A Novel training based QR-RLS channel estimator for MIMO OFDM systems
27 Jun 2010-pp 1-4
TL;DR: In this article, a novel method for OFDM-MIMO channel estimation using QR-RLS(square root-recursive least square) estimator is presented, which uses QR-factorization of the correlation matrix and thus avoids square matrix inverse results in less computations as well as less roundoff error.
Abstract: In this paper, A novel method for OFDM-MIMO channel estimation using QR-RLS(Square Root-Recursive Least Square) estimator is presented. Preamble aided channel estimation is performed in time-domain, estimated channel is then used for data detection during data transmission within that frame. The performance results are compared with wiener RLS channel estimator in terms of channel estimator MSE performance. Wiener based Standard-RLS estimator uses correlation matrix inverse for estimation and recursion, Correlation matrix may become singular under low noise/high correlated channels, results in round-off error. On the other hand, Square-Root estimator use QR-factorization of the correlation matrix and thus avoids square matrix inverse results in less computations as well as less round-off error. The simulation results shows that square root QR-RLS estimator give better results in terms of Estimation error.
01 Dec 2011
TL;DR: This work extended the previous work of QR-RLS based MIMO(Multiple input Multiple output) channel estimation to Mobile Wimax 802.16m system, where both preamble and pilots are jointly used for robust channel estimation.
Abstract: In this paper, We extended our previous work of QR-RLS based MIMO(Multiple input Multiple output) channel estimation to Mobile Wimax 802.16m system. Mobile wimax system provides high data rate, also fulfills user's requirement like VOD(Video on demand)at very high vehicle speed and also provides better cell coverage area. Channel estimation is crucial part to achieve this goals especially in fast fading environment. Generally, Mobile Wimax systems uses Preamble and Pilots for channel estimation purpose. In the proposed method both preamble and pilots are jointly used for robust channel estimation. At First, QR-RLS Estimator uses Preamble for coarse channel estimation at start of every frame. Once the coarse channel is estimated, then pilots (scattered throughout time-frequency grid) are jointly used with the coarse channel component to derive the channel fading rate. This fading rate is then used to finely estimate the channel at pilot as well as data subcarrier. Thus robust estimation results without adding any overhead. Jointly estimated channel is then used with QR-LRL based data detection, where hard decision values are calculated. Simulation results are shown under various slow-fast channel fading conditions. Results are compared with pilot based channel estimation with LS(least square) interpolation, which shows that joint coarse-Fine estimation gives better performance.
TL;DR: Methods for channel estimation based training symbols in MIMO-OFDM systems are discussed and the results confirm the superiority of the represented methods over the existing ones in terms of bandwidth efficiency and estimation error.
Abstract: OFDM combined with the MIMO technique has become a core and attractive technology in future wireless communication systems and can be used to both improve capacity and quality of mobile wireless systems Accurate and efficient channel estimation plays a key role in MIMO-OFDM communication systems, which is typically realized by using pilot or training sequences by virtue of low complexity and considerable performance In this paper, we discuss some methods for channel estimation based training symbols in MIMO-OFDM systems The results confirm the superiority of the represented methods over the existing ones in terms of bandwidth efficiency and estimation error
••02 Jun 2011
TL;DR: The performance of smart antenna in terms of the received signal for linear, circular and planararrays is observed and the adaptive array structure for all the geometry's is trained using modified LMS algorithm and RLS algorithm.
Abstract: Smart Antenna systems have recently received increasing interest as the demand for better quality and new value added services on the existing wireless communication systems. These system of antennas include different array geometry and different adaptive techniques to enhance the received modulated signal with a fixed DOA by suppressing the inferences. Although enormous study has been done on smart antennas, special emphasis has not been provided to the antenna array structure. In this paper the performance of smart antenna in terms of the received signal for linear, circular and planararrays is observed. The adaptive array structure for all the geometry's is trained using modified LMS algorithm and RLS algorithm. Comparison is made for different array structures using both the adaptive techniques.
••07 Feb 2008
TL;DR: Q-RLS based adaptive channel TEQ (Time Domain Equalizer) to make the OFDM systems robust for delay spreads exceeding the CP is presented and simulation results show that this method gives better results as compared to the aforementioned methods.
Abstract: In this paper, QR-RLS based adaptive channel TEQ (Time Domain Equalizer) to make the OFDM systems robust for delay spreads exceeding the CP (cyclic prefix) is presented. The performance of the proposed method is compared with standard LMS and exponentially weighted RLS based TEQ, in terms of computational complexity and BER. QR decomposition using Givens transformation results in better computational complexity compared to standard RLS and QRD based on gram Schmidt orthogonalization process. The simulation results show that this method gives better results as compared to the aforementioned methods.
01 Jan 2008
TL;DR: In this paper, QR-RLS-based adaptive channelnelimpulse response and atarget impulse response ofagiven TEQ(Time Domain Equalizer) tomakethe OFDM systems robustlength.
Abstract: Inthispaper, QR-RLSbasedadaptive channelnelimpulse response andatarget impulse response ofagiven TEQ(Time DomainEqualizer) tomaketheOFDM systems robustlength. Butthemethoddoesn't dealwithtimevarying channel. fordelay spreads exceeding theCP(cyclic prefix) ispresented. The Pravin(2) suggested amodelbasedchannel shortening tech- performance oftheproposed methodiscompared withstandard LMS andexponentially weighted RLSbasedTEQ,intermsof nique forfixed channels using IR filter, with keeping number computational complexity andBER.QR decomposition usingofzeros fixed toCPanddesigned aFIRfilter tocompensate Givens transformation results inbetter computational complexity thepoles. Fortimevarying channels, LMS basedTEQis compared tostandard RLSandQRD basedongramschmidtproposed bySun-Wook Kim(3), whoaddresses theissue using orthogonalization process. Thesimulation results showthatthisinstantaneous values oftheerror toadaptTEQ coefficients. methodgives better results ascompared totheaforementioned Lang Yang(4) suggested LMS-MSSNRbased TEQfor time methods. varying channels, wherehefocused onminimization ofenergy
"A Novel training based QR-RLS chann..." refers background in this paper
...Computation wise, Standard RLS involves matrix inverse which requires (O(N)(3)), On the other hand, QR estimation requires(O(N)(2)) results in Computationally efficient estimation method....
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