 Open Access
 Total Downloads : 13
 Authors : Tulika, Mr. Manoj Arora, Mr. R.S.Chauhan
 Paper ID : IJERTCONV1IS02052
 Volume & Issue : NCEAM – 2013 (Volume 1 – Issue 02)
 Published (First Online): 30072018
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Survey of MIMO OFDM System under Different Estimators: A Review
Survey of MIMO OFDM System under Different Estimators: A Review
Tulika1, Mr. Manoj Arora2, Mr. R.S.Chauhan3 1M.Tech Scholar
Electronics & communication Deptt.
JMIT (Radaur), Yamuna Nagar, INDIA
2,3Asst. Professor Electronics & communication Deptt.
JMIT (Radaur), Yamuna Nagar, INDIA
Abstract: In 4th generation (4G) broadband wireless communication system to achieve high gain can be made possible by the use of multiple antennas not only at transmitter but also at receiver ends. A multiple input multiple output (MIMO) system provides multiple independent transmission channels, thus, under certain conditions, leading to a channel capacity that increases linearly with the number of antennas. Also, orthogonal frequency division multiplexing (OFDM) is an effective technique for high data rate wireless mobile communication. By combining these two promising techniques, the researcher can significantly increase data rate, which is justified by improving bit error rate (BER) performance. this paper describe the concept of MIMO system and OFDM. orthogonality on OFDM in CDM (Code Division Multiplexing) system, as the Walls code plays an important role, orthogonality of OFDM also main point for spreading signals as an unique, meaning receiver finds out distinguish integer cyclic number from each signal. This paper deals with various estimators for the betterment of elements used in wireless communications like SNR (signal to noise ratio), MMSE (minimum mean square error) and least square error.
Index TermsMIMO, OFDM , MMSE , Least square
I.INTRODUCTION
A special blend of advanced techniques and technologies is required to overcome fading and other interference problems in nonlineofsight wireless connections. Elements of a non lineofsight solution should include: high system gain, Fading mitigation, Dispersion mitigation, Multipath compensation.
These can be achieved using technologies such as: Multiple Input MultipleOutput and Orthogonal Frequency Division Multiplexing.
Multipleinput Multipleoutput (MIMO), MIMO is a method of transmitting multiple data beams on multiple transmitters to multiple receivers. The advantage is that the odds of receiving the data are massively increased. Basically, if any one path is faded, there is a high probability that the other paths are not, so the signal still gets through. For MIMO to be effective, the paths need to be de correlated (e.g., the signals traveling on those paths need to behave differently from each other). This can be done using techniques such as spatial separation of the antennas or separation of the transmitted waveforms via time
separation, data sequence separation, polarization separation, frequency separation or modulation separation.
More recently, it has been recognized that the capacity of wireless communication links is increased by using multiple antennas both at the transmitter and the receiver [3], [4]. This scheme is able to provide a high spectral efficiency in a rich and quasistatic scattering environment. Owing to the large computational complexity required for this scheme, a simplified version, called Vertical BLAST (VBLAST) has been proposed in [15]. A BLAST scheme is primarily based on the following three steps: 1) interference nulling to reduce the effect of the other (interfering) signals on the desired one;

ordering to select the substream with the largest signalto noise ratio (SNR); and 3) successive interference cancellation (SIC). Moreover, it was observed also in [14] that a minimum meansquareerror (MMSE) criterion can be used instead of interference nulling (zero forcing) to mitigate both interference and thermal noise.
Orthogonal Frequency Division Multiplexing (OFDM): OFDM is as discrete multitone modulation because, instead of a single carrier being modulated, a large number of subcarriers are modulated by digital modulation techniques like QAM (MQAM), BPSK, QPSK, DQPSK, DQAM etc.
Parallel with the possible data rates, the transmission bandwidth of OFDM systems is also large. Because of these large bandwidths, noise cannot be assumed to be white with flat spectrum across subcarriers. This is a spreadspectrum technique that increases the efficiency of data communications by increasing data throughput because there are more carriers to modulate. In addition, problems with multipath signal cancellation and spectral interference are greatly reduced by selectively modulating the clear carriers or ignoring carriers with high bitrate errors.
In OFDM system design, a number of parameters are up for consideration, such as the number of subcarriers, without a guard time the data receiving in purpose is impossible except on Blind channel [10], symbol duration, subcarrier spacing, modulation type per subcarriers, and the type of FEC coding. IEEE 802.16d uses Orthogonal Frequency Division Multiplexing (OFDM). IEEE 802.16e (mobile) uses
Orthogonal Frequency Division Multiple Access (OFDMA). The Wi MAX forum established that, initially, OFDM256 will be used for fixedservice 802.16d (in 2004). It is referred to as the OFDM 256 FFT Mode, which means there are 256 subcarriers (figure 1 illustrates 5 subcarriers of OFDM signal in frequency domain) available for use in a single channel. Multiple Accesses on a channel is accomplished using TDMA. Alternatively, FDMA may be used.
Figure 1. Five OFDM subcarriers in frequency domain.
Orthogonality on OFDM In CDM (Code Division Multiplexing) system, as the Walls code plays an important role, orthogonality of OFDM also main point for spreading signals as an unique, meaning receiver finds out distinguish integer cyclic number from each signal. Figure 2 shows the signals that even though they are a sum of sinusoids with each corresponding to subcarriers in time domain, they are orthogonal each other in frequency domain.
Mathematical model of orthogonal signals in continuous time at the receiver following:
In discrete time:
Where S signal,
Where is carrier spacing, is number of carriers, is the symbol period. From this equation we can notice that the highest frequency component is means the transmission bandwidth of channel.
Figure 2. Subcarriers and general signal stream for transmission in time domain.
Here section II contains problems faced, section III is for literature review, section IV is analysis of different estimators, and at the last not least section V is the conclusion.
II. PROBLEMS
Among these schemes, the minimum mean square error (MMSE) receiver is a good choice, because it offers better performance than zero forcing (ZF), avoiding the noise enhancement effect of ZF. In MIMO systems, the adaptation of the modulation at the transmitter side according to the channel characteristics allows for reducing the transmission power and/or enhancing the data rates[12]. To do so, a closed form expression is needed, relating the average BER and the required transmission power for different candidate modulation schemes. Although having so many advantages also OFDM is having certain disadvantages.

Sensitive to Doppler shift.

Sensitive to frequency synchronization problems.

High peaktoaveragepower ratio (PAPR), requiring linear transmitter circuitry, which suffers from poor power efficiency.
Another important issue in OFDM transmission is synchronization. There are basically three issues that must be addressed in synchronization. The receiver has to estimate the symbol boundaries an the optimal timing instants that minimize the effects of intercarrier interference (ICI) and intersymbol interference (ISI). In an OFDM system, the sub carriers are exactly orthogonal only if the transmitter and the receiver use exactly the same frequencies. Thus receiver has to estimate and Correct for the carrier frequency offset of the received signal. Further, the phase information must be recovered if coherent demodulation is employed. Another associated problem with OFDM systems is the effect of phase noise. Phase noise is present in all practical oscillators and it manifests itself in the form of random phase modulation of the carrier. Both phasenoise and frequency offset cause significant amount of ICI in an OFDM receiver. The effect these are
worse in OFDM than single carrier systems. The use of efficient frequency and phase estimation schemes can help reduce these effects.

LITERATURE REVIEW
Mobasher et al. (2007) In (MIMO) systems, maximum likelihood (ML) decoding are equivalent to finding the closest lattice point in an Ndimensional complex space. In general, this problem is known to be NPhard. In this paper, a quasi ML algorithm based on semidefinite programming (SDP) is proposed. The proposed relaxation models are also used for soft output decoding in MIMO systems. A method for quasi ML decoding based on two semidefinite relaxation models is introduced. The proposed semidefinite relaxation models provide a wealth of tradeoffs between the complexity and the performance. The strongest model provides a nearML performance with polynomialtime worst case complexity (unlike the SD that has exponentialtime complexity).
Jiang etal.(2009) This paper presents an indepth analysis of the (ZF) and (MMSE) equalizers applied to wireless (MIMO) systems with no fewer receive than transmit antennas. In spite of much prior work on this subject, they reveal several new and surprising analytical results in terms of the wellknown performance metrics of output signaltonoise ratio (SNR), encoded error and outage probabilities, diversitymultiplexing (DM) gain tradeoff, and coding gain.
Babu. T, Dr. Kumar (2009) said that in wireless communication systems, equalization process is needed to suppress ISI caused by multipath channels. Conventional equalization techniques use training signals. A blind equalization index is presented for the purpose of selecting the best equalizer. This paper deals with the equalization techniques using MIMO FIR (finite impulse response) equalizers. They performed a theoretic frame work on blind MIMO – FIR channel equalization is established in this paper. [7]
Ketonen(2012) proposed the use of decision directed (DD) channel estimation in a MIMO and OFDM downlink receiver is studied in this paper. The 3GPP long term evolution (LTE) based pilot structure is used as a benchmark. The space alternating generalized expectationmaximization (SAGE) algorithm is used to improve the performance from that of the pilot symbol based leastsquares (LS) channel estimator. The DD channel estimation improves the performance with high user velocities, where the pilot symbol density is not sufficient. MMSE filtering can also be used in estimating the channel in between pilot symbols. The pilot based LS, MMSE and the SAGE channel estimators are implemented and the performancecomplexity tradeoffs are studied. They concluded that complexity and power consumption of the LS and MMSE estimators are low.. A good performance complexity tradeoff can be achieved by allowing a longer processing delay for the SAGE estimator. The MMSE filter and the SAGE estimator improve the pilot symbol based LS performance with high user velocities when the channel changes frequently between pilot symbols. They said that the MMSE estimator is suitable for systems with high pilot densities. [4]
Khan etal. (2012) said that in pilotsymbolaided OFDM system, MMSE estimator performs better than LS estimator; however, computational complexity associated with the MMSE method is relatively higher than the LS. Although, the LS estimator has lower complexity and requires minimum knowledge the channel state information, the estimator suffers from inherent additive Gaussian noise and Inter Carrier Interference (ICI). Following that, in this study an efficient and improved channel estimation technique is proposed based on the LS algorithm. Simulation results show that the proposed method performs considerably better than the conventional LS method for a range of SNRs. In addition, the performance of the proposed method is found to be almost equal, if compared with the MMSE estimator. Despite the proposed method experiences relatively higher computational complexity than the LS, the complexity is yet to be achieved about 40 % lower than the MMSE. [5]
Ylioina etal. (2013) discussed an iterative receiver for a MIMO OFDM system is considered to jointly decode the transmitted bits and estimate the channel state. The receiver consists of the a posteriori probability (APP) algorithm, the repeataccumulate (RA) decoder, and the leastsquares (LS) channel estimator. The problem of finding the optimal activation schedule for the detector, the channel estimator and the decoder in an iterative receiver was considered. The issue is to study the concept with more realistic complexity definitions in the scheduling and with algorithms being computationally less intensive. [6]

ANALYSIS ON DIFFERENT ESTIMATORS MMSE AND LS ESTIMATORS
The estimation techniques have the general structure as presented in Figure. If the channel vector g is Gaussian and uncorrelated with the channel noise n, the MMSE estimate of g becomes
gMMSE = Rg y Ry y y
I
n N
Where, Rg y = E {gyH } = Rg g FH XH Ry y= E {yyH }= XFRgg FH XH + 2
are the crosscovariance matrix between g and y and the auto
covariance matrix of y. Further Rg g is the auto covariance matrix of g and n2 denotes the noise variance. These two
qualities are assumed to be known. Since the columns in F are orthonormal,
gMMSE generates the frequency domain MMSE estimate hMMSE by
hMMSE = F gM MSE = F QM MSE FH XH y
Channel Estimators for Mobile MIMOOFDM 9781 467350518/12/$31.00 Â©2012 IEEE

Katsumi Konishi, Member, IEEE, and Toshihiro Furukawa, Member, IEEE A Reweighted Least Squares Approach to QAM Detector for Blind Equalization IEEE SIGNAL PROCESSING LETTERS, VOL. 18, NO. 4, APRIL 2011
where, Q
M MSE
can be shown to be

Jari Ylioinas, Juha Karjalainen, Member, IEEE, Markku
Juntti, Senior Member, IEEE, and Olli
QM MSE = Rg g [(FH XHXF)1 n2 + Rg g ]1 (FH XHXF)1
This MMSE channel estimator has the form shown in Figure If g is not Gaussian, hM MSE is not necessarily a minimum meansquare error estimator. It is however the best linear estimator in the meansquare error sense. In either case
(g, Gaussian or not) the channel estimate is to be denoted as hM MSE.
The LS estimator for the cyclic impulse response g minimizes
(yXFg)H (yXFg) and generates
PiirainenScheduling of the Activations in Iterative Detection, Decoding, and Channel Estimation for MIMOOFDM 00906778/13$31.00 c 2013 IEEE

Ram Babu. T Dr.P.Rajesh Kumar Professor Blind Equalization For MIMO FIR Channel In Wireless Communication Systems 9780769538457/09 $25.00
Â© 2009 IEEE

Md. Nazmul Islam Khan Southeast University Dhaka, Bangladesh Md. Jobayer Alam Noise Reduction Algorithm for LS Channel Estimation in OFDM System
Where,
hLS
=FQLS
FH XH y,
9781467348362/12/$31.00 Â©2012 IEEE

Blind Equalization For MIMO FIR Channel In Wireless Communication Systems Ram Babu. T Dr.P.Rajesh
QLS = ( FH XH XF)1
It is to be noted that hLS also corresponds to the estimator structure. Above equation reduces to
hLS =X1y,
The LS estimator is equivalent to what is also referred to s the zero forcing estimators. Both estimators LS and MMSE have their drawbacks. The MMSE estimator suffers from a high complexity, whereas the LS estimate has a high mean square error.


CONCLUSION


In this paper it is concluded that different techniques used for achieving high gain and directivity in wireless communication. Various advantages as well as the problems occurred in such techniques has been discussed. So we have used various estimators to estimated different parameters to find the system efficiency as well as its adaptability. Such parameters are like SNR (Signal to noise Ratio), MMSE (Minimum Mean square error), LS (least Square). The future work can be extended to modify the various algorithms so that to remove such problems caused in estimators like complexity and mean square error.
REFERENCES

R.Matznerand F.Englberger, An SNR estimation algorithm using fourthorder moments, in Proc. IEEE Int. Symp. Inform. Theory, Trondheim, Norway, June 1994, p. 119.

D. R. Pauluzzi and N. C. Beaulieu, A comparison of SNR estimation techniques for the AWGN channel, IEEE Trans. Commun., vol. 48, pp. 1681 1691, Oct. 2000.

S. Boumard, Novel noise variance and SNR estimation algorithm for wireless MIMO OFDM systems, in Proc. IEEE Global Telecommunica tions Conf. (Globecom), vol. 3, Dec. 2003, pp. 13301334.

Johanna Ketonen and Markku Juntti Jari Ylioinas Joseph
R. Cavallaro Implementation of LS, MMSE and SAGE
Kumar 2009 International Conference on Advances in Recent technologies in Communication and Computing

Zhang Liyi1,2, Chen Lei1,2, Sun Yunshan Variable Stepsize CMA Blind Equalization based on Nonlinear Function of Error Signal2009 International Conference on Communications and Mobile Computing 97807695 35012/09 $25.00 Â© 2009 I

D. Palomar, J. Cio, and M. Lagunas, Joint TxRx beamforming design for multicarrier MIMO channels: A unifiedframework for convex optimization, IEEE Transactions on Signal Processing, vol. 51, pp. 2381 2401, September 2003.

L. Zheng and D. Tse, Diversity and multiplexing: A fundamental tradeo in multipleantenna channels, IEEE Transactions on Information Theory, vol. 49, pp. 10731096, May 2003.

J. Zhang, E. K. P. Chong, and D. N. C. Tse, Output mai distribution of linear mmse multiuser receivers in ds cdma systems, IEEE Transactions on Information Theory, vol. 47, p. 2001, March 2001.39