LTE Physical Layer Transmission Mode Selection Over MIMO Scattering Channels

Dissertation Defense1
LTE Physical Layer Transmission Mode Selection
Over MIMO Scattering Channels
In the Name of Jesus-Christ（耶稣上帝已经做了我需要的）
Name: Illa Kolani
Major: Electromagnetic Field and Microwave Technology
Professor Advisor :Jie Zhang
Professor Assistant : Gao zehua

Overview
2.Multi Ring Scattering Channel Model
1. MIMO Channel Modeling
5. Contributions and Suggestions
3. An Adaptive MIMO Switching for LTE Downlink
LTE Physical Layer Dynamic Transmission Mode Selection

MIMO Channel Modeling
Although LTE networks systems profits from recent advanced transmission
techniques as MIMO systems, it encounters particularly two mains challenges:
 MIMO channel Modeling or MIMO channel estimation .
 An Optimal Dynamic MIMO transmission modes switching following the
variation of MIMO Channel

Set up the Space-Time and Frequency correlation function rkl,qs(∆t ,∆f)
a) Parameters Taken into account
-Scattering environment -mobility of the mobile - multipath
-antenna spacing d, D
b) Correlation Function computation method
 Two paths hkl (t, f) and hqs (t+ ∆t ,f+ ∆f )
 rkl,qs(∆t,∆f)= hkl (t) . hqs (t+∆t, f+ ∆f )*
Set up the evolution of rkl,qs(∆t,∆f) in time domain
 In space domain  In Time domain t  In Frequency domain f
General framework

3GPP MIMO Channel Model for LTE Extended from the SCM
 
1
( )
1 2
kl,qs
d
r f
j f τ
 
 
( , )klh t f 
  
  
  
, , ,
, ,
1
, ,
exp sin
exp sin
exp cos
s n m AoD n m
M
u n m AoA
m
n m AoA v
j kd
jkd
jk t


 

      
 
 
 
 
 

v
( )nP f
M


Complexities Lacks of 3GPP LTE MIMO Channel model
 The model is defined for fixed Scattering Cell Environment A,B,C and D
 The model system assumed a single-bounced waves.
 Inaccuracy of the Uniform PDF used of time-delays
 The Model is not suitable to implement a MIMO TM Switching .
 For m path tending infinite the model system turns to the well-known
One-Ring Scattering Channel (1)
3GPP MIMO Channel Model for LTE

α
re
Le
k
q
l
s
An arbitrary path
Base Station
Scattering Ring
Mobile Station
(1)One-Ring Scattering MIMO channel
 Describing only Space-time
correlation function
 Fails to exhibit The frequency
correlation function
3GPP MIMO Channel Model for LTE
-α= re/Le is the beamwidth where Le is the distance between base station and the mobile station
-therefore when the mobile is moving, the channel can experience different beamwidth realization
K,q; s,l denotes the range of each pair of antenna in the respective array

A Novel Frequency Correlation Function
Motivations
 in NLOS ,waves may undergoes multiple bounces scattering before
reaching the Mobile Station.
 The Uniform PDF of time-delays or waves of the 3GPP is not in
accordance with experimental results
Multi Ring Scattering Channel model

α
re
Le
k
q
l
s
An arbitrary path
Base Station
Scattering Ring
Mobile Station
(1)One-Ring Scattering Model (2)Our Channel Model
(1) Waves emitted from the base station reaches a local scattering surrounding the mobile station
(2) Waves emitted from the base station undergoes multiple bounces before reaching a local scattering surrounding
the base station
Base StationBase Station
Scattering Ring
Complex medium of scatterers
distributed randomy
Scatterer m
m
Scatterer ii
m

Mathematical Formulation
 (1) One-ring scattering MIMO
 (2) Our MIMO Channel modeling
 (3) Difference between the two systems
 Waves in (2) are delayed with average delay τi and attenuated with
average attenuation Ci.
 the PDF P(τi) of time-delays in (2 ) is needed
 2
2* *
kl,qs i il iq ik is d i i i i i ir ( t, f C a a b b .exp j2π( f cos( - ) t j . fτ P( )P(τ )d dτ          
 * *
kl,qs il iq ik is d i i ir ( t a a b b .exp j2π( f cos( - ) t P( )d      

Which PDF P(τi) should be used ?
 A Uniform PDF as in the 3GPP MIMO channel Model?
 Experimental results suggests an exponent PDF of delays
 A Gaussian PDF
• Physically acceptable
 A uniform PDF
• Physically inacceptable!!!
• Infinite(∞) time-delays inexistent
   22
0ic' τ τ
i
c'
p τ e
π
 

Gaussian PDF

Novel frequency correlation function
 The novel frequency correlation
 Main Contributions of our Novel correlation function
 More flexible than the 3GPP correlation form
 Calibration functionalities with three parameters τo, C’, τd (1/DFd )
 Wideband channel Bandwidth calibrations
  2
2 2
2 22d o /
kl,qs
jΔf τ c' τ /c'
Δf c'r ( f e e
 

  
 
1
( )
1 2
kl,qs
d
r f
j f τ
 
 

Simulation results
 Assuming τo =0, simulation result show
different form of frequency correlation
with different bandwidths.
 In Practical circumstance ,parameters can
be calibrated in order to match a given
power density function
 The correlation Function can
be used in LTE performance
analysis.
Novel Frequency correlation function

Application to LTE system : General Setting and simulations
Frequency Spacing Δf Antenna
configurations
TMs Channel characteristics
15KHz 2x2 MIMO, 4x2 MIMO TM2,TM3,TM4 Δfd =100 KHz , C=1.e5
Performances of LTE TM are affected in frequency selective channel in comparison with non-
frequency selective channel(Uncor) . The channel should be coded by appropriate Space-frequency
coding

Note
 (1) The space-time and frequency correlation based on the one-ring
scattering channel is rkl,qs(∆t, α)
rkl,qs(∆f) is our New frequency correlation.
 (2) Set up the Evolution of the space-time frequency correlation in the Cell
 kl,qs 0 d
d D d
r t,α exp j2π( s l ) J 2π ( q k ) α( s l ) f t
λ λ λ
   
          
   

The Evolution of the Correlation Function of The Cell MIMO Channel
Motivations
 The 3GPP MIMO channel fails to describe the evolution of Cell MIMO
channel following the mobility of the MS
 Provide the evolution of our proposed rkl,qs(∆f, ∆t, α) with the motion of
the mobile(time ).
 Implement a LTE Downlink Dynamic Transmission Mode selection
Methodology

Methodology
 Split the LTE cell coverage zone into Multiple Sectors environment
 Split Each Sector into Multiple One-ring Scattering Channels
α1 αnα2
α1
S1
S2
Sm
Base station
Cell coverage
MS moving

 kl,qs n 0 n d
d D d
r t,α exp j2π( s l ) J 2π (q k ) α ( s l ) f t
λ λ λ
   
          
   
Methodology
 Adopt the One-ring Scattering Channel as Channel realization rkl,qs(∆f, ∆t, αn)
.
 Each sector as then it own MIMO channel correlation function
 Experimental Results about the Beamwidth αn seen at the base station
 Following experimental results of authors, αn =[tang(0o ) tang(15o ) ]
kl,qsr
 kl,qs n kl,qs n nr d .r α dα 

.
Cluster model and Gaussian model of MRSC
Two models of MRSC for each sector are investigated
 Cluster Model dn ( uniform )
 Each sector consists of multiple clusters of MRSC
 Each cluster consists of continuous variation of On-
ring Scattering
 Gaussian Model
with α1 < α2 < .. αmax
1
1
=n
max
d
α -α
 22
0
2 nc α α
n
.c
d e
π
 


 In cluster Model
 In Gaussian Model
 Transmit, receive and cross correlation q≠k , l=s; q=k , l ≠ s; q≠k ,q≠k
 
d
j2π( s l )
λ
kl,qs max 0 d 2k+1 max
max k=0
λ D(q k ) 2π
r t,α e J 2π f t J d(s l )α
2πd(s l )α λ λ
       
                

With conditions
q = [1, nt]; k = [1, nt];
l = [1, nr]; s = [1, nr ]
   
 
 
 
 
1
2
0 0 0 2 2
0
11 2
2
m
j2π( s l )
kl,ks 0 d m
m
mπ
r t,α e .J 2πf t . J d(s-l )α
λπ .c m!



  
    
 


Kronecker Representation of MIMO MRSC Channel
 Fast fading fd. ∆t ≠0 : MRSC does not respect Kronecker representation
 Slow Fading fd. ∆t =0 : MRSC respects Kronecker requirement
Cross-correlation = transmit correlation X receive correlation
Application of MRSC to LTE performances Analysis

Capacity and BLER Performances Analyses: General Setting
LTE General Parameters MRSC Cluster model MRSC Gaussian Model
 OFDM channel Subcarrier=15Khz
 Bandwidth, Bw=1.4MHz
 Working Frequency, F=2GHz
 Antenna Configuration:4x2
MIMO,2x2 MIMO,2x1 MIMO
 Channel Quality Indicator,
CQI_index=7
 Transmission Modes:
TM2(OLSM rank1)
TM3(OLSM rank2)
TM4(CLSM rank2)
TM6(CLSM rank 1)

-5 0 5 10 15 20 25 30
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Rank 1: Cluster model(C) versus Gaussian model(G)
Capacity[Mbps]
SNR [dB]
CLSM rank1 2x1c
CLSM rank1 2x1c
OLSM rank1 2x2c
OLSM rank1 4x2c
OLSM rank1 4x1c
CLSM rank1 4x1c
OLSM rank1 2x1G
CLSM rank1 2x1G
OLSM rank1 2x2G
OLSM rank1 4x2G
OLSM rank1 4x1G
CLSM rank1 4x1G
-5 0 5 10 15 20 25 30
0
0.5
1
1.5
2
2.5
3
Capacity[Mbps]
SNR [dB]
OLSM rank1 2x1
OLSM rank1 2x2
CLSM rank1 2x1
CLSM rank2 2x2
OLSM rank2 2x2
OLSM rank1 4x2
OLSM rank2 4x2
CLSM rank2 4x2
Capacity Rank1TM: Gaussian vs Cluster model Capacity Rank2 TM: Gaussian vs Cluster model
 We note that, in terms of bit error rate and capacity, rank 2 transmissions in Gaussian MIMO channel
model performs higher than in Cluster MIMO model. This may be explained by assuming that in Cluster
model correlations are relatively high.

-5 0 5 10 15 20 25 30
10
-3
10
-2
10
-1
10
0
Rank 1:Cluster model(C) versus Gaussian model(G)
BLER
SNR [dB]
CLSM rank1 2x1c
CLSM rank1 2x1c
OLSM rank1 2x2c
OLSM rank1 4x2c
OLSM rank1 4x1c
CLSM rank1 4x1c
OLSM rank1 2x1G
CLSM rank1 2x1G
OLSM rank1 2x2G
OLSM rank1 4x2G
OLSM rank1 4x1G
CLSM rank1 4x1G
-5 0 5 10 15 20 25 30
10
-3
10
-2
10
-1
10
0
BLER
SNR [dB]
OLSM rank1 2x1
OLSM rank1 2x2
CLSM rank1 2x1
CLSM rank2 2x2
OLSM rank2 2x2
OLSM rank1 4x2
OLSM rank2 4x2
CLSM rank2 4x2
BLER Rank1 TM: Gaussian vs Cluster model BLER Rank2TM : Gaussian vs Cluster model
 This assumption is in accordance with what we observe that CLSM rank1 (4x1 MIMO) performs well in Cluster
model than in Gaussian model. In fact CLSM rank 1 takes advantage of high correlations in cluster channel
model.
 Correlation decreases the performances of TM

-5 0 5 10 15 20 25 30
0
0.5
1
1.5
2
2.5
3
Rank 2: Cluster model(c) versus Gaussian model(G)
Capacity[Mbps]
SNR [dB]
CLSM rank2 2x2c
OLSM rank2 2x2c
OLSM rank2 4x2c
CLSM rank2 4x2c
CLSM rank2 2x2G
OLSM rank2 2x2G
OLSM rank2 4x2G
CLSM rank2 4x2G
-5 0 5 10 15 20 25 30
0
0.5
1
1.5
2
2.5
3
Capacity[Mbps]
SNR [dB]
OLSM rank1 2x1
OLSM rank1 2x2
CLSM rank1 2x1
CLSM rank2 2x2
OLSM rank2 2x2
OLSM rank1 4x2
OLSM rank2 4x2
CLSM rank2 4x2
Figure5.7 Capacity : MRSC model Figure5.8 Capacity : One-Ring Scattering model
 MRSC can be used also for fixed mobile station .In this case, the simulation results shows that Gaussian model of
MRSC behave similar to the one-ring scattering channel when we assume the beamwidth αo = α.
 Therefore, we may assume that the Gaussian model is more accurate.
 However TM 6performance is increase.
 Gaussian Model of MRSC is more accurate

Note
 Each sector has its own MIMO channel characterized by the beamwidth
 αmax in Cluster model of MRSC
 αo in Gaussian model of MRSC
 The beamwidth αo / αmax is varying
 Each Cell MIMO channel evolution is performed by the variation of sector MIMO
Channel.
 LTE MIMO TM Switching or Selection can be now performed over the variation of
each cell MIMO channel evolution .

An Adaptive MIMO Switching for LTE Downlink

Motivations
 Problematic of a LTE downlink TM Switching
 There is no deep study on the subject
 Telecommunication operators implement only a single TM.
 Lacks of LTE feedback CQI, RI, PMI in implementing a TM switching
 LTE TM performs differently in Different correlation Environments

TM1
TM2
TM3
TM4
TM8
TM5
TM6
TM7
BS
Problematic of LTE Downlink TM switching
?
 How to implement the TM switching?
 Which Suitable MIMO Channel to be
used ?
 MIMO Multi Ring Scattering Channel
Model
 TM1 implements a SISO
 TM2 implements OLSM rank 1 or OSTBC
 TM3 implements OLSM rank2 or SM(CDD)
 TM4 implements CLSM rank2 or SM rank2
 TM5 implements MU-MIMO
 TM6 implements CLSM rank1 or SM rank1
 TM7 implements Beamforming rank1
 TM8 implements Beamforming rank 2
ms

TM2,TM6,TM7
Rank 1
TM
high
correlation
levels
TM3,TM4,TM8
Rank 2
TM
Low
correlation
levels
SISO or
TM1
case of
bad MIMO
channel
Spatial Correlation
Performances of LTE TM following spatial correlations levels
αo1
αo2
αo3
S1
S2 S3
 01kl,ksr t,α
 02kl,ksr t,α
Cell
coverage
Base station
Sector Channel
Multi –Ring Scattering Channel Model
Motion of the mobile in different spatial
correlation levels
0
1

Impact of the amount of correlation on the DMT
Methodology for Delimitation correlations levels
 What is low, medium, high spatial correlation?
 Take into account MIMO signal processing(SP) performance characteristics
 Capacity-error rate tradeoff : Diversity-multiplexing tradeoff gs =f(gd)
 Take into account Impact of correlations on MIMO SP performance
 Correlations measure: Amount of correlation
 Impact of amount of correlation on DMT gs =f(gd)
 gs =f(Ψntnr ,gd )
t r
t r
*
n n kl,qs kl,qs
t r t r k,q=1s.l=1
nn
1
ψ r r
n n (n n 1)
  

.
DMT of Rank2 TM DMT of Rank 1 TM
0
0.2
0.4
0.6
0.8
1
0
0.5
1
1.5
2
0
0.5
1
1.5
2
AMOUNT OF CORRELATION
MULTIPLEXING GAIN
DIVERSITYGAIN
0
0.2
0.4
0.6
0.8
1
0
0.5
1
0
1
2
3
4
AMOUNT OF CORRELATION
MULTIPLEXING GAIN
DIVERSITYGAIN
 2
1
1 ( 1) t r
r
dsm s
t r n n
n
g g n
n n ψ
 
 
 2
1
1 ( 1) t r
t r
dostbc s
t r n n
n n
g g n
n n ψ
 
 
It can be seen that Rank 2 TM can achieve a high
coding rate or rank(max=2) but cannot offer
highest diversity available(diversity gain=4).
It can be seen that Rank1 TMthe can achieve a
high diversity available(max=4)but cannot
offer highest coding rate available(n=2).
Diversity Multiplexing and Amount of Correlations
nt = nr =n=2 nt = nr =n=2

Correlations levels delimitation
1. User Ran2 TM DMT Fonction to fix a Guard diversity gain 𝜇o wanted
2. Use Rank2 TM DMT Fonction to fix a Guard diversity gain 𝜇1
 s t r
t r t r
d s n n o
o
n n n n
g g ,ψ μ
ψ ψ
  
 
   0 t r t r
o
n n n nψ ψ 
TM3,TM4
Rank 2 TM
To Delimit low
correlation
Levels for Rank2 TM
Spatial Correlations levels delimitation
  1
1
s t r
t r t r
d s n n
n n n n
g g ,ψ μ
ψ ψ
  
 
  
TM3,TM4Rank 2 TM
To Delimit medium
correlation
Levels for Rank2 TM
1
t r t r t r
o
n n n n n nψ ψ ψ 

3.Rank 1 DMT Fonction to fix a guard diversity gain 𝜇2
.
4. Finaly for activate SISO or TM1
  2
2
ostbc t r
t r t r
d s n n
n n n n
g g ,ψ μ
ψ ψ
  
 
  
TM2,TM6
Rank 1TM
To Delimit high
correlation
Levels for Rank1 TM
1 2
t r t r t rn n n n n nψ ψ ψ 
2
1t r t rn n n nψ ψ 
Bad MIMO channel

Low
correlation
levels, L
Medium
correlation
levels, M
High
correlations
level, H
Bad
MIMO
channel
B
Propositions
4x2
MIMO
2x2
MIMO
TM TM4,TM3 TM4,TM3 TM2,TM6 TM1
0 0 3t rn n . 
0 0 2t rn n . 
0 3 0 6t rn n. .  0 6 0 95t rn n. .  0 95 1t rn n.  
0 2 0 4t rn n. .  0 4 0 95t rn n. .  0 95 1t rn n.  
Proposition of correlation environments delimitation
(1) Since the highest diversity gain of SM is 1, we suggest 𝜇o=1/2, 𝜇1=1.
(2) since the smallest diversity gain of OSTBC is 1 ,we suggest 𝜇2=0.95 nearest SISO performance
（3）in the literature , it is quite difficult to delimit correlation levels. It often assumed <=0.3 and >= 0.6 as low correlationlevels and high
correlation levels for MIMO systems

 CQ is known
 MIMO correlation environments are delimited
If CSI is known
For Channel correlation case L, M, H, B
If case L,M
TM= TM4
Else if case H
TM= TM6
Else TM=TM1
end
For Channel Correlation case L, M, H, B
If case L,M
TM= TM3
Else if case H
TM= TM2
Else TM=TM1
end
TG=CLSM{TM4,TM6}+TM1TG=OLSM{TM2,TM3}+TM1
PMI=[1,2,3,4]
CQI=[0,1,..15]
chose
MIMO TM switching scheme

Application of the MIMO TM switching Scheme
 Practical Amount of correlation computation
Channel Model Propagation
Environment
Base station spacing Mobile Station
spacing
Sectors Beamwidth
variation
MRSC A, Gaussian
distribution model
Urban macro Cell 1 ,0.5λ spacing 0.2λ
spacing(proposition)
αo =[tang(0o ) tang(15o ) ].
MRSC B, Gaussian
distribution model
Urban macro Cell 2, 4 λ spacing 0.2λ
spacing(proposition)
α o =[tang(0o ) tang(15o ) ].

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35
0.4
0.5
0.6
0.7
0.8
0.9
1
Beamwidth
AMOUNTOFCORRELATION
ANTENNA SPACING d=4.lamda ,D=0.2.lamda
4x2MIMO
2x2MIMO
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
Beamwidth
AMOUNTOFCORRELATION
ANTENNA SPACING d=0.5.lamda,D=0.2.lamda
4x2MIMO
2x2MIMO
Amount of correlation in Cell 1 Amount of correlation in Cell
both 2x2 MIMO and 4x2 MIMO stay in high correlation level
i.e.C3.In this case ,on the basis of the proposed adaptive LTE
MIMO switching, it advisable to allow the eNodeB enabling
the appropriate transmission mode.
Both 2x2 MIMO and 4x2 MIMO systems may experience C2
and C3 level. So in this case, we need to implement a
switching.
[rd] [rd]

Sector
Beamwidth seen
at the base
station
Sector 1
0.1
sector 2
0.001
2x2
MIMO(Amount of
correlation ψ
Ψ=0.3283:
Moderate
correlation level
Ψ=0.84:
high correlation level
4x2
MIMO(Amount of
correlation ψ)
Ψ=0.4094:
moderate
correlation level
Ψ=0.92:
high correlation level
Correlations levels for Sector 1 and sector 2
 Bandwidth
Bw=5MHz
 10 Frame basis
 Working
frequency=2GHz
CQ
 CQI_index
=10
 PMI=1
 RI=1,2
 Receiver: SSD
LTE General Parameters
 BLER and Capacity Analysis in different LTE Sector spatial correlation
environments
Cell1 :6 sectors conf.

Block Error Rate in sector 1: M level
].
Block Error Rate in Sector 2 :H level
-5 0 5 10 15 20 25 30
10
-3
10
-2
10
-1
10
0
BLER, CQI 10, MRSC model,Beamwidth=0.1, 10 subframes
BLER
SNR [dB]
SISO TM1
2x1 MIMO TM6
2x2 MIMO TM2
2x2 MIMO TM3
2x2 MIMO TM4
4x1 MIMO TM6
4x2 MIMO TM2
4x2 MIMO TM3
4x2 MIMO TM4
-5 0 5 10 15 20 25 30
10
-3
10
-2
10
-1
10
0
BLER, CQI 10, MRSC model,Beamwidth=0.001, 10 subframes
BLER
SNR [dB]
SISO TM1
2x1 MIMO TM6
2x2 MIMO TM2
2x2 MIMO TM3
2x2 MIMO TM4
4x1 MIMO TM6
4x2 MIMO TM2
4x2 MIMO TM3
4x2 MIMO TM4
 In Sector1, we can see Rank 1 BLER being a transmit –diversity based performs well than Rank TM BLER ,however it can offer
high capacity while Rank2 TM achieves high capacity and acceptable BLER rate, In this case , the base station can chose to
maximize capacity and this enable Rank2 TM
 In Sector 2, even though transmit diversity-based TM (TM2) performance decrease with the increase of correlation level; it
remains robust against correlation in H state. In contrast, the spatial multiplexing –based TM (TM3, TM4) which is rank 2
transmission mode performances become extremely catastrophic at H level. Indeed, their BLER performances remain
approximately linear lines following the SNR while their capacity behaves similar as rank 1–based transmission(TM 6 and
TM2).
Simulation Results in Different Correlations levels
Different BLER in the Cell1

-5 0 5 10 15 20 25 30
0
5
10
15
20
25
Capacity, CQI 10, MRSC model,Beamwidth=0.1, 10 subframes
Capacity[Mbps]
SNR [dB]
SISO TM1
2x1 MIMO TM6
2x2 MIMO TM2
2x2 MIMO TM3
2x2 MIMO TM4
4x1 MIMO TM6
4x2 MIMO TM2
4x2 MIMO TM3
4x2 MIMO TM4
-5 0 5 10 15 20 25 30
0
2
4
6
8
10
12
Capacity, CQI 10, MRSC model,Beamwidth=0.001, 10 subframes
Capacity[Mbps] SNR [dB]
SISO TM1
2x1 MIMO TM6
2x2 MIMO TM2
2x2 MIMO TM3
2x2 MIMO TM4
4x1 MIMO TM6
4x2 MIMO TM2
4x2 MIMO TM3
4x2 MIMO TM4
Rule(1):In this case the eNodeB can only choose to optimize the transmission reliability(BLER Performance) and thus enable the
rank 1 modes.
Rule(2): Enable TM1 at M et H level instead of TM6 in the case of the switching 2x2 MIMO TM4 and 2x1 TM 6. However if the
switching process is configured between 2x2 MIMO(TM4) and 2x2 MIMO-TM6, the advantage of the TM6 on TM1 will be clearly
seen.
Capacity in Cell 1
Sector 1 : M correlation level Sector 2 : H correlation level

E. Simulation Results from BER and Capacity analyses
 In moderate correlation LTE , sector1
 Enable TM4 if CSI known else TM3
 In High correction , LTE sector 2
 Enable TM6 if CSI is known else TM2
 Both 2x2 MIMO and 4x2 MIMO spatial correlation levels are not in bad MIMO
channel condition and in low correlation level.
 Not need to enable SISO channel
 Results of these analyses are then satisfactory and in agreement in regard
with the proposed adaptive LTE MIMO TM scheme !!!!!!!!

Note
In MIMO MRSC, Each LTE sector n has the space-time and frequency correlation
as:
With
αn =[tang(0o ) tang(15o ) ]
     kl,ks n kl,ks kl,ks nr t,α , f r f r t,α    
   kl,ks kl,ksr f r f  

Conclusion And Suggestions
 In this Thesis ,we have proposed a new frequency correlation function by
assuming multiple bounced - waves emitted from the base station before
reaching the mobile surrounded by a local one-ring scattering channel.
 We further extend the frequency selective channel to propose a full space-time
and frequency MIMO channel named MRSC by assuming that the mobile is
moving through in a LTE cell sectored in multiple sectors.
 We adopt the MRSC model to propose an optimal LTE Physical Dynamic TM
Switching method for LTE TM when the base station is moving through multiple
sectors.
 We propose also a wise tradeoff between parameters for minimizing
correlations and for Designing MIMO Systems (reference in our thesis).

Papers published
(1) I.Kolani And J. Zhang, ‘ MIMO Multi Ring scattering channel Model for Wireless Communications
Microcells’ in proceeding IEEE –2nd ICIST , Wuhan 2012, PP:794-797
(2) I.Kolani And J. Zhang And G. Zehua, ‘ MIMO Channel Modeling: Covariances Estimation based on
Multi Ring Scattering Environments’ ,IEEE-8th WICOM, Shanghai 2012.
l (3) I. Kolani And J. Zhang And G. Zehua,‘Amount of Correlation Function-based Diversity Motivation
Design in MIMO Systems’,International Journal of Information and Computer Science, 2012IJICS Volume 1,
Issue 6, September 2012 PP. 159-162.
l (4) I. Kolani And J. Zhang, Gao Zehua ,R. Germany’ LTE Optimal Transmission Mode Selection
Algorithm in MIMO Scattering Correlation environments’ , Journal of communications(Scopus and EI) ‘
(accepted and under the author review ). 2013, April .
l (5) I.Kolani And J.Zhang, ‘Millimeter Wave Antenna for MIMO Small Antenna and For Mobile Handset’ in
proceeding IEEE-ICCSNT, Harbin 2011, PP: 150-153
l (6) I. Kolani And J. Zhang, ‘A Wise Trade- off between Parameters for Minimizing Correlations and for
Designing MIMO Systems’ ,in proceeding IEEE-ComComAp, Hong Kong 2012 , PP: 396-399.

Thank you !
In the Name of Jesus-Christ（耶稣上帝已经做了我需要的）
耶稣上帝是您天主，真的上帝，他要您的生活完全好，顺利。
请您相信我他是真的上帝，哪，您等什么信他呢？
LTE Physical Layer Transmission Mode Selection
Over MIMO Scattering Channels
感谢张杰与高泽华教授为了他们的辅导

LTE Physical Layer Transmission Mode Selection Over MIMO Scattering Channels

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