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