Recent research shows that the merging Orthogonal Frequency Division Multiplexing (OFDM) technology has faced a problem with finding the current state information of the mobile systems. Channel estimation error and co-channel interference (CCI) problems are among the main Causes of performance degradation in the wireless networks. For mobile applications channel Time-variations in one OFDM symbol introduce inter carrier interference (ICI) which degrades the performance. This becomes more severe as mobile speed, carrier frequency or OFDM symbol duration increases. As delay spread increases, symbol duration should also increase in order to maintain a near-constant channel in every frequency sub band. Also, due to the high demand for bandwidth, there is a trend toward higher carrier frequencies. Therefore, to have an acceptable reception quality for the applications that experience high delay and Doppler spread, there is a need for channel estimation error mitigation within one OFDM symbol. From the given channel estimation methods for OFDM mobile systems available the best technique, which is found from the performance analysis will be investigated and optimized. This thesis paper introduces the literature review and performance analysis of LS and MMSE channel estimation techniques. What makes this area more important is the desire to have noise free information and to recognize the channel state information. To address this we performed channel estimation performance analysis which is suggested from the literature review. It also describes the modeling and software simulation as well as the expected outputs of the system for the selected channel estimation techniques. Finally, from the simulation results we’ve understood that MMSE channel estimation technique has better performance for slow fading signals. Having this, we have suggested that minimizing the channel estimation error which is modeled graphically in our simulation will be the target of our final project.
Introduction
Wireless communications, which has been growing enormously in the recent years, is an emerging field. This rapid expansion of wireless services has fueled the evolution of wireless communication systems from the first-generation to fourth-generation systems. It also supports multidimensional high-speed wireless communications, which leads to an increase in demand for high capacity wireless networks in many applications, such as wireless local area networks (WLANs), and worldwide interoperability for microwave access (WiMax). Moreover, the demand for high-speed mobile wireless communications is also rapidly growing. Orthogonal Frequency Division Multiplexing (OFDM) technology is a key
technique for achieving high data capacity and spectral efficiency requirements in the present,
as well as in the future broadband wireless communication systems. Channel parameters are required for diversity combining, coherent detection and decoding of OFDM systems. Therefore, channel estimation is essential in OFDM system design. The fundamental phenomenon which makes reliable wireless transmission difficult is the multipath fading. Hence, in ODFM mobile Systems, there are two phenomena that cannot be ignored: the distortion and the inter symbol interference (ISI) provoked by frequency selective Fading channels. [1][2]. In order to achieve these performance improvements, accurate CSI
(Channel State Information) is required at the receiver which is obtained via channel estimation. Time-variations introduce Inter Carrier Interference (ICI), which must be mitigated to improve the performance in high delay and Doppler spread environments.
Statement of the Problem
The fourth generation (4G) mobile communication technology and beyond are widely in development nowadays. Among all the technologies orthogonal frequency division multiplexing (OFDM) is the most potential candidate of the 4G system, due to the advantages it can offer in wideband wireless communication. However, it also has some disadvantages. Researchers are trying to find methods to make this scheme perform better. From the given problems of OFDM we are keen to put our legacy on Mitigating Channel Estimation Error for OFDM mobile Systems. Channel estimation error and co-channel
interference (CCI) problems are among the main Causes of performance degradation in wireless networks. For mobile applications channel time-variations in one OFDM symbol introduce inter carrier-interference (ICI) which degrades the performance. This becomes more severe as mobile speed, carrier frequency or OFDM symbol duration increases. As delayspread increases, symbol duration should also increase in order to maintain a near-constant channel in every frequency sub band. Also, due to the high demand for band width, there is a trend toward higher carrier frequencies. Therefore, to have an acceptable reception quality for the applications that experience high delay and Doppler spread, there is a need for channel
estimation error mitigation within one OFDM symbol.
From the given channel estimation methods for OFDM mobile systems based on a block-type pilot sub-carrier arrangement will be investigated. For a block-type structure, pilot information is inserted among all the OFDM carriers along the time axis to estimate the channel response in the time domain and having the ability to overcome the problem of frequency-selective channel. Block type pilot signal estimation can be estimated by least square (LS) and minimum mean square error (MMSE) estimators and assumes that channel remains the same for the entire block. So, in block type estimation, we first estimate the channel and use the same estimates within the entire block.
Objectives of the Project
General Objective
- performance analysis of channel estimation techniques for OFDM systems
Specific Objectives
- Analyzing performance of different channel estimation techniques and selecting the better one.
- Maximizing channel parameters
- Modeling the channel estimation error
- Analyzing the effect of some parameters on the channel estimation techniques
1.4 Methodology for the Work for Frequency Division Multiplexing
Basically, in this project we implemented and evaluated performance efficient OFDM mobile
system. Our earliest work will be clearly identifying the problem that our project targets.
Related previous researches on optimization of channel estimation from standard journals
have been reviewed. This is very important to understand the state-of-the-art in the area. The
Internet remains our main source to search for journals and related works. We have developed mathematical and graphical modeling of our system. This modeling is simpler than the real system and helps to understand it easily. We also have used Mat lab and Simulink modeling to simulate the system. The hardware implementation is not done because it is hard to fulfill the hardware requirements. Finally, we have studied the outputs and we have derived conclusions based on the results.
The methodology we have used to achieve the objectives of this project is summarized in the
diagram below.
Significance of the Project
Wireless communications, which has been growing enormously in the recent years, is an emerging field. This rapid expansion of wireless services has fueled the evolution of wireless communication systems from the first-generation to fourth-generation systems. It also supports multidimensional high-speed wireless communications, which leads to an increase in demand for high capacity wireless networks in many applications, such as WLANs, and WiMAX. Moreover, the demand for high-speed mobile wireless communications is also rapidly growing. OFDM technology is a key technique for achieving high data capacity and spectral efficiency requirements in the present, as well as in the future broadband wireless
communication systems. To achieve this efficiently it needs a deep channel estimation to avoid ISI, ICI and to reduce PAPR. Here in our project we are mainly concerned to mitigate the interferences (ISI and ICI). We have tried to minimize these interferences by pilot insertion among the subcarriers. We have used block type pilot insertion and we have applied LS and MMSE estimators to reduce the interferences.
Limitations of Frequency Division Multiplexing
Despite of many channel estimation techniques which make the performance of OFDM better, we have focused on the two basic channel estimation techniques which are LS and MMSE estimators. This is due to these techniques are the foundation for the derivation of the remaining techniques. In addition to this these techniques have good performance under the condition of slow fading. However, this semester project is limited to the comparison of mean square error and BER performance, despite the different modulation scheme and different channels that has a great effect on the performance analysis. It is also limited to performance comparison.
Thesis Outline and Contributions
This thesis consists of five chapters. Chapter 2 presents background knowledge of wireless communication developments and technologies. In modern wireless communication, a high end-user data rate is one main objective of system design and therefore wider transmission bandwidth is desired. However, an increased transmission bandwidth will increase the frequency selectivity of a radio channel and thus cause severe corruption to the transmitted signal. This problem can be solved by using multicarrier transmission techniques such as OFDM. This chapter also provides detailed technical background for the entire thesis. Firstly, fading characteristics of wireless propagation environments are presented and classified into large-scale fading and small-scale fading from the view of propagation terrain. Secondly, the basic concept of OFDM and its implementation are explained. Finally, we list the advantages and drawbacks of OFDM compared with a conventional single-carrier transmission. we discussed about channel estimation techniques such as training based, blind channel estimation and Semi-blind channel estimation. From those techniques we apply training based which includes block type and comb type pilot insertions. Here in our work we Channel Estimation Techniques use block type pilot based estimation technique by LS and MMSE estimators.
Simulation Results and Discussion is all about simulation results and discussions of our project which includes LS and MMSE comparisons based on mean square error and BER performance.
Finally, Conclusion and Future Work summarizes the whole thesis and lists our contributions in this work. In addition, some future work related to our current research is suggested.
Review of Literatures
Wireless Channels and Frequency Division Multiplexing Technique
In this section, we will present some background knowledge concerning wireless channels and the OFDM technique. We first address the fading characteristics of wireless propagation environments, and then introduce the basic concept and fast Fourier transform (FFT) implementation of OFDM. Finally, advantages and drawbacks of the OFDM technique are compared with conventional single-carrier modulation.
Wireless Channels
In a wireless communication system, the transmitted signal typically undergoes attenuation and distortion over the transmission path. The overall effect on the transmitted signal caused by the transmission path is one main source of system performance degradation in any wireless system. To address and compensate for the attenuation and distortion caused by the transmission path, researchers have studied wireless channels extensively and proposed different channel models [3]. These effects, which are mainly due to path loss, shadowing, scattering and reflecting effects caused by unpredictable objects between the transmitter and receiver, can primarily be categorized into large-scale fading and small-scale fading [6]. In
this section, we will briefly describe both types of fading.
Large-Scale Fading of Frequency Division Multiplexing
Large-scale fading, which is caused by path loss of the signal, can be characterized as a function of transmitted distance and shadowing effects of large obstacles such as buildings and hills. This phenomenon happens when the mobile moves over a large distance (of the order of the cell size), and is typically frequency independent [14]. In an ideal LOS channel with no obstructions between the transmitter and the receiver, the received signal power Pr is given by
Where Pt is the transmitted signal power, λ is the wavelength, d is the distance from the transmitter to the receiver, and Gt , Gr are respectively the power gains of the transmit and receive antennas. The power attenuation PL= Pr/Pt is also referred to as free space path loss and it is obvious that the received signal power Pr is inversely proportional to the square of the distance d between the transmit and receive antennas. However, in real transmission environments, the received signal power Pr does not obey this free space path loss model and it varies randomly due to the terrain. Usually a ray tracing method can be used to trace the signal propagation through a wireless channel. Unfortunately, the free space model and ray tracing method cannot model complex propagation environments accurately. Based on empirical measurements, some empirical models for path loss in typical wireless environments have been developed to predict the average received signal power as the transmitted distance d varies, i.e., the Okumura model, the Hata model, the European Cooperative for Science and Technology (COST) model, and the piece wise linear (multi slope) model [12], [14]. In addition to path loss, the transmitted signal is also subject to shadowing, which is caused by changes in reflecting surfaces and scattering objects along the transmission path. The shadowing causes random attenuation to the transmitted signal.
Typically, the log-normal shadowing model, which has been confirmed empirically to accurately model the variation in received power, is used to characterize this random attenuation.
Small-Scale Fading of Frequency Division Multiplexing
Small-scale fading is due to the constructive and destructive addition of different multipath components introduced by the channel between the transmitter and receiver. Therefore, it is also referred to as multipath fading [15]. This phenomenon usually occurs over a distance of several signal wave lengths and is frequency dependent. Since the transmitted signal over a multipath fading channel experiences randomness we must characterize multipath fading channels statistically. Frequency selectivity and the time-varying nature, which depend on the relative relation between parameters of the transmitted signal (i.e., signal bandwidth and symbol duration) and parameters of multi-path fading channels (i.e., delay spread and Doppler spread), are two important characteristics of multipath fading channels. In the
following, we will briefly describe these two characteristics of multipath fading channels. Depending on the relative relation between transmitted signal bandwidth and delay spread (Or equivalently coherence bandwidth BC), multipath fading channels can be categorized into frequency- nonselective (flat) fading channels and frequency- selective fading channels [8]. The parameter coherence bandwidth is the reciprocal of the delay spread which is defined as the span of the delays of duplicates of the transmitted signal arriving at the receiver via different paths. When the transmitted signal bandwidth is small compared with the coherence bandwidth BC, the channel is called frequency-nonselective or flat fading channel. For a flat fading channel, the spectral components of the transmitted signal are affected in a similar manner so that the multipath components are not resolvable. Otherwise, if the transmitted
signal bandwidth is large compared with the coherence bandwidth BC, the channel is said to be frequency-selective. For a frequency-selective fading channel, the spectral components of
the transmitted signal are affected by different amplitude gains and phase shifts. In a
frequency-selective fading channel, different multipath components with delay differences
significantly exceeding the inverse of the transmitted signal bandwidth are resolvable.
Typically, such a frequency-selective fading channel can be modeled as a tapped delay line
filter with time-variant tap coefficients [8].
In a similar way, the multipath fading channel can be categorized as slow fading or fast fading based on the relative relation between symbol duration and Doppler spread (or equivalently coherence time TC). The parameter coherence time, which measures the period of time over which the channel effects on the transmitted signal does not change, is defined as the reciprocal of the Doppler spread. The fading channel is said to be slow fading if the symbol duration is small compared with the channel coherence time TC; otherwise, it is considered to be fast fading. In a slow fading channel, the transmitted signal is affected by the same amplitude gain and phase shift over at least one symbol duration, while the amplitude gain and phase shift vary within one symbol duration in a fast fading channel. According to the discussion above, we have four types of multipath channel, i.e., slow flat fading channel, slow frequency-selective fading channel, fast flat fading channel, and fast frequency selective fading channel. In this thesis, we focus on the channel state estimation of a fast frequency selective fading channel for an OFDM system.
OFDM Technique
In this portion we look at the principles of an orthogonal division multiplexing (OFDM) system. Since our objective is to investigate channel estimation methods for OFDM systems, it is essential to acquire a solid understanding of OFDM systems before proceeding with the channel estimation investigation.
Multi-carrier Modulation of Frequency Division Multiplexing
Multi-carrier modulation was first proposed in 60’sand forms the basis of the OFDM
modulation technique. In multi-carrier modulation, the available bandwidth was divided into
number of NC sub-bands or sub-carriers, each with a width of
Instead of transmitting the data symbols serially, the multi-carrier transmitter partitions the
data into blocks of NC data symbols that are transmitted in parallel by modulating the NC
carriers. The symbol duration for a modulated carrier is Ts=1/W
.
We will continue stay tuned………
