Publications

Highlights

For a full list see below or go to Google Scholar, and DBLP.

Please go to Research section for category-wise list of papers.

Evaluating Physical-Layer BLE Location Tracking Attacks on Mobile Devices [IEEE S&P 2022]

[Webpage] [paper]

Mobile devices increasingly function as wireless tracking beacons. Using the Bluetooth Low Energy (BLE) protocol, mobile devices such as smartphones and smartwatches continuously transmit beacons to inform passive listeners about device locations for applications such as digital contact tracing for COVID-19, and even finding lost devices. These applications use cryptographic anonymity that limit an adversary’s ability to use these beacons to stalk a user. However, attackers can bypass these defenses by fingerprinting the unique physical-layer imperfections in the transmissions of specific devices. We empirically demonstrate that there are several key challenges that can limit an attacker’s ability to find a stable physical layer identifier to uniquely identify mobile devices using BLE, including variations in the hardware design of BLE chipsets, transmission power levels, differences in thermal conditions, and limitations of inexpensive radios that can be widely deployed to capture raw physical-layer signals. We evaluated how much each of these factors limits accurate fingerprinting in a large-scale field study of hundreds of uncontrolled BLE devices, revealing that physical-layer identification is a viable, although sometimes unreliable, way for an attacker to track mobile devices.

Hadi Givehchian, Nishant Bhaskar, Eliana Rodriguez Herrera, Héctor Rodrigo López Soto, Christian Dameff, Dinesh Bharadia, and Aaron Schulman

ULoc: a cm-accurate, low-latency and power-efficient UWB tag localization system [IMWUT 2021]

[ULoc-Webpage] [paper]

A myriad of IoT applications demand centimeter-accurate localization that is robust to blockages from hands, furniture, or other occlusions in the environment. To address these needs, we developed ULoc, a scalable, low-power, and cm-accurate UWB localization and tracking system. ULoc’s builds a multi-antenna UWB anchor and develops a novel 3D tracking algorithm to deliver a stationary localization accuracy of less than 5 cm and a tracking accuracy of 10 cm in mobile conditions.

Minghui Zhao, Tyler Chang, Aditya Arun, Roshan Ayyalasomayajula, Chi Zhang, Dinesh Bharadia

Two beams are better than one: Towards Reliable and High Throughput mmWave Links [Sigcomm 2021]

[Webpage] [paper]

Millimeter-wave communication with high throughput and high reliability is poised to be a gamechanger for V2X and VR applications. However, mmWave links are notorious for low reliability since they suffer from frequent outages due to blockage and user mobility. We build mmReliable, a reliable mmWave system that implements multi-beamforming and user tracking to handle environmental vulnerabilities. It creates constructive multi-beam patterns and optimizes their angle, phase, and amplitude to maximize the signal strength at the receiver. Multi-beam links are reliable since they are resilient to occasional blockages of few constituent beams compared to a single-beam system. We implement mmReliable on a 28 GHz testbed with 400 MHz bandwidth, and a 64 element phased array supporting 5G NR waveforms. Rigorous indoor and outdoor experiments demonstrate that mmReliable achieves close to 100% reliability providing 2.3x improvement in the throughput-reliability product than single-beam systems.

Ish Kumar Jain, Raghav Subbaraman, and Dinesh Bharadia

SyncScatter: Enabling WiFi like synchronization and range for WiFi backscatter Communication [NSDI 2021]

[Webpage] [paper]

WiFi backscattering can enable direct connectivity of IoT devices with commodity WiFi hardware at low power. However, most existing work in this area has overlooked the importance of synchronization and have, as a result, accepted either limited range between the transmitter and the IoT device, reduced throughput via bit repetition, or both. In this paper, we present SyncScatter, which achieves accurate synchronization to incident signals at the IoT device level, while also achieving sensitivity commensurate with the maximum possible afforded by a backscattering link budget. SyncScatter creates a novel modeling framework, and derives the maximal optimal range and synchronization error that can be achieved without major performance compromises. Next, SyncScatter builds a novel hierarchical wake-up protocol, which together with a custom ASIC, achieves a range of 30+ meters at 2Mbps, with an average power consumption of 25.2µW.

Manideep Dunna, Miao Meng, Po-Han Wang, Chi Zhang, Patrick Mercier, and Dinesh Bharadia

WiForce: Wireless Sensing and Localization of Contact Forces on a Space Continuum [NSDI 2021]

[Webpage]

Contact force is a natural way for humans to interact with the physical world around us. However, most of our interactions with the digital world are largely based on a simple binary sense of touch (contact or no contact). Similarly, when interacting with robots to perform complex tasks, such as surgery, richer force information that includes both magnitude and contact location is important for task performance. To address these challenges, we present the design and fabrication of WiForce which is a ‘wireless’ sensor, sentient to contact force magnitude and location. WiForce achieves this by transducing force magnitude and location, to phase changes of an incident RF signal of a backscattering tag. The phase changes are thus modulated into the backscattered RF signal, which enables measurement of force magnitude and contact location by inferring the phases of the reflected RF signal. WiForce’s sensor is designed to support wide-band frequencies all the way up to 3 GHz. We evaluate the force sensing wirelessly in different environments, including through phantom tissue, and achieve force accuracy of 0.3 N and contact location accuracy of 0.6 mm.

Agrim Gupta, Cédric Girerd, Manideep Dunna, Qiming Zhang, Raghav Subbaraman, Tania Morimoto, and Dinesh Bharadia

DroneScale: Drone Load Estimation Via Remote Passive RF Sensing [Sensys 2020]

[paper]

Drones have carried weapons, drugs, explosives and illegal packages in the recent past, raising strong concerns from public authorities. While existing drone monitoring systems only focus on detecting drone presence, localizing or fingerprinting the drone, there is a lack of a solution for estimating the additional load carried by a drone. In this paper, we present a novel passive RF system, namely DroneScale, to monitor the wireless signals transmitted by commercial drones and then confirm their models and loads. Our key technical contribution is a proposed technique to passively capture vibration at high resolution (i.e., 1Hz vibration) from afar, which was not possible before. We prototype DroneScale using COTS RF components and illustrate that it can monitor the body vibration of a drone at the targeted resolution. In addition, we develop learning algorithms to extract the physical vibration of the drone from the transmitted signal to infer the model of a drone and the load carried by it. We evaluate the DroneScale system using 5 different drone models, which carry external loads of up to 400g. The experimental results show that the system is able to estimate the external load of a drone with an average accuracy of 96.27%. We also analyze the sensitivity of the system with different load placements with respect to the drone’s body, flight modes, and distances up to 200 meters.

Phuc Nguyen (University of Texas at Arlington; University of Colorado Boulder); Vimal Kakaraparthi, Nam Bui, Nikshep Umamahesh (University of Colorado Boulder); Nhat Pham (University of Colorado Boulder; University of Oxford); Hoang Truong (University of Colorado Boulder); Yeswanth Guddeti, Dinesh Bharadia (University of California San Diego); Eric Frew, Richard Han, Daniel Massey (University of Colorado Boulder); Tam Vu (University of Colorado Boulder; University of Oxford)

Pointillism: Accurate 3D Bounding Box Estimation with Multi-Radars [Sensys 2020]

[Webpage] [paper]

Autonomous perception requires high-quality environment sensing in the form of 3D bounding boxes of dynamic objects. The primary sensors used in automotive systems are light-based cameras and LiDARs. However, they are known to fail in adverse weather conditions. Radars can potentially solve this problem as they are barely affected by adverse weather conditions. However, specular reflections of wireless signals cause poor performance of radar point clouds.We introduce Pointillism, a system that combines data from multiple spatially separated radars with an optimal separation to mitigate these problems. We introduce a novel concept of Cross Potential Point Clouds, which uses the spatial diversity induced by multiple radars and solves the problem of noise and sparsity in radar point clouds. Furthermore, we present the design of RP-net, a novel deep learning architecture, designed explicitly for radar’s sparse data distribution, to enable accurate 3D bounding box estimation. The spatial techniques designed and proposed in this paper are fundamental to radars point cloud distribution and would benefit other radar sensing applications.

Kshitiz Bansal, Keshav Rungta, Siyuan Zhu and Dinesh Bharadia

mMobile: Building a mmWave testbed to evaluate and address mobility effects [mmNets 2020]

[Webpage] [paper]

Beamforming methods need to be critically evaluated and improvedto achieve the promised performance of mmWave 5G-NR in highmobility applications like Vehicle-to-Everything (V2X) communi-cation. Conventional beam management methods developed forhigher frequency applications do not directly carry over to the 28GHz mmWave regime, where propagation and reflection character-istics are vastly different. Further, real system deployments and testsare required to verify these methods in a practical setting. In thiswork, we develop mMobile, a custom 5G-NR compliant mmWavetestbed to evaluate beam management algorithms. We describe thearchitecture and challenges in building such a testbed. We then cre-ate a novel, low-complexity beam tracking algorithm by exploitingthe 5G-NR waveform structure and evaluate its performance onthe testbed. The algorithm can sustain almost twice the averagethroughput compared to the baseline.

Ish Kumar Jain, Raghav Subbaraman, Tejas Harekrishna Sadarahalli, Xiangwei Shao, Hou-Wei Lin, Dinesh Bharadia

S3Net: Semantic-Aware Self-supervised Depth Estimation with Monocular Videos and Synthetic Data [ECCV 2020]

[paper]

Solving depth estimation with monocular cameras enables the possibility of widespread use of cameras as low-cost depth estimation sensors in applications such as autonomous driving and robotics. However, learning such a scalable depth estimation model would require a lot of labeled data which is expensive to collect. There are two popular existing approaches which do not require annotated depth maps: (i) using labeled synthetic and unlabeled real data in an adversarial framework to predict more accurate depth, and (ii) unsupervised models which exploit geometric structure across space and time in monocular video frames. Ideally, we would like to leverage features provided by both approaches as they complement each other; however, existing methods do not adequately exploit these additive benefits. We present S 3Net, a selfsupervised framework which combines these complementary features: we use synthetic and real-world images for training while exploiting geometric, temporal, as well as semantic constraints. Our novel consolidated architecture provides a new state-of-the-art in self-supervised depth estimation using monocular videos. We present a unique way to train this self-supervised framework, and achieve (i) more than 15% improvement over previous synthetic supervised approaches that use domain adaptation and (ii) more than 10% improvement over previous self-supervised approaches which exploit geometric constraints from the real data

Bin Cheng, Inderjot Singh Saggu, Raunak Shah, Gaurav Bansal, Dinesh Bharadia

A 28μW IoT Tag That Can Communicate with Commodity WiFi Transceivers via a Single-Side-Band QPSK Backscatter Communication Technique [ISSCC 2020]

[Ubiquitous IoT Project Page] [slides]

More portable, fully wireless smart home setups. Lower power wearables. Batteryless smart devices. These could all be made possible thanks to a new ultra-low power Wi-Fi radio developed by electrical engineers at the University of California San Diego. The device, which is housed in a chip smaller than a grain of rice, enables Internet of Things (IoT) devices to communicate with existing Wi-Fi networks using 5,000 times less power than today’s Wi-Fi radios. It consumes just 28 microwatts of power. And it does so while transmitting data at a rate of 2 megabits per second (a connection fast enough to stream music and most YouTube videos) over a range of up to 21 meters.

P-H. P. Wang, C. Zhang, H. Yang, D. Bharadia, P. P. Mercier

Press cover by UCSD News, Tech Explorist, ACM News, Hacker News

ScatterMIMO: Enabling Virtual MIMO with Smart Surfaces [Mobicom 2020]

[ScatterMIMO-Webpage] [paper]

In the last decade, the bandwidth expansion and MIMO spatial multiplexing have promised to increase data throughput by orders of magnitude. However, we are yet to enjoy such improvement in real-world environments, as they lack rich scattering and preclude effective MIMO spatial multiplexing. In this paper, we present ScatterMIMO, which uses smart surface to increase the scattering in the environment, to provide MIMO spatial multiplexing gain. Specifically, smart surface pairs up with a wireless transmitter device say an active AP and re-radiates the same amount of power as any active access point (AP), thereby creating virtual passive APs. ScatterMIMO avoids the synchronization, interference, and power requirements of conventional distributed MIMO systems by leveraging virtual passive APs, allowing its smart surface to provide spatial multiplexing gain, which can be deployed at a very low cost. We show that with optimal placement, these virtual APs can provide signals to their clients with power comparable to real active APs, and can increase the coverage of an AP. Furthermore, we design algorithms to optimize ScatterMIMO’s smart surface for each client with minimal measurement overhead and to overcome random per-packet phase offsets during the measurement. Our evaluations show that with commercial off-the-shelf MIMO WiFi (11ac) AP and unmodified clients, ScatterMIMO provides a median throughput improvement of 2x over the active AP alone.

Manideep Dunna, Chi Zhang, Daniel Sievenpiper, Dinesh Bharadia

Press cover by UCSD News, Hackster News

Deep Learning based Wireless Localization for Indoor Navigation [Mobicom 2020]

[DLoc-Webpage] [paper]

Location services, fundamentally, rely on two components- a mapping system and a positioning system. The mapping system provides the physical map of the space, and the positioning system identifies the position within the map. Outdoor location services have thrived over the last couple of decades because of well-establishedplatforms for both these components (e.g. Google Maps for mapping, and GPS for positioning). In contrast, indoor location services haven’t caught up because of the lack of reliable mapping and positioning frameworks, as GPS is known not to work indoors. WiFi positioning lacks maps and is also prone to environmental errors. In this paper, we present DLoc, a Deep Learning based wireless localization algorithm that can overcome traditional limitations of RF-based localization approaches (like multipath, occlusions, etc.). DLoc uses data from the mapping platform we developed, MapFind, that can construct location-tagged maps of the environment. Together, they allow off-the-shelf WiFi devices like smartphones toaccess a map of the environment and to estimate their position withrespect to that map. During our evaluation, MapFind has collected location estimates of over 120 thousand points under 10 different scenarios across two different spaces covering 2000 sq. Ft. DLoc outperforms state-of-the-art methods in WiFi-based localizationby 80% (median and 90th percentile) across the 2000 sq. ft. spanningtwo different spaces.

Roshan Ayyalasomayajula, Aditya Arun, Chenfeng Wu, Sanatan Sharma, Abhishek Sethi, Deepak Vasisht, and Dinesh Bharadia

LocAP: Accurate Localization of Existing WiFi Infrastructure [NSDI 2020]

[LocAP-Webpage] [paper] [slides]

Indoor localization has been studied for nearly two decades fueled by wide interest in indoor navigation, achieving the necessary decimeter-level accuracy. However, there are no real-world deployments of WiFi-based user localization algorithms, primarily because these algorithms are triangulation based and therefore assume the location of the Access Points, their antenna geometries, and deployment orientations in the physical map. In the real world, such detailed knowledge of the location attributes of the Access Point is seldom available, thereby making WiFi localization hard to deploy. In this paper, for the first time, we establish the accuracy requirements for the location attributes of access points to achieve decimeter level user localization accuracy. Surprisingly, these requirements for antenna geometries and deployment orientation are very stringent, requiring millimeter level and sub-10 degree of accuracy respectively, which is hard to achieve with manual effort. To ease the deployment of real-world WiFi localization, we present LocAP, which is an autonomous system to physically map the environment and accurately locate the attributes of existing infrastructure AP in the physical space down to the required stringent accuracy of 3 mm antenna separation and 3degree deployment orientation median errors, whereas state-of-the-art report 150 mm and 25degrees respectively.

Shrivatsan Rajagopalan, Shreya Ganesaraman, Aravind Seetharaman, Roshan Ayyalasomayajula, Aditya Arun, Chenfeng Wu, and Dinesh Bharadia

Capttery: Scalable Battery-like Room-level Wireless Power [Mobisys 2019]

Internet-of-things (IoT) devices are becoming widely adopted, but they increasingly suffer from limited power, as power cords cannot reach the billions and batteries do not last forever. Existing systems address the issue with ultra-low-power designs and energy scavenging, which inevitably limit functionality. To unlock the full potential of ubiquitous computing and connectivity, our solution uses capacitive power transfer (CPT) to provide battery-like wireless power delivery, henceforth referred to as “Capttery”. Capttery presents the first room-level (~5 m) CPT system, which delivers continuous milliwatt-level wireless power to multiple IoT devices concurrently. Unlike conventional one-to-one CPT systems that target kilowatt power in a controlled and potentially hazardous setup, Capttery is designed to be human-safe and invariant in a practical and dynamic environment. Our evaluation shows that Capttery can power end-to-end IoT applications across a typical room, where new receivers can be easily added in a plug-and-play manner.

Chi Zhang, Siddharth Kumar, and Dinesh Bharadia

SparSDR: Sparsity-proportional Backhaul and Compute for SDRs [Mobisys 2019]

[SparSDR Webpage] [paper]

We present SparSDR, a resource-efficient architecture for softwaredefined radios whose backhaul bandwidth and compute power requirements scale in inverse proportion to the sparsity (in time and frequency) of the signals received. SparSDR requires dramatically fewer resources than existing approaches to process many popular protocols while retaining both flexibility and fidelity. We demonstrate that our approach has negligible impact on signal quality, receiver sensitivity, and processing latency. The SparSDR architecture makes it possible to capture signals across bandwidths far wider than the capacity of a radio’s backhaul through the addition of lightweight frontend processing and corresponding backend reconstruction to restore the signals to their original sample rate. We employ SparSDR to develop two wideband applications running on a USRP N210 and a Raspberry Pi 3+: an IoT sniffer that scans 100 MHz of bandwidth and decodes received BLE packets, and a wideband Cloud SDR receiver that requires only residential-class Internet uplink capacity. We show that our SparSDR implementation fits in the constrained resources of popular low-cost SDR platforms, such as the AD Pluto.

Moein Khazraee, Yeswanth Guddeti, Sam Crow, Alex C. Snoeren, Kirill Levchenko, Dinesh Bharadia, and Aaron Schulman

SweepSense: Sensing 5 GHz in 5 Milliseconds with Low-cost Radios [NSDI 2019]

[SweepSense Repository and Website]

Wireless transmissions occur intermittently across the entire spectrum. For example, WiFi and Bluetooth devices transmit frames across the 100 MHz-wide 2.4 GHz band, and LTE devices transmit frames between 700 MHz and 3.7 GHz). Today, only high-cost radios can sense across the spectrum with sufficient temporal resolution to observe these individual transmissions. We present “SweepSense”, a low-cost radio architecture that senses the entire spectrum with high-temporal resolution by rapidly sweeping across it. Sweeping introduces new challenges for spectrum sensing: SweepSense radios only capture a small number of distorted samples of transmissions. To overcome this challenge, we correct the distortion with self-generated calibration data, and classify the protocol that originated each transmission with only a fraction of the transmission’s samples. We demonstrate that SweepSense can accurately identify four protocols transmitting simultaneously in the 2.4 GHz unlicensed band. We also demonstrate that it can simultaneously monitor the load of several LTE base stations operating in disjoint bands.

Yeswanth Guddeti, Raghav Subbaraman, Moein Khazraee, Aaron Schulman, and Dinesh Bharadia

This work won the Qualcomm Innovation Fellowship 2019

SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception [CVPR 2019]

Unsupervised learning for geometric perception (depth, optical flow, etc.) is of great interest to autonomous systems. Recent works on unsupervised learning have made considerable progress on perceiving geometry; however, they usually ignore the coherence of objects and perform poorly under scenarios with dark and noisy environments. In contrast, supervised learning algorithms, which are robust, require large labeled geometric dataset. This paper introduces SIGNet, a novel framework that provides robust geometry perception without requiring geometrically informative labels. Specifically, SIGNet integrates semantic information to make depth and flow predictions consistent with objects and robust to low lighting conditions. SIGNet is shown to improve upon the state-of-the-art unsupervised learning for depth prediction by 30% (in squared relative error). In particular, SIGNet improves the dynamic object class performance by 39% in depth prediction and 29% in flow prediction. Our code will be made available online

Yue Meng, Yongxi Lu, Aman Raj, Samuel Sunarjo, Rui Guo, Tara Javidi, Gaurav Bansal, Dinesh Bharadia

BLoc: CSI-based accurate localization for BLE tags [CoNext 2018]

BLoc-webpage [paper] [slides]

Bluetooth Low Energy (BLE) tags have become very prevalent over the last decade for tracking applications in homes as well as businesses. These tags are used to track objects, navigate people, and deliver contextual advertisements. However, in spite of the wide interest in tracking BLE tags, the primary methods of tracking them are based on signal strength (RSSI) measurements. Past work has shown that such methods are inaccurate, and prone to multipath and dynamic environments. As a result, localization using Wi-Fi has moved to Channel State Information (CSI, includes both signal strength and signal phase) based localization methods. In this paper, we seek to investigate what are the challenges that prevent BLE from adopting CSI based localization methods. We identify fundamental differences at the PHY layer between BLE and Wi-Fi, that make it challenging to extend CSI based localization to BLE. We present our system, BLoc, that incorporates novel, BLE-compatible algorithms to overcome these challenges and enable an accurate, multipath-resistant localization system. Our empirical evaluation shows that BLoc can achieve a localization accuracy of 86 cm with BLE tags, a 3X improvement over a state-of-the-art baseline.

Roshan Ayyalasomayajula, Deepak Vasisht, and Dinesh Bharadia

Full duplex radios [Sigcomm 2013]

This paper presents the design and implementation of the first in-band full duplex WiFi radios that can simultaneously transmit and receive on the same channel using standard WiFi 802.11ac PHYs and achieves close to the theoretical doubling of throughput in all practical deployment scenarios. Our design uses a single antenna for simultaneous TX/RX (i.e., the same resources as a standard half duplex system). We also propose novel analog and digital cancellation techniques that cancel the self interference to the receiver noise floor, and therefore ensure that there is no degradation to the received signal. We prototype our design by building our own analog circuit boards and integrating them with a fully WiFi-PHY compatible software radio implementation. We show experimentally that our design works robustly in noisy indoor environments, and provides close to the expected theoretical doubling of throughput in practice.

Dinesh Bharadia, Emily McMilin, Sachin Katti

Citation count > 1700

 

Full List

Evaluating Physical-Layer BLE Location Tracking Attacks on Mobile Devices
Hadi Givehchian, Nishant Bhaskar, Eliana Rodriguez Herrera, Héctor Rodrigo López Soto, Christian Dameff, Dinesh Bharadia, and Aaron Schulman
[IEEE S&P 2022]

ULoc: a cm-accurate, low-latency and power-efficient UWB tag localization system
Minghui Zhao, Tyler Chang, Aditya Arun, Roshan Ayyalasomayajula, Chi Zhang, Dinesh Bharadia
[IMWUT 2021]

Two beams are better than one: Towards Reliable and High Throughput mmWave Links
Ish Kumar Jain, Raghav Subbaraman, and Dinesh Bharadia
[Sigcomm 2021]

SyncScatter: Enabling WiFi like synchronization and range for WiFi backscatter Communication
Manideep Dunna, Miao Meng, Po-Han Wang, Chi Zhang, Patrick Mercier, and Dinesh Bharadia
[NSDI 2021]

WiForce: Wireless Sensing and Localization of Contact Forces on a Space Continuum
Agrim Gupta, Cédric Girerd, Manideep Dunna, Qiming Zhang, Raghav Subbaraman, Tania Morimoto, and Dinesh Bharadia
[NSDI 2021]

DroneScale: Drone Load Estimation Via Remote Passive RF Sensing
Phuc Nguyen (University of Texas at Arlington; University of Colorado Boulder); Vimal Kakaraparthi, Nam Bui, Nikshep Umamahesh (University of Colorado Boulder); Nhat Pham (University of Colorado Boulder; University of Oxford); Hoang Truong (University of Colorado Boulder); Yeswanth Guddeti, Dinesh Bharadia (University of California San Diego); Eric Frew, Richard Han, Daniel Massey (University of Colorado Boulder); Tam Vu (University of Colorado Boulder; University of Oxford)
[Sensys 2020]

Pointillism: Accurate 3D Bounding Box Estimation with Multi-Radars
Kshitiz Bansal, Keshav Rungta, Siyuan Zhu and Dinesh Bharadia
[Sensys 2020]

mMobile: Building a mmWave testbed to evaluate and address mobility effects
Ish Kumar Jain, Raghav Subbaraman, Tejas Harekrishna Sadarahalli, Xiangwei Shao, Hou-Wei Lin, Dinesh Bharadia
[mmNets 2020]

S3Net: Semantic-Aware Self-supervised Depth Estimation with Monocular Videos and Synthetic Data
Bin Cheng, Inderjot Singh Saggu, Raunak Shah, Gaurav Bansal, Dinesh Bharadia
[ECCV 2020]

A 28μW IoT Tag That Can Communicate with Commodity WiFi Transceivers via a Single-Side-Band QPSK Backscatter Communication Technique
P-H. P. Wang, C. Zhang, H. Yang, D. Bharadia, P. P. Mercier
[ISSCC 2020]

ScatterMIMO: Enabling Virtual MIMO with Smart Surfaces
Manideep Dunna, Chi Zhang, Daniel Sievenpiper, Dinesh Bharadia
[Mobicom 2020]

Deep Learning based Wireless Localization for Indoor Navigation
Roshan Ayyalasomayajula, Aditya Arun, Chenfeng Wu, Sanatan Sharma, Abhishek Sethi, Deepak Vasisht, and Dinesh Bharadia
[Mobicom 2020]

LocAP: Accurate Localization of Existing WiFi Infrastructure
Shrivatsan Rajagopalan, Shreya Ganesaraman, Aravind Seetharaman, Roshan Ayyalasomayajula, Aditya Arun, Chenfeng Wu, and Dinesh Bharadia
[NSDI 2020]

Capttery: Scalable Battery-like Room-level Wireless Power
Chi Zhang, Siddharth Kumar, and Dinesh Bharadia
[Mobisys 2019]

SparSDR: Sparsity-proportional Backhaul and Compute for SDRs
Moein Khazraee, Yeswanth Guddeti, Sam Crow, Alex C. Snoeren, Kirill Levchenko, Dinesh Bharadia, and Aaron Schulman
[Mobisys 2019]

SweepSense: Sensing 5 GHz in 5 Milliseconds with Low-cost Radios
Yeswanth Guddeti, Raghav Subbaraman, Moein Khazraee, Aaron Schulman, and Dinesh Bharadia
[NSDI 2019]

SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception
Yue Meng, Yongxi Lu, Aman Raj, Samuel Sunarjo, Rui Guo, Tara Javidi, Gaurav Bansal, Dinesh Bharadia
[CVPR 2019]

BLoc: CSI-based accurate localization for BLE tags
Roshan Ayyalasomayajula, Deepak Vasisht, and Dinesh Bharadia
[CoNext 2018]

FreeRider: Backscatter Communication Using Commodity Radios
Pengyu Zhang, Colleen Josephson, Dinesh Bharadia, Sachin Katti
[CoNext 2017]

Enabling high-quality untethered virtual reality
Omid Abari, Dinesh Bharadia, Austin Duffield, Dina Katabi
[NSDI 2017]

Hitchhike: Practical backscatter using commodity wifi
Pengyu Zhang, Dinesh Bharadia, Kiran Joshi, Sachin Katti
[SenSys 2016]

Cutting the cord in virtual reality
Omid Abari, Dinesh Bharadia, Austin Duffield, Dina Katabi
[HotNets 2016]

Numfabric: Fast and flexible bandwidth allocation in datacenters
Kanthi Nagaraj, Dinesh Bharadia, Hongzi Mao, Sandeep Chinchali, Mohammad Alizadeh, Sachin Katti
[Sigcomm 2016]

Enabling backscatter communication among commodity wifi radios
Pengyu Zhang, Dinesh Bharadia, Kiran Joshi, Sachin Katti
[Sigcomm 2016]

Backfi: High throughput wifi backscatter
Dinesh Bharadia, Kiran Raj Joshi, Manikanta Kotaru, Sachin Katti
[Sigcomm 2015]

Spotfi: Decimeter level localization using wifi
Manikanta Kotaru, Kiran Joshi, Dinesh Bharadia, Sachin Katti
[Sigcomm 2015]

Fastforward: Fast and constructive full duplex relays
Dinesh Bharadia, Sachin Katti
[Sigcomm 2015]

WiDeo: Fine-grained Device-free Motion Tracing using {RF} Backscatter
Kiran Joshi, Dinesh Bharadia, Manikanta Kotaru, Sachin Katti
[NSDI 2015]

Spoton: Indoor localization using commercial off-the-shelf wifi nics
Manikanta Kotaru, Kiran Joshi, Dinesh Bharadia, Sachin Katti
[IPSN 2015]

Robust full duplex radio link
Dinesh Bharadia, Kiran Joshi, Sachin Katti
[Sigcomm 2014]

Full Duplex {MIMO} Radios
Dinesh Bharadia, Sachin Katti
[NSDI 2014]

QualComp: a new lossy compressor for quality scores based on rate distortion theory
Idoia Ochoa, Himanshu Asnani, Dinesh Bharadia, Mainak Chowdhury, Tsachy Weissman, Golan Yona
[BMC bioinformatics 2013]

Full duplex backscatter
Dinesh Bharadia, Kiran Raj Joshi, Sachin Katti
[HotNets 2013]

Full duplex radios
Dinesh Bharadia, Emily McMilin, Sachin Katti
[Sigcomm 2013]

Practical, real-time, full duplex wireless
Mayank Jain, Jung Il Choi, Taemin Kim, Dinesh Bharadia, Siddharth Seth, Kannan Srinivasan, Philip Levis, Sachin Katti, Prasun Sinha
[Mobicom 2011]