speaker Jingkai Lin (Southeast University)

TransformLoc: Transforming MAVs into Mobile Localization Infrastructures in Heterogeneous Swarms

Haoyang Wang, Jingao Xu and Chenyu Zhao (Tsinghua University, China); Zihong Lu (Harbin Institute of Technology, China); Yuhan Cheng and Xuecheng Chen (Tsinghua University, China); Xiao-Ping (Steven) Zhang (Tsinghua University & Toronto Metropolitan University, Canada); Yunhao Liu and Xinlei Chen (Tsinghua University, China)

A heterogeneous micro aerial vehicles (MAV) swarm consists of resource-intensive but expensive advanced MAVs (AMAVs) and resource-limited but cost-effective basic MAVs (BMAVs), offering opportunities in diverse fields. Accurate and real-time localization is crucial for MAV swarms, but current practices lack a low-cost, high-precision, and real-time solution, especially for lightweight BMAVs. We find an opportunity to accomplish the task by transforming AMAVs into mobile localization infrastructures for BMAVs. However, turning this insight into a practical system is non-trivial due to challenges in location estimation with BMAVs' unknown and diverse localization errors and resource allocation of AMAVs given coupled influential factors. This study proposes TransformLoc, a new framework that transforms AMAVs into mobile localization infrastructures, specifically designed for low-cost and resource-constrained BMAVs. We first design an error-aware joint location estimation model to perform intermittent joint location estimation for BMAVs and design a similarity-instructed adaptive grouping-scheduling strategy to allocate resources of AMAVs dynamically. TransformLoc achieves a collaborative, adaptive, and cost-effective localization system suitable for large-scale heterogeneous MAV swarms. We implement TransformLoc on industrial drones and validate its performance. Results show that TransformLoc outperforms baselines by maintaining a localization error under 1m and an average navigation success rate of 95%.

Speaker Haoyang Wang (Tsinghua University)

Haoyang Wang received the B.E. degree from the School of Computer Science and Engineering, Central South University, China, in 2022. He is currently pursuing the Ph.D. degree at the Tsinghua Shenzhen International Graduate School, Tsinghua University, China. His research interests include AIoT, mobile computing, and distributed & embedded AI.

AdaSem: Adaptive Goal-Oriented Semantic Communications for End-to-End Camera Relocalization

Qi Liao (Nokia Bell Labs, Germany); Tze-Yang Tung (Nokia Bell Labs, USA)

Recently, deep autoencoders have gained traction as a powerful method for implementing goal-oriented semantic communications systems. The idea is to train a mapping from source domain directly to channel symbols, and vice versa. However, prior studies often focused on rate-distortion tradeoff and transmission latency, at the cost of increasing end-to-end complexity. Moreover, they used publicly available datasets which cannot validate the observed gains against real-world baseline systems, leading to an unfair comparison. In this paper, we study the problem of remote camera pose estimation and propose AdaSem, an adaptive semantic communications approach by optimizing the tradeoff between inference accuracy and end-to-end latency. We develop an adaptive semantic codec model, which encodes the source data into a dynamic number of symbols, based on the latent space distribution and the channel state information. We utilize a lightweight model for both transmitter and receiver to ensure comparable complexity to the baseline implemented in a real-world system. Extensive experiments on real-environment data show the effectiveness of our approach. When compared to a real implementation of client-server camera relocalization service, AdaSem outperforms the baseline by reducing the latency and estimation error by 75.8% and over 63%, respectively.

Speaker

Speaker biography is not available.

Session Chair

Wenye Wang (NC State University, USA)

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AGR: Acoustic Gait Recognition Using Interpretable Micro-Range Profile

Penghao Wang, Ruobing Jiang and Chao Liu (Ocean University of China, China); Jun Luo (Nanyang Technological University, Singapore)

Recently, gait recognition, a type of biometric identification, has seen extensive application in area access control and smart homes, bolstering convenience, privacy, and personalized experiences. While privacy-preserving wireless sensing solutions have become a research focal point as alternatives to computer vision methods, current strategies are predominantly based on abstract features, inherently suffering from limitations in interpretability and stability. Fortunately, the widespread utilization of smart speakers has opened up opportunities for acoustic sensing, making it possible to extract more interpretable features. In this paper, we further push the limit of acoustic recognition with visual interpretability by sequentially visualizing fine-grained acoustic human gait features. Original gait profiles, with invisible gait indications, are first constructed by matrixing and compressing multipath gait echoes. Then, we achieve interpretable gaits through the novelly proposed micro-range profiles. Key innovations include Mobile Target Detector (MTD) based clutter elimination, farther echo strength compensation, and macro torso migration subtraction. Practical benefits provided by the interpretable gait profiles lie in improving data utilization, optimizing abnormal data handling, and enhancing model stability. Extensive evaluations with an open experimental scenario have been conducted to demonstrate accuracy reaching 97.5% in general, and robust performance against impacts from various practical factors.

Speaker Chao Liu

Chao Liu received his B.S. degree from Ocean University of China in 2011 and his Ph.D. degrees from the Illinois Institute of Technology and Ocean University of China in 2015 and 2016, respectively. He is currently an associate professor in the Department of Computer Science and Technology, Ocean University of China. He is also the chair of IEEE Std 1851-2023 and the vice chair of ISO 21851-2020. His main research interests include acoustic sensing, mobile computing, and wireless sensor networks. He has authored or coauthored more than 70 papers in international journals and conference proceedings, such as the CCS, INFOCOM, JSAC, TIP, TII, TCSVT and the TOSN. He is a member of the ACM and IEEE.

hBP-Fi: Contactless Blood Pressure Monitoring via Deep-Analyzed Hemodynamics

Yetong Cao (Beijing Institute of Technology, China); Shujie Zhang (Nanyang Technological University, Singapore); Fan Li (Beijing Institute of Technology, China); Zhe Chen (Fudan University & AIWiSe Company, China); Jun Luo (Nanyang Technological University, Singapore)

Blood pressure (BP) measurement is significant to the assessment of many dangerous health conditions. Non-invasive approaches typically rely on wearing devices on specific skin areas with consistent pressure. However, this can be uncomfortable and unsuitable for certain individuals, and the accuracy of these methods may significantly decrease due to improper device placements and wearing states. Recently, contactless methods leveraging RF technology have emerged as a potential alternative. However, these methods suffer from the drawback of overfitting deep learning (DL) models without a sound physiological basis, resulting in a lack of clear explanations for their outputs. Consequently, such limitations lead to skepticism and distrust among medical experts. In this paper, we propose hBP-Fi, a contactless BP measurement system driven by hemodynamics acquired via RF sensing. hBP-Fi has several advantages: i) it relies on hemodynamics as the key physical process of heart-pulse activities, ii) it uses beam-steerable RF devices for super-resolution scans of fine-grained pulse activities along arm arteries, and iii) it ensures trustworthy outputs through an explainable (decision-understandable) DL model. Extensive experiments with 35 subjects demonstrate that hBP-Fi achieves an error of -2.05±6.83 mmHg for monitoring systolic blood pressure and 1.99±6.30 mmHg for diastolic blood pressure.

Speaker Yetong Cao (Beijing Institute of Technology, China)

Yetong Cao is currently a research fellow at the College of Computing and Data Science, Nanyang Technological University. She received her Ph.D. degree from the School of Computer Science at Beijing Institute of Technology in 2023, advised by Prof. Fan Li. She received her B.E. degree from Shandong University in 2017. Her research interests include Smart Sensing, Mobile Computing, Mobile Health, and Security & Privacy.

M2-Fi: Multi-person Respiration Monitoring via Handheld WiFi Devices

Jingyang Hu and Hongbo Jiang (Hunan University, China); Tianyue Zheng, Jingzhi Hu and Hongbo Wang (Nanyang Technological University, Singapore); Hangcheng Cao (City University of Hong Kong, China); Zhe Chen (Fudan University & AIWiSe Company, China); Jun Luo (Nanyang Technological University, Singapore)

Wi-Fi signal is commonly used for conventional communication, yet it can also realize low-cost and non-invasive human sensing. However, using handheld devices for Wi-Fi sensing in Multi-person scenarios is still a challenging problem. In this paper, we propose M2-Fi to achieve multi-person respiration monitoring using handheld device. M2-Fi leverages Wi-Fi BFI (beamforming feedback information) performs multi-person respiration monitoring. As a compressed version of The uplink CSI (channel state information), BFI transmission is unencrypted, easily obtained using frame capture, does not require specific firmware or WiFi chipsets to obtain. M2-Fi is based on an interesting experiment phenomenon that when a Wi-Fi device is very close to a subject, near-field channel changes caused by the subject significantly cancel out changes from other subjects. We employed VMD (Variational Mode Decomposition) to eliminate the interference caused by hand movement in the BFI time series. Subsequently, we devised a deep learning architecture based on GAN (Generative Adversarial Networks) to recover fine-grained respiration waveforms from the respiration patterns extracted from the BFI time series. Our experiments on collected 50-hour data from 8 subjects show that M2-Fi can accurately recover the respiration waveforms of multiple persons with personal handheld device.

Speaker Jingyang Hu (Hunan University)

Jingyang Hu is currently pursuiting Ph.D. student with the College of Computer Science and Electronic Engineering, Hunan University, China. From 2022 to 2023, he works as a joint Ph.D. student at the School of Computer Science and Engineering at Nanyang Technological University (NTU), Singapore. He has published papers in ACM Ubicomp, ACM CCS, IEEE INFOCOM, IEEE ICDCS, IEEE TMC, IEEE JSAC, IEEE TITS, IEEE IoT-J, etc. His research interests include mobile and pervasive computing, the Internet of Things, and machine learning.

One is Enough: Enabling One-shot Device-free Gesture Recognition with COTS WiFi

Leqi Zhao, Rui Xiao and Jianwei Liu (Zhejiang University, China); Jinsong Han (Zhejiang University & School of Cyber Science and Technology, China)

In recent years, WiFi-based gesture recognition (WGR) has gained popularity due to its privacy-preserving nature and wide availability of WiFi infrastructure. However, existing WGR systems suffer from scalability issues, i.e., requiring extensive data collection and re-training for each new gesture class. To address these limitations, we propose OneSense, a one-shot WiFi-based gesture recognition system that can efficiently and easily adapt to new gesture classes. Specifically, we first propose a data enrichment approach based on the law of signal propagation in physical world to generate virtual gestures, enhancing the diversity of the training set without extra overhead of real sample collection. Then, we devise an aug-meta learning (AML) framework to enable efficient and scalable few-short learning. This framework leverages two pre-training stages (i.e., aug-training and meta-training) to improve the model's feature extraction and generalization abilities, and ultimately achieves accurate one-shot gesture recognition through fine-tuning. Experimental results demonstrate that OneSense achieves 93% one-shot gesture recognition accuracy, which outperforms the state-of-the-art approaches. Moreover, it maintains high recognition accuracy when facing new environments, user locations, and user orientations. Furthermore, the proposed AML framework reduces 86%+ pre-training latency compared to conventional meta-learning method.

Speaker Leqi Zhao (Zhejiang University)

Leqi Zhao received the BS degree from Zhejiang University in 2023. She is currently a first-year Ph.D. student at Department of Computer Science and Technology, Zhejiang University. Her research interests include wireless sensing, mobile computing, and IoT security.

Session Chair

Hina Tabassum (York University, Canada)

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Quantum BGP with Online Path Selection via Network Benchmarking

Maoli Liu and Zhuohua Li (The Chinese University of Hong Kong, Hong Kong); Kechao Cai (Sun Yat-Sen University, China); Jonathan Allcock (Tencent Quantum Laboratory, Hong Kong); Shengyu Zhang (Tencent Quantum Laboratory, China); John Chi Shing Lui (Chinese University of Hong Kong, Hong Kong)

Large-scale quantum networks with thousands of nodes require topology-oblivious routing protocols to realize. Most existing quantum network routing protocols only consider the intra-domain scenario, where all nodes belong to a single party with complete topology knowledge. However, like the classical Internet, quantum Internet will likely be provided by multiple quantum Internet Service Providers (qISPs). In this paper, we consider the inter-domain scenario, where the network consists of multiple subnetworks owned by mutually-untrusted parties without centralized control. Under this setting, previously proposed quantum path selection mechanisms, which rely on the network topology knowledge, are no longer applicable. We propose a Quantum Border Gateway Protocol (QBGP) for efficiently routing entanglement across qISP boundaries. To guarantee high-quality information transmission, we propose an algorithm named online top-$K$ path selection. This algorithm utilizes the information gain introduced in this paper to adaptively decide on measurement parameters, allowing for the selection of high-fidelity paths and accurate fidelity estimates, while minimizing costs. Additionally, we implement a quantum network simulator and evaluate our protocol and algorithm. Our evaluation shows that QBGP effectively distributes entanglement across different qISPs, and our path selection algorithm increases the network performance by selecting high-fidelity paths with much lower resource consumption than other methods.

Speaker Zhuohua Li (The Chinese University of Hong Kong)

Zhuohua Li is a postdoctoral fellow at the Advanced Networking and System Research Laboratory (ANSRLab) at The Chinese University of Hong Kong (CUHK). He obtained his Ph.D. in Computer Science and Engineering at CUHK in 2022, under the supervision of Prof. John C.S. Lui. Before that, he completed his B.E. in Computer Science and Technology at the University of Science and Technology of China in 2017. His research focuses on the theory and applications of multi-armed bandits, quantum networks, system security, and program analysis.

Routing and Photon Source Provisioning in Quantum Key Distribution Networks

Sun Xu, Yangming Zhao and Liusheng Huang (University of Science and Technology of China, China); Chunming Qiao (University at Buffalo, USA)

Quantum Key Distribution (QKD) is considered to be an ultimate solution to communication security. However, current QKD devices, especially quantum photon sources, are expensive, and they can generate secret keys only at a low rate. In this paper, we design a system named RPSP for trusted relay-based QKD networks to not only minimize the number of photon sources needed in a network to ensure at least one feasible relay path exists for any potential QKD requests but also save the time to complete a batch of end-to-end QKD requests by jointly optimizing the routing of relay paths and the provisioning of photon sources along each relay path. Compared with existing works, RPSP focuses on a more practical scenario where only some of the nodes are equipped with photon sources and it leverages optical switching to enable dynamic photon source provisioning such that we can utilize such QKD devices in a more efficient way. Extensive simulations show that compared with baseline schemes, RPSP can save up to 87% of the photon sources needed in a trusted relay based QKD network, and 36% of the time to complete a batch of QKD requests.

Speaker Sun Xu (University of Science and Technology of China)

Sun Xu received B.S. degree in 2022 from the University of Electronic Science and Technology of China. He is currently studying for a master's degree in the School of Computer Science and Technology, University of Science and Technology of China(USTC). His main research interest is quantum network.

LinkSelFiE: Link Selection and Fidelity Estimation in Quantum Networks

Maoli Liu, Zhuohua Li and Xuchuang Wang (The Chinese University of Hong Kong, Hong Kong); John Chi Shing Lui (Chinese University of Hong Kong, Hong Kong)

Reliable transmission of fragile quantum information requires one to efficiently select and utilize high-fidelity links among multiple noisy quantum links. However, the fidelity, a quality metric of quantum links, is unknown a priori. Uniformly estimating the fidelity of all links can be expensive, especially in networks with numerous links. To address this challenge, we formulate the link selection and fidelity estimation problem as a best arm identification problem and propose an algorithm named LinkSelFiE. The algorithm efficiently identifies the optimal link from a set of quantum links and provides an accurate fidelity estimate of that link with low quantum resource consumption. LinkSelFiE estimates link fidelity based on the feedback of a vanilla network benchmarking subroutine, and adaptively eliminates inferior links throughout the whole fidelity estimation process. This elimination leverages a novel confidence interval derived in this paper for the estimates from the subroutine, which theoretically guarantees that LinkSelFiE outputs the optimal link correctly with high confidence. We also establish a provable upper bound of cost complexity for LinkSelFiE. Moreover, we perform extensive simulations under various scenarios to corroborate that LinkSelFiE outperforms other existing methods in terms of both identifying the optimal link and reducing quantum resource consumption.

Speaker Maoli Liu (The Chinese University of Hong Kong)

Maoli Liu is a fourth-year Ph.D. candidate in the Department of Computer Science and Engineering at the Chinese University of Hong Kong, under the supervision of Prof. John C.S. Lui. Before that, she completed his B.E. in Infomation Engineering at Xi'an Jiaotong University in 2020. Her research focuses on the theory and applications of multi-armed bandits, computer networks, and quantum networks.

Routing and Wavelength Assignment for Entanglement Swapping of Photonic Qubits

Yangyu Wang, Yangming Zhao and Liusheng Huang (University of Science and Technology of China, China); Chunming Qiao (University at Buffalo, USA)

Efficient entanglement routing in Quantum Data Networks (QDNs) is essential in order to concurrently establish as many Entanglement Connections (ECs) as possible, which in turn maximizes the network throughput. In this work, we consider a new class of QDNs with wavelength division multiplexed (WDM) quantum links where each quantum repeater will perform entanglement swapping by measuring two photonic qubits coming from some entangled photon sources directly on the same wavelength. To address unique challenges in achieving a high network throughput in such QDNs, we propose QuRWA to jointly optimize the entanglement routing and wavelength assignment. To this end, we introduce a key concept named Co-Path to improve fault-tolerance: all ELs in a Co-Path set will be assigned the same wavelength and this may serve as backup for some other ELs in the same Co-Path when establishing ECs. We design efficient algorithms to optimize the Co-Path selection and wavelength assignment to maximize resource utilization and fault tolerance. Extensive simulations demonstrate that compared with the methods without introducing Co-Path, QuRWA improves the network throughput by up to 122%.

Speaker Yangyu Wang(University of Science and Technology of China)

Yangyu Wang received B.S. degree in 2020 from the Hubei University. He is currently studying for a master's degree in the School of Computer Science and Technology, University of Science and Technology of China(USTC). His main research interest is the design and optimization of quantum network communication protocols, including research on routing and transmission protocols. Currently, his focus is mainly on issues related to quantum data networks. The paper to be shared at this conference also focuses on solving efficient entanglement routing in quantum data networks to improve resource utilization and network throughput. In the future, he will also conduct more research on scheduling problems in quantum data networks, hoping to have the opportunity to share relevant achievements with researchers in the communication field.

Session Chair

Carlee Joe-Wong (Carnegie Mellon University, USA)

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Program at a Glance