Superimposed Training Sequence Research Articles

Exact Analysis of MIMO Channel Estimation Based on Superimposed Training

In this paper, channel estimation capabilities of a multiple-input multiple-output (MIMO) system using superimposed training sequences are investigated. A new expression for estimation-error variance is derived. It is shown that the training sequences must be balanced and must have specific correlation properties. The latter are required only in a specific zone. Sequences that satisfy these criteria exist and are referred to as zero-correlation zone (ZCZ) solutions. Consequently, by using balanced ZCZ sequences, harmful direct current (DC) offset can be removed. Owing to their zero-cross correlation, interference from other transmitting antennas may be eliminated. Furthermore, a closed-form expression of the estimation-error variance can be obtained due to their impulse-like autocorrelation. To increase the number of antennas in the MIMO system, a new construction of ZCZ sequence set is proposed, in which all sequences are balanced

Receiver design for CDMA‐FBMC/ OQAM systems

This Letter documents BER performance analysis of orthogonal affine precoder (OAP)-based CDMA filter bank multi-carrier offset QAM (CDMA-FBMC/OQAM) systems. The use of OAP cancels the interference introduced by simultaneous transmission of data and training sequence during channel estimation and data detection. This makes the channel estimation and data detection more accurate and hence, BER performance is improved. BER is calculated for different power coefficients of the superimposed training sequence and is compared with that of other FBMC systems. It is observed that the proposed system outperforms the state-of-art FBMC systems from BER perspective.

Superimposed Training Combined Approach for a Reduced Phase of Spectrum Sensing in Cognitive Radio.

This paper presents an approach to exploit the superimposed training (ST)-based primary users’ (PUs) transmissions in the context of spectrum sensing for cognitive radio. In the low signal-to-noise ratio (SNR), the proposed scheme splits the spectrum sensing phase into two sample processing periods, allowing a secondary user (SU) to carry out a training sequence synchronization (with a small probability of error) before the implementation of a robust spectrum sensing algorithm that enhances the detection, based on the deterministic signal components embedded in the ST PU’s signals along with the unknown data signal. The overall sensing performance is improved using a reasonable number of samples to achieve a high probability of detection, resulting in a reduced spectrum sensing duration. Furthermore, a low computational complexity version of the proposed ST combined approach for a reduced phase (SCAR-Phase) of spectrum sensing is presented, which attains the same detection performance with a smaller number of real operations in the low SNR. In the practical consideration of imperfect training sequence synchronizations, the results show the advantages of exploiting the ST sequence to perform spectrum sensing, thus quantifying the significant improvement in detection performance and the maximum SU’s achievable throughput.

GA Based Estimation of Sparse MIMO Channels with Superimposed Training

Multiple-input multiple-output (MIMO) techniques are foreseen to play a vital role in future 5G cellular networks. This paper presents a novel approach that employs genetic algorithm (GA) to estimate the sparse uplink MIMO channels using superimposed training sequence (SiT). At each transmitter (user) a training sequence is mathematically added with the data bits; thus, avoiding the overhead of dedicated frequency/time slots used for the training. On the receiver side (base station), signals received at all the receive antennas are jointly processed by employing the proposed method to obtain channels’ estimate. Then, a linear minimum mean square error (LMMSE) equalizer estimates the data sequences sent by transmitter. A computer simulation based performance analysis of the proposed method is presented, where performance evaluation is done using metrics of normalized channel mean square error (NCMSE), as well as, bit error rate (BER). A comparative analysis of the proposed method with notable SiT least squares (SiT-LS) and SiT-LMMSE methods in the literature is conducted, which clearly demonstrates that the proposed method outperforms both the existing techniques. DOI: http://dx.doi.org/10.5755/j01.eie.24.6.22294

Joint Time/Frequency Synchronization and Chromatic Dispersion Estimation With Low Complexity Based on a Superimposed FrFT Training Sequence

We design a new kind of fractional Fourier transform superimposed training sequence (TS) to simultaneously estimate linear distortions including chromatic dispersion (CD), timing offset (TO), and frequency offset (FO), etc. Without sacrificing additional spectrum efficiency, the scheme can reduce the computational complexity sharply compared to a regular estimation method. The feasibility of the proposed scheme is verified by simulations of 28 GBaud dual polarization quadrature phase-shift keying (DP-QSPK) and 16-ary quadrature amplitude modulation (DP-16 QAM) system. Simulation results show that the superimposed TS has negligible negative effects on the transmission performance considering 7% forward error correction while the TO, FO, and rough CD estimation perform well after a long distance transmission. The max TO estimation (TOE) errors are, respectively, eight samples and 12 samples for QPSK and 16 QAM, and the max FO estimation errors are, respectively, 5.7 and 3.6 MHz for QPSK and 16 QAM. The rough CD estimation (CDE) error is below 946 ps/nm. The TOE and rough CDE separately decrease the complexity of frame synchronization and CDE. In total, the computation complexity of the estimations decreases by 91.8% compared to a regular blind estimation method.

GA Based Sensing of Sparse Multipath Channels with Superimposed Training Sequence

GA Based Sensing of Sparse Multipath Channels with Superimposed Training Sequence

Compressed Sensing of Sparse Multipath MIMO Channels with Superimposed Training Sequence

Recent advances in multiple-input multiple-output (MIMO) systems have renewed the interests of researchers to further explore this area for addressing various dynamic challenges of emerging radio communication networks. Various measurement campaigns reported recently in the literature show that physical multipath MIMO channels exhibit sparse impulse response structure in various outdoor radio propagation environments. Therefore, a comprehensive physical description of sparse multipath MIMO channels is presented in first part of this paper. Superimposing a training sequence (low power, periodic) over the information sequence offers an improvement in the spectral efficiency by avoiding the use of dedicated time/frequency slots for the training sequence, which is unlike the traditional schemes. The main contribution of this paper includes three superimposed training (SiT) sequence based channel estimation techniques for sparse multipath MIMO channels. The proposed techniques exploit the compressed sensing theory and prior available knowledge of channel's sparsity. The proposed sparse MIMO channel estimation techniques are named as, SiT based compressed channel sensing (SiT-CCS), SiT based hardlimit thresholding with CCS (SiT-ThCCS), and SiT training based match pursuit (SiT-MP). Bit error rate (BER) and normalized channel mean square error are used as metrics for the simulation analysis to gauge the performance of proposed techniques. A comparison of the proposed schemes with a notable first order statistics based SiT least squares (SiT-LS) estimation technique is presented to establish the improvements achieved by the proposed schemes. For sparse multipath time-invariant MIMO communication channels, it is observed that SiT-CCS, SiT-MP, and SiT-ThCCS can provide an improvement up to 2, 3.5, and 5.2 dB in the MSE at signal to noise ratio (SNR) of 12 dB when compared to SiT-LS, respectively. Moreover, for $$\mathrm {BER}=10^{-1.9}$$BER=10-1.9, the proposed SiT-CCS, SiT-MP, and SiT-ThCCS, compared to SiT-LS, can offer a gain of about 1, 2.5, and 3.5 dB in the SNR, respectively. The performance gain in MSE and BER is observed to improve with an increase in the channel sparsity.

Disintegrated Channel Estimation in Filter-and-Forward Relay Networks

This paper investigates a disintegrated channel estimation technique required to accomplish the spatial diversity supported by cooperative relays. The relaying strategy considered herein is a filter-and-forward (FF) relaying method with superimposed training sequences for separately estimating the backhaul and access channels. To reduce inter-relay interference, a generalized filtering technique is proposed and investigated thoroughly. Unlike the interference suppression method commonly used in the conventional FF relay networks, the generalized filtering matrix essentially multiplexes the superimposed training sequences from different relays to the destination by time-division multiplexing, frequency-division multiplexing, and code-division multiplexing methods. The Bayesian Cramer–Rao lower bounds (BCRBs) for this channel estimation problem are derived as the estimation performance benchmarks. The mean square errors (MSEs) of the disintegrated channel estimates are also derived. Finally, the improvements offered by the proposed technique are verified by comprehensive computer simulations in conjunction with the calculations of the BCRBs and the MSEs derived in this paper.

GA Based Sensing of Sparse Multipath Channels with Superimposed Training Sequence

class=Abstract>This paper proposes an improved Genetic Algorithms (GA) based sparse multipath channels estimation technique with Superimposed Training (ST) sequences. A non-random and periodic training sequence is proposed to be added arithmetically on the information sequence for energy efficient channel estimation within the future generation of wireless receivers. This eliminates the need of separate overhead time/frequency slots for training sequence. The results of the proposed technique are compared with the techniques in the existing literature -the notable first order statistics based channel estimation technique with ST. The normalized channel mean-square error (NCMSE) and bit-error-rate (BER) are chosen as performance measures for the simulation based analysis. It is established that the proposed technique performs better in terms of the accuracy of estimated channel; subsequently the quality of service (QoS), while retrieving information sequence at the receiver. With respect to its comparable counterpart, the proposed GA based scheme delivers an improvement of about 1dB in NCMSE at 12 dB SNR and a gain of about 2 dB in SNR at 10 -1 BER, for the population size set at twice the length of channel. It is also demonstrated that, this achievement in performance improvement can further be enhanced at the cost of computational power by increasing the population size. DOI: http://dx.doi.org/10.5755/j01.eee.22.1.14114

Channel Estimation for Time-Varying MIMO Relay Systems

In this paper, we investigate the channel estimation problem for multiple-input multiple-output (MIMO) relay communication systems with time-varying channels. The time-varying characteristic of the channels is described by the complex-exponential basis expansion model (CE-BEM). We propose a superimposed channel training algorithm to estimate the individual first-hop and second-hop time-varying channel matrices for MIMO relay systems. In particular, the estimation of the second-hop time-varying channel matrix is performed by exploiting the superimposed training sequence at the relay node, while the first-hop time-varying channel matrix is estimated through the source node training sequence and the estimated second-hop channel. To improve the performance of channel estimation, we derive the optimal structure of the source and relay training sequences that minimize the mean-squared error (MSE) of channel estimation. We also optimize the relay amplification factor that governs the power allocation between the source and relay training sequences. Numerical simulations demonstrate that the proposed superimposed channel training algorithm for MIMO relay systems with time-varying channels outperforms the conventional two-stage channel estimation scheme.

Design of superimposed training sequence for spatially correlated multiple‐input–multiple‐output channels under interference‐limited environments

In this study, the design of a superimposed training (ST) for an interference-limited spatially correlated multiple-input–multiple-output (MIMO) system is addressed and a closed-form solution for designing the training signal is proposed in a sub-optimal way. Earlier papers have considered noise limited MIMO systems. The authors also propose random/orthogonal variable spread factor (OVSF) codes as a choice for the ST signal. The mean-squared error of the channel estimate and symbol error rate performances are obtained through simulation. Considering the power allocation issue between the data and training symbols, a sub-optimal average training power that maximises the lower bound on the effective signal-to-interference ratio is also proposed. Simulation results show that the bit error rate performance of the ST is indistinguishable from the OVSF/random ST sequence.

Time‐interpolated channel estimation for increasing channel capacity of superimposed training‐based precoding schemes

This study proposes a time-interpolated channel estimation method for the superimposed training-based precoding scheme in slowly time-varying channels. The regular inclusion of a superimposed training sequence, which is necessary for explicit time-varying channel estimation, reduces the power allocated to information transmission. Therefore time-interpolated channel estimation, where the transmission without a superimposed training symbol is performed during a few coherence times, makes very efficient use of transmitting power and possible gains in terms of capacity. A closed-form solution of the periodicity of time-interpolated channel estimation was derived using this method together with a Markov channel model. The simulation results confirmed that the use of the time-interpolated channel estimation method improves the performance of channel capacity and possibly provides energy savings for mobile terminals.

Compressed sensing theory-based channel estimation for optical orthogonal frequency division multiplexing communication system

Due to the spare multipath property of the channel, a channel estimation method, which is based on partial superimposed training sequence and compressed sensing theory, is proposed for line of sight optical orthogonal frequency division multiplexing communication systems. First, a continuous training sequence is added at variable power ratio to the cyclic prefix of orthogonal frequency division multiplexing symbols at the transmitter prior to transmission. Then the observation matrix of compressed sensing theory is structured by the use of the training symbols at receiver. Finally, channel state information is estimated using sparse signal reconstruction algorithm. Compared to traditional training sequences, the proposed partial superimposed training sequence not only improves the spectral efficiency, but also reduces the influence to information symbols. In addition, compared with classical least squares and linear minimum mean square error methods, the proposed compressed sensing theory based channel estimation method can improve both the estimation accuracy and the system performance. Simulation results are given to demonstrate the performance of the proposed method.

High-speed Downhole Transmission System and Its Synchronization Algorithm Based on Optical OFDM

To overcome the shortcomings of low transmission rate due to limited bandwidth in traditional wired transmission, this paper proposes a wireless downhole transmission system based on optical OFDM technique. This system is advantageous in both optical communications technique and OFDM technique. It has higher transmission rate. Furthermore, in order to overcome the defects of being sensitive to synchronization of OFDM systems, a frame synchronization algorithm based on superimposed training sequence is proposed. The mirror symmetry property of training sequence is used to shift intercept the received signals. The intercepted signals are mirror symmetrically superimposed, which cause frame synchronization objective function to accumulate more energy to synchronize. At the same time, it effectively avoids complex multiplication of long sequence. The simulation results show that good synchronization performance with low SNR is obtained and the proposed frame synchronization algorithm has lower mean square error. 

Quantization Error's Influence on the Channel Estimation

To improve the transmission performance of wireless channel, the improvement of the Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) channel estimation algorithm based on the superimposed training sequence is proposed. We take advantage of training and information sequences unrelated, without loss of bandwidth preliminary estimate the channel parameters; Then using the weighted factor of channel parameters between adjacent signal weighted average, get the final channel parameters. Compared with previous superimposed training sequence estimation method, using the algorithm has a lower estimation mean square error (MSE), and is suitable for the time-varying channel. The computer simulation results show that the estimation method improves the accuracy and performance of channel transmission.

Channel estimation for asymmetrically clipped optical orthogonal frequency division multiplexing communication system

The channel estimation problem for asymmetrically clipped optical orthogonal frequency division multiplexing wireless communication systems is investigated. In order to resolve the noise-sensitive problem of traditional least squares-based channel estimation method, a new channel estimation method which is based on superimposed training sequence and guarantees the linear minimum mean square error estimate is proposed. Cycle training sequence is added at variable power ratio to the information sequence at the transmitter prior to transmission. Then, statistical average method is employed to separate training and information sequences at the receiver. Simulation results show that the power ratio of training sequence needs to balance between the mean square error (MSE) of estimation and the error bit rate. Moreover, compared with the traditional least squares-based method, the proposed method has significantly improved the estimation performance under the condition of low signal-to-noise ratio, especially, when the MSE of the estimation reduces 1 to 2 orders.

Robust Training Sequence Design for Spatially Correlated MIMO Channel Estimation

A robust superimposed training sequence design is proposed for spatially correlated multiple-input-multiple-output (MIMO) channel estimation. The proposed scheme does not require accurate knowledge of the spatial correlation matrix, and it is shown to outperform previously proposed robust correlated MIMO channel estimators, such as relaxed minimum mean square error (RMMSE) and least-square RMMSE. Since the training sequence is overlaid into the data stream, the spectral efficiency of the system is higher than those that use time-multiplexed pilots. A solution for the sequence can easily be obtained by using a projection on convex-set-based iterative algorithm that is guaranteed to converge as long as the training sequence matrix is initialized to have full rank. Furthermore, it is shown that the proposed scheme is identical to the RMMSE-based schemes when the MIMO channel is spatially uncorrelated. The computational complexity of the proposed algorithm is also illustrated.

An Improved Channel Estimation Algorithm Based on Superimposed Training Sequence

A channel estimation algorithm based on superimposed training sequence is proposed in this paper. The algorithm does not need the allocation of time slot for superimposed training sequence, and it can estimate the channel coefficients without loss of bandwidth. The mean squared difference, Cramer-Rao bound and the lower bound of channel capacity are deduced based on the LS algorithm of channel estimation. It has simple structure, less computational requirement by using the LS algorithm. The result of simulation shows that the performance of system is improved to a large extent by using superimposed training sequence rather than direct training sequence, and the capacity of system is also increased.

Channel estimation in bit-interleaved coded modulation with iterative decoding

This study improves the fading channel estimation in bit-interleaved coded modulation systems with iterative decoding (BICM-ID) and signal space diversity (SSD) by embedding a training sequence. Existing training schemes work well at high signal-to-noise ratio (SNR) or slowly time-varying channels whereas the applications of BICM-ID are beneficial at low SNR and fast time-varying fading channels. Motivated by the power/bandwidth efficiency of the SSD technique and the fact that superimposed training outperforms pilot symbol-assisted modulation (PSAM) training over relatively fast time-varying channels, a new superimposed training sequence is explored. The proposed training sequence inserts pilot bits into the coded bits prior constellation mapping and signal rotation. This becomes a superimposed training sequence in the rotated symbols and helps the estimator to track fast variation of the channel gains. A soft iterative channel estimator is developed to work with the superimposed training sequence. The performance of the proposed scheme, namely SSD-pilot, is shown to be superior to PSAM scheme. To gauge the performance improvement achieved with the proposed channel estimation, an analytical bound on the asymptotic bit error probability for BICM-ID using SSD over correlated fading channels is provided. The Cramer-Rao bound on the mean-square error of the channel estimator is also derived to evaluate the performance of the iterative channel estimator.

Optimal Superimposed Training Sequences for Channel Estimation in MIMO-OFDM Systems

In this work an iterative time domain Least Squares (LS) based channel estimation method using superimposed training (ST) for a Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system over time varying frequency selective fading channels is proposed. The performance of the channel estimator is analyzed in terms of the Mean Square Estimation Error (MSEE) and its impact on the uncoded Bit Error Rate (BER) of the MIMO-OFDM system is studied. A new selection criterion for the training sequences that jointly optimizes the MSEE and the BER of the OFDM system is proposed. Chirp based sequences are proposed and shown to satisfy the same. These are compared with the other sequences proposed in the literature and are found to yield a superior performance. The sequences, one for each transmitting antenna, offers fairness through providing equal interference in all the data carriers unlike earlier proposals. The effectiveness of the mathematical analysis presented is demonstrated through a comparison with the simulation studies. Experimental studies are carried out to study and validate the improved performance of the proposed scheme. The scheme is applied to the IEEE 802.16e OFDM standard and a case is made with the required design of the sequence.