Narrow Results

Orthogonal frequency division multiplexing (OFDM) is adopted in most wireless communication systems, and the performance of OFDM is affected by impulse noise (IN). Better channel estimation (CE) performance is required to detect the channel information on the receiving side. The OFDM system is considered with the number of subcarriers carrying the data and pilot symbols. In the OFDM modulator, the symbols of the frequency domain are transformed into the signals of the time domain using inverse discrete Fourier transformation (IDFT). Effective impulsive noise mitigation is crucial for improving the effectiveness of OFDM communication systems, and it improves the signal-to-noise ratio (SNR) at the receiver. A cyclic prefix is then appended before transmission through the channel. In the receiver noise, the effect of IN is mitigated jointly with CE using the Bayesian matching pursuit (BMP) and Moth-flame algorithm (MFA). In this approach, the IN and channel information is considered as a sparse vector. Here, the data symbols used are considered an unknown parameter. The approach incorporating all subcarriers has a lower mean square error (MSE) of IN estimate for impulsive noise reduction. The MFA algorithm is used to optimize the sensor matrix in BMP. The sparsity of the channel and the impulse response are observed in the time domain to represent the channel and IN together. It exactly recovers the sparse signal for finding the convex objectives of the sparse minimizer, and the sparse solution is obtained with fixed point updates. The proposed BMP algorithm improves the CE by explicitly considering the existence of IN. The BMP is a greedy algorithm that selects the most correlated residuals at each column. Under mutual incoherence, it recovers the signal with a higher probability. The simulated outcomes proved that the proposed CE and noise mitigation model achieved better performance based on the bit error rate (BER), throughput and MSE.

In this paper, we design an optimal training scheme for multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems under spatially correlated time- and frequency- (doubly) selective fading channels. We first develop the optimal pilot symbols and placement of pilot clusters with respect to the minimum mean square error (MMSE) of the linear channel estimate. We then derive the optimal power allocation for pilot symbols in a two-water-level way: by maximizing the averaged capacity lower bound, how much power to be allocated for training is determined subject to the global water level (or the constraint of total transmit power); subsequently, pouring power to the pilot symbols with an approximately optimal water-filling scheme subject to the local water level (or the constraint of assigned power for training). In addition, for a particular OFDM size, the optimal number of pilot clusters is derived by maximizing the capacity lower bound and by minimizing the channel estimate's MMSE.

In time-selective fading channel, the Alamouti orthogonality principle is lost due to the variation of channel from symbol-to-symbol in space–time block-coded orthogonal frequency division multiplexing (STBC-OFDM) system and causes co-channel interference (CCI) effects. To combat the CCI effects, various signal detection schemes have been proposed earlier by assuming that a priori channel state information (CSI) is known to the receiver. However, in practice, the CSI is unknown and therefore accurate estimation of channel is required for efficient signal detection. In this paper, by exploiting circulant properties of the channel frequency response (CFR) autocorrelation matrix $R_{HH}$, we propose an efficient low complexity linear-minimum-mean-square-error (LMMSE) estimator. This estimator applies an expectation–maximization (EM) iterative process to reduce the computational complexity significantly. Finally, we compare the proposed LMMSE-EM estimator with conventional least square (LS) and LMMSE estimator in terms of performance and computational complexity. The simulation results show that the proposed LMMSE-EM estimator achieves exactly the same performance as the optimal LMMSE estimator with much lower computational complexity.

Wireless local area networks (WLANs) are currently playing an important role in serving the indoor traffic demand. Therefore, there is a need for software-defined radio platforms (SDRs) that can enable the solutions used in these systems to be tested in real environments as well as simulated results. In this paper, we present the SDR-based wireless receiver platform for determining the real-time WLANs performance and provide the comparison of the different channel estimation methods for IEEE 802.11g based on orthogonal frequency division multiplexing (OFDM) operations. The implementation of the receiver comprises the universal software radio peripheral and National Instruments LabVIEW. To determine the real-time receiver tool performance, we emphasized necessary signal processing techniques and different channel estimation methods with varying experimental parameters in real wireless environments. Experimental results report that the SDR-based receiver tool with the LabVIEW in real-time provides the throughput of the OFDM wireless network. The captured throughput performance concerning frame error rate by the receiver is also scrutinized with different channel estimation methods.

In this paper, pilot-based time-domain channel estimation (CE) along with peak-to-average power ratio (PAPR) reduction is proposed for universal filtered multicarrier (UFMC) system. The pilots that are inserted in time domain not only estimate the channel behavior but also can be used in the reduction of PAPR. For PAPR reduction, a linear companding scheme, which can treat amplitudes of the UFMC signal separately with a different scale, is proposed. The proposed companding scheme offers more design flexibility and better performance gains by using two inflexion points. However, the proposed companding scheme requires side information (SI) to perform de-companding at the receiver. The transmission of SI decreases the data efficiency, so a pilot-assisted UFMC system that can perform both data recovery and PAPR reduction without the requirement of SI transmission is proposed. In pilot-assisted UFMC system, the inserted time-domain pilots can enable SI cancellation inherently. Furthermore, a hybrid transform, which improves PAPR performance by employing clipping scheme to the linear companded signal, is proposed. Simulation results confirm that the proposed joint CE with linear companding scheme achieves an improved net gain of 6.5dB. Additionally, the proposed hybrid scheme with clipping threshold of 1.4 provides an improved PAPR reduction of 8.8dB and enhanced net gain of 7.3dB. Moreover, the proposed joint time-domain CE with hybrid PAPR reduction scheme of UFMC system is validated over real time by employing wireless open-access research platform (WARP) board.

Chaotic dynamical systems are increasingly considered for use in coding and transmission systems. This stems from their parameter sensitivity and spectral characteristics. The latter are relevant for channel estimation methods. In particular, the logistic map f_λ = λx(1 - x) has been employed in chaotic coding and spread spectrum transmission systems. For λ = 4, the statistical properties of sequences generated by f₄ are considered as ideal drive signals for channel estimation schemes. This assumption is proven in the present paper. To this end, the higher order statistical moments and the autocorrelation of time series generated by f₄ are derived. It is shown that for λ = 4 the zero mean time series is uncorrelated. The adaptation performance of finite impulse response (FIR) digital adaptive filters (DAF) used for channel estimation is analyzed. It is shown that using zero mean sequences of f₄ leads to the maximal possible FIR DAF performance. An optimal value for the damping parameter in the LMS scheme is derived that leads to the maximal performance and ensures stability. The analytic considerations are confirmed by simulation results.

In the high-speed railway wireless communication, a joint channel estimation and signal detection algorithm is proposed for the orthogonal frequency division multiplexing (OFDM) system without cyclic prefix in the doubly-selective fading channels. Our proposed method first combines the basis expansion model (BEM) and the inter symbol interference (ISI) cancellation to overcome the situation that exists with the fast time-varying channel and the normalized maximum multipath channel exceeding the length of the cyclic prefix (CP). At first, the channel estimation and signal detection can be approximated without considering the ISI. Then, the channel parameters and signal detection are updated through ISI cancellation and circular convolution reconstruction from the frequency domain. The simulations show the algorithm can improve the performance of channel estimation and signal detection.

We propose a Second-Order Statistics (SOS)-based channel estimator that finds application in Discrete MultiTone (DMT) systems. Most SOS adaptive channel estimators are based either on Newton–Raphson or on steepest descent methods. These classes of methods have complementary advantages and disadvantages. In this contribution, a new adaptive channel estimator based on combined Conjugate Gradient-NR method is developed. Among a number of attractive features, the combined estimator demands less computational complexity than that required by NR-based estimators, and when compared to CG estimators, the combined estimator exhibits better convergence especially for ill-conditioned channels. All mentioned theoretical results are illustrated by numerical results for estimates of randomly generated and real measured channels. Comparison with other well-known algorithms shows good trade off between performance and complexity.

In order to increase the transmission efficiency of OFDM system, considering the special structure of OFDM system, this paper proposes an EM iteration channel estimation algorithm that uses fewer pilots. The simulation result shows that the algorithm can still converge to the case with given channel parameters when inserting fewer pilots, and the iteration number apparently decreases as the number of pilots increases. The proposed algorithm doesn’t need the statistical characteristic of channels; at the same time, it can realize the trade-off between system performance and complexity by choosing the number of iteration times. The algorithm degenerates to a general EM algorithm when no pilot is used in channel estimation.

Multi-path is a major impairment for mobile OFDM systems. Time variance of the mobile channel leads to a loss of sub-carrier orthogonality. In this paper, linear minimum mean square error estimator (LMMSEE) with superimposed periodic pilot for finite-impulse response (FIR) channel estimation is proposed. Theoretical analysis and computer simulation show that the proposed method are found to exhibit better performance and lower complexity than that of the LS method at SNR values of practical interest.

In this paper, a new blind equalization algorithm based on fuzzy neural network (FNN) is proposed. It makes use of blind estimation (BE) and FNN classifier to equalize. Firstly BE algorithm is used to identify the channel character, the signals are rebuilt by deconvolution, and then the signals are classified by FNN classifier. This algorithm has the merits than the foregoing neural network algorithm, such as faster convergence speed, smaller residual error, lower bit error rate (BER), etc. The validity is proved by simulations.

In this paper, we focus on preamble-based time of arrival (TOA) estimation for orthogonal frequency division multiplexing (OFDM) systems in non-line-of-sight (NLOS) environments. Recent development in wireless communication-based positioning systems exploiting TOA methods faces a major challenge for the TOA estimation in NLOS condition. Because of the possible obstruction of the direct path, the signal component from direct propagation can be very weak and therefore, the performance of TOA estimation will be dramatically degraded. An accurate TOA estimation utilizing channel estimation is presented. The proposed approach consists of three stages. First, we obtain coarse integer TOA estimation by correlation detection. Second, Maximum-likelihood criterion is employed in channel impulse response estimation to get the fine integer TOA estimate. We exploit the multipath interference cancellation with channel equalization in frequency domain. Finally, to break the limitation by sampling interval, fractional TOA estimate by linear fit is obtained. Compared with the off-the-shelf method, the simulation results show that our method achieves more precise TOA estimation.