{"id":10,"date":"2015-02-03T18:40:23","date_gmt":"2015-02-04T02:40:23","guid":{"rendered":"https:\/\/depts.washington.edu\/funlab\/?page_id=10"},"modified":"2023-03-20T19:16:02","modified_gmt":"2023-03-21T02:16:02","slug":"research","status":"publish","type":"page","link":"https:\/\/wp.ece.uw.edu\/funlab\/research\/","title":{"rendered":"Research"},"content":{"rendered":"

Research<\/h3>\n\n\n\n\n\n\n\n
<\/i> 5G Wireless Networks<\/a><\/td>\n<\/tr>\n<\/thead>\n
Lead:<\/strong> Sian Jin, Lyutianyang Zhang<\/td>\n<\/tr>\n
Affiliated Student:<\/strong><\/td>\n<\/tr>\n
5G wireless networks aim to be a leap forward in terms of three new application scenarios defined by: enhanced mobile broadband (eMBB), ultra-reliable and low latency communications (URLLC) and massive machine type communications (mMTC). Current research focuses on:<\/p>\n

Compressed sensing application on channel estimation of 5G MIMO\/SISO:<\/p>\n

Rad-Com for Automotive Applications: Integration of automotive radars with vehicle networks will play a key role in enabling various intelligent driver-assist functions, and ultimately autonomous vehicles. Rad-Com is envisaged as new approaches for managing multi-radar operation for improving radar functionality via integration of vehicular networks (both V-2-V and V-2-I) in support.<\/p>\n

5G Spectrum Info<\/a><\/u>:\u00a0This provides a queryable tool for searching 5G spectrum plans for different regions in the continental US.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n\n\n\n
<\/i> RFI in Radio Astronomy<\/a><\/td>\n<\/tr>\n<\/thead>\n
Lead:<\/strong> Lvtianyang Zhang<\/td>\n<\/tr>\n
Affiliated Student:<\/strong><\/td>\n<\/tr>\n
Radio Astronomy arrays \u2013 such as the Murchison Widefield Array (MWA) in the West Australian desert \u2013 seek to detect extremely faint radiation (the desired signal) in the presence of other \u2018Sky Noise\u2019 (other undesired signals emanating from the galaxy) and terrestrial (man-made) radio frequency interference (RFI). The received signal processing chain must detect and flag these anomalies so they are excised from the database used for astronomy research. The\u00a0 MWA system currently implements AOflagger algorithm that is known to perform sub-optimally under certain RFI (e.g. terrestrial TV). In this project, we seek to improve RFI flagging by enhancing the AOflagger with new algorithmic capabilities tuned to known RFI source characteristics.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n\n\n\n
<\/i> Public Safety Communications Research<\/a><\/td>\n<\/tr>\n<\/thead>\n
Lead:<\/strong> Collin Brady<\/td>\n<\/tr>\n
Affiliated Student:<\/strong><\/td>\n<\/tr>\n
Over the next decade, network providers will roll out an LTE-based public safety network to replace the current system nationwide. Due to the stringent requirements of public safety communications, there will be many challenges associated with the new system. LTE will be forced to add priority queuing for public safety data, a feature currently unused in LTE, and device-to-device (D2D) communications. Research in this area focuses on extending coverage through the use of LTE D2D capability introduced in 3GPP release 12, the use of response systems to disaster-stricken areas like cell on wheels and drone technologies, and network performance under extreme disaster scenarios.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n\n\n\n
<\/i> Center on Satellite Multimedia and Connected Vehicles<\/a><\/td>\n<\/tr>\n<\/thead>\n
Lead:<\/strong> Sumit Roy<\/td>\n<\/tr>\n
Affiliated Student:<\/strong> Xiangyu Gao, Sian Jin<\/td>\n<\/tr>\n
Radar Vision \u2013 Radar Object Recognition<\/strong><\/a><\/p>\n

Millimeter-wave (mmW) radars are being increasingly integrated into commercial vehicles to support new advanced driver-assistance systems (ADAS) by enabling robust and high-performance object detection, localization, as well as recognition \u2013 a key component of new environmental perception.<\/p>\n

We propose a novel radar multiple-perspectives convolutional neural network (RAMP-CNN) that extracts the location and class of objects based on further processing of the range-velocity-angle (RVA) heatmap sequences. To bypass the complexity of 4D convolutional neural networks (NN), we propose to combine several lower-dimension NN models within our RAMP-CNN model that nonetheless approaches the performance upper-bound with lower complexity.<\/p>\n

\"\"<\/a><\/p>\n

Figure: test examples from the city road scenario; For each column, the top-row image is the synchronized camera image for visualization, the second-row image is the corresponding radar RA heatmap, and the bottom-row image is the visualization of the RAMP-CNN model results.<\/p><\/blockquote>\n

Radar Vision \u2013\u00a0High-resolution Radar Imaging<\/strong><\/a><\/p>\n

A key shortcoming for present-day vehicular radar imaging is poor azimuth resolution (for side-looking operation) due to the form factor limits on antenna size and placement.<\/p>\n

We propose a solution via a new multiple-input and multiple-output synthetic aperture radar (MIMO-SAR) imaging technique, that applies coherent SAR principles to vehicular MIMO radar to improve the side-view (angular) resolution. The proposed 2-stage hierarchical MIMO-SAR processing workflow drastically reduces the computation load while preserving image resolution. To enable coherent processing over the synthetic aperture, we integrate a radar odometry algorithm that estimates the trajectory of ego-radar.<\/p>\n

\"\"<\/a><\/p>\n

Figure: MIMO-SAR imaging for roadside experiment 1. (a) The camera image for the imaging environment with two inclinedly parked cars; (b) The MIMO-SAR imaging result where we use two rectangles to cover the parked cars; (c) Range-angle map imaging for single-frame radar data.<\/p><\/blockquote>\n

Automotive Radar Test-bed<\/strong><\/p>\n

Vehicle Radar Testbed is a millimeter-wave (mm-wave) FMCW radar sensor that can be used to test the performance of signal processing algorithms and collect data in field tests. The testbed shown below consists of the AWR1642 BoosterPack and DCA1000EVM from Texas Instruments.
\n\u00a0 \u00a0 Features:<\/em>
\n\u2013 Two available bandwidths: 76-77 GHz and 77-81GHz
\n\u2013 MIMO configuration: Four Receive antennas and two Transmit antennas with Time Division Modulation(TDM)
\n\u2013 Tx Power: 12dbm
\n\u2013 Rx Gain: 30dB
\n\u2013 C674x DSP for FMCW Signal Processing<\/p>\n

Signal Processing: The development of signal processing techniques along with progress in the mm-wave semiconductor technology plays a key role in automotive radar systems. Various signal processing techniques have been developed to provide better resolution and estimation performance in all measurement dimensions: range, azimuth angles, and velocity of the targets surrounding the vehicles.<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\"\"<\/a>\u00a0 \u00a0\u00a0\"\"<\/a><\/strong><\/p>\n

Figure: (left) The lab-scale mmW radar test-bed; (right) The automotive radar test-bed accompanied by cameras mounted on a vehicle.<\/p><\/blockquote>\n

Radar-Comm Coexistence<\/strong><\/p>\n

Wireless mediums provide finite resources for the purposes of radar and data communications. Often, these two functions are at odds with one another and compete for these resources. The broad solution space to this problem encompasses cooperation or codesigning of systems with both radar and communications functions.<\/p>\n

Automotive Applications: Integration of automotive radars with vehicle communication networks will play a key role in enabling various intelligent driver-assist functions, and ultimately autonomous vehicles. Rad-Comm coexistence is envisaged as new approach for managing the multi-radar operation for improving radar functionality (e.g., avoiding multi-radar interference) via integration of vehicular networks (both V-2-V and V-2-I) in support.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n\n\n\n
<\/i> Spectrum Sharing<\/a><\/td>\n<\/tr>\n<\/thead>\n
Lead:<\/strong><\/td>\n<\/tr>\n
Affiliated Student:<\/strong><\/td>\n<\/tr>\n
Radar Wi-Fi Coexistence<\/strong><\/p>\n

Abstract: <\/strong>Over the last decade, the increasing demand for high-speed wireless connectivity has forced us to confront spectrum scarcity. In 2012, the president mandated that a large swathe of spectrum that was under government control be opened up for opportunistic access by commercial systems. In particular, much of the initial focus has been directed at the coexistence of radar systems with broadband wireless networks. Spectrum sharing of 802.11 wireless local area network (WLAN) and radars\u00a0operating in co-\/adjacent channel scenarios (notably 5 GHz) is a problem of considerable importance that requires new innovations. The spectrum sharing explored in this project is based on unilateral action by Wi-Fi networks to prevent unacceptable interference to incumbent radar and also mitigating the interference from radar to Wi-Fi. Specifically, the ability of a single Wi-Fi network inside the exclusion region is to\u00a0speedily detect\u00a0radar operation and to subsequently switch to a clear channel as a means of protecting them. Also, the Wi-Fi systems outside the exclusion region are modified to detect and mitigate the interference from a pulsed search radar such that the WLAN continues to operate with no noticeable performance degradation.<\/p>\n

LTE WiFi Coexistence<\/strong><\/a><\/p>\n

The two most common broadband wireless access networks are cellular and Wi-Fi. Traditionally, these have operated in very different spectrum regulatory domains: cellular over licensed spectrum (i.e. exclusive use, requiring large sums of money for this privilege) whereas Wi-Fi has been designed for the unlicensed bands where there is no interference protection by rule. This difference has led to fundamentally different system architectures: cellular systems are centrally controlled where users are allocated resources in frequency and\/or time in a way so as to minimize intra-cell and inter-cell interference. Wi-Fi (IEEE 802.11) on the other hand, has been designed to operate in an environment where interference between like (other Wi-Fi) and unlike (non Wi-Fi) systems must be tolerated. Of late, driven by the maturation of Small Cell technologies, there has been increasing interest in deploying systems originally intended for licensed, cellular bands in the unlicensed bands (currently primarily used by Wi-Fi) with minimal changes. This creates a new and largely under-explored heterogeneous interference scenario: a scheduled system (cellular) coexisting with a collision avoidance protocol (Wi-Fi)<\/span>.<\/p>\n

 <\/p>\n

The initial ns-3 version of Wi-Fi with LTE-DC coexistence uses the waveform based LTE-DC model for modeling the LTE-DC behavior. The example scenario can be found here:
\nwifi-lte-coexistence.cc<\/a><\/p>\n

For subsequent LTE-DC performance evaluation, we used the LTE-DC capability that was developed under a WiFi Alliance funded project involving collaboration CTTC and UW and updated by Muhammad Iqbal in the 2018 Google Summer of Code; this updates previous LTE\/Wi-Fi coexistence code to the latest ns-3.29 release. This LTE-DC model uses the LTE DutyCycleAccessManager to generate duty-cycled LTE transmissions in ns-3 code. The example scenario can be found here:
\nwifi-lte-coexistence.cc<\/a><\/p>\n

For LTE-U duty cycle manuscripts as of February 2019, the ns-3 coexistence code can be found on the branch \u201clte-dc-analysis\u201d of the Bitbucket repository:
\nhttps:\/\/bitbucket.org\/ns3lteu\/ns-3-dev-lbt<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n\n\n\n
PHY network coding for coded caching in wireless networks<\/td>\n<\/tr>\n<\/thead>\n
Lead:<\/strong><\/td>\n<\/tr>\n
Affiliated Student:<\/strong><\/td>\n<\/tr>\n
Abstract<\/strong>: Coded caching can achieve significant broadcast gain in the content distribution network. It contains two phases: placement phase and delivery phase. The placement phase is done at the off-peak time so that each user will cache some contents. In the peak time the server leverages the caching contents at each user to send coded packets to harvest the broadcast gain in the delivery phase. However, when coded caching is applied to the wireless networks, the non-identical link capacity and packet loss in wireless channels make it inefficient. To address the problems of coded caching in wireless networks, we consider to design a new PHY network coding scheme, which can achieve full rate of each wireless link and rateless transmission. Moreover, the relationship of caching and link capacity is explored to achieve better performance.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n\n\n\n
<\/i> Wireless Network Coding<\/a><\/td>\n<\/tr>\n<\/thead>\n
Lead:<\/strong><\/td>\n<\/tr>\n
Affiliated Student:<\/strong><\/td>\n<\/tr>\n
Random Access with Physical Layer Network Coding<\/strong><\/p>\n

Abstract<\/strong>: Compute-and-forward (C&F) is a promising new physical-layer technique which allows a receiver to recover multiple linear combinations of\u00a0simultaneous transmitted packets. Prior work on C&F mainly focuses on its information-theoretic performance as well as its practical code constructions. However, its potential in improving networking performance, such as throughput and delay, is less well understood.<\/p>\n

In particular, it is unclear what is the benefit of C&F in\u00a0the presence of bursty data traffic and decentralized network\u00a0operations. To understand the networking performance of C&F, we consider the application of C&F to random access protocols such as Slotted ALOHA and variants of CSMA.<\/p>\n

Scenarios and Implementations of NC<\/strong><\/p>\n

Abstract<\/strong>: Network Coding (NC), a technique to increase the spectral efficiency of communication networks by\u00a0combining multiple packets of data destined for different sink nodes at every node, has drawn much\u00a0research attention in the past decade. In a single-source multicast network, it is shown that the\u00a0networks capacity is achievable by utilizing Linear Network Coding (LNC). Also in simple two-way relay\u00a0channel, it\u2019s shown that NC can increase the throughput of the network by %33 at most.\u00a0A new ground for exploring network coding techniques is the rapidly growing dense cellular networks, in\u00a0which a user entity can lie within the overlapping area of two or more cells and hence is able to\u00a0communicate with multiple Base Stations (BS) simultaneously. These situations can evidently be\u00a0modeled as Distributed Antenna Systems (DAS) or Distributed Multi-Input-Multi-Output (DMIMO)\u00a0systems. The research ongoing is to identify and exploit the potentials of such techniques to increase the\u00a0spectral efficiency and hence the throughput of wireless communication networks.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n\n\n\n
Improvements to NS-3 Simulator<\/td>\n<\/tr>\n<\/thead>\n
Lead:<\/strong> Sumit Roy and Tom Henderson<\/td>\n<\/tr>\n
Affiliated Student:<\/strong> Sian Jin, Jun Hyeon Park, Hao Yin, Sachin Nayak<\/td>\n<\/tr>\n
ns-3 Scaling for Next G Wireless Networks (NSF CCRI)<\/strong><\/a><\/p>\n

Abstract:\u00a0<\/strong> This project intends to significantly upgrade the scalability of the wireless models of ns-3 to address the challenges posed by next-generation (dense, hetnets) wireless scenarios.\u00a0 The project also will support educational, training, and sustainment activities.<\/p>\n

Efficient PHY Layer Abstractions<\/strong><\/a>
\nAbstract<\/strong>: Packet-level wireless network simulators face escalating system dimensionalities resulting from dense deployment scenarios supporting wideband, Multi-Input Multi-Output (MIMO), Multi-User (MU) transmission. Managing the resulting network simulation complexity and achieving practical runtimes require continuing enhancements to physical (PHY) layer abstractions. This work improves the state-of-the-art PHY layer abstractions via a new computational workflow that maps extensive offline link simulation results for OFDM\/OFDMA MIMO\/MU-MIMO system performance over frequency-selective channels into Packet Error Ratio (PER) for network simulations. The proposed method is shown to require modest additional storage and the runtime is insensitive to the increase in system dimensionalities (e.g., MIMO dimensions, MU dimensions,
\netc.).<\/p>\n

LTE-WiFi Coexistence Studies<\/strong>
\nAbstract<\/strong>: A joint effort with CTTC and Wi-Fi Alliance is underway to simulate coexistence between Unlicensed LTE and Wi-Fi in the\u00a0ns-3 simulator. To accomplish this, functionality has been added to model LTE and Wi-Fi signal interference, and\u00a0standalone improvements have been made to both the LTE and Wi-Fi modules. Duty-cycled LTE transmission is being\u00a0investigated for fair spectrum sharing with Wi-Fi.<\/p>\n

Radar Wi-Fi Coexistence<\/a>\u00a0<\/strong>
\nAbstract<\/strong>: Over the last decade, the increasing demand for high speed wireless connectivity has forced us to confront spectrum scarcity. In 2012, the president mandated that a large swathe of spectrum that was under government control be opened up for opportunistic access by commercial systems. In particular, much of the initial focus has been directed at the coexistence of radar systems with broadband wireless networks.<\/p>\n

In this project, we aim to address this coexistence scenario by mapping an accurate error model generated through sample level link simulations for the ns-3 network simulator to provide a tool for network coexistence simulations. We will study the effects of radar on Wi-Fi systems in detail to establish expected performance as a function of network and radar parameters.<\/p>\n

\n

Current ns-3 Error models & Spectrum Module<\/strong>
\nAbstract<\/strong>: The ns-3 simulator contains detailed models of the Wi-Fi MAC layer which essentially relies on accurate abstraction at the physical layer.\u00a0 Analytical models for physical layer can provide fairly tight bounds for simple scenarios (AWGN channels with single antennas and limited interference), but the industry relies on detailed link-level simulations. we are currently working to set up and conduct more accurate link simulations using commercial simulator for IEEE 802.11n\/ac performance over AWGN and fading channels. Furthermore implementing link-to-system-mapping technique for fading channels that can determine performance using link simulation results of the AWGN channel. <\/span><\/p>\n

Our broader contributions are the link simulation programs themselves which allow others to reproduce and extend the basic tables( error models) that we provide, and flexibility in the ns-3 implementation to allow additional tables to be added over time.\u00a0<\/span>It is envisioned that this road map will enable support for MIMO and further, MU-MIMO, beam-forming etc for Wi-Fi in particular and OFDM PHY in general.<\/span><\/p>\n

For more information, see our technical report on validation of OFDM error models<\/a>.<\/p>\n<\/div>\n

Validation of Wi-Fi network simulation on ns-3<\/strong>
\nAbstract<\/strong>: <\/span>In this work, we provide a more thorough validation of the 802.11a OFDM ns-3 simulation model under a saturating load, and compare with an analytical model of the DCF previously proposed. We provide throughput results for all 802.11a data rates, we explain the mathematical model employed, and make our simulation programs publicly available for others to reproduce.<\/p>\n

Technical Report: Validation of Wi-Fi network simulation on ns-3<\/a><\/p>\n

ns-3 11ax project<\/a><\/p>\n

Interference Modeling with the CMU Wireless Emulator<\/strong><\/a><\/p>\n

Abstract:<\/strong>\u00a0 This research used the CMU Wireless Emulator to improve the NS-3 network simulator interference model by examining different aspects of interference, determining the validity of the current NS-3 implementation, and suggesting alternative implementations that more closely match reality.<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n\n\n\n
Underwater Acoustic Networking<\/td>\n<\/tr>\n<\/thead>\n
Lead:<\/strong> Payman Arabshahi, Sumit Roy<\/td>\n<\/tr>\n
Affiliated Student:<\/strong><\/td>\n<\/tr>\n
Prior Work<\/strong><\/p>\n

Abstract<\/strong>: Underwater acoustics has been a topic of research for decades. However, the idea of deploying networked teams of underwater vehicles for both deep and shallow water ocean exploration is a more recent topic of interest. The Seaglider, developed at the University of Washington, is one such vehicle. FUNLab along with the Applied Physics Lab (APL) are exploring physical and MAC layer protocols to provide robust, low power, efficient networking solutions to the Seaglider.<\/p>\n

The underwater acoustic channel has properties that make it a very difficult medium for communications. For instance, the long propagation delay of sound, multi-path spread of the medium, frequency selective attenuation, shadowing zones, and other factors make this channel extremely hard to characterize. A commonly used approach for determining the acoustic propagation of sound in the underwater channel is to use ray tracing techniques based on Snell\u2019s Law. Members of the FUNLab are investigating ways to statistically characterize the underwater channel using techniques similar.<\/p>\n

MAC protocols in the underwater environment must be designed with different considerations than those in the terrestrial environment. The long propagation delays of sound make carrier sensing and acknowledgment packets impractical. Additionally, autonomous underwater vehicles (AUVs) are extremely energy-constrained. These and other design considerations, including the lack of position information from GPS, necessitates new MAC design for AUV deployment, which is also an ongoing topic of research between the FUNLab and the APL.<\/p>\n

For more information see the UAN Projects page
\n<\/a>For more information on Ocean-TUNE project see the Ocean-TUNE Project page<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n\n\n\n
Software Defined Wireless Networks<\/td>\n<\/tr>\n<\/thead>\n
Lead:<\/strong> Farzad Hessar<\/td>\n<\/tr>\n
Affiliated Student:<\/strong><\/td>\n<\/tr>\n
CampusLink: A WS-based Campus Network<\/strong><\/p>\n

Abstract<\/strong>: A WS campus network reuses WS spectrum on campus and allows multiple hosts to communicate with others inside WS channels. To accomplish that, a MAC layer is needed to allow communication among multiple bladeRFs, and its objective is to catch most parts of 802.11 standard to allow either ad hoc or infrastructure network. In addition to that, a PAWS is required for information exchange between a WS database and a host, and its purpose is to give hosts a list of recommended channels which are selected by an algorithm to achieve the highest overall throughput.<\/p>\n

Spectrum Observatory<\/a><\/strong><\/p>\n

Cityscape project<\/a><\/strong><\/p>\n

Abstract<\/strong>: A collaborative effort between University of Washington, Shared Spectrum Company and Daintree Technologies is resulting in the first metro-scale spectrum observatory \u2013 CityScape. In this work, we provide an overview of the system architecture (both hardware and software components), the novel features that distinguish this from others and the design and operational challenges encountered. For further details, refer to Cityscape Technical report<\/a>.<\/p>\n

UAS Networking<\/strong><\/a><\/p>\n

Abstract<\/strong>: The advances in UAS technology has opened up the opportunity for UAS networks that promise to be easily scalable and deployable in the absence of any infrastructure. This opportunity comes with challenges \u2013 mobility and going from a 2D space to 3D space, to name a few. This project aims to perform analytical studies to provide a better understanding and optimal values of the parameters involved. Simultaneously, a platform is in the works that uses an SDR to transmit packets from the ground to the SDR stationed on the UAV to test out different scenarios.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"excerpt":{"rendered":"

Research 5G Wireless Networks Lead: Sian Jin, Lyutianyang Zhang Affiliated Student: 5G wireless networks aim to be a leap forward in terms of three new application scenarios defined by: enhanced mobile broadband (eMBB), ultra-reliable and low latency communications (URLLC) and massive machine type communications (mMTC). Current research focuses on: Compressed sensing application on channel estimation … <\/p>\n

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