CHARACTERIZATION PRESENTATION ANAT KLEMPNER SPRING 2012 SUPERVISED BY: MALISA MARIJAN YONINA ELDAR A Compressed Sensing Based UWB Communication System 1

Content 2 Background  UWB – Ultra Wideband  Project Motivation  Compressed Sensing Project overview  Project Goals  Project Tasks Timetable  Current State  Project Schedule

UWB A technology for transmitting information in bands occupying over 500 MHz bandwidth. Used for short-range communication Very low Power Spectral Density 3

UWB - Advantages Useful for communication systems that require:  High bandwidth  Low power consumption  Shared spectrum resources 4

UWB - Applications In communications:  High speed, multi-user wireless networks.  Wireless Personal Area Networks / Local Area Networks  Indoor communication 5

UWB - Applications Radar  Through-wall imaging and motion sensing radar  Underground imaging Long distance, Low data rate applications  Sensor networks  High precision location systems 6

Project Motivation The problem:  The UWB signal has very high bandwidth, and therefore the UWB receiver requires high-speed analog-to-digital converters.  High sampling rates are required for accurate UWB channel estimation. 7

Project Motivation The proposed approach relies on the following UWB signal properties:  The received UWB signal is rich in multipath diversity.  The UWB signal received by transmitting an ultra- short pulse through a multipath UWB channel has a sparse representation. 8

Compressed Sensing 9 The main idea:  A signal is called M-sparse if it can be written as the sum of M known basis functions:

Compressed Sensing 10  An M-sparse signal can be reconstructed using a few number of random projections of the signal into a random basis which is incoherent with the basis in which the signal is sparse, thus enabling reduced sampling rate. Where Φ is the random projection matrix (measurement matrix).

Project Goals We wish to build a simulation environment for an UWB communication system with compressed sensing based channel estimation. The simulation environment will be used to compare different compressed sensing strategies. 11

Simulation Environment 12 Block-Diagram of the system: Signal Generator Multipath Channel Detection Channel Estimation To be implemented according to IEEE a standard Correlator Based Detector/ Rake Receiver

Project Tasks Phase 1 - Simulate the system and perform the channel estimation. Performance parameter: MSE of the estimation error as a function of the number of measurements. Phase 2 - Simulate signal detection methods: correlator-based detector and the RAKE receiver. Performance parameter: BER vs. input SNR for different sampling rates and number of pilot symbols. 13

Project Tasks Phase 3- Compare the MSE and BER performance for the different sampling schemes: the randomized Hadamard scheme, Xampling method, and the random filter. Phase 4 -Compare the MSE and BER performance for the different sampling schemes and the reconstruction algorithms (e.g., OMP, eOMP, and CoSaMP). 14

What has been done Studying the theoretical background and some of the different algorithms to be implemented. Beginning of implementation of the simulation environment for the channel estimation phase. 15

Currently: Phase I – Channel Estimation 16 Block-Diagram of the process: Signal Generator Multipath Channel Analog pre- processing A/D Conversion Reconstruction Algorithm To be implemented according to IEEE a standard Randomized Hadamard Scheme/ Random Filter Variants of the MP algorithm

Schedule Phase I – 2-3 weeks Phase II – 2-3 weeks Phase III + IV – 2-3 weeks 17

18 Thank You!