Dr. Arash Asadi

Head

Contact Information

Building 28, Room 2.W.900
Van Mourik Broekmanweg 6
2628 XE Delft, Netherlands
2.W.900
a.asadi@tudelft.nl
+31 15 27 87627
Homepage

Biography

Arash Asadi is an Assistant Professor at Embedded Systems Group at TU Delft where he leads the Wireless Communication and Sensing Lab (WISE). His research is focused on wireless communication and sensing for 6G networks. He is a recipient of several awards, including Athena Young Investigator award from TU Darmstadt and outstanding PhD and master thesis awards from UC3M. Some of his papers on D2D communication have appeared in IEEE COMSOC best reading topics on D2D communication and IEEE COMSOC Tech Focus.

Offered Theses Topics

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Year

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2024 Open

Deep Learning-based Temporal and Spatial Segmentation in RF Sensing for Human Activity Recognition and Indoor Localization

Supervisor: Arash Asadi

Abstract/Overview: This thesis will explore the use of deep learning models to perform both temporal and spatial segmentation of RF signals in order to enhance human activity recognition and indoor localization. The research will investigate how different neural network architectures can be applied to raw or preprocessed RF data (e.g., from Wi-Fi, mmWave) to segment distinct activities and localize individuals within complex indoor environments. The thesis will aim to improve segmentation accuracy by comparing traditional signal processing techniques with state-of-the-art deep learning methods, as well as exploring the potential of hybrid approaches. The outcomes of this research could be applicable in smart homes, healthcare monitoring, and security systems. Key Step: Dataset Preparation: Gathering RF sensing datasets (or creating a new one) with labeled human activities and spatial locations. Preprocessing and Feature Extraction: Signal filtering, noise reduction, and extraction of key features like signal strength, phase, or frequency characteristics. Segmentation Models: Temporal Segmentation to identify time intervals corresponding to different actions. Spatial Segmentation to partition indoor spaces based on signal reflections or other RF characteristics. Performance Evaluation: Comparison of traditional methods (e.g., thresholding or clustering) with deep learning-based approaches in terms of accuracy, precision, and real-time performance. Applications and Use Cases: Testing the developed models in real-world scenarios like human activity detection (e.g., walking, sitting, falling) and indoor localization in environments like smart homes or hospitals.

Deep Learning-based Temporal and Spatial Segmentation in RF Sensing for Human Activity Recognition and Indoor Localization

2024 Open

Unsupervised learning from video segmentation to person/object tracking in wireless networks

Supervisor: Arash Asadi

There is a large body of work on using commercial wireless devices to detect, identify and localize people as well as their motion, gestures, and even vital signs. The underlying techniques span from machine learning techniques to signal processing and Radar. To some extent, the impact of a person's body/motion on the wireless signals can resemble an image/video. While there has been extensive use of advanced Machine learning techniques for people/object tracking in videos, there is very little work on using these techniques in the wireless domain. For example, applying the works presented here (https://www.youtube.com/watch?v=tSBWZ6nYld0) to wireless sensing. If you find this interesting, send me an email to discuss further details.

2024 Open

mmWave full duplex joint sensing and communication design

Supervisor: Lu Wang Arash Asadi

Joint Sensing and Communication technology is one of the key 6G technologies. It makes your phone/vehicle/device, etc., smarter with the function of sensing and communication simultaneously [1]. Configuring this technology in mmWave band, better performance such as higher date rate can be realized. If you are interested in the joint sensing and communication system design, feel free to contact us. Research objective: 1. Sensing parameters estimation 2. full duplex joint sensing and communication system design Expected gain of knowledge: Wireless communication [1] Y. Cui, F. Liu, X. Jing and J. Mu, “Integrating Sensing and Communications for Ubiquitous IoT: Applications, Trends, and Challenges,” in IEEE Network, vol. 35, no. 5, pp. 158-167, September/October 2021.

2024 Open

FPGA development and experimental analysis of beamforming and beam-tracking in 6G networks

Supervisor: Arash Asadi

Millimeter-wave frequencies (30-300 GHz) will be dominating 6G communications, providing users with tens of Gbps data rates. However, communication at such high frequencies requires using highly directional beams to compensate for the propagation loss. In our group, we have access to unique software-defined radios capable of communication at 70 GHz with 4GHz of bandwidth. If you are interested in performing experimental studies in this area and contributing to the research in the next generation of mobile networks, this could be your topic. Research objective: Test and development of agile beamforming/tracking for 6G systems Expected gain of knowledge: Wireless communication, FPGA programming

2024 Open

Beam management in mmWave full duplex joint sensing and communication system

Supervisor: Lu Wang Arash Asadi

Joint Sensing and Communication technology is one of the key 6G technologies. It makes your phone/vehicle/device smarter with the function of sensing and communication simultaneously [1]. By beaming the transmitted data in a direct way, both sensing and communication performance can be improved. If you are interested in the beam-related design in the joint sensing and communication system, feel free to contact us. Research objective: 1. Sensing parameters estimation 2. Beam management(searching/tracking) and beamforming design 3. full duplex joint sensing and communication system design Expected gain of knowledge: Wireless communication [1] Y. Cui, F. Liu, X. Jing and J. Mu, “Integrating Sensing and Communications for Ubiquitous IoT: Applications, Trends, and Challenges,” in IEEE Network, vol. 35, no. 5, pp. 158-167, September/October 2021.

2024 Open

Going against the tide: Using interpretable machine learning instead of black box DNN for wireless sensing

Supervisor: Arash Asadi

Your WiFi router is constantly monitoring the surrounding. You can analyze the channel state information to detect the location and even trajectory of people in their homes. The majority of these works leverage black box machine learning, which questions their reliability. While many believe that the black box models provide higher performance and are less complex, new studies suggest otherwise [1]. If you are interested in going against the tide and proving interpretable learning can perform similar to black box models in wireless sensing, send me an email. Research objective: Explanation methods for WiFi Sensing Expected gain of knowledge: Wireless communication, Interpretable machine learning [1] C. Rudin, “Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,” Nat Mach Intell, vol. 1, no. 5, pp. 206–215, 2019, doi: 10.1038/s42256-019-0048-x.

2024 Open

Preserving Privacy against WiFi Sensing

Supervisor: Arash Asadi

Your WiFi router is constantly monitoring the surrounding. You can analyze the channel state information to detect the location and even trajectory of people in their homes. There are many other applications, including detecting heartbeat, breathing rate, reading lips, etc. If you are interested in implementing one of these systems using real hardware and finding solutions to fight against it, send me an email. Note that these are rather challenging topics as they require good knowledge of communication as well as signal processing. Research objective WiFi Sensing countermeasures Expected gain of knowledge Wireless communication, Signal processing

2022 Completed

Explainable machine learning to explain blackbox human activity sensing

Supervisor: Arash Asadi

2021 Completed

Privacy-Preserving radiometric fingerprinting (Datenlotsen Awardee)

Supervisor: Arash Asadi

2021 Completed

Device-Free Indoor Localization: A User-Privacy Perspective

Supervisor: Arash Asadi

2021 Completed

Protecting Heartbeat and Respiration Information in WiFi Sensing Applications

Supervisor: Arash Asadi

2018 Completed

Experimental Evaluation on Inband Device-to-Device Communication in LTE

Supervisor: Arash Asadi

2017 Completed

Evaluation of Latency Reduction Techniques for 5th Generation Mobile Network

Supervisor: Arash Asadi

Publications

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Year

Show awards only

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2024 IEEE 2024 International Conference on Communications Conference

Fast Transition-Aware Reconfiguration of Liquid Crystal-based RISs

Mohamadreza Delbari Robin Neuder Alejandro Jiménez-Sáez Arash Asadi Vahid Jamali

BibTeX

2023 Conference on Communications and Network Security (CNS 2023) Conference

BeamSec: A Practical mmWave Physical Layer Security Scheme Against Strong Adversaries

Afifa Ishtiaq Arash Asadi Ladan Khaloopour Waqar Ahmed Vahid Jamali Matthias Hollick

BibTeX DOI: 10.1109/CNS59707.2023.10289003

Abstract

The high directionality of millimeter-wave (mmWave) communication systems has proven effective in reducing the attack surface against eavesdropping, thus improving the physical layer security. However, even with highly directional beams, the system is still exposed to eavesdropping against adversaries located within the main lobe. In this paper, we propose BeamSec, a solution to protect the users even from adversaries located in the main lobe. The key feature of BeamSec are: (i) Operating without the knowledge of eavesdropper’s location/channel; (ii) Robustness against colluding eavesdropping attack and (iii) Standard compatibility, which we prove using experiments via our IEEE 802.11ad/ay-compatible 60 GHz phased-array testbed. Methodologically, BeamSec first identifies uncorrelated and diverse beampairs between the transmitter and receiver by analyzing signal characteristics available through standard-compliant procedures. Next, it encodes the information jointly over all selected beampairs to minimize information leakage. We study two methods for allocating transmission time among different beams, namely uniform allocation (no knowledge of the wireless channel) and optimal allocation for maximization of the secrecy rate (with partial knowledge of the wireless channel). Our experiments show that BeamSec outperforms the benchmark schemes against single and colluding eavesdroppers and enhances the secrecy rate by 79.8% over a random paths selection benchmark.

2023 42nd IEEE Conference on Computer Communications (INFOCOM 2023) Conference

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

Amir Ashtari Gargari Andrea Patricia Ortiz Jimenez Matteo Pagin Anja Klein Matthias Hollick Michele Zorzi Arash Asadi

BibTeX DOI: 10.1109/INFOCOM53939.2023.10228969

Abstract

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 to 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, Safehaul 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.

2022 IEEE Transactions on Wireless Communications Article

RadiOrchestra: Proactive Management of Millimeter-wave Self-backhauled Small Cells via Joint Optimization of Beamforming, User Association, Rate Selection, and Admission Control

Luis F. Abanto-Leon Andres Garcia-Saavedra Arash Asadi Gek Hong Sim Matthias Hollick

BibTeX DOI: 10.1109/TWC.2022.3191744

Abstract

Millimeter-wave self-backhauled small cells are a key component of next-generation wireless networks. Their dense deployment will increase data rates, reduce latency, and enable efficient data transport between the access and backhaul networks, providing greater flexibility not previously possible with optical fiber. Despite their high potential, operating dense self-backhauled networks optimally is an open challenge, particularly for radio resource management (RRM). This paper presents, RadiOrchestra, a holistic RRM framework that models and optimizes beamforming, rate selection as well as user association and admission control for self-backhauled networks. The framework is designed to account for practical challenges such as hardware limitations of base stations (e.g., computational capacity, discrete rates), the need for adaptability of backhaul links, and the presence of interference. Our framework is formulated as a nonconvex mixed-integer nonlinear program, which is challenging to solve. To approach this problem, we propose three algorithms that provide a trade-off between complexity and optimality. Furthermore, we derive upper and lower bounds to characterize the performance limits of the system. We evaluate the developed strategies in various scenarios, showing the feasibility of deploying practical self-backhauling in future networks.

2020 Proceedings of the ACM on Measurement and Analysis of Computing Systems Article

Stay Connected, Leave no Trace: Enhancing Security and Privacy in WiFi via Obfuscating Radiometric Fingerprints

Luis F. Abanto-Leon Andreas Bäuml Gek Hong Sim Matthias Hollick Arash Asadi

PDF BibTeX DOI: 10.1145/3428329

2020 IEEE Internet of Things Journal Article

LIDOR: A Lightweight DoS-Resilient Communication Protocol for Safety-Critical IoT Systems

Milan Stute Pranay Agarwal Abhinav Kumar Arash Asadi Matthias Hollick

PDF BibTeX DOI: 10.1109/JIOT.2020.2985044

2019 IEEE Journal on Selected Areas in Communications Article

SCAROS: A Scalable and Robust Self-backhauling Solution for Highly Dynamic Millimeter-Wave Networks

Andrea Patricia Ortiz Jimenez Arash Asadi Gek Hong Sim Daniel Steinmetzer Matthias Hollick

BibTeX DOI: 10.1109/JSAC.2019.2947925

2019 IEEE Journal on Selected Areas in Communications Article

CBMoS: Combinatorial Bandit Learning for Mode Selection and Resource Allocation in D2D Systems

Andrea Patricia Ortiz Jimenez Arash Asadi Max Engelhardt Matthias Hollick Anja Klein

BibTeX DOI: 10.1109/JSAC.2019.2933764

2018 IEEE/ACM Transactions on Networking Article

An Online Context-Aware Machine Learning Algorithm for 5G mmWave Vehicular Communications

Gek Hong Sim Sabrina Klos Arash Asadi Anja Klein Matthias Hollick

BibTeX DOI: 10.1109/TNET.2018.2869244

2018 37th IEEE Conference on Computer Communications Conference

FML: Fast Machine Learning for 5G mmWave Vehicular Communications

Arash Asadi Gek Hong Sim Matthias Hollick Anja Klein Sabrina Müller

BibTeX DOI: 10.1109/INFOCOM.2018.8485876

2017 IEEE Wireless Communications Article

5G Millimeter-Wave and D2D Symbiosis: 60 GHz for Proximity-based Services

Gek Hong Sim Adrian Loch Arash Asadi Vincenzo Mancuso Jörg Widmer

BibTeX DOI: 10.1109/MWC.2017.1600098

2017 IEEE/ACM Transactions on Networking Article

DORE: An Experimental Framework to Enable Outband D2D Relay in Cellular Networks

Arash Asadi Vincenzo Mancuso Rohit Gupta

BibTeX DOI: 10.1109/TNET.2017.2712285

2017 9th International Conference on Communication Systems and Networks (COMSNETS) Conference

mm-Wave on Wheels: Practical 60 GHz Vehicular Communication Without Beam Training

Adrian Loch Arash Asadi Gek Hong Sim Jörg Widmer Matthias Hollick

BibTeX DOI: 10.1109/COMSNETS.2017.7945351

2017 16th International IFIP TC6 Networking Conference Conference

SEMUD: Secure Multi-hop Device-to-Device Communication for 5G Public Safety Networks

Milan Schmittner Arash Asadi Matthias Hollick

BibTeX DOI: 10.23919/IFIPNetworking.2017.8264846

2017 9th International Conference on Communication Systems & Networks Conference

mm-Broker: Managing Directionality and Mobility Issues of Millimeter-Wave via D2D Communication

Gek Hong Sim Arash Asadi Adrian Loch Matthias Hollick Jörg Widmer

BibTeX

2016 IEEE Transactions on mobile computing Article

Network-assisted Outband D2D-clustering in 5G Cellular Networks: Theory and Practice

Arash Asadi Vincenzo Mancuso

BibTeX DOI: 10.1109/TMC.2016.2621041

2016 35th Annual IEEE International Conference on Computer Communications Conference

An SDR-based Experimental Study of Outband D2D Communications

Arash Asadi Vincenzo Mancuso Rohit Gupta

BibTeX DOI: 10.1109/INFOCOM.2016.7524372

2016 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks Conference

Tie-breaking Can Maximize Fairness without Sacrificing Throughput in D2D-assisted Networks

Vincenzo Mancuso Arash Asadi Peter Jacko

BibTeX DOI: 10.1109/WoWMoM.2016.7523498

2014 ACM SIGMETRICS Performance Evaluation Review Article

Modeling D2D communications with LTE and WiFi

Arash Asadi Peter Jacko Vincenzo Mancuso

BibTeX DOI: 10.1145/2667522.2667540

2014 Computer Communications Article

DRONEE: Dual-radio opportunistic networking for energy efficiency

Arash Asadi Vincenzo Mancuso

BibTeX DOI: 10.1016/j.comcom.2014.02.014

2014 IEEE Communications Surveys & Tutorials Article

A Survey on Device-to-Device Communication in Cellular Networks

Arash Asadi Wang Qing Vincenzo Mancuso

BibTeX DOI: 10.1109/COMST.2014.2319555

2013 IEEE Communications Surveys & Tutorials Article

A Survey on Opportunistic Scheduling in Wireless Communications

Arash Asadi Vincenzo Mancuso

BibTeX DOI: 10.1109/SURV.2013.011413.00082