Code

• XRLoc: Accurate UWB Localization to Realize XR Deployments

  The following codebase open sources the particle filter algorithm in the XRLoc UWB localization system. This system is implemented on 6 clock-synchronized UWB radios, which measure the phase difference of arrival (PDoA) and time difference of arrival (TDoa) of a transmitted UWB signal from a tag. The partcle filter then combines the PDoA and TDoA measurements to furnish the 2D location of the tag with cm-level accuracy. The code is written in Python.

• Crescendo: Towards Wideband, Real-Time, High-Fidelity Spectrum Sensing Systems

  The artifact provides data and code to analyze the case-study evaluations in the paper

• WiROS: WiFi sensing toolbox for robotics

  WiROS makes three concrete contributions, in order to deliver an accurate, versatile and tractable WiFi-sensor framework for ROS. First, it provides a C++ framework to integrate WiFi-sensor specific API’s into ROS. We accomplish this for Nexmon API, however, newer platforms can be similarly integrated with little additional effort. Second, we provide a wireless calibration algorithm and toolkit to measure and calibrate for hardware non-idealities for WiFi sensors. The lastly, we open-source state-of-art algorithms to extract physical parameters like angles of arrival or departure of the WiFi signals.

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

  Our dataset which consists of multiple indoor and outdoor experiments for up to 30 m gNB-UE link. In each experiment, we fixed the location of the gNB and move the UE with an increment of roughly one degrees. The table above specifies the direction of user movement with respect to gNB-UE link, distance resolution, and the number of user locations for which we conduct channel measurements. Outdoor 30 m data also contains blockage between 3.9 m to 4.8 m. At each location, we scan the transmission beam and collect data for each beam. By doing so, we can get the full OFDM channels for different locations along the moving trajectory with all the beam angles. Moreover, we use 240 kHz subcarrier spacing, which is consistent with the 5G NR numerology at FR2, so the data we collect will be a true reflection of what a 5G UE will see.

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

  Our dataset which consists of multiple indoor and outdoor experiments for up to 30 m gNB-UE link. In each experiment, we fixed the location of the gNB and move the UE with an increment of roughly one degrees. The table above specifies the direction of user movement with respect to gNB-UE link, distance resolution, and the number of user locations for which we conduct channel measurements. Outdoor 30 m data also contains blockage between 3.9 m to 4.8 m. At each location, we scan the transmission beam and collect data for each beam. By doing so, we can get the full OFDM channels for different locations along the moving trajectory with all the beam angles. Moreover, we use 240 kHz subcarrier spacing, which is consistent with the 5G NR numerology at FR2, so the data we collect will be a true reflection of what a 5G UE will see.

• BSMA: scalable LoRa networks using full duplex gateways

  The MATLAB simulator allows simulation of LoRa networks with various MAC protocols. The physical layer is abstracted out and the simulator can be used to evaluate the performance of MAC protocols in different network topologies.

• ULoc: a cm-accurate, low-latency and power-efficient UWB tag localization system

  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.

• Two beams are better than one: Towards Reliable and High Throughput mmWave Links

  This repository contains the artifact for submission #441, ACM SIGCOMM 2021. The artifact is composed of simulations and algorithms implemented on real-life mmWave channel estimates.

• Pointillism: Accurate 3D Bounding Box Estimation with Multi-Radars

  This is the official code release for RP-net. It is the deep-learning system of Pointillism which estimates 3D bounding boxes from Cross-Potential point clouds generated by Pointillism.

• Deep Learning based Wireless Localization for Indoor Navigation

  While being the first in in Deep Learning based Indoor Navigation with WiFi data, we want to build WiFi CSI dataset on par with ImageNet to assist further research in WiFi based indoor localization and their applications.

• SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception

  Unsupervised learning for visual perception of 3D geometry is of great interest to autonomous systems. This paper introduces SIGNet, a novel frameworkthat provides robust geometry perception without requiring geometrically informative labels. Specifically, SIGNet integrates semantic information to make unsupervised robust geometric predictions for objects in low lighting and noisy environments. SIGNet is shown to improve upon the state-of-the-art unsupervised learning for geometry perception by 30% (in squared relative error for depth prediction). In addition, SIGNet improves the dynamic object class performance by 39% in depth prediction and 29% in flow prediction.

• SparSDR: Sparsity-proportional Backhaul and Compute for SDRs

  SparSDR’s goal is to make SDRs capture primary transmissions rather than entire channels. While a Full-capture SDR always backhauls data at a fixed rate, SparSDR takes advantage of frequency-time signal sparsity to scale the backhaul rate linearly with the actual occupancy of the channels observed. This allows SparSDR to backhaul more than 100 MHz of bandwidth over a backhaul where a Full-capture SDR could do less than 25 MHz.

• SweepSense: Sensing 5 GHz in 5 Milliseconds with Low-cost Radios

  We propose a new receiver architecture for spectrum sensing radios where sampling is done along with quick sweeping of the center frequency. This is motivated by the intuition that a sweeping radio may miss lesser transmissions than one that sequentially tunes to different bands. We implement this using an open loop VCO fed with a sawtooth voltage waveform. The output of the VCO is used to drive a mixer and implement the sweeping radio. The architecture has been prototyped on a USRP N210 with a CBX daughterboard. Downconverting while sweeping introduces distortions in the signal, which we remove using an 'unsweeping' process and is discussed in the paper.

Datasets

• GreenMO: Enabling Virtualized, Sustainable Massive MIMO with a Single RF Chain

  Our dataset consists of wireless channels and interfering bit-rate transmissions from 2, 3, 4 interfering users collected in a conference room setting with upto 8 antennas. This dataset can be used to evaluate performance of interfering suprresion schemes. Further, we show a simulation framework that creates signal processing for Massive MIMO upto 256 RF chains, as well as power consumption models from a standard digital beamformer and a hybrid beamformer.

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

  Our dataset which consists of multiple indoor and outdoor experiments for up to 30 m gNB-UE link. In each experiment, we fixed the location of the gNB and move the UE with an increment of roughly one degrees. The table above specifies the direction of user movement with respect to gNB-UE link, distance resolution, and the number of user locations for which we conduct channel measurements. Outdoor 30 m data also contains blockage between 3.9 m to 4.8 m. At each location, we scan the transmission beam and collect data for each beam. By doing so, we can get the full OFDM channels for different locations along the moving trajectory with all the beam angles. Moreover, we use 240 kHz subcarrier spacing, which is consistent with the 5G NR numerology at FR2, so the data we collect will be a true reflection of what a 5G UE will see.

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

  Our dataset which consists of multiple indoor and outdoor experiments for up to 30 m gNB-UE link. In each experiment, we fixed the location of the gNB and move the UE with an increment of roughly one degrees. The table above specifies the direction of user movement with respect to gNB-UE link, distance resolution, and the number of user locations for which we conduct channel measurements. Outdoor 30 m data also contains blockage between 3.9 m to 4.8 m. At each location, we scan the transmission beam and collect data for each beam. By doing so, we can get the full OFDM channels for different locations along the moving trajectory with all the beam angles. Moreover, we use 240 kHz subcarrier spacing, which is consistent with the 5G NR numerology at FR2, so the data we collect will be a true reflection of what a 5G UE will see.

• P2SLAM: Bearing based WiFi SLAM for Indoor Robots
• Two beams are better than one: Towards Reliable and High Throughput mmWave Links

  This repository contains the artifact for submission #441, ACM SIGCOMM 2021. The artifact is composed of simulations and algorithms implemented on real-life mmWave channel estimates.

• Pointillism: Accurate 3D Bounding Box Estimation with Multi-Radars

  This is the official code release for RP-net. It is the deep-learning system of Pointillism which estimates 3D bounding boxes from Cross-Potential point clouds generated by Pointillism.

• mMobile: Building a mmWave testbed to evaluate and address mobility effects

  We release 28 GHz full channel (CSI) measurements for a mobile user. The CSI data is tagged with each user location and for each beam index. The CSI consists of 256 subcarriers with a sub-carrier spacing of 240 kHz requisite by 5G NR standards. There are four datasets for various indoor and outdoor environments.

• Deep Learning based Wireless Localization for Indoor Navigation

  While being the first in in Deep Learning based Indoor Navigation with WiFi data, we want to build WiFi CSI dataset on par with ImageNet to assist further research in WiFi based indoor localization and their applications.