Training Based Channel Estimation in MIMO-OFDM Systems
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
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01 Dec 2016
TL;DR: Different channel estimation methods like Least Square, Minimum mean Square Error estimator, modified MMSE and LS methods are discussed in the proposed work.
Abstract: The demand for high speed, reliable communication using available frequency spectrum results in a new technology called OFDM. Channel estimation has a vital role in finding the effect of the channel on OFDM signal. In this work, performance analysis and implementation of pilot aided channel estimation is investigated. Different channel estimation methods like Least Square, Minimum mean Square Error estimator, modified MMSE and LS methods are discussed in the proposed work. This paper concentrates on performance comparison of channel estimation in terms of Symbol error rate and Mean square error.
10 citations
TL;DR: This paper considers the well-known log-MAP decoding algorithm by a linear approximation of the correction function used by the max* operator and proposes a generalized decoding scheme that optimizes the existing MAP algorithm for faster convergence and better throughput on the basis of varying channel conditions.
Abstract: The use of turbo codes enhances the data transmission efficiency and optimizes the performance of a communication system over wireless fading channels. In this paper, we present a brief overview of the various components of the turbo coding scheme, analyze the complexities of the most popular turbo decoding algorithms, and discuss the various implementation methods of the maximum a posteriori (MAP) algorithm. The paper considers the well-known log-MAP decoding algorithm by a linear approximation of the correction function used by the max* operator. We propose a generalized decoding scheme that optimizes the existing MAP algorithm for faster convergence and better throughput on the basis of varying channel conditions. The proposed scheme of decoding reduces complexity and enhances the throughput with only a negligible loss in BER performance.
Cites methods from "Training Based Channel Estimation i..."
...Once the iterations have been completed, a hard bit decision is taken using 2 , 1 Xk k K , where 1 Xk when 2 0 Xk and 0 Xk when 2 Here one modification is that a channel estimator as in [9], is attached with the decoder that will estimate channel condition through received pilot symbols [10,11]....
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References
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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
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
01 Jan 2013
TL;DR: In this article, two major types of pilot arrangement such as block type and comb-type pilot have been focused employing Least Square Error (LSE) and minimum mean square error (MMSE) channel estimators.
Abstract: Orthogonal frequency division multiplexing (OFDM) provides an effective and low complexity means of eliminating inter symbol interference for transmission over frequency selective fading channels. This technique has eceived a lot of interest in mobile communication research as the radio channel is usually frequency selective and time variant. In OFDM system, modulation may be coherent or differential. Channel state information (CSI) is required for the OFDM receiver to perform coherent detection or diversity combining, if multiple transmit and receive antennas are deployed. In practice, CSI can be reliably estimated at the receiver by transmitting pilots along with data symbols. Pilot symbol assisted channel estimation is especially attractive for wireless links, where the channel is time-varying. When sing differential modulation there is no need for a channel estimate but its performance is inferior to coherent system. In this paper we investigate and compare various efficient pilot based channel estimation schemes for OFDM systems. In this present study, two major types of pilot arrangement such as block type and comb-type pilot have been focused employing Least Square Error (LSE) and Minimum Mean Square Error (MMSE) channel estimators. Block type pilot sub-carriers is especially suitable for slow-fading radio channels whereas comb type pilots provide better resistance to fast fading channels. Also comb type pilot arrangement is sensitive to frequency selectivity when comparing to block type arrangement. The channel estimation algorithm based on comb type pilots is divided into pilot signal estimation and channel interpolation. The symbol error rate (SER) performances of OFDM system for both block type and comb type pilot subcarriers are presented in this paper.
412 citations
"Training Based Channel Estimation i..." refers methods in this paper
...Various channel estimation methods including two major methods LS and MMSE [1,2], have been widely used for MIMO-OFDM channel estimation....
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TL;DR: This work exploits the one-to-one correspondences between the recursive least-squares (RLS) and Kalman variables to formulate extended forms of the RLS algorithm that are applicable to a system identification problem and the tracking of a chirped sinusoid in additive noise.
Abstract: We exploit the one-to-one correspondences between the recursive least-squares (RLS) and Kalman variables to formulate extended forms of the RLS algorithm. Two particular forms of the extended RLS algorithm are considered: one pertaining to a system identification problem and the other pertaining to the tracking of a chirped sinusoid in additive noise. For both of these applications, experiments are presented that demonstrate the tracking superiority of the extended RLS algorithms compared with the standard RLS and least-mean-squares (LMS) algorithms.
281 citations
TL;DR: The issue of superimposed training power allocation for complex Gaussian random channels for MIMO systems arising from spatial multiplexing of a single-user signal is addressed.
Abstract: Channel estimation for multiple-input multiple-output (MIMO) time-invariant channels using superimposed training is considered. A user-specific periodic (nonrandom) training sequence is arithmetically added (superimposed) at a low power to each user's information sequence at the transmitter before modulation and transmission. Two versions of a two-step approach are adopted where in the first step we estimate the channel using only the first-order statistics of the data. Using the estimated channel from the first step, a linear minimum mean-square error (MMSE) equalizer and hard decisions, or a Viterbi detector, are used to estimate the information sequence. In the second step of the two-step approach a deterministic maximum-likelihood (DML) approach based on a Viterbi detector or a linear MMSE equalizer-based approach is used to iteratively estimate the MIMO channel and the information sequences sequentially. We also present a performance analysis of the first-order statistics-based approach to obtain a closed-form expression for the channel estimation variance. We then address the issue of superimposed training power allocation for complex Gaussian random (Rayleigh) channels for MIMO systems arising from spatial multiplexing of a single-user signal. Illustrative simulation examples are provided
84 citations