arXiv subjects

Dinesh Bharadia

Publications and source records attributed to Dinesh Bharadia.

At least 37 records · Page 2Linked to original sources

arXiv2023

Multifunctional Metasurface: Simultaneous Beam Steering, Polarization Conversion and Phase Offset

A varactor-based reconfigurable multifunctional metasurface capable of simultaneous beam steering, polarization conversion and phase offset is proposed in this paper. The unit cell is designed to naturally decompose the incident waves into two equal amplitude orthogonal linear components, and by integrating varactors, the reflection phase of the field components can be engineered from $-180^{\circ}$ to $180^{\circ}$.Taking advantage of the infinite states of the varactors, this design integrates a new function, the phase offset. After simulation validation of its capability, a four-layer $7$ by $6$ unit one-dimensional prototype is fabricated as a printed circuit board. It is experimentally demonstrated that it switches between X/Y and circular polarization with more than $10$ dB cross polarization isolation, while reaching $\pm45^{\circ}$ steering and $\pm180^{\circ}$ phase offset.

physics.app-ph↗

arXiv2023

WiROS: WiFi sensing toolbox for robotics

Many recent works have explored using WiFi-based sensing to improve SLAM, robot manipulation, or exploration. Moreover, widespread availability makes WiFi the most advantageous RF signal to leverage. But WiFi sensors lack an accurate, tractable, and versatile toolbox, which hinders their widespread adoption with robot's sensor stacks. We develop WiROS to address this immediate need, furnishing many WiFi-related measurements as easy-to-consume ROS topics. Specifically, WiROS is a plug-and-play WiFi sensing toolbox providing access to coarse-grained WiFi signal strength (RSSI), fine-grained WiFi channel state information (CSI), and other MAC-layer information (device address, packet id's or frequency-channel information). Additionally, WiROS open-sources state-of-art algorithms to calibrate and process WiFi measurements to furnish accurate bearing information for received WiFi signals. The open-sourced repository is: https://github.com/ucsdwcsng/WiROS

cs.RO↗

arXiv2023

EdgeRIC: Empowering Realtime Intelligent Optimization and Control in NextG Networks

Radio Access Networks (RAN) are increasingly softwarized and accessible via data-collection and control interfaces. RAN intelligent control (RIC) is an approach to manage these interfaces at different timescales. In this paper, we develop a RIC platform called RICworld, consisting of (i) EdgeRIC, which is colocated, but decoupled from the RAN stack, and can access RAN and application-level information to execute AI-optimized and other policies in realtime (sub-millisecond) and (ii) DigitalTwin, a full-stack, trace-driven emulator for training AI-based policies offline. We demonstrate that realtime EdgeRIC operates as if embedded within the RAN stack and significantly outperforms a cloud-based near-realtime RIC (> 15 ms latency) in terms of attained throughput. We train AI-based polices on DigitalTwin, execute them on EdgeRIC, and show that these policies are robust to channel dynamics, and outperform queueing-model based policies by 5% to 25% on throughput and application-level benchmarks in a variety of mobile environments.

cs.NI↗

arXiv2023

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

Modern mmWave systems have limited scalability due to inflexibility in performing frequency multiplexing. All the frequency components in the signal are beamformed to one direction via pencil beams and cannot be streamed to other user directions. We present a new flexible mmWave system called mmFlexible that enables flexible directional frequency multiplexing, where different frequency components of the mmWave signal are beamformed in multiple arbitrary directions with the same pencil beam. Our system makes two key 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 called FSDA (Frequency-space to delay-antenna) to estimate delay and phase values for the real-time operation of the delay-phased array. Through evaluations with mmWave channel traces, we show that mmFlexible provides a 60-150% reduction in worst-case latency compared to baselines

eess.SY↗

arXiv2022

Control and Placement of Finite-Resolution Intelligent Surfaces in IoT Systems with Imperfect CSI

In this paper, we study the advantages of using reconfigurable intelligent surfaces (RISs) for interference suppression in single-input single-output (SISO) distributed Internet of Things (IoT) networks. Implementing RIS-assisted networks confronts various problems, mostly related to the control and placement of the RIS. To tackle the control-related challenges, we consider noisy and local channel knowledge, based on which we devise algorithms to optimize the potentially distributed RISs to achieve an overall network objective, such as the sum-rate. We use a network with a centralized RIS as a benchmark for our comparisons. We further assume low-bit phase shifters at the RIS to capture real-world hardware limitations. We also study the placement of the RIS and analytically quantify the minimum required degrees-of-control for the RIS as a function of its location to guarantee a specific network performance metric and verify the results via simulations.

cs.IT↗

arXiv2022

GreenMO: Virtualized User-proportionate MIMO

With the turn of new decade, wireless communications face a major challenge on connecting many more new users and devices, at the same time being energy efficient and minimizing its carbon footprint. However, the current approaches to address the growing number of users and spectrum demands, like traditional fully digital architectures for Massive MIMO, demand exorbitant energy consumption. The reason is that traditionally MIMO requires a separate RF chain per antenna, so the power consumption scales with number of antennas, instead of number of users, hence becomes energy inefficient. Instead, GreenMO creates a new massive MIMO architecture which is able to use many more antennas while keeping power consumption to user-proportionate numbers. To achieve this GreenMO introduces for the first time, the concept of virtualization of the RF chain hardware. Instead of laying the RF chains physically to each antenna, GreenMO creates these RF chains virtually in digital domain. This also enables GreenMO to be the first flexible massive MIMO architecture. Since GreenMO's virtual RF chains are created on the fly digitally, it can tune the number of these virtual chains according to the user load, hence always flexibly consume user-proportionate power. Thus, GreenMO paves the way for green and flexible massive MIMO. We prototype GreenMO on a PCB with eight antennas and evaluate it with a WARPv3 SDR platform in an office environment. The results demonstrate that GreenMO is 3x more power-efficient than traditional Massive MIMO and 4x more spectrum-efficient than traditional OFDMA systems, while multiplexing 4 users, and can save upto 40% power in modern 5G NR base stations.

eess.SP↗

arXiv2022

Users are Closer than they Appear: Protecting User Location from WiFi APs

WiFi-based indoor localization has now matured for over a decade. Most of the current localization algorithms rely on the WiFi access points (APs) in the enterprise network to localize the WiFi user accurately. Thus, the WiFi user's location information could be easily snooped by an attacker listening through a compromised WiFi AP. With indoor localization and navigation being the next step towards automation, it is important to give users the capability to defend against such attacks. In this paper, we present MIRAGE, a system that can utilize the downlink physical layer information to create a defense against an attacker snooping on a WiFi user's location information. MIRAGE achieves this by utilizing the beamforming capability of the transmitter that is already part of the WiFi protocols. With this initial idea, we have demonstrated that the user can obfuscate his/her location from the WiFi AP always with no compromise to the throughput of the existing WiFi communication system and reduce the user location accuracy of the attacker from 2.3m to more than 10m.

cs.CR↗

arXiv2022

Spoofing Attack Detection in the Physical Layer with Commutative Neural Networks

In a spoofing attack, an attacker impersonates a legitimate user to access or tamper with data intended for or produced by the legitimate user. In wireless communication systems, these attacks may be detected by relying on features of the channel and transmitter radios. In this context, a popular approach is to exploit the dependence of the received signal strength (RSS) at multiple receivers or access points with respect to the spatial location of the transmitter. Existing schemes rely on long-term estimates, which makes it difficult to distinguish spoofing from movement of a legitimate user. This limitation is here addressed by means of a deep neural network that implicitly learns the distribution of pairs of short-term RSS vector estimates. The adopted network architecture imposes the invariance to permutations of the input (commutativity) that the decision problem exhibits. The merits of the proposed algorithm are corroborated on a data set that we collected.

cs.LG↗

arXiv2022

Deep Learning Object Detection Approaches to Signal Identification

Traditionally source identification is solved using threshold based energy detection algorithms. These algorithms frequently sum up the activity in regions, and consider regions above a specific activity threshold to be sources. While these algorithms work for the majority of cases, they often fail to detect signals that occupy small frequency bands, fail to distinguish sources with overlapping frequency bands, and cannot detect any signals under a specified signal to noise ratio. Through the conversion of raw signal data to spectrogram, source identification can be framed as an object detection problem. By leveraging modern advancements in deep learning based object detection, we propose a system that manages to alleviate the failure cases encountered when using traditional source identification algorithms. Our contributions include framing source identification as an object detection problem, the publication of a spectrogram object detection dataset, and evaluation of the RetinaNet and YOLOv5 object detection models trained on the dataset. Our final models achieve Mean Average Precisions of up to 0.906. With such a high Mean Average Precision, these models are sufficiently robust for use in real world applications.

cs.SD↗

arXiv2022

BeamScatter: Scalable, Deployable Long-Range backscatter communication with Beam-Steering

WiFi backscatter tags can enable direct connectivity of IoT devices with commodity WiFi hardware at low power. However, most state of the art backscatter tag implementations in this area have limited transmitter to tag range, are not suitable to be deployed in a WiFi mesh network, and do not take full advantage of today's WiFi network capabilities such as MIMO. In this paper, we present BeamScatter, which can realize a backscatter tag based on MIMO techniques that can work at a very long separation of 28m from an access-point and enables their deployment in WiFi mesh networks. BeamScatter presents a novel technique to perform beam-steering on the MIMO tag at a very low power consumption of 88 uW while achieving a peak throughput of 500kbps. Next, BeamScatter creates a novel modeling framework to decide the optimal phase setting on the tag to steer the backscattered signal WiFi access point.

eess.SP↗

arXiv2022

R-fiducial: Reliable and Scalable Radar Fiducials for Smart mmwave Sensing

Millimeter wave sensing has recently attracted a lot of attention given its environmental robust nature. In situations where visual sensors like cameras fail to perform, mmwave radars can be used to achieve reliable performance. However, because of the poor scattering performance and lack of texture in millimeter waves, radars can not be used in several situations that require precise identification of objects. In this paper, we take insight from camera fiducials which are very easily identifiable by the camera, and present R-fiducial tags, which smartly augment the current infrastructure to enable a myriad of applications with mmwave radars. R-fiducial acts as a fiducial for mmwave sensing, similar to camera fiducials, and can be reliably identified by a mmwave radar. We identify a precise list of requirements that a millimeter wave fiducial has to follow and show how R-fiducial achieves all of them. R-fiducial uses a novel spread-spectrum modulation technique that provides low latency with high reliability. Our evaluations show that R-fiducial can be reliably detected upto 25m and upto 120 degrees field of view with a latency of the order of milliseconds. We also conduct experiments and case studies in adverse and low visibility conditions to showcase the applicability of R-fiducial in a wide range of applications.

eess.SP↗

arXiv2022

WiForceSticker: Batteryless, Thin Sticker-like Flexible Force Sensor

Any two objects in contact with each other exert a force that could be simply due to gravity or mechanical contact, such as a robotic arm gripping an object or even the contact between two bones at our knee joints. The ability to naturally measure and monitor these contact forces allows a plethora of applications from warehouse management (detect faulty packages based on weights) to robotics (making a robotic arms' grip as sensitive as human skin) and healthcare (knee-implants). It is challenging to design a ubiquitous force sensor that can be used naturally for all these applications. First, the sensor should be small enough to fit in narrow spaces. Next, we don't want to lay cumbersome cables to read the force values from the sensors. Finally, we need to have a battery-free design to meet the in-vivo applications. We develop WiForceSticker, a wireless, battery-free, sticker-like force sensor that can be ubiquitously deployed on any surface, such as all warehouse packages, robotic arms, and knee joints. WiForceSticker first designs a tiny $4$~mm~$\times$~$2$~mm~$\times$~$0.4$~mm capacitative sensor design equipped with a $10$~mm~$\times$~$10$~mm antenna designed on a flexible PCB substrate. Secondly, it introduces a new mechanism to transduce the force information on ambient RF radiations that can be read by a remotely located reader wirelessly without requiring any battery or active components at the force sensor, by interfacing the sensors with COTS RFID systems. The sensor can detect forces in the range of $0$-$6$~N with sensing accuracy of $<0.5$~N across multiple testing environments and evaluated with over $10,000$ varying force level presses on the sensor. We also showcase two application case studies with our designed sensors, weighing warehouse packages and sensing forces applied by bone joints.

cs.RO↗

arXiv2022

ViWiD: Leveraging WiFi for Robust and Resource-Efficient SLAM

Recent interest towards autonomous navigation and exploration robots for indoor applications has spurred research into indoor Simultaneous Localization and Mapping (SLAM) robot systems. While most of these SLAM systems use Visual and LiDAR sensors in tandem with an odometry sensor, these odometry sensors drift over time. To combat this drift, Visual SLAM systems deploy compute and memory intensive search algorithms to detect `Loop Closures', which make the trajectory estimate globally consistent. To circumvent these resource (compute and memory) intensive algorithms, we present ViWiD, which integrates WiFi and Visual sensors in a dual-layered system. This dual-layered approach separates the tasks of local and global trajectory estimation making ViWiD resource efficient while achieving on-par or better performance to state-of-the-art Visual SLAM. We demonstrate ViWiD's performance on four datasets, covering over 1500 m of traversed path and show 4.3x and 4x reduction in compute and memory consumption respectively compared to state-of-the-art Visual and Lidar SLAM systems with on par SLAM performance.

cs.RO↗

arXiv2022

Two beams are better than one: Enabling reliable and high throughput mmWave links

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. This is an extended version of a paper published in Sigcomm'21.

eess.SP↗

arXiv2022

RadSegNet: A Reliable Approach to Radar Camera Fusion

Perception systems for autonomous driving have seen significant advancements in their performance over last few years. However, these systems struggle to show robustness in extreme weather conditions because sensors like lidars and cameras, which are the primary sensors in a sensor suite, see a decline in performance under these conditions. In order to solve this problem, camera-radar fusion systems provide a unique opportunity for all weather reliable high quality perception. Cameras provides rich semantic information while radars can work through occlusions and in all weather conditions. In this work, we show that the state-of-the-art fusion methods perform poorly when camera input is degraded, which essentially results in losing the all-weather reliability they set out to achieve. Contrary to these approaches, we propose a new method, RadSegNet, that uses a new design philosophy of independent information extraction and truly achieves reliability in all conditions, including occlusions and adverse weather. We develop and validate our proposed system on the benchmark Astyx dataset and further verify these results on the RADIATE dataset. When compared to state-of-the-art methods, RadSegNet achieves a 27% improvement on Astyx and 41.46% increase on RADIATE, in average precision score and maintains a significantly better performance in adverse weather conditions

cs.CV↗

arXiv2022

Pointillism: Accurate 3D bounding box estimation with multi-radars

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.

cs.CV↗

arXiv2021

ZLeaks: Passive Inference Attacks on Zigbee based Smart Homes

Zigbee is an energy-efficient wireless IoT protocol that is increasingly being deployed in smart home settings. In this work, we analyze the privacy guarantees of Zigbee protocol. Specifically, we present ZLeaks, a tool that passively identifies in-home devices or events from the encrypted Zigbee traffic by 1) inferring a single application layer (APL) command in the event's traffic, and 2) exploiting the device's periodic reporting pattern and interval. This enables an attacker to infer user's habits or determine if the smart home is vulnerable to unauthorized entry. We evaluated ZLeaks' efficacy on 19 unique Zigbee devices across several categories and 5 popular smart hubs in three different scenarios; controlled RF shield, living smart-home IoT lab, and third-party Zigbee captures. We were able to i) identify unknown events and devices (without a-priori device signatures) using command inference approach with 83.6% accuracy, ii) automatically extract device's reporting signatures, iii) determine known devices using the reporting signatures with 99.8% accuracy, and iv) identify APL commands in a public capture with 91.2% accuracy. In short, we highlight the trade-off between designing a low-power, low-cost wireless network and achieving privacy guarantees. We have also released ZLeaks tool for the benefit of the research community.

cs.CR↗

arXiv2021

WiForce: Wireless Sensing and Localization of Contact Forces on a Space Continuum

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.

eess.SP↗