(RIA) Call Girls Bhosari ( 7001035870 ) HI-Fi Pune Escorts Service
MIMO-OFDM
1. Perunthalaivar Kamarajar
Institute of Engineering and Technology (PKIET)
Department of Electronics & Communication Engineering
PROJECT REVIEW
(18.04.2017)
1
2. OPTIMIZATION OF PDPR FOR SIMULATING CHANNEL
CAPACITY TO MIMO-OFDM SYSTEMS
by
ARUN PRASANTH.R Reg. No 13TC1306
DURGA SRINIVAS . V Reg. No 13TC1312
KEERTHI.P Reg. No 13TC1321
PARAMESWARI. N Reg. No 13TC1338
Under the Guidance of
Dr. A. SUNDHAR
Assistant Professor
2
3. CONTENT
• Broad Area of Project
• Current Scenario of Project
• Proposed Area of project
• MIMO
• OFDM
• Literature Survey
• Challenges Ahead
• Project Objective
• System Architecture
• Module
• Pilot Patterns
• Simulation Results And Discussion
• References
3
4. Services anywhere and anytime.
Stay reachable all over the globe.
Integrating various wireless access technologies to
satisfies the user needs.
Higher data rates, variety of services, applications and
global roaming of multiple access networks.
BROAD AREA OF PROJECT
( Wireless Communication )
4
5. Optimization of Spectrum Use
Various Multiplexing and different Coding Techniques
Multiple Access Techniques
Internetworking and Convergence Mechanism
Mobility Management and Handover Issues
Network Security
Routing
Traffic allocation and Load balancing Mechanism
Interference Cancellation
CURRENT SCENARIO OF PROJECT
( Project Areas in Wireless Communication )
5
6. MIMO-OFDM SYSTEMS
MIMO , a multi user concept where multiple antennas are
used both at the transmitter and receiver.
Accelerate the channel capacity
OFDM is a modulation technique which encodes digital data
on multiple carrier frequencies
High speed, high data rate, high spectral efficiency
PROPOSED AREA OF PROJECT
6
7. MULTIPLE INPUT MULTIPLE OUTPUT
Multiple antenna array are
used both at transmitter and
receiver
MIMO accelerate the
channel capacity better than
SISO
This technology boosts data rates in rich
scattering environment , diversity gain ,.
7
8. MIMO FEATURES
Increased Data rate
Increase System Capacity by 20% to 40%
It gives Reliable communication
High Diversity Gain
8
10. OFDM FEATURES
Widely used in 4G Technologies
Serve in Channel Interferences
Avoid Fading
Most resistance to frequency selective fading
Sufficient use of spectrum by overlap
Channel Equalization becomes simpler
Eliminates ISI and IFI—Cycle prefix
10
12. S.No Author / Title of the Paper Extract Limitations
1
Gabor Fador and Miklos Telek,”
On the Pilot Data Power Trade
Off In Single Input Multiple
Output Systems” May 2014.
1. Mean square equalization
2. Finds path loss between
mobile station to base
station
1. Number of antenna
grows large
2. Investigate multicell
system
3. Signal cell analyses re-
examined
2
K.Siva Nagamma, K. Udaya
Kiran,”A Novel Approach for
Channel Estimation In MIMO-
OFDM System Using The
Efficient Pilot Patterns”,IEEE
Journals October 2014.
1. Unitary matrix based on
scattered pilot
2. Estimate pilot patterns
3. Channel impulse response
estimation
1. Depends on frequency
response and impulse
response
3
K.Vidhya , K.R.
Shankakumar,”Channel
Estimation and Otimization For
Pilot Design In MIMO OFDM
Systems”,IEEE Journals February
2013.
1. Estimated by BER and
mean square error
2. Compared with random
and orthogonal placement
of pilot
.
1. Estimation done with
BER and mean square
root
LITERATURE SURVEY
12
13. S.No Author / Title of the Paper Extract Limitations
4
Dipika Agnihotri, Mandeep Singh
Saina,”Channel Capacity
Enhancement of Diffferent
Fading Channel Using Hybrid
Algorith In MIMO OFDM
Systems”,IEEE journals
December 2015.
1. Enhance channel capacity
for different faing
channels
2. Hybrid algorithm MIMO-
OFDM
1. Expensive
5
Hlaing Minn, Daniel Munoz,
“Pilot Design for Channel
Estimation OF MIMO OFDM
Systems with Frequency –
Dependent I/Q Imbalances”,
IEEE Transactions August 2010.
1. I/Q imbalance
2. Efficient pilot design
3. Estimate mean square
error optimally
1. Depends upon
resources , users and
channels
6
Chao Zhang, Yichen Wang ,
“Optimal Relay Power Allocation
for Amplify and Forward Relay
Network With Non-Linear Power
Amplifiers”, April 2011.
1. Optimal relay power
allocation
2. Frame work of non-linear
distorted aware receiver
1. Not proposed to
symmetric networks
13
14. S.No Author / Title of the Paper Extract Limitations
7
V. K. Varma Gottumukkala,
Hlaing Minn, “ Capacity Analysis
and Pilot Data Power Allocation
For MIMO-OFDM With
Transitter and Receiver IQ
Imbalances and Residual Carrier
Frequency Offset”, IEEE
Transactions February 2012.
1. Residual carrier
frequencies offset
2. IQ imbalance
3. Pilot data power allocation
1. Channel capacity is
more sensitive to CFO
8
H. Chamkhia, A. Omri, R.
Bouallegue, “Improvement Of
LTE System Performance By
Using A New Pilot Structure “ ,
IEEE Journals February 2012.
1. LTE downlink system
2. LTE pilot structure
1. Used less number of
pilot
2. Based only DVB
structure
9
Samir Kapoor, Daniel J.
Marchok,” Pilot Assisted
Synchronization For Wireless
OFDM Systems Over Fast Time
Varying Fading Channels “ , May
1998.
1. Frequency
synchronization technique
2. Frequency offsets are
estimated
3. Real time
complementation
1. Approximately
algorithm used
2. Only for frequency
selective fading
14
15. Using pilot patterns with optimized PDPR
More power required for pilot than data
Increasing the SNR not the bandwidth
Channel capacity similar to optimum capacity, but its
capacity is improved by power to pilot and data sub
carriers.
CHALLENGES AHEAD
15
16. To simulate the channel capacity in order to
accelerate the high speed, high data rate to improve the
existing cellular mobile communications IEEE 802.11 and
LTE (4G) technologies using MIMO OFDM systems by
optimizing PDPR with the pilot patterns.
PROJECT OBJECTIVE
16
17. 4G TECHNOLOGIES
HIGH SPEED, HIGH DATA RATE
INCREASE BANDWIDTH INCREASE SNR
LEADS TO MULTIPLEXING
COMPLEXITY
CHANNEL
CAPACITY
PILOT
SYMBOLS
OFDM
TECHNOLOGY
OPTIMIZE PDPR
MIMO
TECHNOLOGY
PILOT
PATTERNS
(3 TYPES)
SYSTEM ARCHITECTURE
17
19. MIMO-OFDM AND CHANNEL ESTIMATION
OFDM used in
conjunction with MIMO to
provide high speed
Wireless,4G and mobile
communications.
The above technology is
highly advantageous only
if the channel estimation is
properly done.
For proper channel
estimation we send
“pilots” along with data.
19
21. CONCEPT OF PILOT SYMBOLS
Just symbols transmitted along with data symbols
Pilot symbols doesn’t carry any data
It estimates the Unknown channel
Pilot symbols infused in to each OFDM frame
Channel estimation with three pilot pattern's for
capacity
21
22. CONCEPT OF PDPR
Pilot to data power ratio
Optimize the ratio--Channel capacity
Large impact on spectral and energy efficiency
It depends on the number of Antenna’s
22
23. PILOT PATTERNS
There are three principal pilot patterns under our consideration:
1. Independent Pilot pattern (Time division Multiplexed)
2. Scattered Pilot Pattern(Frequency Division Multiplexed)
3. Orthogonal Pilot Pattern(Code Division Multiplexed)
23
24. INDEPENDENT PILOT PATTRENS
E[
The channel estimation error
covariance defined as
Single antenna is used to
broadcast pilot tones
Other antennas for data
symbols
More amount of data
sent than pilot
24
25. SCATTERING PILOT PATTRENS
The channel estimation error
covariance is given by
Pilot tones transmitted at
different antennas at different
carriers
In Same carriers, data also
send
It require greater power
than data
25
26. ORTHOGONAL PILOT PATTERN
The channel estimation error
covariance is given by
It follows code division
multiplexing
AWGN channel is constant
for no. of OFDM symbols
26
27. CAPACITY LOWER BOUND
Capacity Lower Bound is defined as the amount of mutual
information between the known and unknown signals over the
symbol period over which the data is to be transmitted. It is
represented by
Where H is the Hermitian matrix
It has been observed that the is different for different pilot patterns
Clb =
1
𝐾
E[ log2
det(1+ρ
eff
HH)]
27
28. for IPP
for SPP
for OPP
OPTIMAL PDPR
When an optimal PDPR is achieved is maximized and hence it
maximizes the ergodic capacity lower bound
𝜎 𝐻2 = Pr Pt D – (Pr Pt LD/ Gϒp+L)
𝜎 𝐻2 = Pr Pt Dac – (Pr Pt LD/ Gϒp+L)
𝜎 𝐻2 = Pr Pt D - (Pr Pt LD/Pt σp2+L)
28
29. we get the optimal PDPR to maximize the capacity as
Independent/Orthogonal design:
Scattered pilot design:
The results portray that that the capacity of the independent
and orthogonal pilot patterns is identical
29
32. The channel capacity
increases along with the
decrease of the pilot power
Three different pilot patterns
were compared with the
perfect channel
Shannon’s capacity rule
CHANNEL CAPACITY AND PERCENTAGE OF PILOT POWER
10
0
10
1
0
0.5
1
1.5
2
2.5
3
Percentage of pilot Power
Capacity(bits/secs/hz)
Channel Capacity and Percentage of Pilot Power
Perfected ch
Ipp
Spp
Opp
32
33. The effective capacity of
the signal varies with the
QoS.
Compared 2x2 , 3x3 , 4x4
At low effective capacity
The MIMO 4x4 shows
high QoS
EFFECTIVE CAPACITY
33
10
0
10
1
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
QoS Statistical exponents
Capacity(bits/secs/hz)
Effective Capacity
Mt=4,Mr=4
Mt=3,Mr=3
Mt=2,Mr=2
34. ENERGY EFFICIENCY OF PDPR AND MIMO
It shows different config.,for
both PDPR and MIMO
The Average Power constraint
were calculated w.r.t effective
capacity
It shows the PDPR and MIMO
For different config. of antennas.
0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
0
1000
2000
3000
4000
5000
6000
Average Power Contraint
Capacity(bits/secs/hz)
Energy Efficiency of PDPR and MIMO
PDPR FOR MT=2,MR=2
MIMO FOR MT=2,MR=2
PDPR FOR MT=3,MR=3
MIMO FOR MT=3,MR=3
PDPR FOR MT=4,MR=4
MIMO FOR MT=4,MR=4
34
35. CHANNELCAPACITY AND SNR
The SNR simulated with the
channel capacity
It shows Increasing of the
SNR along with the
increasing Effective capacity .
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
SNR
Capacity(bits/secs/hz)
SNR VS Channel Capacity
PDPR FOR MT=2,MR=2
MIMO FOR MT=2,MR=2
PDPR FOR MT=3,MR=3
MIMO FOR MT=3,MR=3
PDPR FOR MT=4,MR=4
MIMO FOR MT=4,MR=4
35
36. REFERENCES
1. Ye Zhang ; Wei-Ping Zhu, “Energy – efficient pilot and data allocation
in massive MIMO communication systems based on MMSE channel
estimation”, IEEE International Conference on Acoustics, Speech and
Signal Processing (ICASSP), pp.3571,3575, March 2016.
2. Minn, H.; Munoz, D., "Pilot Designs for Channel Estimation of MIMO
OFDM Systems with Frequency-Dependent I/Q Imbalances,"
Communications, IEEE Transactions on , vol.58, no.8, pp.2252,2264,
August 2010GJ.
3. Gottumukkala, V.K.V.; Minn, H., "Capacity Analysis and Pilot-Data
Power Allocation for MIMO-OFDM With Transmitter and Receiver IQ
Imbalances and Residual Carrier Frequency Offset," Vehicular
Technology, IEEE Transactions on , vol.61, no.2, pp.553,565, Feb. 2012
4. Moshavi, S.; Yellin, D.; Sadowsky, J.S.; Perets, Y.; Pick, K., "Pilot
interference cancellation technology for CDMA cellular networks,"
Vehicular Technology, IEEE Transactions on , vol.54, no.5,
pp.1781,1792, Sept. 2005
5. Qinfei Huang; Ghogho, M.; Freear, S., "Pilot Design for MIMO OFDM
Systems With Virtual Carriers," Signal Processing, IEEE Transactions on
, vol.57, no.5, pp.2024,2029, May 2009 doi: 10.1109/TSP.2008.2011824 36
37. 7. Kezhi Wang; Yunfei Chen; Alouini, M.-S.; Feng Xu, "BER and Optimal
Power Allocation for Amplify-and-Forward Relaying Using Pilot-Aided
Maximum Likelihood Estimation," Communications, IEEE Transactions on
, vol.62, no.10, pp.3462,3475, Oct. 2014
8. Cicerone, M.; Simeone, O.; Spagnolini, U., "Channel Estimation for
MIMO-OFDM Systems by Modal Analysis/Filtering," Communications,
IEEE Transactions on , vol.54, no.10, pp.1896,1896, Oct. 2006 doi:
10.1109/TCOMM.2006.881401
9. A. Dowler and A. Nix, “Performance evaluation of channel estimation
techniques in a multiple antenna OFDM system,” in Proc., IEEE Veh.
Technology Conf., vol. 2, Oct. 2003, pp. 1214–1218
10. Fodor, Gabor; Fodor, Gabor; Telek, Miklos; Telek, Miklos, "On the Pilot-
Data Power Trade Off in Single Input Multiple Output Systems," European
Wireless 2014; 20th European Wireless Conference; Proceedings of, vol.,
no., pp.1,8, 14-16 May 2014
11. Young-Keum Song; Dongwoo Kim; Zander, J., "Pilot power adjustment for
saving transmit power in pilot channel assisted DS-CDMA mobile
systems," Wireless Communications, IEEE Transactions on , vol.9, no.2,
pp.488,493, February 2010
37