The document discusses various techniques for mitigating wireless channel impairments, including diversity, MIMO, and OFDM techniques. It explains approaches such as space, time, and frequency diversity to enhance signal reliability and efficiency, highlighting the importance of adaptive methods based on specific channel conditions. Additionally, it covers how MIMO systems utilize multiple antennas to improve performance, as well as the role of OFDM in efficiently transmitting data over degraded channels.

1. Wireless
Channel
Impairment
Mitigation
Techniques
Reference: Haesik Kim, “ Wireless Communications Systems Design-
From Theory to Design”, Ch. 5, 2015 1

2. There are many types of wireless channel impairments
such as noise, path loss, shadowing, and fading and
impairment Mitigation techniques should be adopted
according to system requirements and channel
environments.
There are many techniques to mitigate wireless
channel impairments. For example: For the purpose of
mitigating delay spreads, Global System for Mobile
Communications (GSM) system uses adaptive channel
equalization techniques and Code Division Multiple
Access (CDMA) system uses a rake receiver.
Introduction
2

3. Diversity Techniques
3
Diversity techniques mitigate multipath fading effects
and improve the reliability of a signal by utilizing
multiple received signals with different characteristics.
Space diversity uses multiple antennas
Time diversity uses different time slots
Frequency diversity uses different frequency
slots.

4. 4
Diversity channels

5. Space diversity
5
Space diversity uses multiple antennas and is classified
into macroscopic diversity and microscopic diversity.
Macroscopic diversity mitigates large‐scale fading
caused by log normal fading and shadowing. To achieve
macroscopic diversity, antennas are spaced far enough
and we select an antenna which is not shadowed. Thus,
we can increase the signal to noise ratio.
 Microscopic diversity mitigates small‐scale fading
caused by multipath. To achieve microscopic diversity, a
multiple antenna technique is used as well and an
antenna is selected to have a signal with small fading
effect.

6. 6
Space diversity
Example of channel
responses and the
average of two
channel responses

7. Time diversity
7
Time diversity uses different time slots.
Basically, consecutive signals are highly
correlated in wireless channels. Thus, a time
diversity technique transmits same signal
sequences in different time slots. The time
sequence difference should be larger than the
channel coherence time. An interleaving
technique is one of time diversity techniques.

8. 8
Time diversity

9. Frequency diversity
9
Frequency diversity uses different frequency
slots. It transmits a signal through different
frequencies or spreads it over a wide frequency
spectrum. Frequency diversity is based on he
fact that the fading effect is differently
appeared in different frequencies separated by
more than the channel coherence bandwidth.
When the channel coherence he transmission
bandwidth is greater than bandwidth (namely,
it is a broadband system), the frequency
diversity Technique is useful.

10. 10
Frequency diversity

11. Combining Techniques for Diversity
11
Maximal Ratio Combining(MRC)
Equal Gain Combining (EGC)
Selection Combining (SC)
The signal, s(t), is transmitted through L different
channels. The each received signal, rl(t), through
different channels is represented by Channel: gain
(αl) and phase
rotation (φl)
nl(t) : Gaussian
noise

12. 12
MRC technique

13. 13
The received signal,
r(t), is weighted by wl
SNR, γ, of the
received
signal, r(t),

14. 14
Schwartz’s
inequality
The equality holds if
for all l, where K is an arbitrary complex constant

15. 15
The maximum SNR, γmax, can be found when

16. 16
EGC technique
Combining all signals using phase estimation and
unitary weight to achieve a high SNR

17. 17
the SNR, γ, of the
received signal, r(t),

18. SC technique
18

19. Summary: Diversity techniques
19

20. Multi-Input Multi-OutPut Techniques
20
MIMO techniques use the multiple antennas at a
transmitter and receiver.
They are very effective to mitigate the degradation of
fading channels and enhance the link quality between a
transmitter and a receiver. Especially, they improve
Signal to Noise Ratio (SNR), Signal to Interference plus
Noise Ratio (SINR), spectral efficiency, and error
probability.
The MIMO techniques are classified into spatial
diversity techniques, spatial multiplexing techniques,
and beamforming techniques.

21. Spatial diversity
21
•Spatial diversity techniques target to decrease the error
probability. A transmitter sends multiple copies of the
same data sequence and a receiver combines them.
MIMO as spatial diversity technique

22. 22
Transmit diversity: The transmitter has multiple
antennas and pre‐processing blocks for combining the
multiple same data sequences. We typically assume
the receiver has channel knowledge. Several
well‐known spatial diversity techniques are
Space‐Time Block Codes (STBCs) and Space‐Time
Trellis Codes (STTCs) for improving the reliability of
the data transmission. The STBC provides us with
diversity gain only. However, the STTC uses
convolutional codes and provides us with both code
gain and diversity gain.
Receiver diversity: The receiver has multiple antennas
and combining techniques such as MRC, EGC, and SC.

23. Spatial multiplexing
23
Spatial multiplexing techniques substantially increase
spectral efficiency. A transmitter sends Nt data
sequences simultaneously in the different antennas and
same frequency band and a receiver detects them using
an interference cancellation algorithm.
MIMO as spatial multiplexing technique

24. 24
This technique improves spectral efficiency because
multiple data sequences are transmitted in parallel.
Thus, the spectral efficiency is improved by increasing
the number of the transmit antennas (Nt). In addition,
It can be expanded into multi‐user MIMO or Space‐
Division Multiple Access (SDMA). Multi‐user MIMO
techniques assign each data sequence to each user.
Multiuser MIMO

25. Beamforming techniques
25
They are signal processing techniques for directional
transmission The beamformer with Nt antenna elements
combines Radio Frequency (RF) signals of each antenna
element to have a certain direction by adjusting phases
and weighting of RF signals. Thus, this technique brings
antenna gain and suppresses interferences in multiuser
environments.
In the modern wireless communications, the cell size is getting
smaller and the number of cells is getting bigger. The
interferences among cells became serious problem. Thus, the
interference mitigation technique among cells is an essential part
of wireless communication systems. The beamforming technique
is very effective to mitigate interferences.

26. 26
MIMO as beamforming technique

27. Fundamentals of MIMO Techniques
27
Spatial diversity techniques: MISO
Alamouti scheme with 2 × 1 antennas

28. Signal mapping of Alamouti scheme with 2 × 1 antennas
28
Each symbol experiences different channel responses
(h1 and h2). The received symbols
“*” =
complex
conjugate

29. 29
In matrix form
The combining technique in the receiver is performed.
Based on the orthogonal properties of H matrix

30. 30
The combined symbols are sent to ML detector and we
estimate the transmitted symbol
ML detection
The
combined
symbols

31. Spatial diversity Techniques: SIMO
31
We transmit s[t] and receive yi[t] via receive
antenna i at the time index t.
The receiver collects yi[t] using combining techniques
and obtains more reliable received symbols.
When dealing with spatial diversity techniques, it is
important to maintain uncorrelated antennas. Under
the uncorrelated condition, we can obtain diversity gain
which means SNR or SINR increases. If antennas are
strongly correlated, we cannot obtain diversity gain.

32. 32
Spatial diversity technique with 1 × Nr antennas

33. Spatial multiplexing techniques: BLAST & D-BLAST
33
BLAST : Bell laboratories layered space time
D-BLAST: Diagonal BLAST V-BLAST: Vertical BLAST
 It is especially useful for uplink systems due to the
limited number of antennas at mobile stations.
Sometime, this is called collaborative MIMO or
collaborative spatial multiplexing.

34. 34
D‐BLAST and V‐BLAST transmitter
with four antennas

35. 35
D‐BLAST and V‐BLAST transmitted data sequences
( Four-antenna Example)

36. Homework 1
36
Show with mathematical analysis how Channel
Impairment Mitigated in BLAST system.

37. • MIMO: A communications
technology that enhances the
transmission
• data rate without increasing the
signal bandwidth by
• combining multiple transmitters and
receivers.
37

38. Beamforming Techniques
38
They use array gain and control the direction of
signals by adjusting the magnitude and phase at each
antenna array. The array gain means a power gain of
multiple antennas with respect to single antenna.
Simple beamformer
Plane wave
departs from
omnidirectional
antennas

39. 39
The delay of departure
among antennas
Each signal si(t) at each omnidirectional antenna

40. 40
a= Antenna array
steering vector
controls the
direction of the
signals
Add the weighting vector w

41. 41
Thus, the signal power is strengthened in the desired
direction and weakened in the undesired direction. The
beamforming performance depends on finding the
suitable weighting vector w, antenna array
arrangement, distance d between antenna arrays, and
signal correlation.

42. Summary: MIMO
42

43. Orthogonal Frquency-Division Multiplexing
43
The OFDM technique is based on Frequency Division
Multiplexing (FDM) which transmits multiple signals in
multiple frequencies simultaneously.
FDM with three carriers

44. 44
One disadvantage of the FDM is a long guard band
between the carriers which makes spectral efficiency
of the FDM system worse. The OFDM uses the similar
concept but increases the spectral efficiency by
reducing the guard band between the subcarriers. This
can be achieved by orthogonality characteristic of the
OFDM system.
The OFDM system uses multiple subcarriers. Thus, it
needs multiple local oscillators to generate them and
multiple modulators to transmit them. However, a
practical OFDM system uses Fast Fourier Transform
(FFT) to generate this parallel data sequences.

45. 45
OFDM with three subcarriers

46. Advantages and Disadvantages of OFDM
46
The OFDM equalizer is much simpler to
implement than those in Code Division Multiple
Access (CDMA).
The OFDM system is almost completely
resistant to multipath fading due to very long
symbols.
The OFDM system is ideally suited to MIMO
techniques due to easy matching of transmit
signals to the uncorrelated wireless channel.
Advantages

47. 47
It is sensitive to frequency errors and phase
noises due to close subcarrier spacing.
It is sensitive to the Doppler shift which creates
interferences between subcarriers.
It creates a high peak to average power ratio.
It is more complex than other communication
systems when handling interferences at the cell
edge.
Disadvantage

48. System Modeling and implementation
48
Using Fast Fourier Transform (FFT) to implement
OFDM system is a big benefit because a local oscillator
is expensive and not easy to implement.
 In the transmitter of the OFDM system, the data
sequences are passed to Inverse FFT (IFFT) and these
data sequences are converted into parallel data
sequences which are combined by multiple subcarriers
with maintaining the orthogonality between
subcarriers.
In the receiver, the parallel data sequences are
converted into the serial data sequences by FFT.

49. 49
Although the OFDM system overcomes interferences
in frequency domain by orthogonality, the interference
problem still exists in time domain.
 One of the major problems in wireless
communication systems is Inter‐ISI. This is caused by
multipath and considered one important reason is a
distorted original signal.
 In the OFDM system, a Cyclic Prefix (CP) or Zero
Padding (ZP) is used to mitigate the effects of
multipath propagation. This can be represented as a
guard period which is located just in front of the data
and is able to mitigate delay spreads.

50. 50
OFDM transmitter with N parallel data sequence

51. 51
The baseband modulated symbol of the OFDM
Xk is the baseband modulated symbol such as BPSK,
QPSK, or QAM and N is the total number of subcarriers

52. 52
The subcarrier spacing

53. 53
In addition, we can regard this signal as a discrete OFDM
symbol when sampling the signal in every Ts/N.

54. 54
The OFDM transmitter
can be represented using
IFFT (Inverse Discrete
Fourier Transform, IDFT)

55. Cyclic prefix as a guard interval
55
When a cyclic prefix is inserted as a guard interval
Tg is a cyclic prefix length
The OFDM symbol

56. 56
Summary: OFDM

57. Transmitted OFDM Signal
57
This baseband signal is up‐converted to a carrier frequency fc
and we obtain the following the transmitted OFDM signal
The complex baseband signal x(t)
The received signal

58. 58
Up‐conversion
from the base
band signal to
the passband
signal
Down‐conver
sion from the
pass band
signal to the
baseband
signal

59. 59
Synchronization process is performed at trhe receiver using the
baseband signal y(t). The OFDM signal is very sensitive to
synchronization errors such as ISI and inter‐carrier interference.
Thus, this process is very important and should be implemented
very carefully. Generally, the synchronization of the OFDM
system is composed of three stages which are symbol Timing
synchronization, carrier frequency/phase offset
synchronization, and sampling clock/sampling frequency
synchronization.
The orthogonal frequency division multiple access
(OFDMA) is a multiple access scheme based on the
OFDM technique. The subcarriers in the OFDM
system are shared by multiple users in the OFDMA
system.

60. 60OFDM‐based communication system

61. Equalization
61
Due to a time dispersive channel by multipath fading,
Inter‐Symbol Interference (ISI) occurs and an equalizer
plays an important role in ISI compensation.
Channel model for equalization

62. 62
The frequency response of the equalizer should be
designed to satisfy the following equation
F(f) = Combined frequency response of the transmit
filter, channel, and receive filter

63. Zero Forcing Equalizer
63
One of simple equalizers using the inverse of the
channel frequency response.
Channel model for zero forcing equalizer

64. 64
This technique basically ignores AWGN.
The complexity is low but the performance is not
good.
However, the equalization in the OFDM system is not
much important because multipath fading is
compensated in the OFDM system itself. Each
subcarrier of the OFDM symbol experiences a flat
fading channel. Thus, the OFDM system does not need
a strong equalization and the one‐tap zero forcing
equalizer in frequency domain is enough to compensate
subcarrier distortions.

65. 65
In order to estimate how well an equalizer works, the
Mean Squared Error (MSE) is used as the metric. It is
defined as the mean squared error between the
received signal and the desired signal as follows

66. Channel Estimation
66
estimate the channel
response Hc(f )
The first type is to use training symbols (preambles) or
pilot symbols and the other type is a blind channel
estimation. The method using preambles in the OFDM
system is to reserve several OFDM symbols. The receiver
knows which symbol the transmitter sends without
interpolation.
Frame structure including preambles and data symbols

67. Types of Pilot Structures in the OFDM System
67
Block‐type pilot structure in the OFDM system

68. 68
Comb‐type pilot structure in the OFDM system

69. 69
Lattice‐type pilot structure in the OFDM system

70. 70
Block‐type pilot structure
Suitable for a frequency selective channel. The pilot
interval in time domain should be satisfied by
fd = Doppler spread and TOFDM
= OFDM symbol duration
Comb‐type pilot structure
Suitable for a time selective channel
τmax = Maximum delay spread
Δf = Subcarrier spacing in
frequency domain

71. 71
Lattice‐type pilot structure in the OFDM system
Combination of both the block‐type pilot structure and
the comb‐type pilot structure. It allocates a pilot signal
to a part of subcarriers and time slots as maintaining
specific interval. This structure is suitable for a
frequency and time selective channel. The pilot
interval inntime domain and frequency domain should
be satisfied by the following both equations

72. Homework 2
72
The Least Square (LS)
estimation and the MMSE
estimation are important
channel estimations based on
the training symbols or pilot
symbols.
Discuss with aid of mathematical analysis

73. Summary: Equalization
73