A NEW MODELING & DESIGN APPROACH FOR WIRELESS CHANNELS WITH PREDICTABLE PATH GEOMETRIES

Narayan B. Mandayam

Challenges in Enabling Wireless Data

Wireless Data is uneconomical in cellular like systems

v cents / min of voice = 13v cents/Mbyte of Data

“Free” Bits – the real challenge of the wireless internet Need “Many-Time Many-Where” solutions as opposed to “Any-Time Any-Where”

Small, separated “cells”Low power (~100 mw)Brief connections (~1 sec)Very high bit rate (~1 G bps)Simple infrastructure (LAN on a pole, IP access)Unlicensed bands

Maps and images

Internet access

Music, voicemail,

news

InfostationsA system of sweet spots for “free”

bits

Why New ?

??? Receiver

Transmitter

Cellular systems Infostations - Power (>=1W) - Low power (~100mW) - Low bit rates - Very high bit rates - Connectivity always, - Brief connections (~seconds), everywhere localized-Narrowband - Wideband (e.g., U-NII bands,100MHz) - Large distances (~ km) - Short range (<20m)

Maps and images

Music, voicemail,

news

Modeling ApproachDeterministic

approach(indoor wireless)

Ray-tracing (Valenzuela,

Rappaport, ...)

Stochastic approach(wide-area cellular)

Measurements (Greenstein, COST, Rappaport,

Hata, ...)

Deterministic-plus-stochastic approach

Ray-tracing AND Measurements

• Predictable user behavior (trajectory)• Short-range • No shadowing• LOS always present + Possible scatter

Methodology

1. SCENARIOS (Account for different environments)

2. POSTULATED RESPONSES (Ray-tracing – geometry, antenna patterns, reflection coefficients)

3. MEASUREMENTS (Moving antenna and fixed antenna experiments)

4. CHANNEL MODEL (Refine the postulated response with model of the scatter component)

Infostations Channel Modeling

Scenario 1: 2-ray model - channel almost Gaussian

Scenario 2: 4-ray model - channel almost Gaussian

Scenario 3: Ricean channel - K ~ 10 dB

DomazetovicGreensteinMandayam

Seskar

Measurements

Moving antenna Fixed antenna

• Measure path gain vs. distance• Confirm ray-tracing approach

• Measure time variations• Augment deterministic model with stochastic component

Scenario 1: Open Roadway with Trees

Delay, +

Time-varying scatter

Deterministic component

IN OUT

)t,d(h)t,d(f)t,d(E tttr )t,d(f t

DeterministicStochastic

)t,d(h t

(dt)

(dt) (dt)

dt – ground distancebetween antennas

Base

ant e

nn

a

heig

ht

hb

Mobile

an

tenna

heig

ht

hmLOS

GR

dt

user trajectory - d

dt

])t(costcos[E)t,d(E cc0tr

t

mb1

d

hhtan

t

mb1

dhh

tan

)d(R)(R t

)(g)(g tr

)(g)(g tr

LOS

0rt

d

d)(g)(g

GR

0rtt d

d)(g)(g)d(R

c

dd LOSGR

Scenario 1: Deterministic component model

R – reflection coefficientg – antenna gains

Scenario 1: Stochastic component model

)]dlog(10[6.078P

log10 tt

2

78.0f/1~)f(S

h(dt,t) – zero mean, complex Gaussian with standard deviation [derived from measurements]

Scenario 1: Path Gain vs. Distance

Scenario 2: Roadway with Buildings on the Sides

Delay, +

Time-varying scatter

Deterministic component

IN OUT

Delay,

Delay,

1 1

2 2

3 3

)t,d(h)t,d(f)t,d(E tttr

DeterministicStochastic

)t,d(h t

)t,d(f t

Scenario 2: Path Gain vs. Distance

Scenario 3: Parking Lot

+

Space-varying scatter

Deterministic component

IN OUT

)t,d(h)t,d(f)t,d(E tttr

DeterministicStochastic

)t,d(h t

)t,d(f t

h(dt,t) – zero mean, complex Gaussian with standard deviation changing with user position

user t

raje c to

ry

d

Scenario 3: Path Gain vs. Distance

Ricean process with K factor constant over distance K ~ 10 dB

Refining the Channel Models• Power measurements insufficient to characterize the

stochastic part of the channel• Stochastic part characterization critical for simulation

=> Reasonable assumptions need to be made

1. Power decay profile: “Spike + Exponential” 2. RMS delay spread:

Channels 1 & 2: 5 ns for EXP part

Channel 3: Case I; Low: 2 ns (5ns for EXP) Case II; High: 20 ns (50 ns for EXP) – ref. Linnartz

Infostations Modem Design

OBJECTIVE : To design a low cost (complexity) receiverthat can provide high data rates (order of 100s of Mbps) under different channel conditions within 20m

Two-ray model - channel

Four-ray model - channel

Ricean channel - K ~ 10 dB

Ideal matched filter sufficient for lower order MQAM

SC+Equalization or OFDM+Channel Est. with low complexity required

for higher order MQAM

Ideal matched filter not sufficient

SC+Equalization or OFDM+Channel Est. with higher

complexity required

“BAD” CHANNELS

“GOOD” CHANNELS

PatelMandayamSeskar

Modem: Infostation Proposals

� Matched filter receiver: Channels 1 and 2, low rates only Low implementation complexity

� Low complexity OFDM or SC+DFE Equalization Systems:

Channels 1, 2 and 3: Case I only Channel perfectly known:

� SC slightly better than OFDM systems Channel unknown:

� OFDM systems outperform SC systems 2.5 % training overhead sufficient

� High complexity OFDM systems: All channels 5 % training overhead required

Example ApplicationsExample Applications

Application Walking User(2 mph)

Driving User(30 mph)

DVD (min)(2 Gbytes/ 60 min)

49 3

CD (min)(500 Mbytes/ 74 min)

242 16

Digital Photos(5 Mbytes each)

328 22

Above results for rate 3/4 coded OFDM system with 64-QAM

modulation scheme