MIMO – OFDM Adaptive pilot channel estimation Hybrid Adaptive Relay Technique Partially Connected Hybrid Beamforming Technique

1. Performance Analysis of MIMO–OFDM for PCHBF , RELAY Technique with
MMSE For Time-Varying and Frequency Selective Fading Channel
Viva Voce
Presented by

2. Overview
• Objectives
• Motivation & Requirements
• Literature summary
• Problem Statement
• Research Objectives
• Research Modules (I, II, III, IV)
• Conclusion
• Publications
• References
2

3. Objectives
 To improve the performance of SNR at the receiver and
reduce the BER value in MIMO-OFDM system through the
techniques related to channel estimation, Adaptive
Modulation, Relay and Beam forming.
3

4. Motivation & Requirement
• Motivation
 Data requirement for every user became very high in last decade
 Almost 6 billion Mobile devices/connections
 60% Video traffic is utilized in mobile communication
• Requirements
 Support high data rate
 Need Uniform Coverage
 Security & privacy
4

5. Literature Summary
S. No Title Limitations
1
Samuli Tiiro, Jari Ylioinas, Markus Myllyla, and
Markku Juntti, "Implementation of the Least Squares
Channel Estimation Algorithm for MIMO-OFDM
Systems", In Proceedings of International ITG
Workshop on Smart Antennas, Berlin, Germany, Feb
2009.
The least squares (LS)
equalization, pilot-based channel
estimation.
2
Saqib Saleem and Qamar-ul-Islam, "On Comparison of
DFT-Based and DCT-Based Channel Estimation for
OFDM System", International Journal of Computer
Science Issues, Vol. 8, No. 2, pp. 353-358, May 2011.
LSE has low complexity,
performance and enhanced by
using LMMSE, having more
complexity.
3
Hussein Hijazi and Laurent Ros, "Polynomial
Estimation of Time-varying Multi-path Gains with ICI
Mitigation in OFDM Systems", IEEE Transactions on
Vehicular Technology, Vol. 58, pp. 140-151, Jan 2009.
Compared to LS, MMSE has better
performance.
5

6. 4
Kun-Chien Hung and David W. Lin, “Pilot-Based
LMMSE Channel Estimation for OFDM Systems With
Power–Delay Profile Approximation,” IEEE Trans.
Veh. Technol, vol. 59, no. 1, pp.150-159, Jan. 2010
Approximation for channel power -
delay profile (PDP), two parameters,
the mean delay and the root-mean-
square (RMS) delay spread.
5
Xianhua Dai, Han Zhang, and Dong Li, “Linearly
Time-Varying Channel Estimation for MIMO/OFDM
Systems Using Superimposed Training,” IEEE Trans.
on Commun., vol. 58, no. 2, pp.681-693, Feb. 2010
Super Imposed training sequence, a
fixed lower-bound as the training
6
Chao-Cheng Tu and Benoît Champagne, “Subspace-
Based Blind Channel Estimation for MIMO-OFDM
Systems With Reduced Time Averaging,” IEEE Trans.
Veh. Technol, vol. 59, no. 3, pp.1539-1545, Mar. 2010.
Blind channel estimation,
Complexity more
7
Ayman Assra, Walaa Hamouda, and Amr Youssef,
“A Channel-Estimation and Data-Detection scheme
for Multiuser MIMO-CDMA Systems in Fading
Channels,” IEEE Trans. Veh. Technol, vol. 59, no. 6,
pp.2830-2844, July 2010
Channel estimation and data
detection scheme based on the
superimposed training technique
6

7. 8
Vincent S, Faouzi B, Yves L ; A Joint MMSE
Channel and Noise Variance Estimation for
OFDM/OQAM Modulation. IEEE Transactions on
Communications 63: 4254-4266. (2015)
Low rank approximation method.
9
Bai, Z., Jia, J., Wang, C.-X., & Yuan, D. (2015).
Performance analysis of SNR-based incremental
hybrid decode-amplify-forward cooperative
relaying protocol. IEEE Transactions on
Communications, 63, 2094–2106.
New relaying scheme, the three node
cooperative relaying system, named as
incremental decode – amplify - forward
relaying
10
Liu, W.-C., & Liu, Y.-C. (2016). Performance
analysis of relay selection with enhanced dynamic
decode-and-forward and network coding in two-
way relay channels. Information Theory, 20 Jan
2016. arXiv:1602.07943.
Relay selection (RS), enhanced dynamic
decode – and - forward (EDDF), and
network coding (NC)
11
Sun, H., et al. (2017). Hop-by-hop relay selection
strategy for multi-hop relay networks with
imperfect CSI. IET Communications, 11(9), 1387–
1395.
Relay can be implemented in a
distributed manner without the need for
a central controller
7

8. 12
Yuzgecioglu M, Jorswieck E; Hybrid Beamforming
with Spatial Modulation in Multi-user Massive
MIMO mmWave Networks. arXiv: 1709.04826v1.
(2017)
Spatial modulation (SM) with
Beamforming. Digital combining at the
receiver.
13
Duy, T. T., & Kong, H. Y. Performance analysis of
mixed amplify and forward and decode and
forward protocol in underlay cognitive networks.
IEEE China Communications, 13(3), 115–126. (2016)
One relay always operates in AF mode,
while the remaining one always
operates in DF mode. Proposed relay
selection method, which relies on the
decoding status at the DF relay.
14
Molisch AF, Ratnam VV, Han S, Li Z, Nguyen SLH,
et al. Hybrid beamforming for massive MIMOa
survey. arXiv preprint arXiv: 1609.05078. (2016)
Channel state information
15
Guangxu Z, Kaibin H, Vincent KN Lau, Bin Xia,
Xiaofan Li, et al. Hybrid Interference Cancellation in
Millimeter-Wave MIMO Systems. 2016 IEEE
International Conference on Communication
Systems (ICCS). (2017)
Phase adjustment - limitation of a
phase array and limited RF chains
render the conventional design
8

9. Problem Statement
• It is a necessity to design a system that required an advancement and perfect
transmission of data over wireless multipath channels. (i.e. maintain QoS).
• MIMO is technique chosen for realizing the same. But fails to meet the
demands of wireless communication system in all case. Many researchers
working on to improve channel capacity, bandwidth, gain, polarization
diversity etc.
• Similarly technology like OFDM, is an effective technique that combats
frequency selective fading channels. It fails when increase more number of
subcarriers. It address the issue like PAPR, phase offset.
– A peak in the signal power will occur when all, or most, of the sub-carriers align themselves in
phase.
9

10. Research Objectives
 To improve the performance of SNR in the receiver and reduce the
BER in MIMO-OFDM system includes with this techniques.
 Module I - Adaptive Pilot Channel Estimation Technique with Equalization
 Module II - Adaptive Modulation and Coding
 Module III - Hybrid Adaptive Relay
 Module IV - Partially Connected Hybrid Beam Forming
10

11. Module 1
Adaptive pilot channel estimation technique with
equalization
11

12. Channel Estimation
 The link quality will be estimated based on the BER value at
received signal.
Three type of channel estimation
 Blind
 Use inherent structure of signal
 Constrained to special cases, less accurate
 Semi-blind
 Use minimal amount of known symbols with inherent structure of signal
 Training-based
 Use pilot symbols/tones
 More accuracy
 Consume bandwidth, capacity loss
12

13. Training-based Channel Estimation
 The pilot tones are fixed for both channel conditions like Doppler
spread and Delay spread means, then spectral efficiency will be less
for good channel conditions using OFDM transmission.
 Several investigations have deal to found out the best pilot tone
arrangements to estimate the channel as well as to increase the
spectral efficiency.
 Using different pilot pattern like rectangular and hexagonal etc., are
used to estimate the signal but here also the pilot tone arrangements
are fixed.
13

14. Adaptive Pilot Channel Estimation
 The proposed method of Adaptive Pilot Channel Estimation (APCE) is
expected to overcome the fading channel constraints and to increase the
spectral efficiency.
 Based on the BER value at the receiver side, the transmitter can
maximize or minimize the pilot tones.
 Here pilot tones are arranged in more appropriate and adaptive way
based on the CSI. i.e APCE dynamically utilized the pilot tones based on
the channel conditions like Doppler spread or delay spread.
 APCE integrates the goodness in the frequency domain and time
domain. For equalization purposes, set of equalizers such as LS, ZF,
MMSE are employed. 14

15. Adaptive Pilot Channel Estimation
∆p t= 4
∆pf=6
B1
∆pf=4
∆p t= 4
B4
∆pf=6 ∆p t= 3
A1
∆pf=8
∆p t= 5
B3
∆p t= 5
∆pf=6 B2
A 1 - Basic Pilot B1 - Low Doppler Spread
B2 - High Doppler Spread
B3 - Low delay Spread
B4 - High Delay Spread
15

16. Flow Chart for APCE Model
Pilot Signal Decrease
Doppler spread or
Delay spread =Th
CSI
Pilot Signal Increase
Doppler spread or
Delay spread >Th
No
Yes
Yes No
16

17. Adaptive Pilot Channel Estimation Comm.
system
CQI Feedback
Source
Modulation
Adaptive Pilot
Insertion
Adaptive Pilot
Insertion
S
/
P Guard
Insertion
Guard
Insertion
IDFT
IDFT
P/S
P/S
Channel Quality
Indicator
S/P
Guard
Removal
DFT
DFT
P
/
S
Demodulator
Destination
Adaptive
Pilot
Channel
Estimation
Channel
Predictions
Equalizer /
Detector
S/P
Guard
Removal
17

18. Estimation of Channel
Where eq(k) denotes summation of the estimation error at subcarrier k
Where iq(k) denotes summation of the estimation error at subcarrier k for noise
1 1
, (t 1)
( ),p,q ,
1 0 0
q
1
(k) (l) h ( , ( ) e (k)) (s)
MT N L M
k s
m i p q p
P s l i
Rq b l m i X
 

   
 
1 1
, (t 1)
( ),p,q ,
1 0 0
q
1
(k) (l) h ( , ( ) (k) (s) )
MT N L M
k s
m i p q p
P s l i
Rq b l m i X i
 

   
 
18

19. Simulation Parameters
Parameters Description
Modulation QPSK
Estimation / Equalization APCE / LS, ZF, MMSE
No. of Sub Carrier 64, 128
Channel Model Rayleigh
Antennas Configurations 2x2, 4x4
19

20. Performance analysis of SISO and MIMO
20

21. Analysis of APCE at target BER 10−3
Spec. SNR (dB)
SISO MIMO-APCE -64 SC MIMO-APCE-128 SC
1×1 2×2 4×4 2×2 4×4
~ 30 19.9 17.5 19 16
 Compared between 128SC with 64SC for 4X4 antenna, 8.5% performance is increased
 Compared between 128SC with 64SC for 2X2 antenna, 4.5% performance is increased
21

22. Performance analysis of different equalization
technique for 100km/hr
22

23. Performance analysis of different equalization
for 200km/hr
23

24. Analysis of different speed for 128 SC with
MMSE at target BER 10−3
Spec. 100Km/hr 200Km/hr
Antenna 2×2 4×4 2×2 4×4
SNR (dB) 22.2 20 28.2 27
24

25. Achievable Rate Analysis of MIMO APCE
25

26. Achievable Rate Analysis for SISO and MIMO
Spec. Pt=4, Pf=6 Pt=4, Pf=4
Antenna SISO 1×1 APCE MIMO 4×4 APCE SISO 1×1 APCE MIMO 4×4 APCE
SNR (25dB) 2.25 3.8 1.7 3.5
26

27. Module – 1 - Result
• In this phase an APCE technique is used to estimate the channel,
together with MMSE used for equalizations to predict the channel
conditions.
• With reference to the CSI signal, the transmitter can allocate the
resource block dynamically to transmit the data thereby fulfilling the
objective of this phase.
• On performing a comparative analysis based on BER vs SNR,
MIMO-OFDM system with MMSE technique along with QPSK
modulation with 4X4 antennas and 128SC gives an improved
efficiency of 9.3%.
27

28. Module 2
Adaptive Modulation and Coding
28

29. Why adaptive modulation?
• The adaptive modulation approach is utilized towards channel
adoption conditions, which maximize the spectral efficiency and
reduce the transmission loss. Therefore QoS of the time-varying
channel can be controlled.
– (i.e., adaptive modulation and coding) increases the spectral
efficiency and reliability of the link.
– Adaptive modulation is an important method for effective usage of
the spectrum in MIMO-OFDM systems.
29

30. Adaptive modulation
• A set of pilot signals used to get the information about the channel
and the modulation type, jointly.
• Here the APCE and AM will jointly maximize the spectral efficiency
over the fading channel.
• In adaptive modulation scheme, three different modulation schemes
are considered for evaluations.
– The modulation schemes are BPSK, QPSK, and QAM modulation.
• When the received SNR value is less than 10dB means BPSK
modulation, if it is above 10dB and below 20dB means QPSK else
QAM will be employed (for this case).
30

31. Flow Chart for APCE and AM Model
Pilot Signal Decrease
then modulation rate
increase
Doppler spread or
Delay spread =Th
CSI
Pilot Signal Increase
then modulation rate
decrease
Doppler spread or
Delay spread >Th
No
Yes
Yes No
31

32. System Model for Adaptive Modulation with
APCE
Channel
Quality
Indicator
S/P
Guard
Removal
DFT
DFT
P
/
S
Demodulator
Destination
Adaptive
Pilot
Channel
Estimation
Channel
Predictions
Equalizer /
Detector
S/P
Guard
Removal
Modulation
mode Selector
CQI Feedback
Source
Adaptive
Modulation
Adaptive Pilot
Insertion
Adaptive Pilot
Insertion
S
/
P Guard
insertion
Guard
insertion
IDFT
IDFT
P/S
P/S
Modulation
mode
Controller
32

33. Adaptive Rate and Power Schemes
• Spectral efficiency is maximizing by power adaptation policy
• Spectral efficiency is given by
– γ - average snr:
– k - power loss factor
– The constellation size associated with each γ by discretizing the range of channel fade levels.
2log ( ) p( ) d
KK
R
K
B



  

33

34. Performance analysis of AM for BPSK
34

35. Performance analysis of AM for QPSK
35

36. Performance analysis of AM for QAM
36

37. Numerical analysis for APCE and AM at target
BER 𝟏𝟎−𝟑
Modulation
Level
64 SC 128 SC
2×2 4×4 2×2 4×4
BPSK
SNR(dB)
18(dB) 15.7(dB) 16.8(dB) 14.6(dB)
QPSK 19.8 17.2 18.4 15.6
QAM 20.4 19.6 18 16
37

38. Modulation level Comparisons with BEP vs SNR
BPSK
QPSK
QAM
38

39. Scatter plot of the modulation M= 2 Scatter plot of the modulation M= 4 Scatter plot of the modulation M= 8
Symbol Mapping for different modulation level
39
39

40. Scatter plot of the modulation M= 16 Scatter plot of the modulation M= 32 Scatter plot of the modulation M= 64
Symbol Mapping for different modulation level
40

41. Comparisons for BEP vs Doppler Frequency with
different SNR
41

42. Comparisons for BEP vs Doppler Frequency with
different SNR
42

43. Module - 2 - Result
• APCE and AM jointly works to identify the perfect modulation for
transmission and it maximizes the spectral efficiency.
• It is observed that the required SNR for BPSK is 6.4% less than the
required SNR for QPSK. Similarly for QPSK it is 2.5% less than the
required SNR for QAM, this improves the overall performance of the
system.
43

44. Module 3
Hybrid Adaptive Relay Technique
44

45. Need for Relay
– The relay technique is an alternative way to mitigate channel fading
and improve the coverage area.
– To reduce power consumption at the receiver end.
– To increase coverage area without involving new base station.
– Relay node reduces the channel impairments due to multi-path.
45

46. Cont..
• In this module a Hybrid Adaptive Relay (HAR) is refers to as a
truncated function of Amplify and forward relay and Dynamic
Decoding relay.
• Global knowledge of all Relay metrics is needed for the source to
determine which relay is the best to coordinate else to determines not
via executing a back-off mechanism.
• Every relay node should monitor the instantaneous channel state
conditions towards the source and the destination
46

47. Relaying Strategies
47
Relay types
Amplify and forward Decode and forward Compress-and-forward
Hybrid Adaptive Relay
Source Receiver
Hybrid Adaptive Relay
(HAR)
Multi hop
hr,d
hs,d
hs,r
Hybrid Adaptive Relay
(HAR)

48. Yes
Yes
No
No
Use CRC
to rectify
error
Dynamic
decode
Forward
Relay work
If not, ARQ will
initiate the
Source to
retransmit msg.
Receiver
No
Yes
No
Amplify
Forward
Relay
Received
signal = 1st
threshold
value
No
sync
Relay (R)
YesR in
Coverage
area
Receiver
Source (S)
TX message
Check Threshold
48
Flowchart

49. Hybrid Adaptive Relay (HAR) with APCE, AM
HAR
HAR
P/S
P/S
CQI Feedback
Source
Adaptive
Modulation
Adaptive Pilot
Insertion
Adaptive Pilot
Insertion
S
/
P Guard
Insertion
Guard
Insertion
IDFT
IDFT
Modulation
mode Controller
S / P
Guard
Removal
DFT
DFT
P
/
S
Demodulator
Destination
Adaptive Pilot
Channel
Estimation
Channel
Predictions
Equalizer /
Detector
S / P
Guard
Removal
Channel
Quality
Indicator
Modulation
mode Selector
49

50. Performance analysis of APCE, AM and HAR for
MIMO-OFDM - BPSK
50

51. Performance analysis of APCE, AM and HAR for
MIMO-OFDM -QPSK
51

52. Performance analysis of APCE, AM and HAR for
MIMO-OFDM – QAM
52

53. Numerical results of HAR for MIMO-OFDM at
target 𝟏𝟎−𝟑
Modulation
64 SC 128 SC
2×1×2 4×1×4 2×1×2 4×1×4
BPSK
SNR(dB)
14.2(dB) 11.5(dB) 13.2(dB) 10.8(dB)
QPSK 15.2 13 14 11.4
QAM 16 13.8 15 12.2
53

54. Perfect CSI vs Imperfect CSI
54

55. Average Channel Capacities for HAR with AF
and DF
55

56. Outage Probability for HAR with DFR
BER
SNR (dB)
HAR DF
10-3 11.5 12.5
56

57. Numerical Results of AM and HAR for MIMO-OFDM for
128SC at target 𝟏𝟎−𝟑
Modulation
AM HAR
2×2 4×4 2×1×2 4×1×4
BPSK
SNR(dB)
16.8(dB) 14.6(dB) 13.2(dB) 10.8(dB)
QPSK 18.4 15.6 14 11.4
QAM 18 16 15 12.2
57

58. Result
 The HAR completely decodes the received information or it only amplifies the
received signal and then it forward towards the receiver.
 Here, the coverage area and outage probability is significantly improved by HAR
and the transmitter works with low computation cost, thereby the network results
are enhanced.
 Totally 26% performance significantly increases with the optimal decoding.
APCE,AM HAR
128 4X4 15.6 11.4
0
5
10
15
20
SNR
SNR Comparison
58

59. Module 4
Partially Connected Hybrid Beamforming Technique
59

60. Need for Beamforming
 Much higher Gain than Omni directional antennas
– Increased coverage and reduce number of antennas elements
 Reject interference
– Improve SNR and system capacity
 Provides diversity gain
– Increases the signal quality
60

61. Beam Forming
• Beamforming is a powerful signal processing technique used an
antennas can be steered to transmit the radio signal in a specific
direction.
• Antennas arrays allows to control the radiation pattern by adjusting
the amplitude and phase of the signal received from each element.
• It can reduce the beam power for nearby users and hence interference
issues near to the cell towers can be avoided.
– Coverage – good
– Interference – very less
– Capacity – high
61

62. Partial Connected Hybrid Beamforming
• PC-HBF overcomes the limitations of both analog and digital BF.
– Single signal is fed to each antenna element or multiple
– Can manage and generate only one signal beam or multiple beams
– Recover both the amplitudes and phases
– Requires high DSP process
– Hardware complexity ( expense)
• How Many Phase Shifters are Needed? - Improve Spectral Efficiency
62
Analog Beamforming Digital Beamforming
Partial Connected Hybrid Beamforming

63. Beamforming with different radiating element
63

64. Transceiver block with RF chain
64
Data
Modulation
Digital
Equalizer
RF Chain 1
RF Chain 2
RF Chain n
Analog
Precoder
Ant1,u
Ant2,u
Antn,u
BS Digital processRx0 Analog process
1-bit quantized value for feedback
S1,u
S2,u
Sn,u
1-bit quantized feedback
Bit Tx1
Bit Tx2
Bit Txn
Data
Modulation
RF Chain 1
RF Chain 2
RF Chain n
Digital
Precoder
K=NA
Analog
Precoder
Ant1,u
Ant2,u
Antn,u

65. RF Chain
RF Chain
Digital
Baseband
Beamformer
(FBB)
Bits
.
.
k
N
Analog Beamformer Interferes
Intended users
Partially Connected Hybrid Beamforming
This architecture type uses separate antenna array (known as "sub-array") for RF beam
former of individual RF chain. Channel fading index (CI=0) for flat fading and CI=1 for
frequency selective fading.
65

66. Estimation
2 2
, , , , , , ,
1 1 

   H H
k i k i k j k j k i l j k i
j j
l k
y h v x h v n
The received signal at user 𝑘
H H H H H H
BB RF X BB RF ISI BB RFy F F G F F H F F n  
Hence, the below equation Y gives the output of the hybrid beam former of the kth
user is given
66

67. Flowchart
67
Decrease the Pilot Signal,
modulation increase
Doppler spread or
Delay spread =Th
CQI
Increase the Pilot Signal,
modulation decrease
No
Yes
Yes No
Doppler spread or
Delay spread >Th
Beamforming vectors change dynamically

68. System model with APCE, AM, HAR and PCHBF
HAR
HAR
S / P
Guard
Removal
DFT
DFT
P
/
S
Demodulator
Destination
Adaptive Pilot
Channel
Estimation
Channel
Predictions
Equalizer /
Detector
S / P
Guard
Removal
Channel
Quality
Indicator
Modulation
mode Selector
P/S
P/S
CQI Feedback
Adaptive Pilot
Insertion
Adaptive Pilot
Insertion
Guard
Insertion
Guard
Insertion
IDFT
IDFT
Source
Adaptive
Modulation
S
/
P
Modulation
mode
Controller
Transmit
Beamformin
g
AoA
Information
68

69. Simulation Parameters
69
Parameter Value
No. of antennas elements array 64
Number of RF chains 4, 8
The number of channel paths 4
Channel Rayleigh fading

70. Performance analysis of PC-HBF for MIMO-
OFDM BPSK
70

71. Performance analysis of PC-HBF for MIMO-
OFDM - QPSK
71

72. Performance analysis of PC-HBF for MIMO-
OFDM -QAM
72

73. APCE, AM, HAR and PCHBF for MIMO 10−3
Modulation
Level
Antenna Configuration and Subcarrier
64 SC 128 SC
2×1×2 4×1×4 2×1×2 4×1×4
BPSK
SNR(dB)
10.2(dB) 9.4(dB) 8.8(dB) 7.6(dB)
QPSK 11.4 10.6 9.2 8.3
QAM 14 11.4 12.6 10
73

74. AoA estimations (deg) vs Sum Rate
74

75. Sum rate analysis for antennas arrays
75

76. Loss calculation for multipath channel
76

77. Module – 4 - Result
• An extensive set of analysis is carried out to analyze the efficiency of the PC-
HBF for MIMO-OFDM systems with varying number of antenna arrays, set
of subcarriers and RF links.
• The results indicate that the system capacity for the applied model is
increased significantly and the ISI is reduced with an increased number of
subcarriers, antennas array, as well as RF links. It is also noted that PC-HBF
is found to be better than analog BF but not than fully digital BF.
• The results show that the PC-HBF achieves better tradeoff between
computational complexity and a good channel capacity with reduction of
ICI.
77

78. Research Outcomes
MMSE with APCE, AM, HAR and PC-HBF for MIMO-OFDM
78

79. MMSE with APCE, HAR and PCHBF for MIMO-
OFDM 64SC, 2×2
79

80. MMSE with APCE, HAR and PCHBF for MIMO-
OFDM 64SC 4×4
80

81. MMSE with APCE, HAR and PCHBF for MIMO-
OFDM 128SC 2×2
81

82. MMSE with APCE, HAR and PCHBF for MIMO-
OFDM 128SC 4×4
82

83. Numerical results of APCE, AM and HAR for
MIMO-OFDM for 128SC at target 10−3
Modulation
APCE, AM HAR PC-HBF
2×2 4×4 2×1×2 4×1×4 2×1×2 4×1×4
BPSK
SNR(dB)
16.8(dB) 14.6(dB) 13.2(dB) 10.8(dB) 9.4(dB) 7.4(dB)
QPSK 18.2 16 14 11.8 10.2 8.4
QAM 19.4 16 15 12 13 10
83

84. 0
2
4
6
8
10
12
14
16
18
APCE,AM HAR PC-HBF
SNR ComparisonSNR(dB)
84

85. APCE, AM vs HAR and APCE, AM, HAR vs PC-HBF
for QPSK at 10−3
Spec.
MMSE with APCE and
AM
MMSE with APCE, AM
and HAR
MMSE with APCE, AM,
HAR, and PC-HBF
SC 2×2 4×4 2×1×2 4×1×4 2×1×2 4×1×4
64
SNR(dB)
20(dB) 17.5(dB) 15.2(dB) 13.8(dB) 11(dB) 10(dB)
128 18.2 16 14 12 10.2 8.4
85

86. • Comparisons between APCE, AM and HAR is increased by 25%.
• Comparisons between HAR and PC-HBF is increased by 27%.
• Comparisons between APCE,AM and PC-HBF is increased by 44%.
25.00%
27.00%
44.00%
APCE,AM vs HAR HAR vs PC-HBF APCE,AM vs PC-HBF
Overall Performance
86

87. Summary
• By mitigating the interference present in the wireless
system APCE, AM, HAR and PC-HBF that
considerably enhanced the system performance.
• This system should dynamically share the network
resources to meet the requirements of all users.
• Cooperating Systems
– Multiple fold increase in spectral efficiency
– Less energy consumption.
– More controls (yields in better performance).
– Improved capacity, coverage, and cell edge throughput.
– Increased system complexity, and the large signaling overhead,
which is reduced by distributed optimization.
PC-HBF
HAR
APCE,
AM
87
SNR

88. Conclusion
• The proposed work has mainly focused on four different ways to improve the
overall spectral efficiency, system capacity, and reduction of ISI of the system.
• The performance of MIMO-OFDM system is suggested with different channel
estimations and data detection techniques for time varying and frequency selective
fading channels using the techniques like APCE, AM, HAR and PC-HBF system
significantly improves the performance of overall system.
• The proposed system has been simulated for different scenario and the performance
and results have also been verified. It is also clear that the increase in number of
antennas, number of subcarriers and number of relays it reduces the required SNR
for a given BER.
• Reduce the transmitter power of the network and will ensure eco-friendly
communication.
88

89. Summary
MMSE with APCE, AM, HAR and PC-HBF for MIMO-OFDM
Future Work
• NOMA ,
• Adaptive carrier
• Numerology,
• Index Modulation
AM
Different modulation
level to increase system
performance
APCE
Reduce Pilot Symbols
and increase spectral
efficiency
APCE AM HAR PC-HBF Future Work
HAR
Increase coverage area
reduce transmitter
power
PC-HBF
Increase SNR and
spectral efficiency
89

90. Future work
• Several methods to enhance the spectral efficiency of OFDM based wireless systems are
proposed in this thesis. However there are many possible areas which can be explored
further.
• In future, the presented research work can be extended to use jointly optimizing the TX and
RX beam formers to maximization SNR and the link adaption.
• The proposed techniques is also suitable for single carrier FDMA (SC-FDMA) in order to
avoid the problem in OFDM such as High peak to average power ratio (PAPR) due to wide
bandwidth (large number of subcarrier) and the requirement of high power amplifier leads
to nonlinear distortion.
• Adaptive Multiplexing techniques schemes also a another issue further investigation. By
incorporating Numerology technique the spectral efficiency is still more increased and
throughput also increased. Similarly adaptive subcarrier also a novel technique to utilize the
resource in good manner and still more supporting to enhance the through put up to 1Gbps.
90

91. Publications
91

92. International Journals
1. Lenin .S.B and Malarkkan .S (2018), “A Hybrid adaptive relay Technique for Cooperative Communication
System” published in Journal of Wireless Personal Communications, Springer. PP 2245–2258 (2018).
(SCI Indexed ; Impact factor. 1.302).
2. Lenin .S.B and Malarkkan .S (2014), “An Extensive Review of Significant Researches on Channel Estimation
in MIMO-OFDM”, published in Journal of Theoretical and Applied Information Technology on 20th June
2014, Vol. 64, No.2. ISSN: 1992-8645.
3. Lenin .S.B and Malarkkan .S (2014), “Performance Analysis of Adaptive Modulation for High Mobility”
published in Journal of Theoretical and Applied Information Technology on 20th September 2014, Vol. 67,
No.2. ISSN: 1992-8645.
4. Lenin .S.B and Malarkkan .S (2019), “Hybrid Adaptive Channel Estimation Technique in Time and Frequency
Domain for MIMO-OFDM Systems,” published in International Journal of Recent Technology and
Engineering(TM) on 11th February 2019, vol.7 issue – 5c. ISSN: 2277-3878.
5. Lenin .S.B and Malarkkan .S (2019), “MMSE Partially Connected Hybrid Beam forming in MIMO-OFDM
Systems” published in Journal of Telecommunications System & Management on 17th June 2019, Vol 8(2),
No.2. ISSN: 2167-0919.
92

93. International conference
• S.B. Lenin and Dr. S. Malarkkan “IMPACT OF ROBUST CHANNEL ESTIMATION FOR HIGH
MOBILITY SYSTEMS” presented oral presentation in “IEEE international conference on
Electrical, Computer and Communication Technologies (ICECCT)” held at SVS college of
Engineering, Coimbatore during 05 -07 March 2015.
• S.B. Lenin and Dr. S. Malarkkan “ADAPTIVE CHANNEL ESTIMATION FOR HIGH DOPPLER
CHANNEL” presented as oral presentation in “IEEE international conference on Signal
Processing, Communication and Networking (ICSCN 2015)” held at Anna University, Madras
Institute of Technology Campus, Chennai during 26th -28th March 2015.
93

94. References
1. A. Saad, M. Ismail and N. Misran, "Correlated MIMO Rayleigh Channels: Eigenmodes and Capacity
Analyses", International Journal of Computer Science and Network Security, Vol. 8 No. 12, pp. 75-81,
Dec 2008.
2. Jlanxuan Du and Ye (Geoffrey) Li, "D-BLAST OFDM with Channel Estimation", EURASIP Journal on
Applied Signal Processing, Vol.5, pp. 605-612, 2004.
3. Veena M.B and M.N. Shanmukha Swamy, "Implementation of Channel Estimation and Modulation
Technique for MIMO System", International Journal of Wireless & Mobile Networks, Vol. 3, No. 2, pp.
126-136, April 2011.
4. Bjorn Olav Hogstad, Gulzaib Rafiq, Valeri Kontorovitch and Matthias Patzold, "Capacity Studies of
Spatially Correlated MIMO Rice Channels", In Proceedings of 5th International Symposium on
Wireless Pervasive Computing, pp. 45-50, 2010.
5. Hou Xiao-Yun, Zheng Bao-Yu, Xu You-Yun and Song Wen-Tao, “An Improved Channel Estimation
with Multipath Search for MIMOOFDM Systems”, Journal of Zhejiang University Science A, Vol.7,
No.2, pp. 149-155, 2006.
6. V. Loshakov and Z. Vadia, "Adaptive Modulation of Signals in MIMO Channels", Journal of
Telecommunications Problems, No.1, Vol.1, pp. 102-108, 2010.
94

95. References
7. N. Noori and H. Oraizi, "Evaluation of MIMO Channel Capacity in Indoor Environments using Vector
Parabolic Equation Method", Progress in Electromagnetics Research B, Vol. 4, pp. 13– 25, 2008.
8. A. Rusko, V. Novikovs and G. Balodis, "Distance and Bandwidth Estimation for MIMO Channel",
elektronika ir elektrotechnika, No.8, pp. 49-52, 2007.
9. Daniel W. Bliss, Keith W. Forsythe, and Amanda M. Chan, "MIMO Wireless Communication", Lincoln
Laboratory Journal, Vol.15, No.1, pp. 97-126, 2005.
10. Ye (Geoffrey) Li, Jack H. Winters and Nelson R. Sollenberger, "MIMO-OFDM for Wireless
Communications: Signal Detection with Enhanced Channel Estimation", IEEE Transactions on
Communications, Vol. 50, No. 9, pp. 1471-1477, Sep 2002.
11. Baosheng Li and Milica Stojanovic, "A Simple Design for Joint Channel Estimation and Data
Detection in an Alamouti OFDM System", In Proceedings of OCEANS, pp. 1-5, Seattle,2010.
12. Dun Cao, Hongwei Du and Ming Fu, "Cubic Hermite Interpolation-based Channel Estimator for
MIMO-OFDM", Journal of Computational Information Systems Vol. 6, No. 14, pp. 4699- 4704, 2010.
95

96. References
13. Fabien Delestre and Yichuang Sun, "MIMO-OFDM with Pilot-Aided Channel Estimation for WiMax
Systems", In Proceedings of First International Workshop on the Performance Enhancements in MIMO-
OFDM Systems, pp. 586-590, 2010.
14. Stefano Tomasin, Alexei Gorokhov, Haibing Yang, and Jean-Paul Linnartz, "Iterative Interference
Cancellation and Channel Estimation for Mobile OFDM", IEEE Transactions on Wireless
Communications, Vol. 4, No. 1, pp. 238-245, Jan 2005.
15. Haowei Wu, Shizhong Yang, Jinglan Ou and Lisheng Yang, "Improved ICI Mitigation Scheme over
Time-varying Channels for High- Mobility OFDM Systems", Journal of Convergence Information
Technology, Vol. 6, No. 4. pp. 264-272, April 2011.
16. Seung Won Kang and KyungHi Chang, "A Novel Channel Estimation Scheme for OFDM/OQAM-IOTA
System", ETRI Journal, Vol. 29, No. 4, pp. 430-436, Aug 2007.
17. K. Vinoth Babu, K.V.N. Kavitha and K. Murali Babu, "Low -Complex ICI Reduction due to Channel
Estimation Error in OFDM systems using VSB-VBL Technique for High Mobility Applications",
International Journal of Computer Theory and Engineering, Vol. 1, No. 3, pp. 262-265, Aug 2009.
96

97. Indian Examiner - Questions
1. Table 3.1, from where the simulation parameters taken. Are they in conformity with current benchmarks of
wireless communications?
Antenna diversity, yes.
2. While calculating the BER with respect to the SNR, what value of noise floor has been taken for the
computation? Further MIMO shows better results over SISO; please explain the reason for it.
10dB.
For comparison between MIMO and SISO, power savings of 5-15dB in 4X4 MIMO system with respect SISO system and the throughput will increase 2.5X times
3. Has the antenna mobility qualitative related to SNR for long haul wireless communication? Please justify
your answer with appropriate reasoning quantitatively.
SNR is refers to the variance between the received signal and the noise floor. The noise floor is an incorrect background signal in the wireless medium
that is received from other devices. If the noise level is close to the signal level leads to data corruption, which will degrades the throughput and
latency in the wireless environment.
4. Figure 3.8: what sets the lower limit for average achievable rate for zero SNR? Further SISO APC actually shows a saturation tendency
for higher SNR. What are the possible reasons for the same?
For theoretical the consideration is variable
No diversity
Power cannot be increased at transmitted to a certain level
97

98. Cont.
5. Please explain equation 4.15 qualitatively.
4.15 is expressed about BER related to SNR comparison for modulation variation.
BERi γiS S′ ≤ BERmax; 0 ≤ i ≤ N − 1
6. Fig 4.11: why is BEP minimum for M= 4 as shown in the figure. Is there an analytical expression to prove the
variation as shown? Further as SNR increases, the BEP for M=16 also reduces, why?
BER is maximum at this point due to Doppler shift,
98

99. Foreign Examiner - Questions
1. Refer figure 3.2 and table 3.3 – you wrote „‟ additionally, it is observed that the BER is again decreased with
an increased number of antennas. ? Explain why this happens?
Due to Spatial diversity (antenna diversity), the SNR will be increased, so that the BER will decrease when number of antennas increase.
2. Refer figure 4.2. explain the comparative analysis in detail
The comparative analysis shows that the BER and SNR of 128SC with 4×4 APCE is less when compared to other systems, when the number of
subcarriers and antennas increases the BER decreases correspondingly the SNR decreases.
3. Refer figure 6.6 – explain the sum rate comparisons in detail
Figure show that when the number of RF chains increases, the average sum rate also increases. The F-DBF with 8-RF links performs well compared to
other approaches and better than the fully digital with 4-RF links.
4. Give more details of future research suggestions.
1. Several methods to enhance the spectral efficiency of OFDM based wireless systems are proposed in this thesis. However, there are many possible
areas, which can be explored further. It is found in a work that OFDM performs better than multi carrier spread is spectrum under full load
conditions whereas the reverse is true for low load situations. Therefore, hybrid adaptive multiplexing techniques, which combine these two
schemes suitably, can be a possible issue for further investigation.
2. The proposed techniques is also suitable for single carrier FDMA (SC-FDMA) in order to avoid the problem in OFDM such as High peak to
average power ratio (PAPR)due to wide bandwidth (large number of subcarrier) and the requirement of high power amplifier leads to nonlinear
distortion.
99

100. Thank you
Any Query?
100