open source tab

Author: aybanerj

6g.md

@@ -4,10 +4,10 @@ heading: Towards 6G
 pubtag: Communications
 carousels:
   - images:
-    - image: /atkinson.jpeg
-    - image: /rice_chip.jpeg
-    - image: /pcb.jpeg
-    - image: /bearl.jpeg
+    - image: /assets/images/atkinson.jpeg
+    - image: /assets/images/rice_chip.jpeg
+    - image: /assets/images/pcb.jpeg
+    - image: /assets/images/bearl.jpeg
 ---
 
 We at the Wireless Sensing group of WCSNG, focus on designing, researching, developing, and deploying wireless localization and sensing systems for a wide range of applications, including Extended Reality, Indoor Robotics, Navigation, Warehouse management and Industrial IoT 4.0, with the goal of providing accurate and reliable location and sensing estimates for humans, devices, and robots.

_data/navigation.yml

@@ -88,3 +88,30 @@ header:
       tr      : &TR       Arşivdekiler
       # @end locale config
     url: /publist.html
+
+  - titles:
+      # @start locale config
+      en      : &EN       Open Source
+      en-GB   : *EN
+      en-US   : *EN
+      en-CA   : *EN
+      en-AU   : *EN
+      zh-Hans : &ZH_HANS  关于
+      zh      : *ZH_HANS
+      zh-CN   : *ZH_HANS
+      zh-SG   : *ZH_HANS
+      zh-Hant : &ZH_HANT  關於
+      zh-TW   : *ZH_HANT
+      zh-HK   : *ZH_HANT
+      ko      : &KO       소개
+      ko-KR   : *KO
+      fr      : &FR       À propos
+      fr-BE   : *FR
+      fr-CA   : *FR
+      fr-CH   : *FR
+      fr-FR   : *FR
+      fr-LU   : *FR
+      tr      : &TR       Hakkında
+      # @end locale config
+    url: /code.html
+

_layouts/publication.html

@@ -3,8 +3,8 @@
 --- 
 <i> {{ page.authors }} </i>
 
-{% if page.highlight %}
-  <div class="row"><h4>{{ page.highlight }}</h4></div>
+{% if page.extra %}
+  <div class="row"><h4>{{ page.extra }}</h4></div>
 {% endif %}
 
 <div class="row">

_posts/2013-08-27-full-duplex.md

@@ -7,7 +7,7 @@ cover: /assets/images/pubpic/fullduplex.png
 authors: "Dinesh Bharadia, Emily McMilin, Sachin Katti"
 conference: "Sigcomm 2013"
 paper: https://dl.acm.org/citation.cfm?id=2486033
-highlight: This paper has been cited over 1700 times!
+extra: This paper has been cited over 1700 times!
 ---
 
 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.

_posts/2019-02-26-SweepSense.md

@@ -8,7 +8,8 @@ authors: "Yeswanth Guddeti, Raghav Subbaraman, Moein Khazraee, Aaron Schulman, D
 conference: "NSDI 2019"
 paper: https://www.usenix.org/conference/nsdi19/presentation/guddeti
 github: https://github.com/ucsdsysnet/SweepSense/tree/master
-highlight: This work won the Qualcomm Innovation Fellowship 2019!
+extra: This work won the Qualcomm Innovation Fellowship 2019!
+osd: "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."
 ---
 
 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.

_posts/2019-06-12-SparSDR.md

@@ -9,6 +9,7 @@ conference: "Mobisys 2019"
 paper: http://people.csail.mit.edu/moein/papers/sparsdr-mobisys19.pdf
 video: https://www.youtube.com/embed/019AI3Q0s4g
 github: https://github.com/ucsdsysnet/sparsdr
+osd: "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."
 ---
 
 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.

_posts/2019-06-16-SIGNet.md

@@ -8,6 +8,7 @@ authors: "Yue Meng, Yongxi Lu, Aman Raj, Samuel Sunarjo, Rui Guo, Tara Javidi, G
 conference: "CVPR 2019"
 paper: http://openaccess.thecvf.com/content_CVPR_2019/html/Meng_SIGNet_Semantic_Instance_Aided_Unsupervised_3D_Geometry_Perception_CVPR_2019_paper.html
 github: https://github.com/mengyuest/SIGNet
+osd: "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."
 ---
 
 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.

_posts/2020-09-21-DLoc.md

@@ -14,6 +14,7 @@ other:
 video: https://www.youtube.com/embed/b19C7U9jNFs
 dataset: https://wcsng.ucsd.edu/wild/
 github: https://github.com/ucsdwcsng/DLoc_pt_code
+osd: "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."
 ---
 
 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. spanning two different spaces.

_posts/2020-09-25-mMobile.md

@@ -10,6 +10,7 @@ paper: /files/mmobile.pdf
 dataset: https://github.com/ucsdwcsng/mMobile
 slides: /files/mMobile_slides.pdf
 video: https://www.youtube.com/embed/5vfE90wR03Q
+osd: "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."
 ---
 
 Beamforming methods need to be critically evaluated and improved to achieve the promised performance of millimeter wave (mmWave) 5G NR in high mobility applications like Vehicle-to-Everything (V2X) communication. Conventional beam management methods developed for higher frequency applications do not directly carry over to the 28 GHz mmWave regime, where propagation and reflection characteristics are vastly different. Further, real system deployments and tests are required to verify these methods in a practical setting. In this work, we develop mMobile, a custom 5G NR compliant mmWave testbed to evaluate beam management algorithms. We describe the architecture and challenges in building such a testbed. We then create a novel, low-complexity beam tracking algorithm by exploiting the 5G NR waveform structure and evaluate its performance on the testbed. The algorithm can sustain almost twice the average throughput compared to the baseline.

_posts/2020-11-17-Pointillism.md

@@ -18,6 +18,8 @@ press2:
   date: "November 17, 2020"
   url: https://jacobsschool.ucsd.edu/news/release?id=3161
   headline: Upgraded Radar Can Enable Self-Driving Cars to See Clearly No Matter the Weather
+
+osd: "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."
 ---
 
 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.