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.
Citations
More filters
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.
2 citations
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
2 citations
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.
References
More filters
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01 Jan 1986
TL;DR: In this paper, the authors propose a recursive least square adaptive filter (RLF) based on the Kalman filter, which is used as the unifying base for RLS Filters.
Abstract: Background and Overview. 1. Stochastic Processes and Models. 2. Wiener Filters. 3. Linear Prediction. 4. Method of Steepest Descent. 5. Least-Mean-Square Adaptive Filters. 6. Normalized Least-Mean-Square Adaptive Filters. 7. Transform-Domain and Sub-Band Adaptive Filters. 8. Method of Least Squares. 9. Recursive Least-Square Adaptive Filters. 10. Kalman Filters as the Unifying Bases for RLS Filters. 11. Square-Root Adaptive Filters. 12. Order-Recursive Adaptive Filters. 13. Finite-Precision Effects. 14. Tracking of Time-Varying Systems. 15. Adaptive Filters Using Infinite-Duration Impulse Response Structures. 16. Blind Deconvolution. 17. Back-Propagation Learning. Epilogue. Appendix A. Complex Variables. Appendix B. Differentiation with Respect to a Vector. Appendix C. Method of Lagrange Multipliers. Appendix D. Estimation Theory. Appendix E. Eigenanalysis. Appendix F. Rotations and Reflections. Appendix G. Complex Wishart Distribution. Glossary. Abbreviations. Principal Symbols. Bibliography. Index.
16,062 citations
TL;DR: In this article, the authors examined the performance of using multi-element array (MEA) technology to improve the bit-rate of digital wireless communications and showed that with high probability extraordinary capacity is available.
Abstract: This paper is motivated by the need for fundamental understanding of ultimate limits of bandwidth efficient delivery of higher bit-rates in digital wireless communications and to also begin to look into how these limits might be approached. We examine exploitation of multi-element array (MEA) technology, that is processing the spatial dimension (not just the time dimension) to improve wireless capacities in certain applications. Specifically, we present some basic information theory results that promise great advantages of using MEAs in wireless LANs and building to building wireless communication links. We explore the important case when the channel characteristic is not available at the transmitter but the receiver knows (tracks) the characteristic which is subject to Rayleigh fading. Fixing the overall transmitted power, we express the capacity offered by MEA technology and we see how the capacity scales with increasing SNR for a large but practical number, n, of antenna elements at both transmitter and receiver.
We investigate the case of independent Rayleigh faded paths between antenna elements and find that with high probability extraordinary capacity is available. Compared to the baseline n = 1 case, which by Shannon‘s classical formula scales as one more bit/cycle for every 3 dB of signal-to-noise ratio (SNR) increase, remarkably with MEAs, the scaling is almost like n more bits/cycle for each 3 dB increase in SNR. To illustrate how great this capacity is, even for small n, take the cases n = 2, 4 and 16 at an average received SNR of 21 dB. For over 99% of the channels the capacity is about 7, 19 and 88 bits/cycle respectively, while if n = 1 there is only about 1.2 bit/cycle at the 99% level. For say a symbol rate equal to the channel bandwith, since it is the bits/symbol/dimension that is relevant for signal constellations, these higher capacities are not unreasonable. The 19 bits/cycle for n = 4 amounts to 4.75 bits/symbol/dimension while 88 bits/cycle for n = 16 amounts to 5.5 bits/symbol/dimension. Standard approaches such as selection and optimum combining are seen to be deficient when compared to what will ultimately be possible. New codecs need to be invented to realize a hefty portion of the great capacity promised.
10,526 citations
Book•
01 Feb 1975
TL;DR: An in-depth and practical guide, Microwave Mobile Communications will provide you with a solid understanding of the microwave propagation techniques essential to the design of effective cellular systems.
Abstract: From the Publisher:
IEEE Press is pleased to bring back into print this definitive text and reference covering all aspects of microwave mobile systems design. Encompassing ten years of advanced research in the field, this invaluable resource reviews basic microwave theory, explains how cellular systems work, and presents useful techniques for effective systems development. The return of this classic volume should be welcomed by all those seeking the original authoritative and complete source of information on this emerging technology. An in-depth and practical guide, Microwave Mobile Communications will provide you with a solid understanding of the microwave propagation techniques essential to the design of effective cellular systems.
9,064 citations
"A Novel training based QR-RLS chann..." refers background in this paper
...Assuming isotropic scattering, the autocorrelation of the path gains as shown in[10][6] is rl(τ) = E[β∗ l (t)βl(t + τ)] = σ 2 l J0(2πfDmaxτ) (3) where σ(2) l is the power of the lth path gain, J0(x) is the bessel function of the first kind of order 0, and fDmax is the maximum Doppler frequency which is related to the velocity v of movement and the wavelength λ of the carrier frequency fDmax = v/λ....
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Book•
19 Apr 1996
TL;DR: The main thrust is to provide students with a solid understanding of a number of important and related advanced topics in digital signal processing such as Wiener filters, power spectrum estimation, signal modeling and adaptive filtering.
Abstract: From the Publisher:
The main thrust is to provide students with a solid understanding of a number of important and related advanced topics in digital signal processing such as Wiener filters, power spectrum estimation, signal modeling and adaptive filtering. Scores of worked examples illustrate fine points, compare techniques and algorithms and facilitate comprehension of fundamental concepts. Also features an abundance of interesting and challenging problems at the end of every chapter.
2,549 citations
"A Novel training based QR-RLS chann..." refers background in this paper
...(9) involves inverse of a matrix which is solved using woolbury identity in Standard-RLS based MIMO channel estimation and is explained in[6][12]....
[...]
25 Jul 1995
TL;DR: The authors present the MMSE and LS estimators and a method for modifications compromising between complexity and performance and the symbol error rate for a 18-QAM system is presented by means of simulation results.
Abstract: The use of multi-amplitude signaling schemes in wireless OFDM systems requires the tracking of the fading radio channel. The paper addresses channel estimation based on time-domain channel statistics. Using a general model for a slowly fading channel, the authors present the MMSE and LS estimators and a method for modifications compromising between complexity and performance. The symbol error rate for a 18-QAM system is presented by means of simulation results. Depending upon estimator complexity, up to 4 dB in SNR can be gained over the LS estimator.
1,647 citations