Iterative Channel Estimation Research Articles

A Multi-user Receiver for Inter-Cell Interference Reduction in LTE PUCCH Signaling

In 3rd generation partnership project (3GPP) long term evolution (LTE) systems, when no resources has been assigned in the uplink to a given user equipment (UE), the control information associated with layers 1 and 2 in the protocol stack is conveyed back to the base station through the so-called physical uplink control channel (PUCCH). In PUCCH, the data streams transmitted by multiple UEs are multiplexed in the time-domain and in the frequency-domain with the aid of spreading codes. Although the spreading codes associated with UEs within the same cell can be assumed to be orthogonal, the presence of inter-cell interference (ICI) in multi-cell scenarios severely limits receiver performance. In particular, the Format 1 of PUCCH, which is associated with the transmission of hybrid automatic repeat request acknowledgements (ACK/NACK) and scheduling requests, has a major impact on system performance, since an incorrectly decoded ACK/NACK message may introduce significant delay in data transmission. In this contribution, we propose a new multi-user receiver for ICI reduction in PUCCH LTE that operates both in cooperative and non-cooperative multi-cell architectures. The proposed receiver relies on a constrained tensor modeling of the received signal in PUCCH signaling, and affords an iterative joint channel estimation and symbol detection by simultaneously exploiting the energy of the data symbols and the pilot tones present in PUCCH. The formulation of the proposed algebraic receiver model incorporates symbol-basis hopping and slot-basis hopping signaling schemes, which are interference randomization techniques existing in the 3GPP specifications of LTE system. Computer simulation results show the remarkable performance gains of the proposed receiver compared to the conventional time-frequency decorrelator based receiver.

Interference suppression with iterative channel and spatial covariance matrix estimation for LTE downlink

In LTE downlink systems with a high frequency reuse factor, inter-cell interference has been viewed as a predominant factor limiting system performance. Therefore, we develop an iterative interference suppression receiver to mitigate inter-cell interference for LTE downlink systems in this paper. The key challenge here is how to accurately estimate the channel state information and spatial interference covariance matrix by using limited available pilots to improve the interference suppression capability and the successful probability of signal detection. To overcome the challenge, an iterative receiver making full use of the time–frequency correlation of channel is proposed. Simulation results show that the proposed iterative receiver can obtain a lower packet-error ratio compared with the existing algorithms.

ITERATIVE QR DECOMPOSTION CHANNEL ESTIMATION FOR MIMO-OFDM SYSTEMS

Channel estimation algorithms have a key role in si gnal detection in MIMO-OFDM systems. In this system , the number of channel components which need to be estimated is much more than conventional SISO wireless systems. Consequent ly, the computational process of channel estimation is highly intensive. In addition, the high performance channel estimatio n algorithms mostly suffer from high computational complexity. In the other words, the system undergoes intensive computations if high performance efficiency is desired. However, there is an alternative solution to achieve both high performance efficiency and rel atively low level of computational complexity. In this solution, high ef ficient channel estimation is firstly designed, and then it is simplified using alternative mathematical expressions. In this paper , Iterative channel estimation based on QR decompos ition for MIMO-OFDM systems is proposed. From simulation results, the i terative QRD channel estimation algorithm can provi de better mean-square-error and bit error rate performance than conventional me thods.

A Modified Nonparametric Message Passing Algorithm for Soft Iterative Channel Estimation

Based on the factor graph framework, we derived a Modified Nonparametric Message Passing Algorithm (MNMPA) for soft iterative channel estimation in a Low Density Parity-Check (LDPC) coded Bit-Interleaved Coded Modulation (BICM) system. The algorithm combines ideas from Particle Filtering (PF) with popular factor graph techniques. A Markov Chain Monte Carlo (MCMC) move step is added after typical sequential Important Sampling (SIS) -resampling to prevent particle impoverishment and to improve channel estimation precision. To reduce complexity, a new max-sum rule for updating particle based messages is reformulated and two proper update schedules are designed. Simulation results illustrate the effectiveness of MNMPA and its comparison with other sum-product algorithms in a Gaussian or non-Gaussian noise environment. We also studied the effect of the particle number, pilot symbol spacing and different schedules on BER performance.

Iterative channel estimation and data detection for MIMO-OFDM systems operating in time-frequency dispersive channels under unknown background noise

In this paper, the challenging problem of joint channel estimation and data detection for multiple-input multiple-output orthogonal frequency division multiplexing systems operating in time-frequency dispersive channels under unknown background noise is investigated. Based on two different but equivalent signal models, two expectation-maximization algorithm-based iterative schemes for joint data detection and channel and noise variance estimation are proposed. The first scheme jointly detects data and estimates the channel and noise variance, but the computational complexity is high, owing to the simultaneous detection and estimation for all antennas. To reduce the computational complexity, a complexity-reduced scheme that is detecting data and estimating channel for only one antenna during each iteration and holding the unknown quantities of other antennas to their last values is proposed, whose performance only slightly degrades compared to the first scheme. Moreover, both schemes are derived as closed-form expressions, and therefore, our proposed schemes are free of exhaustive search. Simulation results demonstrate quick convergence of the proposed algorithm, and after convergence, the performance of the proposed algorithm is close to that of the optimal channel estimation and data detection case, which assumes full training and perfect channel state information.

Doubly-Selective Channel Estimation Using Superimposed Training and Weighted First-Order Statistics

Doubly-selective channel estimation using superimposed training and complex exponential basis expansion model is considered. By taking a weighted averaging operation of the received data, a weighted first-order statistical estimator is proposed, where the time-varying channel estimation is reduced to the simple average-based solution of time-invariant coefficients and the dominant effect of information-induced interference on channel estimation can be suppressed. To further improve the estimation performance with a limited training power, a joint iterative channel estimation and symbol detection scheme is developed where the detected symbol is exploited to enhance estimation performance instead of being viewed as interference. Theoretical analysis and simulation results show that the proposed scheme is superior to data-dependent superimposed training scheme and competitive with the conventional time-multiplexed training in terms of symbol error rate over doubly-selective channels.

Performance comparison of Different Pilot Aided Channel Estimation Techniques for OFDM IEEE 802. 11e (Mobile Wimax) Systems

Orthogonal frequency-division multiplexing (OFDM) modulation is a promising technique for achieving the high bit rates required for a wireless multimedia service. In this paper we present different channel estimation algorithms for OFDM mobile communication systems based on the use of pilot subcarriers. Specifically we present LS (Least Square), MMSE (Minimum Mean Square Error) and LMS iterative channel estimation techniques to show the performance comparison of channel estimation techniques supported by n IFFT-based time channel estimation. The channel estimation is based on the principle of pilotsymbol aided channel estimation (PACE), and it has been implemented using block-type pilot subcarrier arrangements. The low complexity of LMS algorithm, has a higher efficiency than conventional methods (without channel estimation, LS& LMMSE) and it can work in lower amount of SNRs. We have compared the performances of all schemes by measuring bit error rate with 16QAM as modulation scheme, and multi-path Rayleigh fading channel as channel model. Simulation results validate that the LMS Iterative channel estimation scheme can achieve tremendous performance as the existing channel estimation methods. Keyword: Channel estimation (CE), OFDM (Orthogonal Frequency Division Multiplexing), QAM (Quadrature Amplitude Modulation).

Iterative carrier phase and channel estimation algorithm in BICM-ID

This paper proposed an iterative carrier phase and channel estimation algorithm for 16APSK in Bit-Interleaved Coded Modulation with Iterative Decoding(BICM-ID).The algorithm was based on Maximum Likelihood(ML) estimation,which made use of the decision information provided by the decoder and exchanged information between phase,channel estimation and decoder through iteration,so as to combine phase,channel estimation with decoder.The simulation results show the algorithm can get the performance only 0.5 dB lower than the ideal one,and can effectively estimate the phase difference from-20 degree to 20 degree.

Scheduling of the Activations in Iterative Detection, Decoding, and Channel Estimation for MIMO-OFDM

An iterative receiver for a multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system is considered to jointly decode the transmitted bits and estimate the channel state. The receiver consists of the a posteriori probability (APP) algorithm, the repeat-accumulate (RA) decoder, and the least-squares (LS) channel estimator. An obvious problem, with more than two blocks in an iterative receiver, is to find the optimal activation schedule of the different blocks. This paper proposes to use extrinsic information transfer (EXIT) charts to characterize the behavior of the receiver blocks and find out the optimal activation schedule for them. A semi-analytical expression of the EXIT function is derived for the decision directed LS channel estimator. An algorithm is proposed to generate the EXIT function of the APP algorithm as a function of channel estimate mutual information (MI). Surface fitting is used to get closed form expressions for the EXIT functions of the APP algorithm and the RA decoder. Trellis search based algorithms are shown to find the convergence with the lowest possible complexity using the EXIT charts. With the proposed concept, the activation scheduling can be adapted to prevailing channel circumstances and unnecessary iterations will be avoided.

Iterative LMMSE Channel Estimation and Decoding Based on Probabilistic Bias

Iterative channel estimation and decoding based on probabilistic bias is investigated. In order to control the occurrence probability of transmitted symbols, biased convolutional codes (CCs) are proposed. A biased CC is obtained by puncturing the parity bit of a conventional (unbiased) CC and by inserting a fixed bit at the punctured position when the state is contained in a certain subset of all possible states. A priori information about the imposed bias is utilized for the initial linear minimum mean-squared error (LMMSE) channel estimation. This paper focuses on biased turbo codes that are constructed as the parallel concatenation of two biased CCs with interleaving, and proposes an iterative LMMSE channel estimation and decoding scheme based on approximate belief propagation. The convergence property of the iterative LMMSE channel estimation and decoding scheme is analyzed via density evolution (DE). The DE analysis allows one to design the magnitude of the bias according to the coherence time, in terms of the decoding threshold. The proposed scheme is numerically shown to outperform conventional pilot-based schemes in the moderate signal-to-noise ratio (SNR) regime, at the expense of a performance degradation in the high SNR regime.

Merging Belief Propagation and the Mean Field Approximation: A Free Energy Approach

We present a joint message passing approach that combines belief propagation and the mean field approximation. Our analysis is based on the region-based free energy approximation method proposed by Yedidia et al. We show that the message passing fixed-point equations obtained with this combination correspond to stationary points of a constrained region-based free energy approximation. Moreover, we present a convergent implementation of these message passing fixed-point equations provided that the underlying factor graph fulfills certain technical conditions. In addition, we show how to include hard constraints in the part of the factor graph corresponding to belief propagation. Finally, we demonstrate an application of our method to iterative channel estimation and decoding in an orthogonal frequency division multiplexing system.

Iterative Compressive Estimation and Decoding for Network-Channel-Coded Two-Way Relay Sparse ISI Channels

We propose an iterative compressive channel estimation and decoding scheme for network-channel-coded two-way relay sparse intersymbol interference channels. The underlying system model comprises two source nodes and one relay node. In the first time slot, the two sources simultaneously transmit their independent training pilots along with the channel-coded data. In the second time slot, the relay applies analog network coding to the sum of the received signals and broadcasts the result to the two sources. Each source then uses iterative compressive sensing (CS) based estimation and decoding to estimate the composite channels and recover the transmitted signal. We analyze the performance of our proposed CS-based scheme and demonstrate its efficacy over existing ones.

Iterative Message Passing for Pilot-Assisted Multiuser Detection in MC-CDMA Systems

Application of the belief propagation (BP) algorithm is proposed for pilot-assisted reception of multi-carrier code division multiple access systems where the input symbols are binary phase shift keying symbols. The proposed method utilizes a Bayesian network graph that includes channel parameters as explicit nodes representing random variables. Channel parameters in both frequency and time domains are included in the graph. The messages on the graph are obtained by making large system assumptions for the user symbols and Gaussian assumptions for the messages related to the channel parameters. Density evolution analysis is performed whose results match well with the simulation results. For the simulated scenario it is found that BP with the proposed graph outperforms channel estimation followed by graph based detection, and non-graph based iterative channel estimation.

Two iterative channel estimation algorithms in single‐input multiple‐output (SIMO) LTE systems

ABSTRACTAccurate channel estimation is a challenging problem in a long term evolution systems especially in highly selective channels. In this paper, two iterative channel estimation algorithms are proposed to efficiently estimate channels over the orthogonal frequency‐division multiplexing (OFDM) symbols containing pilot symbols. The first one is proposed based on a simple interpolation algorithm, named as simple iterative frequency interpolation (SIFI), and improves the system performances with low implementation complexity. The second one is derived from the traditional expectation maximisation (EM) algorithm in OFDM systems. In order to deal with the ‘null’ subcarriers in the guard band and make the algorithm converge, the truncated singular value decomposition method is integrated to truncated singular value decomposition expectation‐maximisation (TSVD‐EM). Both channel estimation algorithms are followed by a simple linear interpolation in time domain to obtain channel estimates over the whole subframe. Complexities of the proposed algorithms are also analysed and compared. Simulation results show that the SIFI algorithm outperforms the traditional interpolation method with a low complexity, and the TSVD‐EM algorithm approaches the best achievable performance with a reduced complexity.Copyright © 2012 John Wiley & Sons, Ltd.

A joint timing synchronization, channel estimation, and SFD detection for IR-UWB systems

This paper proposes a joint timing synchronization, channel estimation, and data detection for the impulse radio ultrawideband systems. The proposed timing synchronizer consists of coarse and fine timing estimation. The synchronizer discovers synchronization points in two stages and performs adaptive threshold based on the maximum pulse averaging and maximum (MAX-PA) method for more precise synchronization. Then, iterative channel estimation is performed based on the discovered synchronization points, and data are detected using the selective rake (S-RAKE) detector employing maximal ratio combining. The proposed synchronizer produces two signals-the start signal for channel estimation and the start signal for start frame delimiter (SFD) detection that detects the packet synchronization signal. With the proposed synchronization, channel estimation, and SFD detection, an S-RAKE receiver with binary pulse position modulation binary phase-shift keying modulation was constructed. In addition, an IEEE 802.15.4a channel model was used for performance comparison. The comparison results show that the constructed receiver yields high performance close to perfect synchronization.

Iterative Channel Estimation for the Chinese Digital Television Terrestrial Broadcasting Systems with the Multiple-Antenna Receivers

Orthogonal frequency division multiplexing is an effective against multipath fading and high data throughput wireless channel transmission technology. Assistance with the inverse fast Fourier transform and fast Fourier transform operation, orthogonal frequency division multiplexing modulation and demodulation operations of the system convenient and convenient hardware implementation, orthogonal frequency division multiplexing, so in the modern digital television terrestrial broadcasting the system is widely used to support high performance bandwidth-efficient multimedia services. Broadband multi-carrier orthogonal frequency division multiplexing with multi-antenna and multi-antenna receiving system, to increase the diversity gain and improve the capacity of the system in different multipath fading channel. Accurate channel estimation in a simple channel equalization and decoding of broadband multi-carrier orthogonal frequency division multiple-antenna receiver and channel estimation accuracy and multiplexing system is very important, is the key to the performance of the overall broadband multi-carrier orthogonal frequency division multiplexing system in the multi-antenna receiver bit error rate. In this paper, iterative channel estimation to plan for digital terrestrial television broadcasting broadband multi-carrier orthogonal frequency division multiple antenna receiver multiplexing system proposal.

Iterative Channel Estimation Scheme for the WLAN Systems with the Multiple-Antenna Receivers

Alleviate the multipath delay spread and suitable for broadband transmission efficiency, orthogonal frequency division multiplexing wireless local area network (WLAN) is widely used to assist inverse fast Fourier transform and fast Fourier transform operation domain. Orthogonal frequency division multiplexing is a blow to the broadcast channel multipath fading and high data throughput, transmission, wireless fading channel method, which is widely used to support high performance bandwidth-efficient wireless multimedia services. Several times in the transmitter and receiver antenna technology allows data transfer rate and spectrum efficiency and the use of multiple transmit antennas and multiple receive antennas through spatial processing. High-precision channel estimation scheme is very important wideband multi-carrier orthogonal frequency complex WLAN systems use multiple antenna receiver based division of labor and the overall multi-carrier orthogonal frequency multiplexing division of performance-based WLAN system is to crucial antenna to receive the symbol error rate. In this article, the iterative channel estimation scheme proposed multi-carrier orthogonal frequency division multiplexed using multiple antennas receiver-based WLAN system.

MIMO Capacity Analysis Using Adaptive Semi Blind Channel Estimation with Modified Precoder and Decoder for Time Varying Spatial Channel

Multiple Input Multiple Output(MIMO) has been in much importance in recent past because of high capacity gain over a single antenna system. In this article, analysis over the capacity of the MIMO channel systems with spatial channel with modified precoder and decoder has been considered when the channel state information (CSI) is considered partial. Due to delay in acquiring transmitted information at the receiver end, the time selective fading wireless channel often induces incomplete or partial CSI. The dynamic CSI model has also been implemented consisting channel mean and covariance which leads to extracting of channel estimates and error covariance which then further applied with the modified precoder and decoder utilizing both the parameters indicating the CSI quality since these are the functions of temporal correlation factor, and based on this, the model covers data from perfect to statistical CSI, either partially or fully blind. It is found that in case of partial and imperfect CSI, the capacity depends on the statistical properties of the error in the CSI which has been manipulated according to the precoder and decoder conditions. Based on the knowledge of statistical distribution of the deviations in CSI knowledge, a new approach which maximizes the capacity of spatial channel model with modified precoder and decoder has been tried. The interference then interactively reduced by employing the iterative channel estimation and data detection approach, where by utilizing the detected symbols from the previous iteration, multiuser/MIMO channel estimation and symbol detection is improved.

Dual-Diagonal LMMSE Channel Estimation for OFDM Systems

We propose a low-complexity dual-diagonal (DD-) linear-minimum-mean-square-error (LMMSE) channel estimation algorithm for orthogonal frequency-division multiplexing (OFDM) systems involving iterative channel estimation and signal detection. Computational complexity and mean-square-error (MSE) analysis are presented to evaluate the efficiency of the proposed algorithm. A closed-form expression is derived for the asymptotic MSE of the DD-LMMSE channel estimator. Both analysis and numerical results show that DD-LMMSE performs close to the well-known LMMSE estimator with much lower complexity.

Capacity Analysis of MIMO Spatial Channel Model using Novel Adaptive Semi Blind Estimation Scheme

Multiple Input Multiple Output (MIMO) has been remained in much importance in recent past because of high capacity gain over a single antenna system. In this article, analysis over the capacity of the MIMO channel systems with spatial channel has been considered when the channel state information (CSI) is considered imperfect or partial. The dynamic CSI model has also been tried consisting of channel mean and covariance which leads to extracting of the channel estimates and error covariance. Both parameters indicated the CSI quality since these are the functions of temporal correlation factor, and based on this, the model covers data from perfect to statistical CSI, either partial or full blind. It is found that in case of partial and imperfect CSI, the capacity depends on the statistical properties of the error in the CSI. Based on the knowledge of statistical distribution of the deviations in CSI knowledge, a new estimation approach which maximizes the capacity of spatial channel model has been tried. The interference interactively reduced by employing the iterative channel estimation and data detection approach.