Superimposed Training Sequence Research Articles

On the performance of data-dependent superimposed training without Cyclic Prefix for SISO/MIMO systems

Compared with channel estimation method based on explicit training sequences, bandwidth is saved for those methods using superimposed training sequences, while it is wasted when Cyclic Prefix (CP) is added. In previous work of McLernon, the Mean Square Error (MSE) performance of Data-Dependent Superimposed Training (DDST) without CP for Single-Input Single-Output (SISO) system was analyzed under the assumption that the data-dependent sequence matrix was a circulant matrix and not interfered by others. In fact, for the system without CP, the data-dependent sequence matrix is not circulant any more and will be interfered. This paper derives the exact expression of MSE for the system without CP and also gives its extension to Multiple-Input Multiple-Output (MIMO) system without CP.

Optimum superimposed training for mobile OFDM systems

Superimposed training (SIT) design for estimating of time-varying multipath channels is investigated for mobile orthogonal frequency division multiplexing (OFDM) systems. The design of optimum SIT consists of two parts: The optimal SIT sequence is derived by minimizing the channel estimates' mean square error (MSE); the optimal power allocation between training and information data is developed by maximizing the averaged signal to interference plus noise ratio (SINR) under the condition of equal powered paths. The theoretical analysis is verified by simulations. For the metric of the averaged SINR against signal to noise ratio (SNR), the theoretical result matches the simulation result perfectly. In contrast to an interpolated frequency-multiplexing training (FMT) scheme or an SIT scheme with random pilot sequence, the SIT scheme with proposed optimal sequence achieves higher SINR. The analytical solution of the optimal power allocation is demonstrated by the simulation as well.

Power allocation of data dependent superimposed training

Data Dependent Superimposed Training (DDST) scheme outperforms the traditional superimposed training by fully canceling the effects of unknown data in channel estimator. In DDST, however, the channel estimation accuracy and the data detection or channel equalization performance are affected significantly by the amount of power allocated to data and superimposed training sequence, which is the motivation of this research. In general, for DDST, there is a tradeoff between the channel estimation accuracy and the data detection reliability, i.e., the more accurate the channel estimation, the more reliable the data detection; on the other hand, the more accurate the channel estimation, the more demanding on the power consumption of training sequence, which in turn leads to the less reliable data detection. In this paper, the relationship between the Signal-to-Noise Ratio (SNR) of the data detector and the training sequence power is analyzed. The optimal power allocation of the training sequence is derived based on the criterion of maximizing SNR of the detector. Analysis and simulation results show that for a fixed transmit power, the SNR and the Symbol Error Rate (SER) of detector vary nonlinearly with the increasing of training sequence power, and there exists an optimal power ratio, which accords with the derived optimal power ratio, among the data and training sequence.

Analysis for MIMO correlated frequency-selective channel in the presence of interference

The space time spreading, superimposed training sequences, and space-time coding are used to presenta multiple input and multiple output (MIMO) systems model, and a closed-form of average error probability up-per bound expression for MIMO correlated frequency-selective channel in the presence of interference (co-channelinterference and jamming signals) is derived. Moreover, the correlation at both ends of the wireless link that canbe incorporated equivalently into correlation at the transmit end is also derived, which is significant to analyzespace-time link algorithm of MIMO systems.

On Doubly Selective Channel Estimation Using Superimposed Training and Discrete Prolate Spheroidal Sequences

Channel estimation and data detection for frequency-selective time-varying channels are considered using superimposed training. We employ a discrete prolate spheroidal basis expansion model (DPS-BEM) to describe the time-varying channel. A periodic (nonrandom) training sequence is arithmetically added (superimposed) at low power to the information sequence at the transmitter before modulation and transmission; therefore, there is no loss in data transmission rate compared to time-multiplexed (TM) training. We first estimate the channel using DPS-BEM and only the first-order statistics of the observations. In this estimator the unknown information sequence acts as interference resulting in a poor signal-to-noise-and-interference ratio (SNIR) for channel estimation. We then apply a data-dependent superimposed training sequence, to either totally or partially cancel out the effects of the unknown information sequence at the receiver on channel estimation. In total cancellation, at certain frequencies, the information-bearing components are nulled. To compensate for this information loss, we investigate a partially-data-dependent (PDD) superimposed training scheme where a tradeoff is made between interference cancellation and frequency integrity. Design of certain parameters for PDD superimposed training is also investigated. Finally, a deterministic maximum likelihood (DML) approach is used iteratively to enhance channel estimation and data detection. Computer simulation examples show that the proposed approaches are competitive with the conventional TM training without incurring data-rate loss.

Doubly-Selective Channel Estimation Using Data-Dependent Superimposed Training and Exponential Basis Models

Channel estimation for single-user frequency- selective time-varying channels is considered using superimposed training. The time-varying channel is assumed to be well- approximated by a complex exponential basis expansion model (CE-BEM). A periodic (non-random) training sequence is arithmetically added (superimposed) at low power to the information sequence at the transmitter before modulation and transmission. In existing first-order statistics-based channel estimators, the information sequence acts as interference resulting in a poor signal-to-noise ratio (SNR). In this paper a data-dependent superimposed training sequence is used to cancel out the effects of the unknown information sequence at the receiver on channel estimation. A performance analysis is presented. We also consider the issue of superimposed training power allocation. Several illustrative computer simulation examples are presented.

On superimposed training for channel estimation: performance analysis, training power allocation, and frame synchronization

Channel estimation for single-input multiple-output (SIMO) time-invariant channels using superimposed training has been recently considered by several authors. A periodic (nonrandom) training sequence is arithmetically added (superimposed) at a low power to the information sequence at the transmitter before modulation and transmission. In particular, in , the channel is estimated using only the first-order statistics of the data under a fixed power allocation to training and under the assumption that the superimposed training sequence at the receiver is time-synchronized with its transmitted counterpart (frame synchronization). In this paper, we remove these restrictions. We first present a performance analysis of the approach of 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. Using the developed channel estimation variance expression, we cast the power allocation problem as one of optimizing a signal-to-noise ratio for equalizer design. Finally, we propose a novel approach for frame synchronization. All the results are illustrated via simulation examples involving frequency-selective Rayleigh fading. Simulation comparisons with an existing approach to frame synchronization is also provided.

Optimal training and redundant precoding for block transmissions with application to wireless OFDM

The adoption of orthogonal frequency-division multiplexing by wireless local area networks and audio/video broadcasting standards testifies to the importance of recovering block precoded transmissions propagating through frequency-selective finite-impulse response (FIR) channels. Existing block transmission standards invoke bandwidth-consuming error control codes to mitigate channel fades, and training sequences to identify the FIR channels. To enable block-by-block receiver processing, we design redundant precoders with cyclic prefix and superimposed training sequences for optimal channel estimation and guaranteed symbol detectability, regardless of the underlying frequency-selective FIR channels. Numerical results are presented to access the performance of the designed training and precoding schemes.