IEEE INFOCOM 2023

Program at a Glance

IEEE INFOCOM 2023

Session Keynote: Opening, Awards, and Keynote

Session Break-1-Day1: Coffee Break Session Lunch-Day1: Conference Lunch Session Dinner-Day1: Chartered Cruise Dinner (for attendees with Full Conference Registrations)

Session A-1: Cloud/Edge Computing 1 Session A-2: Wireless/Mobile Learning Session A-3: Security and Privacy

Session B-1: Federated Learning 1 Session B-2: Federated Learning 2 Session B-3: Federated Learning 3

Session C-1: LoRa and LPWAN Session C-2: Satellite/Space Networking Session C-3: Internet Routing

Session D-1: mmWave 1 Session D-2: mmWave 2 Session D-3: mmWave 3

Session E-1: Video Streaming 1 Session E-2: Video Streaming 2 Session E-3: Video Streaming 3

Session F-1: Datacenter and Switches Session F-2: Memory/Cache Management 1 Session F-3: Internet Measurement

Session G-1: Theory 1 Session G-2: Theory 2 Session G-3: 5G

Session Demo-1: Demo Session 1

Rotation Speed Sensing with mmWave Radar

Rong Ding, Haiming Jin and Dingman Shen (Shanghai Jiao Tong University, China)

Machines with rotary parts are prevalent in industrial systems and our daily lives. Rotation speed measurement is a crucial task for monitoring machinery health. Previous approaches for rotation speed sensing are constrained by limited operation distance, strict requirement for illumination, or strong dependency on the target object's light reflectivity. In this work, we propose mRotate, a practical mmWave radar-based rotation speed sensing system liberated from all the above constraints. Specifically, mRotate separates the target signal reflected by the rotating object from the mixed reflection signals, extracts the high quality rotation related features, and accurately obtains the rotation speed through the customized radar sensing mode and algorithm design. We implement mRotate on a commercial mmWave radar and extensively evaluate it in both lab environments and in a machining workshop for field tests. mRotate achieves an MAPE of 0.24% in accuracy test, which is 38% lower than that produced by the baseline device, a popular commercial laser tachometer. Besides, our experiments show that mRotate can measure a spindle whose diameter is only 5mm, maintain a high accuracy with a sensing distance as far as 2.5m, and simultaneously measure the rotation speeds of multiple objects.

Speaker Haiming Jin (Shanghai Jiao Tong University)

I am currently a tenure-track Associate Professor in the Department of Computer Science and Engineering at Shanghai Jiao Tong University (SJTU). From August 2021 to December 2022, I was a tenure-track Associate Professor in the John Hopcroft Center (JHC) for Computer Science at SJTU. From September 2018 to August 2021, I was an assistant professor in JHC at SJTU. From June 2017 to June 2018, I was a Postdoctoral Research Associate in the Coordinated Science Laboratory (CSL) of University of Illinois at Urbana-Champaign (UIUC). I received my PhD degree from the Department of Computer Science of UIUC in May 2017, advised by Prof. Klara Nahrstedt. Before that, I received my Bachelor degree from the Department of Electronic Engineering of SJTU in July 2012.

mmEavesdropper: Signal Augmentation-based Directional Eavesdropping with mmWave Radar

Yiwen Feng, Kai Zhang, Chuyu Wang, Lei Xie, Jingyi Ning and Shijia Chen (Nanjing University, China)

With the popularity of online meetings equipped with speakers, voice privacy security has drawn increasing attention because eavesdropping on the speakers can quickly obtain sensitive information. In this paper, we propose mmEavesdropper, a mmWave based eavesdropping system, which focuses on augmenting the micro-vibration signal via theoretical models for voice recovery. Particularly, to augment the receiving signal of the target vibration, we propose to use beam-forming to facilitate the directional augmentation by suppressing other orientations and use Chirp-Z transform to facilitate the distance augmentation by increasing the range resolution compared with traditional FFT. To augment the vibration signal in the IQ plane, we build a theoretical model to analyze the distortion and propose a segmentation-based fitting method to calibrate the vibration signal. To augment the spectrum for sound recovery, we propose to combine multiple channels and leverage an encoder-decoder based neural network to reconstruct the spectrum for voice recovery. We perform extensive experiments on mmEavesdropper and the results show that mmEavesdropper can reach the accuracy of 93% on digit and letter recognition. Moreover, mmEavesdropper can reconstruct the voice with an average SNR of 5dB and peak SNR of 17dB.

Speaker Yiwen Feng (Nanjing University)

Yiwen Feng is currently a PhD student at Nanjing University. She received her bachelor degree from the School of Computer Science and Engineering, South China University of Technology in 2021. Her research interests are in the areas of wireless and smart sensing.

mmMIC: Multi-modal Speech Recognition based on mmWave Radar

Long Fan, Lei Xie, Xinran Lu, Yi Li, Chuyu Wang and Sanglu Lu (Nanjing University, China)

With the proliferation of voice assistants, the microphone-based speech recognition technology usually cannot achieve good performance in the situation of multiple sound sources and ambient noises. In this paper, we propose a novel mmWave-based solution to perform speech recognition to tackle the issues of multiple sound sources and ambient noises, by precisely extracting the multi-modal features from lip motion and vocal-cords vibration from the single channel of mmWave. We propose a difference-based method for feature extraction of lip motion to suppress the dynamic interference from body motion and head motion. We propose a speech detection method based on cross-validation of lip motion and vocal-cords vibration, so as to avoid wasting computing resources on nonspeaking activities. We propose a multi-modal fusion framework for speech recognition by fusing the signal features from lip motion and vocal-cords vibration with the attention mechanism. We implemented a prototype system and evaluated the performance in real test-beds. Experiment results show that the average speech recognition accuracy is 92.8% in realistic environments.

Speaker Long Fan (Nanjing University)

Long Fan is a Ph.D. candidate at State Key Laboratory for Novel Software Technology, Nanjing University(NJU). He received a master's degree from the School of Electrical and Information Engineering, Tianjin University(TJU), in 2020. His research focuses on machine learning, millimeter-wave radar perception, and mobile sensing.

Universal Targeted Adversarial Attacks Against mmWave-based Human Activity Recognition

Yucheng Xie (Indiana University-Purdue University Indianapolis, USA); Ruizhe Jiang (IUPUI, USA); Xiaonan Guo (George Mason University, USA); Yan Wang (Temple University, USA); Jerry Cheng (New York Institute of Technology, USA); Yingying Chen (Rutgers University, USA)

Human activity recognition (HAR) systems based on millimeter wave (mmWave) technology have evolved in recent years due to their better privacy protection and enhanced sensor resolution. With the ever-growing HAR system deployment, the vulnerability of such systems has been revealed. However, existing efforts in HAR adversarial attacks only focus on untargeted attacks. In this paper, we propose the first targeted adversarial attacks against mmWave-based HAR through designed universal perturbation. A practical iteration algorithm is developed to craft perturbations that generalize well across different activity samples without additional training overhead. Different from existing work that only develops adversarial attacks for a particular mmWave-based HAR model, we improve the practicability of our attacks by broadening our target to the two most common mmWave-based HAR models (i.e., voxel-based and heatmap-based HAR models). In addition, we consider a more challenging black-box scenario by addressing the information deficiency issue with knowledge distillation (KD) and solving the insufficient activity sample with a generative adversarial network (GAN). We evaluate the proposed attacks on two different mmWave-based HAR models designed for fitness tracking. The evaluation results demonstrate the efficacy, efficiency, and practicality of the proposed targeted attacks with an average success rate of over 90%.

Speaker Yucheng Xie (Indiana University–Purdue University Indianapolis)

Yucheng Xie is a fifth-year Ph.D. student in the Department of Electrical and Computer Engineering at Indiana University-Purdue University Indianapolis. He holds a master’s degree from the Department of Computer Science at Stevens Institute of Technology. His research focuses on machine learning and large data analysis for mobile computing, artificial intelligence in smart health, mobile sensing and mobile healthcare, and cybersecurity and privacy.

Session Chair

Igor Kadota

Realizing Uplink MU-MIMO Communication in mmWave WLANs: Bayesian Optimization and Asynchronous Transmission

Shichen Zhang (Michigan State University, USA); Bo Ji (Virginia Tech, USA); Kai Zeng (George Mason University, USA); Huacheng Zeng (Michigan State University, USA)

With the proliferation of mobile devices, the marriage of millimeter-wave (mmWave) and MIMO technologies is a natural trend to meet the communication demand of data-hungry applications. Following this trend, mmWave multi-user MIMO (MU-MIMO) has been standardized by the IEEE 802.11ay for its downlink to achieve multi-Gbps data rate. Yet, its uplink counterpart has not been well studied, and its way to wireless local area networks (WLANs) remains unclear. In this paper, we present a practical uplink MU-MIMO mmWave communication (UMMC) scheme for WLANs. UMMC has two key components: i) an efficient Bayesian optimization (BayOpt) framework for joint beam search over multiple directional antennas, and ii) a new MU-MIMO detector that can decode asynchronous data packets from multiple user devices. We have built a prototype of UMMC on a mmWave testbed and evaluated its performance through a blend of over-the-air experiments and extensive simulations. Experimental and simulation results confirm the efficiency of UMMC in practical network settings.

Speaker Shichen Zhang (Michigan State University)

Shichen Zhang is currently a Ph.D. student in the Department of Computer Science and Engineering at Michigan State University (MSU), East Lansing, MI. He received his B.Eng degree in Automation from Beijing University of Technology, Beijing, China, in 2018 and M.Eng degree in Electrical and Computer Engineering from Cornell University, Ithaca, NY, in 2019. His current research interests focus on wireless networks, sensing systems, and machine learning.

mmFlexible: Flexible Directional Frequency Multiplexing for Multi-user mmWave Networks

Ish Kumar Jain, Rohith Reddy Vennam and Raghav Subbaraman (University of California San Diego, USA); Dinesh Bharadia (University of California, San Diego, USA)

Modern mmWave systems cannot scale to a large number of users because of the inflexibility in performing directional frequency multiplexing. All the frequency components in the mmWave signal are beamformed to one direction via pencil beams and cannot be streamed to other user directions. We present mmFlexible, a flexible mmWave system that enables flexible directional frequency multiplexing, allowing different frequency components to radiate in multiple arbitrary directions with the same pencil beam. We make two important contributions: 1. We propose a novel mmWave front-end architecture called a delay-phased array that uses a variable delay and variable phase element to create the desired frequency-direction response. 2. We propose a novel algorithm to estimate delay and phase values for the real-time operation of the delay-phased array. Our front-end architecture creates an abstraction that allows any OFDMA scheduler to operate flexibly like sub-6 without any fixed direction constraints. Our evaluation with indoor and outdoor mmWave channel traces shows 1.3x throughput improvement over traditional phased array architecture and 3.9x improvement over true-time delay architecture; while providing a 72% reduction in worst-case latency.

Speaker Ish Kumar Jain (UC San Diego)

Ish is a fifth-year PhD candidate at UC San Diego with Prof. Dinesh Bharadia. He holds a master's degree from New York University and bachelors' degree from IIT Kanpur, India. His research focuses on optimizing mmWave connectivity by improving reliability, latency, scalability, and practical deployments. Ish has received the Qualcomm Innovation Fellowship and VMWare research grant, and his research has been published in leading venues such as Sigcomm, NSDI, Infocom, and IEEE journals.

On the Effective Capacity of RIS-enabled mmWave Networks with Outdated CSI

Syed Waqas Haider Shah (IMDEA Networks Institute, Spain & Information Technology University, Pakistan); Sai Pavan Deram and Joerg Widmer (IMDEA Networks Institute, Spain)

Reconfigurable intelligent surfaces (RIS) have great potential to improve the coverage of mmWave networks; however, acquiring perfect channel state information (CSI) of a RIS-enabled mmWave network is challenging and costly. On the other hand, finding an optimal RIS configuration in the presence of an
outdated CSI, which provides paradigmatic system performance, is difficult. To this end, this work aims to provide practical insights into the tradeoff between the outdatedness of the CSI and the system performance by using the effective capacity as analytical tool. We consider a RIS-enabled mmWave downlink whereby the base station operates under statistical quality-of-service constraints. We find a closed-form expression for the effective capacity that incorporates the degree of optimism of packet scheduling and correlation strength between instantaneous and outdated CSI. Moreover, our analysis allows us to find optimal values of the signal-to-interference-plus-noise-ratio (SINR) distribution parameter and their impact on the effective capacity in different network scenarios. Simulation results demonstrate that better effective capacity can be achieved with suboptimal RIS configuration when the channel estimates are known to be outdated. It allows us to design system parameters that guarantee better performance while keeping the complexity and cost associated with channel estimation to a minimum.

Speaker Joerg Widmer (IMDEA Networks)

Joerg Widmer is Research Professor and Research Director of IMDEA Networks in Madrid, Spain. His research focuses on wireless networks, ranging from extremely high frequency millimeter-wave communication and MAC layer design to mobile network architectures. Joerg Widmer authored more than 200 conference and journal papers and received several awards such as an ERC consolidator grant, the Friedrich Wilhelm Bessel Research Award of the Alexander von Humboldt Foundation, as well as nine best paper awards. He is an IEEE Fellow and Distinguished Member of the ACM.

flexRLM: Flexible Radio Link Monitoring for Multi-User Downlink Millimeter-Wave Networks

Aleksandar Ichkov and Aron Schott (RWTH Aachen University, Germany); Petri Mähönen (RWTH Aachen University, Germany & Aalto University, Finland); Ljiljana Simić (RWTH Aachen University, Germany)

Exploiting millimeter-wave (mm-wave) for high-capacity multi-user networks is predicated on jointly performing beam management for seamless connectivity and efficient resource sharing among all users. Beam management in 5G-NR actively monitors candidate beam pair links (BPLs) on the serving cell to simply select the user's best beam, but neglects the multi-user resource sharing problem, potentially leading to severe throughput degradation on overloaded cells. We propose flexRLM, a coordinator-based flexible radio link monitoring (RLM) framework for multi-user downlink mm-wave networks. flexRLM enables flexible configuration of monitored BPLs on the serving and other candidate cells and beam selection jointly considering link quality and resource sharing. flexRLM is fully 5G-NR-compliant and uses the LTE coordinator in non-standalone mode to continuously update the monitored BPLs via measurement reports from periodic downlink control synchronization signals. We implement flexRLM in ns-3 and present full-stack simulations to demonstrate the superior performance of flexRLM over default 5G-NR RLM in multi-user networks. Our results show that flexRLM's continuous updating of monitored BPLs improves both link quality and stability. By monitoring BPLs on candidate cells other than the serving one, flexRLM also significantly decreases handover decision delays. Importantly, flexRLM's low-complexity coordinated load-balancing achieves a per-user throughput close to the single-user baseline.

Speaker Aleksandar Ichkov (RWTH Aachen University)

Aleksandar Ichkov has received his Bachelor of Engineering and Master of Science degrees from Ss. Cyril and Methodius University in Skopje in 2014 and 2017, respectively. He is currently purchasing his Doctor of Philosophy degree at the RWTH Aachen University. His main research interests are in the areas of millimeter-wave networks, beam management and multi-user provisioning.

Session Chair

Falko Dressler

MIA: A Transport-Layer Plugin for Immersive Applications in Millimeter Wave Access Networks

Zongshen Wu (University of Wisconsin Madison, USA); Chin-Ya Huang (National Taiwan University of Science and Technology, Taiwan); Parmesh Ramanathan (WISC, France)

The highly directional nature of the millimeter wave (mmWave) beams pose several challenges in using that spectrum for meeting the communication needs of immersive applications. In particular, the mmWave beams are susceptible to misalignments and blockages caused by user movements. As a result, mmWave channels are vulnerable to large fluctuations in quality, which in turn, cause disproportionate degradation in end-to-end performance of Transmission Control Protocol (TCP) based applications. In this paper, we propose a reinforcement learning (RL) integrated transport-layer plugin, Millimeter wave based Immersive Agent (MIA), for immersive content delivery over the mmWave link. MIA uses the RL model to predict mmWave link bandwidth based on the real-time measurement. Then, MIA cooperates with TCP's congestion control scheme to adapt the sending rate in accordance with the predictions of the mmWave bandwidth. To evaluate the effectiveness of the proposed MIA, we conduct experiments using a mmWave augmented immersive testbed and network simulations. The evaluation results show that MIA improves end-to-end immersive performance significantly on both throughput and latency.

Speaker Zongshen Wu (University of Wisconsin - Madison)

Zongshen Wu is a PhD candidate at University of Wisconsin - Madison.

High-speed Machine Learning-enhanced Receiver for Millimeter-Wave Systems

Dolores Garcia and Rafael Ruiz (Imdea Networks, Spain); Jesús O. Lacruz and Joerg Widmer (IMDEA Networks Institute, Spain)

ML is a promising tool to design wireless PHY components. It is particularly interesting for mmwave and above, due to the more challenging hardware design and channel environment at these frequencies. Rather than building individual ML-components, in this paper, we design an entire ML-enhanced mmwave receiver for frequency selective channels. Our ML-receiver jointly optimizes the channel estimation, equalization, phase correction and demapper using Convolutional Neural Networks. We also show that for mmwave systems, the channel varies significantly even over short timescales, requiring frequent channel measurements, and this situation is exacerbated in mobile scenarios. To tackle this, we propose a new ML-channel estimation approach that refreshes the channel state information using the guard intervals (not intended for channel measurements) that are available for every block of symbols in communication packets. To the best of our knowledge, our ML-receiver is the first work to outperform conventional receivers in general scenarios, with simulation results showing up to 7 dB gains. We also provide the first experimental validation of an ML-enhanced receiver with a 60 GHz FPGA-based testbed with phased antenna arrays, which shows a throughput increase by a factor of 6 over baseline schemes in mobile scenarios.

Speaker Rafael Ruiz Ortiz

Rafael Ruiz Ortiz received the bachelor’s degree in industrial electronics and automation engineering from the Universidad Politecnica de Cartagena, Cartagena, Spain. He is currently a PhD student at Universidad Carlos III, Madrid, Spain and a research engineer with IMDEA Networks Institute. His research interests include digital embedded system design and the implementation of accelerators based on FPGA devices.

Argosleep: Monitoring Sleep Posture from Commodity Millimeter-Wave Devices

Aakriti Adhikari and Sanjib Sur (University of South Carolina, USA)

We propose Argosleep, a millimeter-wave (mmWave) wireless sensors based sleep posture monitoring system that predicts the 3D location of body joints of a person during sleep. Argosleep leverages deep learning models and knowledge of human anatomical features to solve challenges with low-resolution, specularity, and aliasing in existing mmWave devices. Argosleep builds the model by learning the relationship between mmWave reflected signals and body postures from thousands of existing samples. Since a practical sleep also involves sudden toss-turns, which could introduce errors in posture prediction, Argosleep designs a state machine based on the reflected signals to classify the sleeping states into rest or toss-turn, and predict the posture only during the rest states. We evaluate Argosleep with real data collected from COTS mmWave devices for 8 volunteers of diverse ages, gender, and height performing different sleep postures. We observe that Argosleep identifies the toss-turn events accurately and predicts 3D location of body joints with accuracy on par with the existing vision-based system, unlocking the potential of mmWave systems for privacy-noninvasive at-home healthcare applications.

Speaker Aakriti Adhikari (University of South Carolina)

Aakriti Adhikari is currently pursuing her Ph.D. in the Department of Computer Science and Engineering at the University of South Carolina, Columbia. Her research focuses on wireless systems and ubiquitous sensing, particularly in developing at-home wireless solutions in the healthcare domain using millimeter-wave (mmWave) technology in 5G and beyond devices. Her research has been regularly published in top conferences in these areas, such as IEEE SECON, ACM IMWUT/UBICOMP, HotMobile, and MobiSys. Aakriti has received multiple awards, including student travel grants for conferences like IEEE INFOCOM (2023), ACM HotMobile (2023), and Mobisys (2022). Additionally, she currently has three patents pending. She has also been invited to participate in the CRA-WP Grad Cohort for Women (2023) and Grace Hopper Celebration (2020, 2021).

Safehaul: Risk-Averse Learning for Reliable mmWave Self-Backhauling in 6G Networks

Amir Ashtari Gargari (University of Padova, Italy); Andrea Ortiz (TU Darmstadt, Germany); Matteo Pagin (University of Padua, Italy); Anja Klein (TU Darmstadt, Germany); Matthias Hollick (Technische Universität Darmstadt & Secure Mobile Networking Lab, Germany); Michele Zorzi (University of Padova, Italy); Arash Asadi (TU Darmstadt, Germany)

Wireless backhauling at millimeter-wave frequencies (mmWave) in static scenarios is a well-established practice in cellular networks. However, highly directional and adaptive beamforming in today's mmWave systems have opened new possibilities for self-backhauling. Tapping into this potential, 3GPP has standardized Integrated Access and Backhaul (IAB) allowing the same base station serve both access and backhaul traffic. Although much more cost-effective and flexible, resource allocation and path selection in IAB mmWave networks is a formidable task. To date, prior works have addressed this challenge through a plethora of classic optimization and learning methods, generally optimizing a Key Performance Indicator (KPI) such as throughput, latency, and fairness, and little attention has been paid to the reliability of the KPI. We propose Safehaul, a risk-averse learning-based solution for IAB mmWave networks. In addition to optimizing average performance, Safe- haul ensures reliability by minimizing the losses in the tail of the performance distribution. We develop a novel simulator and show via extensive simulations that Safehaul not only reduces the latency by up to 43.2% compared to the benchmarks but also exhibits significantly more reliable performance (e.g., 71.4% less variance in achieved latency).

Speaker Andrea Ortiz (TU Darmstadt)

Dr.-Ing. Andrea Ortiz is a Post-Doctoral researcher at the Communications Engineering Laboratory at Technische Universität Darmstadt, Germany. Her research focuses on the application of reinforcement learning and signal processing for resource allocation in wireless communications. She is the recipient of several awards including the “Dr. Wilhelmy-VDE-Preis” given by the German Association for Electrical, Electronic and Information Technologies (VDE), and the Best Dissertation Award from the Electrical Engineering and Information Technology Department of Technische Universität Darmstadt.

Session Chair

Joerg Widmer

Program at a Glance