FuNLab Projects

Spectrum Observatory for White Space Networks

Lead: Farzad Hessar

Affiliated Student: Chang Wook Kim

Transition of TV broadcasters from analog to digital resulted in a more efficient utility of wireless spectrum in the V/UHF band. This essentially means less wireless spectrum is required for transmission of current TV channels and it leaves behind parts of V/UHF band vacant, known as TV White Space (TVWS). With the permission of FCC, TV white space is now open to unlicensed radio transmitters subject to following FCC rules for protecting TV receivers. This is a great opportunity for examination of cognitive radio networks, which has received a lot of attention in recent years, in a real commercial application.

In this project, we analyze availability of TV white space spectrum over the entire US with a focus on following:

  • How much spectrum is actually opened up in TV white space spectrum?
  • Performance analysis of Cognitive Radio Networks in TV White Space.
  • Critical design parameters for optimum performance of wireless networks in TV white space.

Related Links:


Communications, Estimation and Controls for Smart Distributed Grid

Lead: Yue Yang

The future Smart Grid will include distributed monitoring and state estimation of power system based on the introduction of advanced sensing/computing/communication devices such as Phase Measurement Units (PMU), which can measure voltage phasors. The resulting enhanced visibility of network state will be critical to monitoring of wide area power systems, particularly with the anticipated integration of renewable energy sources and plug-in hybrid vehicles, and the attendant stochastic dynamics.

In this project, we explore various aspects of distributed grid operations involving the deployment of new sensors, communication networks and distributed algorithmic approaches in support of a future Smart Distribution Grid.


Vehicular Networks


Throughout the years, the vehicular safety systems have evolved from passive systems (seatbelt, airbag) to active systems (ABS, EPS). Recent researches show that leveraging the state-of-art wireless networking technology is the next logical step towards a pro-active vehicular safety system. Such pro-active system will provide reliable safety services while concurrently support commercial data service. Vehicle-to-vehicle ad hoc communication and vehicle-to-infrastructure communication are involved. Currently we are investigating:

  • Emergency Warning Message (EWM) propagation MAC protocol in vehicle-to-vehicle ad hoc networks for collision avoidance applications. The stringent latency and reliability requirements are satisfied at all costs.
  • Efficient data dissemination protocol at layer-2 and layer-3. Commercial data service will greatly accelerate the development and deployment of vehicular networks. The success of commercial data service relies on harmonious integration of data acquisition through vehicle-to-infrastructure communication and data sharing through vehicle-to-vehicle communication.

For more information see the VANET Project Homepage


Network Coding and Compressed Sensing

Affiliated Student: Aliasghar Tarkhan

Linear Network coding has received considerable attention in recent years for its potential for achieving the theoretical upper bound (max-flow) of network resource utilization via the introduction of coding concepts at the network layer. It has been shown that with simple distributed linear coding in place of the usual forwarding at intermediate nodes, system throughput can be increased in several canonical network topologies. In this project we investigate novel use of network coding for a different purpose - to locate congested link(s) inside a network through end-to-end measurements at (external) boundary nodes. Current link monitoring schemes suffer from identifiability problems; i.e. they are unable to infer link status in many canonical network topologies. We show that network coding offers the promise of being able to identify congested links in any `logical' network.

According to Nyquist Sampling theorem, a band-limited signal can be reconstructed accurately if the sampling rate exceeds twice the maximum frequency of the signal. In many scenarios, this Nyquist sampling rate cannot be achieved due to hardware limitations. Compressive sensing (CS) is a technique to reconstruct a signal from sub-Nyquist samples, given that the signal is sparse in a known domain. The CS technique is applied to different areas in the field of communications and networking, including cognitive radio, network tomography, radar clutter estimation, and compressive imaging.

For more information click Here


Cognitive Heterogeneous Networks

Affiliated Student: Hossein Safavi

Due to the rapidly increasing demands of wireless bandwidth, available spectral resources become scarce. Moreover, current fixed spectral allocation suffers from underutilization as in the case of TV bands. These are the motivations of Cognitive Radio (CR) network, which is defined as a radio that makes secondary users actively search for the idle channels NOT used by the primary users by scanning the whole spectrum. Our current research mainly focuses on channel modeling, search algorithm analysis and channel detection in CR network. Currently for CR, we are investigating the following:

  • We are studying efficient dynamic spectrum access strategies using Markov Chain and Queuing Theory. The adaptive strategy of scanning for spectral opportunities in a dynamic scenario, where the usage pattern of primary users change is also included in potential directions to extend our work. The modelling of channel state information using correlated Markov model is expected to be utilized to design such a strategy.

For more information see the Cognitive Radio Project Homepage


Underwater Acoustic Networking

PI: Payman Arabshahi, Co PI: Sumit Roy

Affiliated Student: Shwan Ashrafi

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.

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's Law. Members of the FUNLab are investigating ways to statistically characterize the underwater channel using techniques similar.

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.

For more information see the UAN Projects page

For more information on Ocean-TUNE project see the Ocean-TUNE Project page

Advanced Imaging Approaches for Detecting Obscured Objects

Principal Investigator (PI): Sumit Roy, Co PI: Yasuo Kuga

Investigator: Sermsak Jaruwatanadilok and Thomas Chan

This research aims to develop new methods based on the synergy between electromagnetic wave imaging and detection and network techniques to solve a difficult problem of detection and imaging of targets-of-interest in the presence of complex, unstructured, and obscured environments. We are currently investigating:

- Detection and imaging of a target on a random rough surface using correlation function including the analysis on probability of detection and probability of false alarm performance

- Imaging of a target in cluttered environments using passive coherent radar and angle-of-arrival algorithm

For further details, please refer to this link

For more information see the Advanced Imaging of Obscured Objects Project Homepage


RFID Sensor Networks

Principal Investigator (PI): Sumit Roy, Co PI: Vikram Jandhyala

Affiliated Student: Colby Boyer

RFID system deployment suffers from asymmetry of the uplink and downlink ranges. Passive RFID tags are traditionally assumed to be downlink limited since typical tag sensitivity is considerably poorer than reader sensitivity, due to stringent power limitations. The goal of our research is to demonstrate that judicious choice and use of IC impedance for backscatter modulation will be needed to simultaneously maximize tag read and write ranges as passive tag designs improve with technology scaling. In order to boost bit error rate, multiple RFID tags are now being integrated into a single system. However, depending on positioning and orientation of tags with respect to each other, such systems suffer from crosstalk between multiple tags that can negatively impact uplink communication and degrade bit error rate. Currently we are investigating:

-Range maximization of passive RFID tags and the uplink versus downlink range trade-off with technology scaling. This work investigates the impact of impedance modulation indices on the read/write range for passive RFID tags. Using a link budget analysis leveraged by EM simulation, we put forth the choice of ASK impedance modulation indices that maximize the operating range as a function of key system parameters - notably the tag sensitivity and bit error rate at the reader.

- Crosstalk effects between multiple RFID tags that are integrated on the same object to boost bit error rate. Our work aims to study the negative impact of mutual coupling between tag antennas since it can directly impact backscatter from each tag leading to degraded bit error rate at the reader.

For more information see the RFID Sensor Networks Project Homepage


Improvements to NS-3 Simulator

Lead: Hossein Safavi, Benjamin Cizdziel
Past Contributors: Trevor Bosaw

Cognitive Phy + QP-CSMA

We aim to improve interference modeling in the ns-3 simulator to allow for the usage of a spectrum-aware channel. The aim of the work is to enhance the basic functionality in ns-3 and lay the groundwork for network simulations encompassing cognitive networks, whitespace devices, and dense WiFi networks. In that vein, we have made modifications to ns-3 for basic spectrum sensing operation and created a simplified MAC algorithm following the ideas in:
“QP-CSMA-CA: A Modified CSMA-CA-based Cognitive Channel Access Mechanism with Testbed Implementation,” Proc. IEEE DySPAN, Seattle, WA, 2012.
Cognitive Phy + QP-CSMA/CA

802.11 Interference Modeling in NS-3 Simulator

Prior Work by Trevor Bosaw

Modeling of interference in 802.11b wireless LAN is not well-defined in NS-3 Simulator. We are enhancing the NS-3 interference model of 802.11b WLAN by introducing capture effects, both in the preamble and payload sections of the data packet, while validating our results using CMU Emulator and Simulink Communication blocksets.

For more information see the 802.11b Interference Modeling in NS-3 Simulator Project Homepage

TV Transmitter Model

We have developed a television transmitter PHY model 'TvSpectrumTransmitter' that has been part of the ns-3 simulator since release ns-3.23. The model enables transmission of realistic TV signals to be simulated using the ns-3 Spectrum module and can be used for interference modeling. Configurable settings are provided to make the TV transmitters highly customizable, along with a helper API called 'TvSpectrumTransmitterHelper' to assist users in setting up simulations.