empirical analysis of channel estimation procedures with ... · empirical analysis of channel...

Empirical Analysis of ChannelEstimation Procedures with Enhanced

Kalman Filter Algorithm overMIMO-OFDM Environment

G. RAJENDER1, DR.T. ANILKUMAR2

and DR.K.SRINIVASA RAO2

1Research Scholar,JNTUH University, Hyderabad,

Telangana, [email protected]

2Professor ECE Department,MVSR Engineering College, Hyderabad,

Telangana, [email protected]

3Professor & Principal TRR College of Engineering,Hyderabad, Telangana, India-502319

[email protected]

January 11, 2018

Abstract

In the wireless communication environment, ChannelConditions are the major cause to reducing the performanceand quality of communications and its effectiveness. InWireless Communication System, the Quality of Service[QOS] is reduced based on several quality affection factorssuch as: noises, duplication or fading, Multi-Path communi-cation, Doppler shifting and so on. Several approaches are

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International Journal of Pure and Applied MathematicsVolume 118 No. 19 2018, 2957-2970ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu

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derived already in past by many researchers to improve thequality of wireless communications by reducing the affec-tions of channel conditions as well as improving the QoS inreceiver side. In the present strategy, the identified Chan-nel medium is hugely different, so there is a high focus re-quired for developing the Channel Estimation Algorithmsin wireless communication environment. Within all the de-veloped approaches, Kalman Filter are producing the betterresults compare to the other approaches and this is widelydeployed in wireless communications and channel estima-tions. Kalman Filters are unique to achieving high-accuracyratio with low-coding complexity, however the communica-tion range or channel intensity is high while applying thisin MIMO-OFDM nature of communications, the KalmanFilter accuracy level is not predictable and it produces theresults based on probability, so the new filter is introducedcalled Enhanced Kalman Filter [EKF], which integrates allthe benefits of Kalman Filter and provides support to largerange wireless communication with same level of accuracyas well as with improved Quality of Service [QoS] in MIMO-OFDM environments.

Key Words : Wireless Communication, Enhanced KalmanFilter, MIMO-OFDM, EKF, Quality of Service, ChannelEstimation.

1 INTRODUCTION

In wireless communications, the channel assignment and condi-tions trapping are the crucial areas need to be take care of. Thevulnerability in channel conditions has corrupted the estimationperformance/execution of customary channel estimator rationaleand requirements an Updation. The feedback/criticism estima-tors are consequently created to accomplish the estimation perfor-mance/execution under variation channel condition. Among differ-ent methodologies of channel estimation rationale, Kalman basedestimators are utilized as an input estimator, which deals with thetarget of channel following. The estimation procedure is adminis-tered by a state move rationale, where the procedure of estimationand Updation is done to acquire a gauge.

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During the time spent MIMO-OFDM channel estimation, theuse of Kalman channels are made for its less difficult coding andestimation performance/execution. In any case, with the expansionin correspondence approaches and offered administrations, the ordi-nary models of Kalman filtration are should be moved forward. Todetermine better performance/execution for such framework, dis-tinctive techniques were created in past systems. In [1] frequencyselective fading channel estimation is proposed. The issue of chan-nel flaw is investigated. A novel technique for pilot development isproposed to catch multipath flag emerge in the MIMO framework.

A Decision feedback/criticism Kalman channel called EnhancedKalman Filter [EKF] is proposed for direct following in correspon-dence framework. The approach of adaptable pilot adaptable cod-ing is been displayed. A joint estimation of channel pick up andstage commotion in MIMO framework is investigated in [2]. Achoice coordinated broadened Kalman channel for stage clamor fol-lowing is created. The approach of stretched out Kalman channel isseen to be more steady in following stage commotion estimation. Acomparative approach of direct estimation in semi-daze approachis proposed in [3]. A recursive estimation Updation in Kalmanbased estimation rationale utilizing hinders for MIMO frameworkis utilized.

A dynamic square handling development demonstrates is dis-played to channel estimation for quick blurring channel. The issueof assorted variety in MIMO-OFDM framework is been investigatedin late time. In [4] to accomplish a speedier difference arrangement,a channel estimation calculation in view of Kalman channel is pro-duced. A State Transfer Coefficient [STC] is determined with alimit revision rationale for time differing condition. Towards theissue of client portability, in [5] [6] new plan of versatility worryin MIMO-OFDM framework is recommended. An insight rationaleutilizing fuzzification was produced for variation configuration ofmoderate, quick and medium speed portability. The Kalman chan-nel is intended to play out the channel estimation for the flag gotunder portability condition.

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1.1 Enhanced Kalman Filter [EKF]

Generally Kalman Filter applications are used for associative normsin communication means, this Enhanced Kalman Filter [EKF] isintroduced to provide clear decision making analysis along withproper guidance and earlier analysis strategies. This EnhancedKalman Filter provides the solution decision based on time con-straints, statistical noise estimations in communication channel andproducing estimation of un-known variables. Based on these strate-gies the Enhanced Kalman Filter provides a better decision to se-lect the correct pathway to provide better Quality of Service overcommunication. A Kalman Filter is an ideal estimator - that isit surmises parameters of enthusiasm from roundabout, mistakenand unverifiable perceptions. It is recursive with the goal that newestimations can be handled as they arrive. On the off chance thatall clamor is Gaussian, the Kalman Filter limits the mean squareblunder of the evaluated parameters. An Enhanced Kalman Filter[EKF] is so main stream in view of the accompanying reasons:

(i) Good outcomes by and by because of optimality and structure.

(ii) Convenient frame for online constant handling.

(iii) Easy to figure and actualize given an essential comprehension.

(iv) Measurement conditions require not be altered.

2 SUMMARIZATION AND OVERVIEW

In this approach of estimation utilizing Enhanced Kalman ChannelFiltration, the issues which are should be investigated to enhancethe exhibitions are recorded as,

A. Estimation Efficiency under Data Rate Variation

The execution/Performance of estimation productivity undervariation information rate condition are describes in this summary.As in current communication frameworks, the offered administra-tions are of variation arrange, the designated information rate isprogressively connected by asset assignment units. The versatile as-set designation brings about higher throughput, be that as it may,

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the estimator rationales are should be tuned with these dynamicnature. The transmitted divert in such framework are transmittedover the remote direct in a dynamic transmission rate, henceforththe impedance experience in the channel is too unique. The issueat that point turns into a double issue, where the information rateand the channel both are seen to be powerfully changing with time.Thus, the current channel estimator utilizing Enhanced KalmanChannel Filter should be enhanced for better operation.

B. MIMO-OFDM Diversity Issues

An issue saw in the MIMO-OFDM framework is diversity issuesin the transmitted flag over the communication channel. The diver-sity variety is seen after some time and frequency/recurrence for thetransmitting bits. Despite the fact that the utilization of multipleradio wire models in OFDM gives a higher throughput, the use ofvarious reception apparatus clusters and frequency/recurrence divi-sion in OFDM, brings about higher assorted variety of transmittingbits. Consequently, the Enhanced Kalman Channel Filter at the es-timator unit takes a more drawn out time of joining, bringing aboutslower preparing.

C. User Mobility Impact over Multi-Path Flag Signaling

The flag multi-pathing and its impact because of client/userversatility is described into the following summary. The issue ofportability is seen in the entire current communication framework.On account of MIMO-OFDM, because of numerous receiving wiresthe transmitting signals are engendered in various allotted powerand frequency/recurrence. These signs are transmitted over thechannel, which goes through numerous channels scattered by meansof various medium, and impediment experienced. The flag scram-bling brings about multipathing and the got signals are seen atbring down quality. In spite of the fact that Kalman filtration forvarious blurring issues are been produced, it have to enhance to ex-perience the issue of variety in blurring inside every communicationperiod, which changes because of client portability.

The experienced blurring in such case is dynamic, as with theversatility of client the conveying channel continues changing with

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time. With these targets in this work, an enhanced Kalman fil-tration in thought to variation information rate, channel diversityvariety, and versatility concern is engaged. The goal of taking careof the dissimilarity issue all the more viably and ideal state esti-mation is been engaged. Another technique for Kalman filtrationunder multipath got motion with various channel impacts is to becreated, experienced in current MIMO-OFDM framework.

3 LITERATURE SURVEY

In the year of 2015 the authors ”MingFei Siyau, Tiancheng Li,Javier Prieto, Juan Corchado, Javier Bajo” proposed a paper titled”A Novel Pilot Expansion Approach for MIMO Channel Estimationand Tracking” in that they described such as: Enabling high prac-tical mobility of MIMO-OFDM systems requires accurate channelestimation under severe frequency and time selective fading chan-nels. Conventional methods rely on non-realistic approximations orresult in impractical complexities. In this paper, a new practicaltraining-based MIMO channel estimation scheme is presented. Thenew pilot bits designed from the ’Paley-Hadamard’ matrix allowsexploiting its orthogonal and ’Toeplitz-like’ structures for minimis-ing its pilot length. Additionally, a novel pilot expansion techniqueis proposed to estimate the length of the channel impulse response,by flexibly extending its pilot length as required to capture thenumber of multipath existed within the MIMO channel.

The proposed pilot expansion enables to deduce the initial chan-nel variation and its Doppler rate, which can be subsequently ap-plied for MIMO channel tracking using decision feedback Kalmanfilter during the data payload.

In the year of 2014 the authors ”Hani Mehrpouyan, Ali A. Nasir,Steven D. Blostein, Thomas Eriksson, George K. Karagiannidis,and Tommy Svensson” proposed a paper titled ”Joint Estimationof Channel and Oscillator Phase Noise in MIMO Systems” in thatthey described such as: Oscillator phase noise limits the perfor-mance of high speed communication systems since it results in timevarying channels and rotation of the signal constellation from sym-bol to symbol. In this paper, joint estimation of channel gains andWiener phase noise in multi-input multi-output (MIMO) systems

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is analyzed.The signal model for the estimation problem is outlined in det

il nd new expressions for the Cr m r-Rao lower bounds (CRLBs)for the multi-parameter estimation problem are derived. A data-aided least-squares (LS) estimator for jointly obtaining the channelgains and phase noise parameters is derived. Next, a decision-directed weighted least-squares (WLS) estimator is proposed, wherepilots and estimated data symbols are employed to track the time-varying phase noise parameters over a frame. In order to reducethe overhead and delay associated with the estimation process, anew decision-directed extended Kalman filter (EKF) is proposedfor tracking the MIMO phase noise throughout a frame.

Numerical results show that the proposed LS, WLS, and EKFestimators’ performances are close to the CRLB. Finally, simulationresults demonstrate that by employing the proposed channel andtime-varying phase noise estimators the bit-error rate performanceof a MIMO system can be significantly improved.

4 EXPERIMENTAL RESULTS

The proposed channel estimation coding for progressive and ran-domized assessments is assessed for various commotion thickness.The recurrence reaction of such channel is seen in Figure.1, inthat variance of TOP: Schmidt Kalman and BOTTOM: SchmidtKalman Filters are clearly shown.

Figure 1: TOP: Schmidt Kalman and BOTTOM: Kalman Filter

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A test in a sinusoidal configuration is taken, at a recurrenceof 5.5 KHz, with 1024 specimens. The watched input test is asappeared in figure 1. This information flag is process with addedsubstance Gaussian clamor, and versatile filtrations are connectedto assess the flag. The measuring parameter of Mean Square De-viation (MSD) and union time is figured. The created approachis assessed over various estimations of channel parameters. Thefirst test flag is engendered by means of 4 multipath channels, de-lighting every recurrence determination segregate. To assess theestimation proficiency of the two channel show (composed and ir-regular), Normalized MSD is registered. The acquired MSD for thetwo strategies is as laid out in figure 2.

Figure 2: Normalized MSD For N=4, At∑

=0.8

The following figure shows the structural graphical representa-tion of RMS VEL Strategies and its present view in output nature.This is clearly explained into the following figure.3.

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Figure 3: RMS VEL Representation

The RMS corresponding to noise level and its procedural strat-geies are illustrated into the following figure.

Figure 4: RMS Corresponding to Noise

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5 CONCLUSION

The proposed approach clearly demonstrates the nature of filtersand how efficiently Enhanced Kalman Filters [EKF] operates to re-duce the noise levels and improving the Channel Conditions as wellas Quality of Service [QoS] over Wireless Communication environ-ments. The powerful estimation logic helps MIMO-OFDM systemto perform more efficiently without any error scenarios as well asthe developed approach is to minimize the fading and error ratesunder different channel conditions by using the Enhanced KalmanFilter. The general performance/execution was demonstrated to beideal under channel decent variety condition, for irregular as wellas facilitated impedance show. The blunder performance/executionapproves the use of proposed approach as contrasted with EnhancedKalman Filtration technique.

References

[1] MingFei Siy u, Tiancheng Li, J vier Prieto, Juan Corch do, Jvier Bajo, A Novel Pilot Expansion Approach for MIMO Chan-nel Estimation and Tracking, IEEE,ICUWB, pp.1-5, 2015.

[2] Hani Mehrpouyan, Ali A. Nasir, Steven D. Blostein, ThomasEriksson, George K. Karagiannidis, and Tommy Svensson,Joint Estimation of Channel and Oscill tor Phase Noise inMIMO Systems, IEEE Transactions on Sign l Processing,Vol.60,(9),pp.4790 4807, 2014.

[3] Movahedian.A, McGuire, M., Efficient nd Accurate Semi blindEstimation of MIMO-OFDM Doubly-Selective Channels, 80thIEEE,VTC F ll, pp.1-5, 2014.

[4] Shijie Zhang, DanaWang and Jun Zhao A K lm n FilteringChannel Estimation Method B sed on State Transfer Coeffi-cient Using Threshold Correction for UWB Systems, Interna-tional Journal of Future Generation Communication and Net-working, Vol.7, No.1, pp.117-124, 2014.

[5] Vinod Gutta, Kamal Kanth Toguru Anand, Teja Sri VenkataSaidhar Movva, Bhargava Rama Korivi, Santosh Killamsetty,

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and Sudheesh padmanabhan, Low Complexity Channel Es-timation using Fuzzy Kalman Filter for Fast Time VaryingMIMO-OFDM Systems, IEEE, ICACCI, pp. 1771 1774, 2015.

[6] Takahiro Natori, Nari Tanabe and Toshihiro Furukaw , MI-MOOFDM channel estimation algorithm for highspeed move-ment environments, IEEE, ISCCSP, pp.348 351, 2014.

[7] Jinesh P. Nairand Ratnam V. Raja Kumar, Optimal Superim-posed Training Sequences for Channel Estimation in MIMO-OFDM Systems, Hindawi Publishing Corporation, EURASIPJournal on Advances in Signal Processing, pp.13, Springer,2010.

[8] Ke Zhong, Xi Lei, Binhong Dongand Shaoqi n Li, Channel esti-mation in OFDM systems operating under high mobility usingWiener filter combined basis expansion model, EURASIP Jour-nal on Wireless Communications and Networking, Vol.186,Springer, 2012.

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[13] Te-Lung Kung and Keshab K Parhi, Semi blind frequency-domain timing Synchronization and channel estimation forOFDM systems, EURASIP Journal on Advances in Signal Pro-cessing, Springer, 2013.

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[17] MingFei Siyau, Tiancheng Li, Javier Prieto, Juan Corchado,Javier Bajo, A Novel Pilot Expansion Approach for MIMOChannel Estimation and Tracking, IEEE,ICUWB, pp.1-5,2015.

[18] Hani Mehrpouyan, Ali A. Nasir, Steven D. Blostein, ThomasEriksson, George K. Karagiannidis, and Tommy Svensson,Joint Estimation of Channel and Oscillator Phase Noise inMIMO Systems, IEEE Transactions on Signal Processing,Vol.60,(9),pp.4790 4807, 2014.

[19] Movahedian.A, McGuire, M., Efficient and Accurate Semiblind Estimation of MIMO-OFDM Doubly-Selective Channels,80th IEEE,VTC F ll, pp.1-5, 2014.

[20] Shijie Zhang, Dan Wang and Jun Zhao A K lm n FilteringChannel Estimation Method Based on State Transfer Coeffi-cient Using Threshold Correction for UWB Systems, Interna-

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tional Journal of Future Generation Communication and Net-working, Vol.7, No.1, pp.117-124, 2014.

[21] Vinod Gutta, Kamal Kanth Toguru Anand, Teja Sri VenkataSaidhar Movva, Bhargava Rama Korivi, Santosh Killamsetty,and Sudheesh padmanabhan, Low Complexity Channel Es-timation using Fuzzy Kalman Filter for Fast Time VaryingMIMO-OFDM Systems, IEEE, ICACCI, pp. 1771 1774, 2015.

[22] T kahiro Natori, Nari Tanabe and Toshihiro Furukaw , MI-MOOFDM channel estimation algorithm for highspeed move-ment environments, IEEE, ISCCSP, pp.348 351, 2014.

[23] Jinesh P. Nairand Ratnam V. R j Kumar, Optimal Superim-posed Training Sequences for Channel Estimation in MIMO-OFDM Systems, Hindawi Publishing Corporation, EURASIPJournal on Advances in Signal Processing, pp.13, Springer,2010.

[24] Ke Zhong, Xi Lei, Binhong Dong and Shaoqian Li, Channel es-timation in OFDM systems operating under high mobility usingWiener filter combined basis expansion model, EURASIP Jour-nal on Wireless Communications and Networking, Vol.186,Springer, 2012.

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empirical analysis of channel estimation procedures with ... · empirical analysis of channel...

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