UWB Channels: Time-Reversal Signaling NEWCOM, Dept. 1 Meeting Paris, 13 May 2005 Erdal Arıkan Bilkent University Ankara, Turkey.

Slides:



Advertisements
Similar presentations
UWB Channels – Capacity and Signaling Department 1, Cluster 4 Meeting Vienna, 1 April 2005 Erdal Arıkan Bilkent University.
Advertisements

1 Small-scale Mobile radio propagation Small-scale Mobile radio propagation l Small scale propagation implies signal quality in a short distance or time.
Comparison of different MIMO-OFDM signal detectors for LTE
Noise on Analog Systems
ISWCS’06, Valencia, Spain 1 Blind Adaptive Channel Shortening by Unconstrained Optimization for Simplified UWB Receiver Design Authors: Syed Imtiaz Husain.
Propagation Characteristics
Amir-Hamed Mohsenian-Rad, Jan Mietzner, Robert Schober, and Vincent W.S. Wong University of British Columbia Vancouver, BC, Canada {hamed, rschober,
M. Stemick, S. Olonbayar, H. Rohling Hamburg University of Technology Institute of Telecommunications PHY-Mode Selection and Multi User Diversity in OFDM.
Mobile Radio Propagation - Small-Scale Fading and Multipath
3F4 Optimal Transmit and Receive Filtering Dr. I. J. Wassell.
Ultra-Wideband Part II David Yee. Overview a.k.a. impulse radio because it sends pulses of tens of picoseconds( ) to nanoseconds (10 -9 ) Duty cycle.
Ger man Aerospace Center Transfer Chart Analysis of Iterative OFDM Receivers with Data Aided Channel Estimation Stephan Sand, Christian Mensing, and Armin.
Wireless Communication Channels: Small-Scale Fading
Matched Filters By: Andy Wang.
DSP Group, EE, Caltech, Pasadena CA IIR Ultra-Wideband Pulse Shaper Design Chun-yang Chen and P.P. Vaidyananthan California Institute of Technology.
Amir-Hamed Mohsenian-Rad, Jan Mietzner, Robert Schober, and Vincent W.S. Wong University of British Columbia Vancouver, BC, Canada {hamed, rschober,
Mohammad Tamim Alkhodary Ali Al-Saihati
ECE 480 Wireless Systems Lecture 14 Problem Session 26 Apr 2006.
Receiver Design for Ultrawideband PPM Communication Systems Vijay Ullal Clemson University July 29, SURE Program.
Formatting and Baseband Modulation
Modulation, Demodulation and Coding Course
Lecture II Introduction to Digital Communications Following Lecture III next week: 4. … Matched Filtering ( … continued from L2) (ch. 2 – part 0 “ Notes.
Digital Communication I: Modulation and Coding Course
Dept. of EE, NDHU 1 Chapter Three Baseband Demodulation/Detection.
Rake Reception in UWB Systems Aditya Kawatra 2004EE10313.
EE 6332, Spring, 2014 Wireless Communication Zhu Han Department of Electrical and Computer Engineering Class 3 Jan. 22 nd, 2014.
The Wireless Channel Lecture 3.
Abdul-Aziz .M Al-Yami Khurram Masood
Doc.: n-proposal-statistical-channel-error-model.ppt Submission Jan 2004 UCLA - STMicroelectronics, Inc.Slide 1 Proposal for Statistical.
Baseband Demodulation/Detection
EE 6331, Spring, 2009 Advanced Telecommunication Zhu Han Department of Electrical and Computer Engineering Class 7 Feb. 10 th, 2009.
1 Chapter 1 Introduction to spread-spectrum communications Part I.
Doc.: IEEE /0553r1 Submission May 2009 Alexander Maltsev, Intel Corp.Slide 1 Path Loss Model Development for TGad Channel Models Date:
Doc.: IEEE /270 Submission July 2003 Liang Li, Helicomm Inc.Slide 1 Project: IEEE P Working Group for Wireless Personal Area Networks (WPANs)
Adaphed from Rappaport’s Chapter 5
Chapter 6. Effect of Noise on Analog Communication Systems
Chapter 4: Baseband Pulse Transmission Digital Communication Systems 2012 R.Sokullu1/46 CHAPTER 4 BASEBAND PULSE TRANSMISSION.
EE 3220: Digital Communication
Digital Communications Chapeter 3. Baseband Demodulation/Detection Signal Processing Lab.
OFDM Based WLAN System Song Ziqi Zhang Zhuo.
September, 2005 Doc: IEEE a Qi, Li, Umeda, Hara and Kohno (NICT) SlideTG4a1 Project: IEEE P Working Group for Wireless Personal.
3: Diversity Fundamentals of Wireless Communication, Tse&Viswanath 1 3. Diversity.
Doc.: IEEE /287 Submission July 2002 Intel Research and Development Slide 1 Project: IEEE P Working Group for Wireless Personal Area Networks.
Outline Transmitters (Chapters 3 and 4, Source Coding and Modulation) (week 1 and 2) Receivers (Chapter 5) (week 3 and 4) Received Signal Synchronization.
Baseband Receiver Receiver Design: Demodulation Matched Filter Correlator Receiver Detection Max. Likelihood Detector Probability of Error.
FMT Modulation for Wireless Communication
1 On the Channel Capacity of Wireless Fading Channels C. D. Charalambous and S. Z. Denic School of Information Technology and Engineering, University of.
1 WELCOME Chen. 2 Simulation of MIMO Capacity Limits Professor: Patric Ö sterg å rd Supervisor: Kalle Ruttik Communications Labortory.
Single Correlator Based UWB Receiver Implementation through Channel Shortening Equalizer By Syed Imtiaz Husain and Jinho Choi School of Electrical Engineering.
Impulse Response Measurement and Equalization Digital Signal Processing LPP Erasmus Program Aveiro 2012 Digital Signal Processing LPP Erasmus Program Aveiro.
Lecture 24-27: Ultra Wideband Communications Aliazam Abbasfar.
Ultra WideBand Channel Models for IPS Choi JeongWon Wireless and Mobile Communication System lab. Information & Communication Engineering dept. Information.
EEE 441 Wireless And Mobile Communications
Small-Scale Fading Prof. Michael Tsai 2016/04/15.
Mobile Radio Propagation - Small-Scale Fading and Multipath
الخبو صغير المقياس أو(المدى)
Shadowing.
1.) Acquisition Phase Task:
Basics of Small Scale Fading: Towards choice of PHY
PHY Design Considerations for af
Outline Introduction Signal, random variable, random process and spectra Analog modulation Analog to digital conversion Digital transmission through baseband.
Lecture 1.30 Structure of the optimal receiver deterministic signals.
Principios de Comunicaciones EL4005
Channel Estimation Field for EDMG OFDM PHY in 11ay
Wireless Communication Channel Capacity
<month year> doc.: IEEE < e>
Wireless Communication Channel Capacity
UWB Receiver Design Simplification through Channel Shortening
On the Design of RAKE Receivers with Non-uniform Tap Spacing
Malong Wang Ting-change
Presentation transcript:

UWB Channels: Time-Reversal Signaling NEWCOM, Dept. 1 Meeting Paris, 13 May 2005 Erdal Arıkan Bilkent University Ankara, Turkey

2 Outline Time-reversal signaling UWB channel model Signaling and achievable rates for the UWB channel –Fixed power –Time reversal –Water filling Simulation results Conclusions

3 Time Reversal Signaling If channel is reversible, h RT (t) = h TR (t). R receives h TR (-t)  h TR (t), which is likely to be peaky. C receives h TR (-t)  h TC (t), which is unlikely to be peaky if C is sufficiently far from R. h XY (t) likely to have low coherence in time and space for high delay-bandwidth product channels, such as the UWB channel. 1) R sends an impulse 3) T transmits h RT (-t) T R 2) T receives h RT (t) C 4) R receives h RT (-t)  h TR (t) 5) R receives h RT (-t)  h TC (t)

4 Correlations of channel responses

5 UWB Channel (FCC 2002) Frequency range: 3.1–10.6 GHz Radiated power: < dBm/MHz Min. Bandwidth: 500 MHz Bandwidth > 20% of center frequency

6 UWB Channel Indoor Emissions Limit GPS Band GPS Band -41 dBm/MHz 7.5 GHz

7 Maximum power emission: –41.3 dBm/MHz  7.5 GHz = 0.56 mW.  UWB systems are not energy limited. UWB Energy

8 Spread or not? With fixed transmitter energy: Spreading the energy uniformly over a wide band  deterioration of channel estimates  collapse of achievable rates (Médard-Gallager, Telatar-Tse, Subramanian- Hajek) In the UWB model, transmitter energy is allowed to increase as more bandwidth is used  there is no collapse of achievable rates  use all available bandwidth if possible.

9 UWB Channel Model The channel is modeled as a linear filter with additive white Gaussian noise. The channel impulse response follows the Saleh- Valenzula model. + h(t) x(t) y(t) z(t) s(t)

10 Saleh-Valenzula Model for UWB The channel impulse response is modeled as –X lognormal shadowing gain –L number of clusters –T l delay of cluster l –k index over rays within a cluster –  k,l excess delay of ray l in cluster k –Details in Report no r0P80215 (

11 Model Characteristics ParameterValue (CM1) Line of SightYES Range (m)0-4 Coherence time (  s) 200 Mean excess delay (nsec)4.9 RMS delay (nsec)5 No. multipath components within 10 dB of peak component, NP 10dB 13.3 No. paths capturing 85% of energy, NP(85%) 21.4 Channel energy mean (dB)-0.5 Channel energy std (dB)2.9

12 Sample of a channel impulse response

13 Frequency Domain Channel Model An OFDM-like channel with subchannels –Z i ~ CN(0,N o ) are independent noise –In each use of the vector channel, a new set of A i are chosen from a fixed distribution –K= W T s where W=RF bandwidth, T s = signaling period –Input constraint: E[ X i 2 ]  E s for each i –Assumption: Transmitter and receiver have perfect knowledge of the channel coefficients A i

14 Perfect Channel Knowledge Assumption For the UWB channel, typical values are: –Coherence time T c  100 – 200  s. –Impulse response duration T d  50 – 100 ns  The receiver can estimate the channel impulse response with negligible overhead and feed it back to the transmitter. The signaling period should be chosen so as to satisfy T d << T s <<T c.

15 Achievable Rates for the Given Channel Model For any channel input X= (X 0,..., X K-1 ) with a given covariance C X, the achievable rate is bounded by where Y=(Y 0,..., Y K-1 ) is the channel output and A = diag(A 0,..., A K-1 ). Equality holds iff X ~ CN(0,C X ).

16 Fixed Power Allocation Suppose each carrier is encoded independenly with X k ~ CN(0,E s ), k=0,...K-1. Then, the achievable rate is given by This signaling scheme does not require the transmitter to know the channel transfer function.

17 Water Filling SolutionWater-Filling WF maximizes the achievable rate by optimum power allocation. In WF, the channel inputs X k are independent Gaussian with optimal powers. The achievable rate by WF is given by Here, total power is constrained not the power spectral density. Solution usually violates the UWB power constraint.

18 Pulse Amplitude Modulation (PAM) Samples of tranmitted signal: p k = pulse samples, c k = data m samples r pulses per signaling period K = mr samples index is mod K to simplify FD description

19 PAM in Frequency Domain In frequency domain, PAM is given by Note that C i is is periodic with period r.

20 Time-Reversal: A form of PAM In TR signaling, X i =C i A i *, i.e. transmitted pulse is the time-reversed channel impulse response. Then Here, C 0,...,C r-1 can be chosen independently, but the rest are determined by periodicity. In this study, we take C 0,...,C r-1 independent Gaussian with C 0 ~ CN(0,  i 2 ) subject to

21 Time Reversal Achievable Rates The achievable rate by TR is given by –m = # samples between successive pulses –r = # pulses per frame –Frame length K=mr –m=1 maximizes C TR, but also ISI

22 TR with Fixed Power C 0,...,C r-1 are independent Gaussian with The achievable rate is then

23 Simulation Results IEEE Channel Model 1 Bandwidth: GHz 8192 carriers

24 Time Reversal + Water Filling

25 Simulation Results IEEE Channel Model 1 Bandwidth: GHz 8192 carriers

26 Achievable Rates at Low SNR As SNR = E s /N 0  0, WF power allocation becomes more frequency selective compared to FP and TR/FP. Under the assumption carrier gains are i.i.d. A k ~ CN(0,1), it can be shown that

27 Achievable Rates at High SNR At the SNR increases, FP allocation becomes near optimal: TR deviates from optimal as the SNR increases: where m is the number of samples between successive TR pulses.

28 Power Allocation Against Channel Opaqueness Allocated power Carrier no.

29 Power Allocation: SNR = 10 dB E s /N 0 = 10 dB Power constraint TR grossly violates power constraint

30 Power Allocation: SNR = 0 dB E s /N 0 = 0 dB Power constraint TR violates power constraint

31 Power Allocation: SNR = -10 dB E s /N 0 = -10 dB TR & WF violate power constraint

32 Power Allocation: SNR = -20 dB WF violates power constraint E s /N 0 = -20 dB

33 Conclusions Fixed power allocation is the only power allocation method consistent with the UWB specification. WF may achieve significantly higher rates than FP but they does so by violating the power spectral density constraint, especially at low SNR. The rate deficiency of TR/FP at low SNR can be fixed by TR/WF which combines TR with WF. At high SNR TR/WF and TR/FP have similar performance. TR should be used only at medium to low SNR and if possible in combination with WF.

34 Other problems Multi-user power allocation: –Centralized algorithm with full knowledge of all channels –Comparison of achievable rates Channel estimation problems