In the present thesis, the concept for beyond 3G mobile radio systems is described. A service area concept is introduced in order to combat the performance limiting interferences present in the cellular mobile communication systems, with each service area consisting of a various simultaneously active mobile terminals, a number of fixed access points and a central unit per- forming signal processing. About uplink transmission, the main characteristic of this service area system is that with the aid of joint detection of the transmit signals from the mobile ter- minals performed at uplink transmission, all interferences between the simultaneously active mobile terminals using the same bandwidth is drastically reduced. Moreover the use of OFDM subcarrierwise in the described service area based system allows for intersymbol interference free communication and for simple equalization in the frequency domain.
Through this subcarrierwise equalization the service area based system is equivalent with a number of smaller parallel systems, a fact that affects in a reduced computational complexity in the case of optimum multiuser operating with the maximum likehood principle and subop- timum linear detector zero-forcing. In this thesis the parallel interference cancellation detector is introduced, according to which the multi access interference is iteratively reconstructed and subtracted from the received signal. Parallel interference cancellation detector is compared in terms of performance with suboptimum linear detector, due to the reduced computational com- plexity.
Using standardized COST 207 channel models, the performance of parallel interference can- cellation detector compared with suboptimum linear detector has been investigated for a frozen channel, with the same snapshot using the same parameters of the channel, as well as for a number of system loads. A fact that can be observed in simulations results is that parallel in- terference cancellation detectors could achieve the same performance with a reduction of the complexity as suboptimum linear detector zero-forcing in the case of no estimate refinement and with estimate refinement by hard quantization depending on the load system. With esti- mate refinement by soft quantization the performance of the parallel interference cancellation detector is improved, having better performance than zero-forcing detector in cases with nor- mal load. Moreover with the improvement raised in this thesis, in this normal system load, the performance is more improved. On the other hand in the case of full load system, the PIC detector can not substract all the multi access interference producing error flow, this thing is not too important taking in account that the fully loaded system case should not be never present.
Coherent Optical Orthogonal Frequency Division Multiplexing (CO-OFDM )BhaSkar Nath
Principle of orthogonal frequency-division multiplexing (OFDM)
Optical transmitter for CO-OFDM
Optical spectral efficiency for CO-OFDM
Channel model for CO-OFDM
The channel model describes the behavior of communications systems, thus fundamentally determining the performance of the systems
Coherent Optical Orthogonal Frequency Division Multiplexing (CO-OFDM )BhaSkar Nath
Principle of orthogonal frequency-division multiplexing (OFDM)
Optical transmitter for CO-OFDM
Optical spectral efficiency for CO-OFDM
Channel model for CO-OFDM
The channel model describes the behavior of communications systems, thus fundamentally determining the performance of the systems
Performance Analysis of Massive MIMO Downlink System with Imperfect Channel S...IJRES Journal
We investigate the ergodic sum rate and required transmit power of a single-cell massive
multiple-input multiple-output (MIMO) downlink system. The system considered in this paper is based on two
linear beamforming schemes, that is, maximum ratio transmission (MRT) beamforming and zero-forcing (ZF)
beamforming. What’s more, we use minimum mean square error (MMSE) channel estimation to get imperfect
channel state information (CSI). Compared with the perfect CSI case, both theoretical analysis and simulation
results show that the system performance is different when the imperfect CSI is taken into account.
OPTIMIZED RATE ALLOCATION OF HYPERSPECTRAL IMAGES IN COMPRESSED DOMAIN USING ...Pioneer Natural Resources
This paper studies the application of bit allocation using JPEG2000 for compressing multi-dimensional remote sensing data. Past experiments have shown that the Karhunen- Lo
`
e
ve transform (KLT) along with rate distortion optimal(RDO) bit allocation produces good compression perfor-mance. However, this model has the unavoidable disadvan-tage of paying a price in terms of implementation complex-ity. In this research we address this complexity problem byusing the discrete wavelet transform (DWT) instead of theKLT as the decorrelator. Further, we have incorporated amixed model (MM) to find the rate distortion curves instead of the prior method of using experimental rate distortioncurves for RDO bit allocation. We compared our results tothe traditional high bit rate quantizer bit allocation modelbased on the logarithm of variances among the bands. Our comparisons show that by using the MM-RDO bit rate al-location method result in lower mean squared error (MSE)compared to the traditional bit allocation scheme. Our ap- proach also has an additional advantage of using DWT asa computationally efficient decorrelator when compared tothe KLT
Performance Analysis of Massive MIMO Downlink System with Imperfect Channel S...IJRES Journal
We investigate the ergodic sum rate and required transmit power of a single-cell massive
multiple-input multiple-output (MIMO) downlink system. The system considered in this paper is based on two
linear beamforming schemes, that is, maximum ratio transmission (MRT) beamforming and zero-forcing (ZF)
beamforming. What’s more, we use minimum mean square error (MMSE) channel estimation to get imperfect
channel state information (CSI). Compared with the perfect CSI case, both theoretical analysis and simulation
results show that the system performance is different when the imperfect CSI is taken into account.
OPTIMIZED RATE ALLOCATION OF HYPERSPECTRAL IMAGES IN COMPRESSED DOMAIN USING ...Pioneer Natural Resources
This paper studies the application of bit allocation using JPEG2000 for compressing multi-dimensional remote sensing data. Past experiments have shown that the Karhunen- Lo
`
e
ve transform (KLT) along with rate distortion optimal(RDO) bit allocation produces good compression perfor-mance. However, this model has the unavoidable disadvan-tage of paying a price in terms of implementation complex-ity. In this research we address this complexity problem byusing the discrete wavelet transform (DWT) instead of theKLT as the decorrelator. Further, we have incorporated amixed model (MM) to find the rate distortion curves instead of the prior method of using experimental rate distortioncurves for RDO bit allocation. We compared our results tothe traditional high bit rate quantizer bit allocation modelbased on the logarithm of variances among the bands. Our comparisons show that by using the MM-RDO bit rate al-location method result in lower mean squared error (MSE)compared to the traditional bit allocation scheme. Our ap- proach also has an additional advantage of using DWT asa computationally efficient decorrelator when compared tothe KLT
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
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Subscribers of ViralQR receive detailed analytics and tracking tools in light of having a view of the core values of QR code performance. Our analytics dashboard shows aggregate views and unique views, as well as detailed information about each impression, including time, device, browser, and estimated location by city and country.
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Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
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Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
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Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
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Parallel Interference Cancellation in beyond 3G multi-user and multi-antenna OFDM systems
1. David Research Group for RF Communications 2003-5-26 1
Parallel interference cancellation
in beyond 3G multi-user and
multi-antenna OFDM systems
David Sabater Dinter
University of Kaiserslautern
dinter@rhrk.uni-kl.de
Supervisor: A. Sklavos
2. David Research Group for RF Communications 2003-5-26 2
summary
• service area based system in the uplink
• transmission model
• subcarrierwise investigation
• optimum and suboptimum linear detection
• parallel interference cancellation
• PIC with improved estimate refinement
• simulation results
• conclusions
3. David Research Group for RF Communications 2003-5-26 3
uplink transmission in a service area
MT
AP
CU
AP
AP
MT
MT
( )1
ˆd
( )2
ˆd
( )
ˆ K
d
( )1
d
( )2
d
( )K
d
4. David Research Group for RF Communications 2003-5-26 4
( ) ( )
( ) ( )
( ) ( )B B
1,1 ,1
1,2 ,2
1, , B
(1)
(2)
( )B
(1)
(2)
( )
(1)
(2)
( )
K
K
K K K K KK
•= +
H H
H H
H H
d
d
d
n
n
n
e
e
e
% %L
% %L
M O M
% %L
M
%
%
M
%
%
%
M
%
transmission model
• Additive noise vector at AP :
• Received signal vector at AP :
Bk
Bk
B B B F( ) ( ,1) ( , ) T
( )k k k N
n n=n% % %K
B B B F( ) ( ,1) ( , ) T
( )k k k N
e e=e% % %K
B F FK N KN× F 1KN ×B F 1K N × B F 1K N ×
• data symbol vector sent by MT k: F( , )(k) ( ,1) T
( )k Nk
d d=d K
5. David Research Group for RF Communications 2003-5-26 5
subcarrierwise investigation
( )
( )
( )F
1
2
N
÷
÷
= ÷
÷
÷ ÷
H 0 0
0 H 0
H
0 0 H
% L
% L%
M M O M
%L
( ) ( ) ( )
( ) ( ) ( )
( ) ( ) ( )B B B
1,1 2,1 ,1
1,2 2,2 ,2
1, 1, ,
K
K
K K K K
÷
÷
= ÷
÷
÷
H H H
H H H
H
H H H
% % %L
% % %L%
M M O M
% % %L
B F F block diagonal matrixK N xKN
•conversion of totalsystem to smaller parallel systems
•significant effort reduction in
•linear ZF, MMSE
•non linear MLVE
FN
B F F matrixsparseK N xKN
6. David Research Group for RF Communications 2003-5-26 6
( )
( )
( )
( ) ( ) ( )
F
F
F FF F
2
all data vectors equiprobable and Gauss noise
maximum likehood vector estimator (MLVE)
ˆ arg min
n
n
K
n nn n
∈
→
= −
d
d
d e H d
g
%%
D
optimum non-linear and
suboptimum linear detector
( )
( )
( ) ( )
( ){ }F
F F Fˆ arg max |n
n n n
K
P
∈
=
d
d d e%
D
F F F
( ) ( ) ( )ˆ .
n n n
=d D e% %
( )F F F F
1( ) ( )*T ( ) ( )*Tn n n n−
=D H H H% % % %
• Optimum multiuser detector,
• suboptimum linear detection
• Example: ZF criterion
F
F F
( )( ) ( )
2
min
nn n
−e H d%%
7. David Research Group for RF Communications 2003-5-26 7
parallel interference cancellation
( )( )
( )
1
*T
*T
diag
diag
−
=
=
F H H
R H H
% % %
% % %
*T
H%
e% r%
F%
R%
$ ( )pd
$
( )ˆ 1p −d
-bank
of MF
( )ˆ pu
iterative MUD
estimaterefinement
andFECdecoding
• Forward matrix:
• Feedback matrix:
8. David Research Group for RF Communications 2003-5-26 8
no estimate refinement
F
, , (non zero) eigenvalues ofKNλ λ1 FR% %K
• no estimate refinement,
• PIC convergent if
• convergence value
ˆˆ ˆ=d d
( ) 1ρ ≤FR% %
$d FECdemod
ˆd
ˆu
$ˆd
estimate refinement and
FEC decoding
spectral radius
( ) { }F
FR max , , KNρ λ λ1=% % K
ZF
ˆ ˆ( )=∞d d
9. David Research Group for RF Communications 2003-5-26 9
spectral radius example
2,...,8K =
{ }P Rρ ≤
divergenceconvergence
•
• exp. Channel
snapshot
B 8K =
R
10. David Research Group for RF Communications 2003-5-26 10
estimate refinement by hard quantization
$d FECdemod
ˆd
ˆu
$ˆd
estimate refinement and
FEC decoding
• exploit knowledge of discrete
• quantization of to the modulation constellation
F( , )k n
d ∈D
$d
{ }F
2ˆˆ ˆarg min ( )
KN
p
∈
= −
d
d d d
D
D
11. David Research Group for RF Communications 2003-5-26 11
estimate refinement by soft quantization
$d FECdemod ˆu
$ˆd
estimate refinement and FEC decoding
$$ ( )
( )
2
min E
k
k
m md d
−
$$ ( )k
md must satisfy
estim.
2
dσ
2
d
ˆ2 σ
( )tanh 2•
( )sign •
mod
( ) ( )
{ }ˆk k
m mL d d
12. David Research Group for RF Communications 2003-5-26 12
-10 -5 0 5 10 15 20
10
-3
10
-2
10
-1
10
0
simulation results
F( )
1n
ρ >
•
•
• subc.
• exp. channel
• no quant.
4K =
B 4K =
( )10 b 010log / /dBE N
bP
AWGN ZF
PICMF
13. David Research Group for RF Communications 2003-5-26 13
-10 -5 0 5 10 15 20
10
-3
10
-2
10
-1
10
0
simulation results
F( )
1n
ρ >
( )10 b 010log / /dBE N
bP
•
•
• subc.
• exp. channel
• hard quant.
4K =
B 4K =
AWGN
ZF
PICMF
14. David Research Group for RF Communications 2003-5-26 14
-10 -5 0 5 10 15 20
10
-3
10
-2
10
-1
10
0
simulation results
F( )
1n
ρ >
( )10 b 010log / /dBE N
bP
•
•
• subc.
• exp. channel
• soft quant.
4K =
B 4K =
AWGN
ZF
PICMF
15. David Research Group for RF Communications 2003-5-26 15
-10 -5 0 5 10 15 20
10
-3
10
-2
10
-1
10
0
simulation results
bP
( )10 b 010log / /dBE N
•
•
• subc.
• exp. channel
• no quant.
2K =
B 4K =
F( )
1n
ρ ;
AWGN
PIC
(even iterations)
PIC
(odd iterations)
ZF
16. David Research Group for RF Communications 2003-5-26 16
PIC with improved estimate refinement
*T
H%
e% r%
F%
$ ( )1d
bank
of MF
iterative MUD
first iterationsecond iterationthird iteration
*T
H%
e% r%
F%
R%
$ ( )2d
-bank
of MF
iterative MUD
demod
( )ˆ 2u
$
( )ˆ 1d
*T
H%
e% r%
F%
R%
$ ( )3d
-bank
of MF
iterative MUD
demod
( )ˆ 3u
$
( )ˆ 2d
hard Q
or
soft Q
• principle: input MAI-free at quantization process
• starting estimate refinement at the third iteration, errors
introduced by quantization method can be reduced
$d
17. David Research Group for RF Communications 2003-5-26 17
-10 -5 0 5 10 15 20
10
-3
10
-2
10
-1
10
0
simulation results
( )10 b 010log / /dBE N
bP
•
•
• subc.
• exp. channel
• hard quant.
• hard mod.
quant.
2K =
B 4K =
F( )
1n
ρ >
AWGN
MF
ZF
PIC
PIC mod
18. David Research Group for RF Communications 2003-5-26 18
-10 -5 0 5 10 15 20
10
-3
10
-2
10
-1
10
0
simulation results
( )10 b 010log / /dBE N
bP
•
•
• subc.
• exp. Channel
• soft quant.
• soft mod.
quant.
2K =
B 4K =
F( )
1n
ρ >
AWGN
MF
ZF
PICPIC mod
19. David Research Group for RF Communications 2003-5-26 19
-10 -5 0 5 10 15 20
10
-3
10
-2
10
-1
10
0
simulation results
( )10 b 010log / /dBE N
bP
•
•
•
• exp. Channel
• hard quant.
• hard mod.
quant.
3K =
B 6K =
F 32N =
AWGN
MF
ZF
PIC
PIC mod
20. David Research Group for RF Communications 2003-5-26 20
-10 -5 0 5 10 15 20
10
-3
10
-2
10
-1
10
0
simulation results
( )10 b 010log / /dBE N
bP
•
•
•
• exp. Channel
• soft quant.
• soft mod.
quant.
3K =
B 6K =
F 32N =
AWGN
MF
ZF
PIC mod
PIC
21. David Research Group for RF Communications 2003-5-26 21
conclusions
• PIC is a flexible JD scheme
• PIC is not always convergent
• performance improvement with modified
estimate refinement
• more investigation towards PIC necessary