Adaptively Regularized Least-Squares Estimator for Decision-Directed Channel Estimation in Transmit-Diversity OFDM Systems (original) (raw)
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IEEE Transactions on Wireless Communications, 2000
The number of transmit antennas that can be employed in the context of least-squares (LS) channel estimation contrived for orthogonal frequency division multiplexing (OFDM) systems employing multiple transmit antennas is limited by the ratio of the number of subcarriers and the number of significant channel impulse response (CIR)-related taps. In order to allow for more complex scenarios in terms of the number of transmit antennas and users supported, CIR-related tap prediction-filtering-based parallel interference cancellation (PIC)-assisted decision-directed channel estimation (DDCE) is investigated. New explicit expressions are derived for the estimator's mean-square error (MSE), and a new iterative procedure is devised for the offline optimization of the CIR-related tap predictor coefficients. These new expressions are capable of accounting for the estimator's novel recursive structure. In the context of our performance results, it is demonstrated, for example, that the estimator is capable of supporting L = 16 transmit antennas, when assuming K = 512 subcarriers and K 0 = 64 significant CIR taps, while LS-optimized DDCE would be limited to employing L = 8 transmit antennas. Index Terms-Decision-directed channel estimation (DDCE), multiple transmit antennas, orthogonal frequency division multiplexing (OFDM), parallel interference cancellation (PIC). I. MOTIVATION I N RECENT years, the family of single-and multiuser orthogonal frequency division multiplexing (OFDM) schemes [1] using time-domain, frequency-domain, as well as spatialdomain spreading [2] has enjoyed a renaissance. Hence, OFDM has found its way into numerous wireless systems that require accurate channel estimation. Accordingly, the topic of decisiondirected channel estimation (DDCE) has been addressed in a variety of contributions, notably, for example, in the detailed discussions of [3]-[8], in the context of single-user single-transmit antenna OFDM environments. The basic idea Manuscript
Efficient Decision-Directed Channel Estimation for OFDM Systems with Transmit Diversity
IEEE Communications Letters, 2000
To reduce the complexity involved in decisiondirected channel estimation (DDCE) in orthogonal frequencydivision multiplexing (OFDM) systems with transmit diversity, both data decoupling and direct cancellation of inter-antenna interference (IAI) suffer from residual IAI caused by channel frequency selectivity and time selectivity. In this paper, we propose a new algorithm to improve the performance of low complexity DDCE in OFDM systems with transmit diversity. The proposed algorithm includes a new data decoupling scheme with weaker assumptions regarding channel frequency response, and residual IAI cancellation using the results of the DDCE. Simulation results demonstrate that the proposed algorithm improves the performance of MSE and BER considerably.
2003
OFDM systems employing multiple transmit antennas have recently drawn wide interest in the context of both space-time coded-and multiuser space-division multiple access (SDMA) arrangements. A prerequisite for using coherent detection at the receiver is the availability of reliable channel transfer factor estimates. Robust parallel interference cancellation (PIC) assisted decision-directed channel estimation (DDCE) has been shown in the literature to be also applicable to scenarios, where the number of users is in excess of the number of OFDM subcarriers-normalized to the number of Channel Impulse Response (CIR) related taps to be estimated-which imposed a limitation in the context of least-squares assisted DDCE techniques invoked in conjunction with multiple transmit antennas. In this paper we will demonstrate that the Recursive Least-Squares (RLS) algorithm is applicable to optimizing the predictors' coefficients on a CIR-related tap-by-tap basis. Compared to 'robust', non-adaptive approaches the proposed solution has the advantage of a potentially lower estimation MSE and a higher resilience to erroneous subcarrier symbol decisions.
SDSELMS-A Simplified Adaptive Channel Estimation Algorithm for MIMO-OFDM System
The demand for high speed wireless applications increased the popularity of MIMO-OFDM systems, which provides high data rates with improved link reliability and capacity. One of the key challenges faced by a MIMO-OFDM system is accurate Channel Estimation (CE). Adaptive Channel Estimation (ACE) is widely used for CE as it can track the time varying wireless channel rapidly. Among different ACE algorithms Least Mean Square (LMS) algorithm is the one with low complexity. But it suffers from high Mean Square Error. Normalized LMS algorithm overcomes this disadvantage with low MSE but the complexity is very high. Main design aim of any communication system is to minimize the complexity. In order to further reduce the complexity of LMS algorithm, several simplified algorithms are used. It includes Sign Error LMS (SELMS) algorithm, Sign Data Normalized LMS (SDNLMS) algorithm etc. In this paper, a new, low complex Sign Data Sign Error LMS (SDSELMS) algorithm is proposed which improves the convergence rate and reduces the computational complexity of the existing algorithms which is required for making channel estimation simpler.
Implementation of LS, MMSE and SAGE channel estimators for mobile MIMO-OFDM
2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2012
The use of decision directed (DD) channel estimation in a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (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 expectation-maximization (SAGE) algorithm is used to improve the performance from that of the pilot symbol based least-squares (LS) channel estimator. The DD channel estimation improves the performance with high user velocities, where the pilot symbol density is not sufficient. Minimum mean square error (MMSE) filtering can also be used in estimating the channel in between pilot symbols. The DD channel estimation can be used to reduce the pilot overhead without any performance degradation by transmitting data instead of pilot symbols. The pilot overhead is reduced to a third of the LTE pilot overhead, obtaining a ten percent increase in throughput. The pilot based LS, MMSE and the SAGE channel estimators are implemented and the performance-complexity trade-offs are studied.
Adaptive Channel Estimation Techniques for MIMO OFDM Systems
… Journal of Advanced Computer Science and …, 2011
Abstract—In this paper, normalized least mean (NLMS) square and recursive least squares (RLS) adaptive channel estimator are described for multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. These CE methods uses ...
Enhanced Adaptive Channel Estimation Technique for MIMO-OFDM Wireless Systems
International Journal of Computer Applications, 2014
Multiple Input Multiple Output (MIMO) in combination with Orthogonal Frequency Division Multiplexing (OFDM) can provide spectrally efficient and ISI free communication. Channel estimation is of great importance in order to recover the signal at the receiver side. Therefore accurate channel state information is essential for proper detection and decoding in MIMO-OFDM wireless systems. To estimate channel state information various types of techniques are being deployed in these systems. Accuracy and precision of channel estimation depends on the techniques used for the purpose of estimating channel state information. The more the accuracy of the technique, more will be the accurate performance of the system. In this paper an enhanced adaptive channel estimation using RLMS technique has been purposed. It is the combination of LMS and RLS algorithm. This technique provides better performance which can be judged by the BER performance. Comparison of the technique is done with the simple LMS and LLMS which is the combination of two LMS algorithms. Simulation results show that the purposed algorithm outperforms the latter algorithms. BPSK and QPSK modulations are used for analysis purposes.
A REDUCED COMPLEXITY AND AN EFFICIENT CHANNEL ESTIMATION FOR OFDM
During the last few years, the progress in wireless communication is widely increasing to mitigate the ever increasing demand of higher data rates. OFDM (Orthogonal Frequency Division Multiplexing) techniques using more densely packed carriers, thus achieving higher data rates using similar channels.
Analysis of adaptive channel estimation techniques in MIMO- OFDM system
2013
Multiple input multiple output (MIMO) can be used to improve information carrying capacity in orthogonal frequency division multiplexing (OFDM).In MIMO‐OFDM system channel estimation task becomes more difficult due to the change in channel parameter. Hence, to find appropriate channel estimation method which is able to follow changes at moment is matter of high significance. In this paper types of adaptive algorithm for channel estimation in MIMO‐OFDM system is discussed. The adap‐ tive filter can be least mean square (LMS), recursive least square (RLS) or KALMAN which does not require prior knowledge of second order statistic (SOS) of channel and noise. The adaptive RLS channel estimation with adaptive forgetting factor outper‐ forms in frequency selective fading. The LMS adaptive estimation can be used to improve the channel performance.But the low convergence speed and serious estimation error led to the combination of LMS with KALMAN filter for more accurate estima‐ tion. Howeve...
CHANNEL ESTIMATION USING ADAPTIVE FILTERING IN OFDM SYSTEM
Superimposed pilot aided channel estimation attracts interest due to its potential spectral efficiency. Unfortunately, its channel estimation performance is affected by the embedded data. To mitigate the embedded data effects, adaptive filtering based channel estimation with superimposed training is proposed for orthogonal frequency division multiplexing (OFDM) system. The performance of four different adaptive algorithms is evaluated and compared through simulation.