Orthogonal frequency-division multiplexing (OFDM) effectively mitigates intersymbol interference (ISI) caused by the delay spread of wireless channels. Therefore, it has been used in many wireless systems and adopted by various standards. In this paper, we present a comprehensive survey on OFDM for wireless communications. We address basic OFDM and related modulations, as well as techniques to improve the performance of OFDM for wireless communications, including channel estimation and signal detection, time- and frequency-offset estimation and correction, peak-to-average power ratio reduction, and multiple-input-multiple-output (MIMO) techniques. We also describe the applications of OFDM in current systems and standards.

OFDM and Its Wireless Applications: A Survey

Channel prediction is an appealing technique to mitigate the performance degradation due to the inevitable feedback delay of the channel state information (CSI) in modern wireless systems. We first propose a general MIMO-OFDM channel prediction framework, which exploits both the spatial and temporal correlations among antennas. Then we derive two predictors which select data for auto-regressive (AR) predictors in different ways based on the proposed framework. The first predictor chooses the data set via minimizing the mean square error (MSE) of prediction model. The second predictor chooses the data in a heuristic way, which aims to reduce the computational complexity. Our algorithms can be applied to improve the precoding performance in multi-user MIMO-OFDM systems. Simulation results show that the proposed methods can overcome the feedback delay effectively, even when the channel changes rapidly.

MIMO-OFDM Wireless Channel Prediction by Exploiting Spatial-Temporal Correlation

Early multiple-input multiple-output with orthogonal frequency division multiplexing (MIMO-OFDM) channel estimation techniques treat channels as spatially uncorrelated. However, in many situations, MIMO-OFDM channels tend to be spatially correlated, for example, due to limited scattering. For such channels, estimation performance can be improved through exploitation of prior knowledge of the channel spatial correlation, for example, by means of the linear multiple mean square error (MMSE) technique. This knowledge is, however, not always available. As an alternative, we investigate techniques in the angle domain, where the MIMO-OFDM channel model lends itself to a physical interpretation. Our theoretical analysis and simulation results indicate that the proposed angle-domain approximated MMSE (AMMSE) channel estimation technique performs well in terms of the mean square error (mse) for various channel models representing different indoor environments. When a suitable threshold is chosen, we can use the angle-domain most-significant-taps selection technique instead of the angle-domain AMMSE technique to simplify the channel estimation procedure with little performance loss.

/pdf/pilot-aided-angle-domain-channel-estimation-techniques-for-1p4xrxtsxh.pdf

Pilot-Aided Angle-Domain Channel Estimation Techniques for MIMO-OFDM Systems

In this paper, the training sequence design for multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems under the minimum mean square error (MMSE) criterion is addressed The optimal training sequence for channel estimation in spatially correlated MIMO-OFDM systems was not known for an arbitrary signal-to-noise ratio (SNR) Only one class of training sequences was proposed in the literature in which the power allocation is given only for the extreme conditions of low and high SNRs The current paper presents a necessary and sufficient condition for the optimal training sequence, and reformulates the training design problem as a convex optimization problem whose optimal solution is efficiently solved In addition, tight upper bounds for MMSE and resulting low complexity iterative algorithms with the closed-form expression in iterations to find the optimum training sequence are derived Simulation results confirm the superiority of the proposed design over the existing one in terms of both MSE estimation and BER performance The proposed methods are also shown to be robust with respect to the spatial correlation mismatch at the transmitter

Optimized Training Sequences for Spatially Correlated MIMO-OFDM

Multiple-input multiple-output (MIMO) system using orthogonal frequency division multiplexing (OFDM) technique has become a promising method for reliable high data-rate wireless transmission system in which the channel is dispersive in both time and frequency domains. Due to multiple cochannel interferences in a MIMO system, the accuracy of channel estimation is a vital factor for proper receiver design in order to realize the full potential performance of the MIMO-OFDM system. A robust and improved channel estimation algorithm is proposed in this paper for MIMO-OFDM systems based on the least squares (LS) algorithm. The proposed algorithm, called improved LS (ILS), employs the noise correlation in order to reduce the variance of the LS estimation error by estimating and suppressing the noise in signal subspace. The performance of the ILS channel estimation algorithm is robust to the number of antennas in transmit and receive sides. The new algorithm attains a significant improvement in performance in comparison with that of the regular LS estimator. Also, with respect to mean square error criterion and without using channel statistics, the ILS algorithm achieves a performance very close to that of the minimum mean square error (MMSE) estimator in terms of the parameters used in practical MIMO-OFDM systems. A modification of the ILS algorithm, called modified ILS (MILS), is proposed based on using the second order statistical parameters of channel. Analytically, it is shown that the MILS estimator achieves the exact performance of the MMSE estimator. Due to no specific data sequences being required to perform the estimation, in addition to the training mode, the proposed channel estimation algorithms can also be extended and used in the tracking mode with decision-aided method.

Robust and Improved Channel Estimation Algorithm for MIMO-OFDM Systems

Multiple transmit and receive antennas (MIMO) have been used in OFDM systems for capacity improvement. In practice, channel state information has to be estimated for diversity combining or space-time decoding. Previous work on channel estimation assumes that MIMO channels are independent and identically distributed (i.i.d.). In certain propagation environments, there exists spatial correlation among channels corresponding to different pairs of transmit and receive antennas. The spatial correlations can he exploited to improve channel estimation. In this paper, we develop a minimum mean-square-error (MMSE) channel estimator for MIMO-OFDM systems that can make full use of the spatial correlation. We also design optimum training sequences that minimize the channel estimation error. When MIMO channels are i.i.d., the training sequences for different transmit antennas are orthogonal and with equal power. However, when MIMO channels are spatially correlated, the power allocation for training sequences can be further optimized. Our simulation results show that the proposed MMSE estimator can exploit spatial and frequency correlations of MIMO channels in OFDM systems and therefore has good performance.

Channel estimation for MIMO OFDM in correlated fading channels

Multiple transmit and receive antennas (MIMO) have been used with orthogonal frequency division multiplexing (OFDM) for capacity improvement in frequency-selective channels. In certain propagation environments, there exists spatial correlation among channels corresponding to different pairs of transmit and receive antennas. In this paper, we investigate training sequence design for channel estimation in MIMO-OFDM systems. We develop necessary conditions for a training sequence to minimize the mean-square error (MSE) of channel estimation when spatial correlation of MIMO channel is known to the transmitter and discuss training sequence design for some special cases. The performance improvement of the designed training sequences is confirmed by simulation examples

Optimum training symbol design for MIMO OFDM in correlated fading channels

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Channel estimation for MIMO OFDM in correlated fading channels

Optimum training symbol design for MIMO OFDM in correlated fading channels