modeling mutual coupling and ofdm system with ...ece.drexel.edu/telecomm/talks/kirsch.pdf · ofdm...
TRANSCRIPT
Modeling Mutual Coupling and OFDM System with Computational Electromagnetics
Nicholas J. KirschDrexel University Wireless Systems
Laboratory
Telecommunication SeminarOctober 15, 2004
Introduction
MIMO CommunicationsMutual Coupling and Local ScatteringMIMO-OFDMMobile Ad-hoc NetworkingConclusions
Modeling MIMO Channels
Effects of scatterers in the near fieldIncorporate effects of OFDMExtend simulations to experimental test bed
Mutual Coupling and Local Scattering
MotivationDetermine the degree of importance of including mutual coupling (MC)Accurately model vehicular mobile ad hoc networks (MANETs) which include local scattering (LS)
Hybrid Computational Electromagnetic Simulation
Near fieldMethod of Moments (NEC, FEKO)
Far fieldRay tracing (FASANT)
Inclusion of mutual coupling and local scattering
Run simulation with isotropic radiation patterns to determine the electric fields and geometric solutions for multipath rays : Eθ(θ) and Eφ(φ)
Inclusion cont’d
Create antenna e-field weight signal components with appropriate radiation pattern value (which includes MC and LS)Sum L the appropriate multipath Ez rays for every pairwise combination of transmit and receive antennas to create the channel matrix H
Inclusion cont’d
( ) ( ) ( ) ( )ϕϕϕϕ ϕ nmmn
weight RETE =),(
( ) ( ) ( ) ( )θθθθ θ nmmn
weight RETE =),(
∑−
=
=1
0
),(,,
L
l
mnlzmn Eh
[ ]MNmnh ×
= ,H
Method of AnalysisSpatial Multiplexing (SM) Capacity - to evaluate spectral efficiency due to multipath richness (without path loss)Beamforming (BF) Capacity –used to compare the effectiveness of antennas in vehicular MANET
SM and BF capacities are used to evaluate which signaling technique is best in a MANET
( )
+=
)(log2 H
HHIHα
ρM
CH
N
( )
+=
)()(
1log2max
2 HHH
αρλ
B
21)(FM
HH =α
Angular spread
Angular Spread – used to understand the multipath richness
20
211FF
−=Λ
∫ −=π
θ θθ2
0
)( depF jkk
Simulations
Inclusion of MC at both ends of the link
4 x 4 MIMO System, ULA
Performance analysis of a vehicular MANET
4x4 MIMO, antennas arranged on each side of vehicle
Radiation patterns2.4 GHz dipole antenna in a λ/2 ULA2.4 GHz patch antenna on the front of a bumper of a vehicleRadiation patterns due to near field effects are shown
Results: Inclusion of MC at both ends of the link
Inclusion of MC at both ends of the link results in greater capacity in both the LOS and NLOS cases.
0.3240Philadelphia - LOS
0.6960Philadelphia - NLOS
Angle SpreadCase
Results: Performance evaluation of a vehicular MANET
Due to the array geometry, some transmitting antennas may not communicate with certain receiving antennas even in a multipath rich environmentA rank deficient channel will have lower spatial multiplexing capacity, but higher beamforming capacity
MIMO-OFDM
Current research on MIMO-OFDM based on statistical channel modelsStatistical channel model: either insufficiently wideband or not suited for the physical environment of interestRay-tracing simulation: site-specific information
Electromagnetic Ray TracingComputational electromagnetic-based simulation technique;ERT is computationally complex and demanding;ERT has been applied to narrowband system so far.
Channel Response Extrapolation
AssumptionsSubcarrier frequencies are not so far apart that the geometric ray solutions remain the same across all subcarriersThe number and type of multipath signal components remain the same from one subcarrier to another
Channel Response Extrapolation
FASANT computes the electric field of each ray
The response Ez,n at carrier fn can be computed by extrapolation
where
mm
fcdj
zdjk
zmz eEeEEπ2
,
−− ==
fcdj
mz
fcdj
znz eEeEE n ∆−−==
ππ 2
,
2
,
mn fff −=∆
SISO Channel Model
Time-varying channel
For a finite bandwidth receiver, some of the rays cannot be distinguished in time. Group them to be a cluster The time-clustered channel is
∑ −
= −= 10
2)(, )(),(
)(Ni i
tfjimz
ideEt ττδτα π
∑ == lp
iimzl tEt
1)(
, )()(β
∑ −
= −= 10 )()(),( L
l ll tt ττδβτα
MIMO Channel Model and Capacity
Input Output relationFrequency response of wideband channel matrix at kth subcarrier
If CSI is not available at the transmitter
If CSI is known at the transmitter: use water-filling to maximize mutual information.
∑−
=−=
1
0)()(
L
ll lnn xHy
∑ −
=−=
1
0/2/2 )( L
lNlkj
lNkj ee ππ HH
( ) ( )∑∑−
=
−
=
+==
1
0
/2/222
1
0detlog11 N
k
NkjHNkj
ntMr
N
kk ee
NMP
NI
NI ππ
σHHI
Simulated Capacity in Ad Hoc network
Center frequency = 2.4GHzNumber of carriers = 64Channel spacing = 312.5KHz
1 2 3
4 5 6
7 8 9
Street
Street
50m 20m
50m
20m
Simulated Capacity
Capacities fluctuate at different subcarriers
0 10 20 30 40 50 608
9
10
11
12
13
14
15
16Capacity vs Subcarrier(Node 7 to Node 3)
Subcarrier
Cap
acity
(bit/
sec/
Hz)
Equal power SNR=15dBEqual power SNR=20dB1D Waterfilling SNR=15dB1D Waterfilling SNR=20dB
0 10 20 30 40 50 606
7
8
9
10
11
12
13
14
15
16Capacity vs Subcarrier(Node 9 to Node 2)
Subcarrier
Cap
acity
(bit/
sec/
Hz)
Equal power SNR=15dBEqual power SNR=20dB1D Waterfilling SNR=15dB1D Waterfilling SNR=20dB
Eigenvalues vs Subcarriers
The larger the eigenvalue an eigenmode has, the more power flows to that modeFrequency diversity achieved
0 10 20 30 40 50 60 702
2.5
3
3.5
4Dominant eigenvalues vs Subcarrier(Node 9 to Node 2)
Subcarrier
Eig
enva
lue
#1
0 10 20 30 40 50 600
0.5
1
1.5
2
Subcarrier
Eig
enva
lue
#2
0 10 20 30 40 50 60 702
2.5
3
3.5Dominant eigenvalue vs Subcarrier(Node 7 to Node 3)
Subcarrier
Eig
enva
lue
#1
0 10 20 30 40 50 60 700
0.5
1
1.5
2
Subcarrier
Eig
enva
lue
#2
Mobile Ad hoc Network
Ad-hoc network MIMO CommunicationsSoftware Defined RadioModular Networking LayerWide bandwidth (10MHz Baseband)
Summary
Demonstrated importance of including MC and LS for design of ad hoc network nodes making use of antenna arraysDeveloped simulation methodology for including these practical electromagnetic effects
Summary cont’d
Fast way to generate channel responses in OFDM system with CEM.This technique is used for simulating MIMO-OFDM Ad Hoc network, which shows both spatial and frequency diversity are achieved.Combine ray tracing channel model with correlation (stochastic) channel model to provide site-specific information as well as numerous channel realizations – Semi-stochastic spatial-temporal channel model.