The document details a project on optimizing pilot-to-data power ratio (PDPR) for MIMO-OFDM systems to enhance channel capacity in wireless communications. It outlines various project components such as system architecture, challenges, literature review, and proposed pilot patterns for effective channel estimation. Simulation results indicate that optimizing PDPR can improve channel capacity alongside increased signal-to-noise ratio (SNR) in high-speed mobile communication environments.

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

9. ORTHOGONAL FREQUENCY DIVISION MULTIPLEXING
9

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

11. OFDM DIAGRAM
11

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

18. MODULE
OPTIMIZATION OF PDPR FOR SIMULATINGCHANNEL
CAPACITY TO MIMO-OFDM SYSTEMS
18

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

20. Time-Frequency
representation of
OFDM symbol and
OFDM Frame
20

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

30. PILOT AND DATA SYMBOL INSERTION IN LTE
30

31. SIMULATION RESULTS AND DISCUSSION
USING
MATLAB
31

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
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