Publications
2024
- NeWRF: A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel PredictionHaofan Lu, Christopher Vattheuer, Baharan Mirzasoleiman, and Omid AbariIn Forty-first International Conference on Machine Learning 2024
We present NeWRF, a novel deep-learning-based framework for predicting wireless channels. Wireless channel prediction is a long-standing problem in the wireless community and is a key technology for improving the coverage of wireless network deployments. Today, a wireless deployment is evaluated by a site survey which is a cumbersome process requiring an experienced engineer to perform extensive channel measurements. To reduce the cost of site surveys, we develop NeWRF, which is based on recent advances in Neural Radiance Fields (NeRF). NeWRF trains a neural network model with a sparse set of channel measurements, and predicts the wireless channel accurately at any location in the site. We introduce a series of techniques that integrate wireless propagation properties into the NeRF framework to account for the fundamental differences between the behavior of light and wireless signals. We conduct extensive evaluations of our framework and show that our approach can accurately predict channels at unvisited locations with significantly lower measurement density than prior state-of-the-art.
- Can IoT Devices be Powered up by Future Indoor Wireless Networks?Tianxiang Li, Mohammad Hossein Mazaheri, Kalaivani Kamalakannan, Haofan Lu, and Omid AbariIn Proceedings of the 25th International Workshop on Mobile Computing Systems and Applications 2024
The number of wireless access points (such as WiFi and 5G) is rapidly growing in indoor environments. In this paper, we ask whether these indoor wireless access points can transfer power to IoT devices besides their communication capability. In particular, our vision is that most indoor access points will be underutilized during nights, and hence, why not use them to transfer power to IoT devices during those times. To evaluate this idea, we first perform a comprehensive study on the feasibility of using different frequency bands to transfer power. Our analysis shows that high-frequency signals (such as mmWave) are the best candidates to transfer power, and have the potential to power IoT devices up to 15 meters. However, achieving this requires addressing multiple challenges. In this paper, we review some of these challenges and propose solutions, enabling IoT devices to harvest energy from mmWave signals with spending (almost) zero energy.
- Enhancing IoT Communication and Localization via Smarter AntennaTianxiang Li, Haofan Lu, and Omid AbariIn Under submission 2024
The convergence of sensing and communication functionalities is poised to become a pivotal feature of the sixth-generation (6G) wireless networks. This vision represents a paradigm shift in wireless network design, moving beyond mere communication to a holistic integration of sensing and communication capabilities, thereby further narrowing the gap between the physical and digital worlds. While Internet of Things (IoT) devices are integral to future wireless networks, their current capabilities in sensing and communication are constrained by their power and resource limitations. On one hand, their restricted power budget limits their transmission power, leading to reduced communication range and data rates. On the other hand, their limited hardware and processing abilities hinder the adoption of sophisticated sensing technologies, such as direction finding and localization. In this work, we introduce Wi-Pro, a system which seamlessly integrates today’s WiFi protocol with smart antenna design to enhance the communication and sensing capabilities of existing IoT devices. This plug-and-play system can be easily installed by replacing the IoT device’s antenna. Wi-Pro seamlessly integrates smart antenna hardware with current WiFi protocols, utilizing their inherent features to not only enhance communication but also to enable precise localization on low-cost IoT devices. Our evaluation results demonstrate that Wi-Pro achieves up to 150% data rate improvement, up to five times range improvement, accurate direction finding, and localization on single-chain IoT devices.
2023
- A Millimeter Wave Backscatter Network for Two-Way Communication and LocalizationHaofan Lu, Mohammad Mazaheri, Reza Rezvani, and Omid AbariIn Proceedings of the ACM SIGCOMM 2023 Conference 2023
Millimeter wave (mmWave) technology enables wireless devices to communicate using very high-frequency signals. Operating at those frequencies provides larger bandwidth which can be used to enable high-data-rate links, and very accurate localization of devices. However, radios operating at high-frequencies consume significant amount of power, making them unsuitable for applications with limited energy sources. This paper presents MilBack, a backscatter network operating at mmWave bands. Backscattering is the most energy-efficient wireless communication technique, where nodes piggyback their data on an access point’s signal instead of generating their own signals. Eliminating the need for signal generation significantly reduces the energy-consumption of the nodes. In contrast to past mmWave backscatter work which supports only uplink, MilBack is the first mmWave backscatter network which supports uplink, downlink and accurate localization. MilBack addresses the key challenges that prevent existing backscatter networks to enable both uplink and downlink at mmWave bands. We implemented MilBack and evaluated its performance empirically. Our results show that MilBack is capable of achieving accurate localization, uplink and downlink communication at up to 10 m while consuming only 32 mW and 18 mW, respectively.
- WiFi Physical Layer Stays Awake and Responds When it Should NotAli Abedi, Haofan Lu, Alex Chen, Charlie Liu, and Omid AbariIEEE Internet of Things Journal 2023
WiFi communication should be possible only between devices inside the same network. However, we find that all existing WiFi devices send back acknowledgments (ACK) to even fake packets received from unauthorized WiFi devices outside of their network. Moreover, we find that an unauthorized device can manipulate the power-saving mechanism of WiFi radios and keep them continuously awake by sending specific fake beacon frames to them. Our evaluation of over 5,000 devices from 186 vendors confirms that these are widespread issues. We believe these loopholes cannot be prevented, and hence they create privacy and security concerns. Finally, to show the importance of these issues and their consequences, we implement and demonstrate two attacks where an adversary performs battery drain and WiFi sensing attacks just using a tiny WiFi module which costs less than ten dollars.
2022
- Bringing WiFi Localization to Any WiFi DevicesHaofan Lu, Tianxiang Li, Reza Rezvani, Ali Abedi, and Omid AbariIn Proceedings of the 21st ACM Workshop on Hot Topics in Networks 2022
Recent years have seen significant advances in WiFi Localization. However, existing systems require either multiple access points to cooperate with each other or a single access point to have multiple antennas and transceiver chains. Therefore, they cannot be integrated into most IoT WiFi chipsets which have only a single transceiver chain. This paper presents \name, a novel approach to bringing WiFi localization to any WiFi devices, especially those with a single RF chain. We propose a WiFi antenna design and use the inherent properties of the 802.11 protocol to measure Angle-of-Arrival (AoA) and Time-of-Flight (ToF) using a single transceiver chain. Our proof-of-concept simulation and real world experiments promise the feasibility of this approach.
- Deep learning techniques elucidate and modify the shape factor to extend the effective medium theory beyond its original formulationHaofan Lu, Yi Yu, Ankit Jain, Yee Sin Ang, and Wee-Liat OngInternational Journal of Heat and Mass Transfer 2022
The effective medium theories (EMTs) can reliably approximate the property of a composite using properties of the inclusion and matrix phase. However, their inherent assumptions and the availability of mathematical forms for describing the inclusion structure limit their accuracy and applicability. In this work, we utilize the capabilities of a deep learning method to ameliorate the latter restriction for a particular EMT formulation. Our deep learning models elucidate the inclusion structure using several physics-based descriptors and can be easily adapted for other inclusion shapes through transfer learning. Using our models, we shed light on the interpretation of the shape factor in the chosen EMT. More importantly, we extend, not replace, the EMT for cases beyond its original formulation. Our proposed transfer learning approach requires relatively low computation cost and a small sample number, making it especially useful when new data is limited.