CHANNEL ESTIMATION IN MOBILE WIRELESS SYSTEMS

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International Journal of Electronics and Communication Engineering and Technology
(IJECET)
Volume 10, Issue 3, May-June 2019, pp. 01-07, Article ID: IJECET_10_03_001
Available online at
http://www.iaeme.com/IJECET/issues.asp?JType=IJECET&VType=10&IType=3
ISSN Print: 0976-6464 and ISSN Online: 0976-6472
© IAEME Publication
CHANNEL ESTIMATION IN MOBILE
WIRELESS SYSTEMS
Rashmi Mohan, Sagar S, Thejaswini N, Varsha P, Akshaya Y M and Pramila B
Department of Electronics and Communication, East West Institute of Technology
Bangalore, India
ABTRACT
One of vital parts of the mobile wireless channel is channel estimation significantly
improve the performance of the system, especially for 4G LTE and 5G systems. The
main goal of this project is to test the time-variant channel estimation using F-OFDM
model. It investigates the Mean Square Error (MSE), the Least Squares (LS) method
and compares performance of OFDM and F-OFDM using BER vs SNR.
Keywords: 4G LTE, 5G, OFDM, F-OFDM, MSE, LSE.
Cite this Article: Rashmi Mohan, Sagar S, Thejaswini N, Varsha P, Akshaya Y M and
Pramila B, Channel Estimation in Mobile Wireless Systems, International Journal of
Electronics and Communication Engineering and Technology, 10(3), 2019, pp. 01-07.
http://www.iaeme.com/IJECET/issues.asp?JType=IJECET&VType=10&IType=3
1. INTRODUCTION
The increasing demands require wireless transmission systems that can support a large number
of data hungry users that may be densely located and/or travelling at high velocities. In addition,
it is very likely that future wireless systems need to support not only the traditional devices that
are directly controlled by a user such as a mobile phone or a computer, but also more or less
autonomous devices that communicate amongst each other, the so-called Internet-of-Things
(IoT), causing the number of user equipment to increase.
Channel estimation is an important technique especially in mobile wireless network systems
where the wireless channel changes over time, usually caused by transmitter and/or receiver
being in motion at vehicular speed. Mobile wireless communication is adversely affected by
the multipath interference resulting from reflections from surroundings, such as hills, buildings
and other obstacles. In order to provide reliability and high data rates at the receiver, the system
needs an accurate estimate of the time-varying channel. Furthermore, mobile wireless systems
are one of the main technologies which used to provide services such as data communication,
voice, and video with quality of service (QOS) for both mobile users and nomadic. The
knowledge of the impulse response of mobile wireless propagation channels in the estimator is
an aid in acquiring important information for testing, designing or planning wireless
communication systems.

In 4G LTE networks, orthogonal frequency division multiplexing (OFDM) has served as
an elegant solution to combat the frequency selectivity and to boost the spectrum efficiency.
Recently, it is becoming a consensus that the basic waveform of 5G should be able to offer
including but not limited to: 1. Tailored services to different needs and channel characteristics,
2. Reduced out-of-band emission (OOBE), 3. Extra tolerance to time-frequency misalignment.
To avoid the limitations of OFDM and to meet the new challenges faced by 5G waveform,
here, in this paper, we present a new enabler for flexible waveform, named as filtered-OFDM
(f-OFDM). With subband-based splitting and filtering, independent OFDM systems (and
possibly other waveforms) are closely contained in the assigned bandwidth. In this way, f-
OFDM is capable of overcoming the drawbacks of OFDM whilst retaining the advantages of
it.
2. DESIGN METHODOLOGY OF OFDM AND F-OFDM
2.1. ORTHOGONAL FREQUENCY DIVISION MULTIPLEXING (OFDM)
Orthogonal frequency division multiplexing can accommodate high data rate in the mobile
wireless systems in order to handle multimedia services. It is important to understand the
OFDM technology because the channel estimation is an integral part of OFDM system. OFDM
technology can be used effectively to avoid the effect of frequency-selective fading and
narrowband interference from parallel closely spaced frequencies in mobile networks. If there
is no orthogonality in the channel, inter-channel interference (ICI) can be experienced. With
these vital advantages, OFDM technology has been widely used by many wireless standards
such as WLAN, WMAN, and DVB. In OFDM scheme, complex filters are not required and
time-spreading can be used without any complications in OFDM.
Figure 2.1 OFDM in mobile wireless system
OFDM scheme can also help to manage the single frequency networks (SFN) by sending
the same signals at the same frequency using adjacent transmitters without interfering each
other. OFDM has been popularly used for various wireless communication systems such as
fourth generation 4G wireless system.
The technology is intended for downlink in the physical layer. The OFDM channel is
divided into several narrowband sub-carriers and designed to be orthogonal with each other in
the frequency domain for carrying data symbols. Each sub-carrier has parallel narrow band-
pass channels at low symbol rate. The advantage of using a low symbol rate is that it is possible
to use a guard interval between symbols and to avoid the inter-symbol interference. In the
OFDM the total data rate for sub-carriers are equivalent to the single carrier modulation with
the same bandwidth. The OFDM plays an important role in the wideband of mobile wireless
systems.

Channel Estimation in Mobile Wireless Systems
2.2. FILTERED- ORTHOGONAL FREQUENCY DIVISION MULTIPLEXING
(F-OFDM)
It is a well-known fact that for higher data rates, higher Bandwidth is required. The fifth
generation of mobile communication would have higher bandwidth to fulfil this need. Filtered-
OFDM would utilize this bandwidth in such way that the entire spectrum is divided into several
smaller sub-bands. Each of these sub-bands would contain a personalized waveform in order to
satisfy the requirements of different services offered by the network. This means that
parameters like cyclic prefix (CP) transmit time interval (TTI) and pacing between the sub-
carriers in each sub-band would be in accordance with the nature of service. After this, each of
these sub-bands would be passed through a tailored filter in order to reduce interference among
each other. On the other hand, to support asynchronous transmission across sub-bands and rule
out the need of global synchronization, the time domain-based orthogonality between the sub-
bands is fragmented purposefully. Consequently, F-OFDM would also reduce the spectral
consumption of the guard band, which would increase the spectral efficiency of the system.
Figure 2.2 F-OFDM Transmitter
Figure 2.3 F-OFDM Receiver
Figure 2.2 and Figure 2.3 show the basic transmitter and receiver structural design for F-
OFDM respectively. The primary advantage of F-OFDM is that the space between the
subcarriers in each of the sub-band can be different. It has been mentioned earlier that the each
of these sub-bands, depending upon the services, can have different parameters.
It is worth mentioning that the sub-bands do not overlap each other and instead of a guard
band, a guard tone is added between them. This guard tone is smaller than that of the guard
band used in CP-OFDM and only utilizes 2% of the allocated spectrum which in the case of
CP-OFDM was 10%.
N-Point
IFFT
Add
Cp
Filter
F(n)
N-Point
IFFT
Add
Cp
Filter
F(n)
N-Point
IFFT
Add
Cp
Filter
F(n)
Added
Data from 1st
Sub Band
Data from 2nd Sub Band
Data from Kth Sub Band
Transmitter
Equalizer
Remove
Cp
Filter
F*(-n)
Equalizer
Remove
Cp
Filter
F*(-n)
Equalizer
Remove
Cp
Filter
F*(-n)
Received Data
Reciever
N-Point
IFFT
N-Point
IFFT
N-Point
IFFT
Received Data
Received Data

2.3. Operations of Transmitter and Receiver of F-OFDM
• At the transmitter side, the signals[n] obtained after N-point IFFT and CP insertion is
then passed through a designed spectrum shaping filter f [n], i.e., the transmitted signal
can be expressed as
s[n] = s[n] ∗ f [n]. (1)
• The receive module has inverse operations as transmit module. The received signal is
firstly passed through the filter f ∗ [−n] which is matched to the transmitter filter . Then
the signal of F-OFDM at receiver is processed as the same as regular OFDM receiver.
• The IFFT/CP insertion block, filtering module is used. In f-OFDM, a filter is applied to
time domain OFDM symbol in order to improve out of band radiation of sub-band
signal, while at the same time complex domain orthogonality of the OFDM symbols is
being maintained.
3. SOFTWARE IMPLEMENTATION
An OFDM and F-OFDM system is modeled using Matlab to allow various parameters of the
system to be varied and tested. The aim of doing the simulations is to measure the performance
of these systems under different channel conditions, and to allow for different configurations to
be tested.
3.1. Flow chart Main Toolbox
Figure 3.1 Main tool box
Fig 3.1 represents the overall implementation work flow of our proposed method. While
implementation the overall work is divided into four choices. Choice 1 initializes the channel
parameters only if choices == 1. Once channel parameters are initialized the choice is switched
to choice == 2 where Transmitter is incorporated to transmit the information bits. Once
transmitter bits are implemented it has to send to the receiver so in that case choice is switched
to choice==3. So during choice == 3 receiver is implemented to receive the transmitted bits.
Once transmission and reception is done successfully than choice is switched to choice = =4 to
determine the performance of bit error rate (transmission and reception bit error rate).
Details the channel parameters are initialized before transmission which include defining
the number of sub carriers and carrier frequency required to transmit information over channel.
Start
Repeat =1
Choice == 1
While(Repeat)
Create Menu Dialog box and
stored in choice variable
Repeat = 0
Stop
chnlParameters
Transmitter
ReceiverChoice == 3
Choice == 2
Choice == 4 Performance

Specifying the length of transmitted filter and receiving filter. Initialize the OFDM and FOFDM
modulation at the transmitter side.
At the transmitter side declare the velocity and power delay for transmitting bits. Assign the
transmitted power to the bits. Fast fading refers to variation in bit strength with respect to time.
This variation can be result of wireless communication channel between the transmitter and
receiver. Map binary data to transmitting symbol and specify Pilot & Date Locations.
Transmitted signal in the time domain using OFDM and FOFDM Modulation which further
convolve or multiplies with channel. Calculate the transmitted power over time and power
spectral density.
At the transmitter bits are transmitted over a channel while before this transmitted bits
receives at the receiver this transmitted bits are multiplied with noise called as channel noise.
So the received bits are the combination of noise and transmitted bits. Once transmitted
information bits are received it is demodulated using OFDM and FOFDM. For channel
estimation extract the received pilots and determine the least square and mean square
estimation. Perform interpolation to estimation of a value within two known values in a
sequence of values. Quantize the received symbols and perform de-mapping to bits. Finally
calculate the BER.
4. RESULT
The result of our simulation with the different parameters for the F-OFDM systems are shown
in the below graph using MSE and LSE.
Figure 4.1 Interpolated, MSE and LSE signal
The performance of the OFDM and F-OFDM is compare using BER vs SNR. The obtained
graph is as shown.

Figure 4.2 Comparison of performance of OFDM vs F-OFDM
The following table shows the values of Bit Error Ratio to the respective Signal to Noise
Ratio:
Table. Comparison values of performance of OFDM and
SNR (dB) -5 0 5 10 20 30
OFDM 0.4236 0.3616 0.2743 0.1915 0.0397 0.0047
F-OFDM 0.4744 0.4311 0.4056 0.3422 0.1361 0.0155
F-OFDM using BER vs SNR
5. CONCLUSION
In this work, we have studied LSE and MMSE estimators for both block type and comb type
pilot arrangement. The estimators in this study can be used to efficiently estimate the channel
in an OFDM and F-OFDM systemS given a certain knowledge about channel statistics. For
high SNRs the LSE estimator is both simple and adequate. The MMSE estimator has good
performance but high complexity. The LSE estimator has low complexity, but its performance
is not as good as that MMSE estimator basically at low SNRs. We also used interpolation
techniques for channel estimation.
REFERENCES
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Generation Cellular
[3] QiweiZheng, FanggangWang, Xia Chen,Yinsheng Liu, DeshanMiao, and Zhuyan Zhao,
“Comparison of 5G Waveform Candidates in High Speed Scenario”, IEEE 32nd URSI
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[5] Swati Mishra, Mohammad Umar, Shalini Kashyap, ”Performance Analysis Of OFDM For
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CHANNEL ESTIMATION IN MOBILE WIRELESS SYSTEMS

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