Can We Remove the Ground? Obstacle-aware Point Cloud Compression for Remote Object Detection

Efficient point cloud (PC) compression is crucial for streaming applications, such as augmented reality and cooperative perception. Classic PC compression techniques encode all the points in a frame. Tailoring compression towards perception tasks at the receiver side, we ask the question, "Can we remove the ground points during transmission without sacrificing the detection performance?" Our study reveals a strong dependency on the ground from state-of-the-art (SOTA) 3D object detection models, especially on those points below and around the object. In this work, we propose a lightweight obstacle-aware Pillar-based Ground Removal (PGR) algorithm. PGR filters out ground points that do not provide context to object recognition, significantly improving compression ratio without sacrificing the receiver side perception performance. Not using heavy object detection or semantic segmentation models, PGR is light-weight, highly parallelizable, and effective. Our evaluations on KITTI and Waymo Open Dataset show that SOTA detection models work equally well with PGR removing 20-30% of the points, with a speeding of 86 FPS.

CalTag: Robust calibration of mmWave Radar and LiDAR using backscatter tags

The rise of automation in robotics necessitates the use of high-quality perception systems, often through the use of multiple sensors. A crucial aspect of a successfully deployed multi-sensor systems is the calibration with a known object typically named fiducial. In this work, we propose a novel fiducial system for millimeter wave radars, termed as \name. \name addresses the limitations of traditional corner reflector-based calibration methods in extremely cluttered environments. \name leverages millimeter wave backscatter technology to achieve more reliable calibration than corner reflectors, enhancing the overall performance of multi-sensor perception systems. We compare the performance in several real-world environments and show the improvement achieved by using \name as the radar fiducial over a corner reflector.

DOLOS: Tricking the Wi-Fi APs with Incorrect User Locations

Wi-Fi-based indoor localization has been extensively studied for context-aware services. As a result, the accurate Wi-Fi-based indoor localization introduces a great location privacy threat. However, the existing solutions for location privacy protection are hard to implement on current devices. They require extra hardware deployment in the environment or hardware modifications at the transmitter or receiver side. To this end, we propose DOLOS, a system that can protect the location privacy of the Wi-Fi user with a novel signal obfuscation approach. DOLOSis a software-only solution that can be deployed on existing protocol-compliant Wi-Fi user devices. We provide this obfuscation by invalidating a simple assumption made by most localization systems -- "direct path signal arrives earlier than all the reflections to distinguish this direct path prior to estimating the location". However, DOLOS creates a novel software fix that allows the user to transmit the signal wherein this direct path arrives later, creating ambiguity in the location estimates. Our experimental results demonstrate DOLOS can degrade the localization accuracy of state-of-art systems by 6x for a single AP and 2.5x for multiple AP scenarios, thereby protecting the Wi-Fi user's location privacy without compromising the Wi-Fi communication performance.

CommRad: Context-Aware Sensing-Driven Millimeter-Wave Networks

Millimeter-wave (mmWave) technology is pivotal for next-generation wireless networks, enabling high-data-rate and low-latency applications such as autonomous vehicles and XR streaming. However, maintaining directional mmWave links in dynamic mobile environments is challenging due to mobility-induced disruptions and blockage. While effective, the current 5G NR beam training methods incur significant overhead and scalability issues in multi-user scenarios. To address this, we introduce CommRad, a sensing-driven solution incorporating a radar sensor at the base station to track mobile users and maintain directional beams even under blockages. While radar provides high-resolution object tracking, it suffers from a fundamental challenge of lack of context, i.e., it cannot discern which objects in the environment represent active users, reflectors, or blockers. To obtain this contextual awareness, CommRad unites wireless sensing capabilities of bi-static radio communication with the mono-static radar sensor, allowing radios to provide initial context to radar sensors. Subsequently, the radar aids in user tracking and sustains mobile links even in obstructed scenarios, resulting in robust and high-throughput directional connections for all mobile users at all times. We evaluate this collaborative radar-radio framework using a 28 GHz mmWave testbed integrated with a radar sensor in various indoor and outdoor scenarios, demonstrating a 2.5x improvement in median throughput compared to a non-collaborative baseline.

Densify & Conquer: Densified, smaller base-stations can conquer the increasing carbon footprint problem in nextG wireless

Connectivity on-the-go has been one of the most impressive technological achievements in the 2010s decade. However, multiple studies show that this has come at an expense of increased carbon footprint, that also rivals the entire aviation sector's carbon footprint. The two major contributors of this increased footprint are (a) smartphone batteries which affect the embodied footprint and (b) base-stations that occupy ever-increasing energy footprint to provide the last mile wireless connectivity to smartphones. The root-cause of both these turn out to be the same, which is communicating over the last-mile lossy wireless medium. We show in this paper, titled DensQuer, how base-station densification, which is to replace a single larger base-station with multiple smaller ones, reduces the effect of the last-mile wireless, and in effect conquers both these adverse sources of increased carbon footprint. Backed by a open-source ray-tracing computation framework (Sionna), we show how a strategic densification strategy can minimize the number of required smaller base-stations to practically achievable numbers, which lead to about 3x power-savings in the base-station network. Also, DensQuer is able to also reduce the required deployment height of base-stations to as low as 15m, that makes the smaller cells easily deployable on trees/street poles instead of requiring a dedicated tower. Further, by utilizing newly introduced hardware power rails in Google Pixel 7a and above phones, we also show that this strategic densified network leads to reduction in mobile transmit power by 10-15 dB, leading to about 3x reduction in total cellular power consumption, and about 50% increase in smartphone battery life when it communicates data via the cellular network.

XRLoc: Accurate UWB Localization for XR Systems

Understanding the location of ultra-wideband (UWB) tag-attached objects and people in the real world is vital to enabling a smooth cyber-physical transition. However, most UWB localization systems today require multiple anchors in the environment, which can be very cumbersome to set up. In this work, we develop XRLoc, providing an accuracy of a few centimeters in many real-world scenarios. This paper will delineate the key ideas which allow us to overcome the fundamental restrictions that plague a single anchor point from localization of a device to within an error of a few centimeters. We deploy a VR chess game using everyday objects as a demo and find that our system achieves $2.4$ cm median accuracy and $5.3$ cm $90^\mathrm{th}$ percentile accuracy in dynamic scenarios, performing at least $8\times$ better than state-of-art localization systems. Additionally, we implement a MAC protocol to furnish these locations for over $10$ tags at update rates of $100$ Hz, with a localization latency of $\sim 1$ ms.

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

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.

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.

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.