Data-dependent Superimposed Training Research Articles

Joint Model and Data-Driven Receiver Design for Data-Dependent Superimposed Training Scheme With Imperfect Hardware

Data-dependent superimposed training (DDST) scheme has shown the potential to achieve high bandwidth efficiency, while encounters symbol misidentification caused by hardware imperfection. To tackle these challenges, a joint model and data driven receiver scheme is proposed in this paper. Specifically, based on the conventional linear receiver model, the least squares (LS) estimation and zero forcing (ZF) equalization are first employed to extract the initial features for channel estimation and data detection. Then, shallow neural networks, named CE-Net and SD-Net, are developed to refine the channel estimation and data detection, where the imperfect hardware is modeled as a nonlinear function and data is utilized to train these neural networks to approximate it. Simulation results show that compared with the conventional minimum mean square error (MMSE) equalization scheme, the proposed one effectively suppresses the symbol misidentification and achieves similar or better bit error rate (BER) performance without the second-order statistics about the channel and noise.

Identification of Widely Linear Systems Using Data-Dependent Superimposed Training

In this correspondence, we address the identification of widely linear (WL) systems using data-dependent superimposed training (DDST). The analysis shows that the nonlinear nature of WL systems can be exploited to decouple the finite impulse responses of the filters that constitute the system under identification. Unlike the DDST scheme considered for strictly linear systems, for the WL scenario two data-dependent sequences are required to distort the transmitted data and avoid interference with the training sequence during the estimation process. Closed form expressions for sequences with a complete second order characterization (good periodic autocorrelation and zero complementary periodic autocorrelation) are also provided. The performance of the method is compared with other techniques using numerical simulations.

A Precoding Scheme for Eliminating Data Identification Problem in Single Carrier System Using Data-Dependent Superimposed Training

In single carrier (SC) systems using a data-dependent superimposed training (DDST) scheme, the data-induced interference that occurs during channel estimation is eliminated, but at the expense of data distortion. This distortion causes a data identification problem (DIP), in which the data sequence cannot be uniquely identified at the receiver end. Accordingly, the present study proposes a precoding scheme which solves the DIP in SC systems using DDST. Five design criteria are derived for the precoding matrix in order to eliminate DIP, achieve frequency diversity, maintain a low peak-to-average power ratio (PAPR), and minimize the receiver complexity. In addition, a low-complexity detection method is proposed for reducing the search space of the detection process. The simulation results show that the proposed precoding scheme successfully eliminates the DIP in traditional DDST schemes while maintaining the same PAPR as that of the traditional SC systems based on DDST.

Joint I/Q imbalances estimation using data-dependent superimposed training

In this paper, we address the joint estimation of the channel impulse response and frequency-dependent in-phase and quadrature-phase (I/Q) imbalances using data-dependent superimposed training (DDST). The analysis developed shows that it is possible to use the first-order statistics of the received process to achieve synchronization and identify the resulting widely linear system that encompasses the radio frequency impairments considered. Furthermore, it is also verified that for the joint estimation of the transmitter and the receiver I/Q imbalances, additional constraints than those required for strictly linear systems should be imposed on the training sequences employed. The results of numerical simulations show that DDST has comparable performance with methods reported in the literature.

Iterative MIMO Detection and Channel Estimation Using Joint Superimposed and Pilot-Aided Training

This paper presents a novel iterative detection and channel estimation scheme that combines the effort of superimposed training (ST) and pilot-aided training (PAT) for multiple-input multiple-output (MIMO) flat fading channels. The proposed method, hereafter known as joint mean removal ST and PAT (MRST-PAT), implements an iterative detection and channel estimation that achieves the performance of data-dependent ST (DDST) algorithm, with the difference that the data arithmetic cyclic mean is estimated and removed from data at the receiver’s end. It is demonstrated that this iterative and cooperative detection and channel estimator algorithm surpasses the effects of data detection identifiability condition that DDST has shown when higher orders of modulation are used. Theoretical performance of the MRST-PAT scheme is provided and corroborated by numerical simulations. In addition, the performance comparison between the proposed method and different MIMO channel estimation techniques is analyzed. The joint effort between ST and PAT shows that MRST-PAT is a solid candidate in communications systems for multiamplitude constellations in Rayleigh fading channels, while achieving high-throughput data rates with manageable complexity and bit-error rate (BER) as a figure of merit.

Elimination of Data Identification Problem for Data-Dependent Superimposed Training

In data-dependent superimposed training (DDST) schemes, the data-induced interference that occurs during channel estimation is eliminated at the expense of data distortion. Unfortunately, this distortion causes a data identification problem (DIP), where data sequence cannot be uniquely determined at the receiver end. Although DIP has been widely investigated in the literature, an effective solution for preventing its occurrence has yet to be proposed. Accordingly, in contrast to previous studies, which explained the occurrence of DIP only for a special case, the present study explores the theoretical foundations for DIP in DDST schemes using a signal subspace technique and derives the general conditions under which DIP occurs. It is shown mathematically that the occurrence of DIP is related to the adopted modulation scheme and the pilot pattern. Therefore, a pilot design criterion that theoretically eliminates DIP is proposed. The simulation results show that the proposed pilot design successfully eliminates the error floor in the bit error rate performance of DDST schemes.

Constellation rotation and symbol detection for data‐dependent superimposed training

The problem of symbol misidentification (SMI) for data-dependent superimposed training (DDST) is considered. The constraint conditions on the discrete Fourier transform matrix are derived and constellation rotation (CR) at the transmitter to avoid the SMI is proposed. Simulation results show that the DDST with CR can eliminate the symbol error floor and yield better detection performance than the original one.

Full-Hardware Architectures for Data-Dependent Superimposed Training Channel Estimation

Channel estimation based on superimposed training (ST) has been an active research topic around the world in recent years, because it offers similar performance when compared to methods based on pilot assisted transmissions (PAT), with the advantage of a better bandwidth utilization. However, physical implementations of such estimators are still under research, and only few approaches have been reported to date. This is due to the computational burden and complexity involved in the algorithms in conjunction with their relative novelty. In order to determine its suitability for commercial applications, counting with the performance and complexity analysis of the ST approaches is mandatory. This work proposes, at a first time, a full-hardware channel estimator architectures for an ST receiver and for one of its variants, known as data-dependent superimposed training (DDST). The architectures were described using Verilog HDL and implemented in Xilinx Virtex-5 XC5VLX110T FPGA. A fixed-point analysis has been carried out, allowing the design to produce practically equal performance to those achieved with the floating-point models. The synthesis results showed a reduced slices consumption (2%) and frequencies operation of 149 MHz, which allows to conclude that ST/DDST receivers can be utilized in practical developments.

Performance evaluation of time-multiplexed and data-dependent superimposed training based transmission with practical power amplifier model

The increase in the peak-to-average power ratio (PAPR) is a well known but not sufficiently addressed problem with data-dependent superimposed training (DDST) based approaches for channel estimation and synchronization in digital communication links. In this article, we concentrate on the PAPR analysis with DDST and on the spectral regrowth with a nonlinear amplifier. In addition, a novel Gaussian distribution model based on the multinomial distribution for the cyclic mean component is presented. We propose the use of a symbol level amplitude limiter in the transmitter together with a modified channel estimator and iterative data bit estimator in the receiver. We show that this setup efficiently reduces the regrowth with the DDST. In the end, spectral efficiency comparison between time domain multiplexed training and DDST with or without symbol level limiter is provided. The results indicate improved performance for DDST based approaches with relaxed transmitter power amplifier requirements.

Configurable Transmitter and Systolic Channel Estimator Architectures for Data-Dependent Superimposed Training Communications Systems

In this paper, a configurable superimposed training (ST)/data-dependent ST (DDST) transmitter and architecture based on array processors (APs) for DDST channel estimation are presented. Both architectures, designed under full-hardware paradigm, were described using Verilog HDL, targeted in Xilinx Virtex-5 and they were compared with existent approaches. The synthesis results showed a FPGA slice consumption of 1% for the transmitter and 3% for the estimator with 160 and 115 MHz operating frequencies, respectively. The signal-to-quantization-noise ratio (SQNR) performance of the transmitter is about 82 dB to support 4/16/64-QAM modulation. A Monte Carlo simulation demonstrates that the mean square error (MSE) of the channel estimator implemented in hardware is practically the same as the one obtained with the floating-point golden model. The high performance and reduced hardware of the proposed architectures lead to the conclusion that the DDST concept can be applied in current communications standards.

Channel Estimation and Symbol Detection with ICI Cancellation Based on Superimposed Training for OFDM Systems

Channel estimation using data-dependent superimposed training (DDST) is developed to doubly selective channels of Orthogonal Frequency Division Multiplexing (OFDM) systems; it consumes no extra bandwidth. An Inter-carrier interference (ICI) Self-cancelation method based on DDST scheme, IS-DDST, is designed which mitigates the interference from adjacent subcarriers to estimation. Moreover, a dual-iteration detection method is proposed to mitigate the ICI for IS-DDST scheme. Theoretical analysis and simulations show that the proposed scheme can achieve better Mean Square Error (MSE) and Bit Error Ratio (BER) performance than the existing DDST based scheme.

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.

A System on a Programmable Chip Architecture for Data‐Dependent Superimposed Training Channel Estimation

Channel estimation in wireless communication systems is usually accomplished by inserting, along with the information, a series of known symbols, whose analysis is used to define the parameters of the filters that remove the distortion of the data. Nevertheless, a part of the available bandwidth has to be destined to these symbols. Until now, no alternative solution has demonstrated to be fully satisfying for commercial use, but one technique that looks promising is superimposed training (ST). This work describes a hybrid software‐hardware FPGA implementation of a recent algorithm that belongs to the ST family, known as Data‐dependent Superimposed Training (DDST), which does not need extra bandwidth for its training sequences (TS) as it adds them arithmetically to the data. DDST also adds a third sequence known as data‐dependent sequence, that destroys the interference caused by the data over the TS. As DDST′s computational burden is too high for the commercial processors used in mobile systems, a System on a Programmable Chip (SOPC) approach is used in order to solve the problem.

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.

Carrier Frequency Offset Estimation using Data-Dependent Superimposed Training

In this letter we propose, for the first time, a solution to the problem of carrier frequency offset (CFO) estimation within the data dependent superimposed training (DDST) framework for channel estimation. While time division multiplexed (TDM) trained systems can use the TDM sequence to determine the CFO, the original attraction of DDST for channel estimation was that it avoided any TDM training. So in this letter we show how CFO estimation can still be very effectively performed with the DDST algorithm, while continuing to preclude the need for any additional bandwidth-consuming TDM training. Finally, simulations are presented that verify the theoretical results.

Iterative Mean Removal Superimposed Training for SISO and MIMO Channel Estimation

This contribution describes a novel iterative radio channel estimation algorithm based on superimposed training (ST) estimation technique. The proposed algorithm draws an analogy with the data dependent ST (DDST) algorithm, that is, extracts the cycling mean of the data, but in this case at the receiver's end. We first demonstrate that this mean removal ST (MRST) applied to estimate a single-input single-output (SISO) wideband channel results in similar bit error rate (BER) performance in comparison with other iterative techniques, but with less complexity. Subsequently, we jointly use the MRST and Alamouti coding to obtain an estimate of the multiple-input multiple-output (MIMO) narrowband radio channel. The impact of imperfect channel on the BER performance is evidenced by a comparison between the MRST method and the best iterative techniques found in the literature. The proposed algorithm shows a good tradeoff performance between complexity, channel estimation error, and noise immunity.

Frame/Training Sequence Synchronization and DC-Offset Removal for (Data-Dependent) Superimposed Training Based Channel Estimation

Over the last few years there has been growing interest in performing channel estimation via superimposed training (ST), where a training sequence is added to the information-bearing data, as opposed to being time-division multiplexed with it. Recent enhancements of ST are data-dependent ST (DDST), where an additional data-dependent training sequence is also added to the information-bearing signal, and semiblind approaches based on ST. In this paper, along with the channel estimation, we consider new algorithms for training sequence synchronization for both ST and DDST and block (or frame) synchronization (BS) for DDST (BS is not needed for ST). The synchronization algorithms are based on the structural properties of the vector containing the cyclic means of the channel output. In addition, we also consider removal of the unknown dc offset that can occur due to using first-order statistics with a non-ideal radio-frequency receiver. The subsequent bit error rate (BER) simulations (after equalization) show a performance not far removed from the ideal case of exact synchronization. While this is the first synchronization algorithm for DDST, our new approach for ST gives identical results to an existing ST synchronization method but with a reduced computational burden. In addition, we also present analysis of BER simulations for time-varying channels, different modulation schemes, and traditional time-division multiplexed training. Finally, the advantage of DDST over (conventional, non semi-blind) ST will reduce as the constellation size increases, and we also show that even without a BS algorithm, DDST is still superior to conventional ST. However, iterative semiblind schemes based upon ST outperform DDST but at the expense of greater complexity

Performance of data-dependent superimposed training without cyclic prefix

A recent improvement in superimposed training for channel estimation, where the training sequence is actually added to the information data, is called data-dependent superimposed training (DDST). Here we show (theoretically and via simulations) that the performance of DDST (both for channel estimation and equalisation) may suffer minimal degradation when implemented without the use of a cyclic prefix (which carries only redundant information).