It is necessary to estimate channel state information coherently to equalize the received signal in wireless communication systems. The pilot symbol, known at the receiver, aided channel estimator degrades the transmission efficiency because it requires the signal spaces and the energy for the transmission. In this paper, we assume a fixed wireless communication system in line of sight slowly varying channel and propose a new blind channel estimation method without help from the pilot symbol for Orthogonal Frequency Division Multiplexing systems. The proposed estimator makes use of the Expectation-Maximization algorithm and the correlation property among the channel frequency responses by considering the assumed channel environment. By computer simulations, we show that the proposed estimator can asymptotically achieve bit error rate performance by using the ideal channel estimation.

In this paper, we present a maximum a posteriori probability (MAP) approach to the problem of blind estimation of single-input, multiple-output (SIMO), finite impulse response (FIR) channels. A number of methods have been developed to date for this blind estimation problem. Some of those utilize prior knowledge on input signal statistics. However, there are very few that utilize channel statistics too. In this paper, the unknown channel to be estimated is assumed as the frequency-selective Rayleigh fading channel, and we incorporate the channel prior distributions (and hyperprior distributions) into our model in two different ways. Then for each case an iterative MAP estimator is derived approximately. Performance comparisons over existing methods are conducted via numerical simulation on randomly generated channel coefficients according to the Rayleigh fading channel model. It is shown that improved estimation performance can be achieved through the MAP approaches, especially for such channel realizations that have resulted in large estimation error with existing methods.

A blind channel estimation algorithm based on the subspace method for single-input multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) systems is proposed in this letter. With the aid of a repetition index, the conventional algorithm is a special case of our algorithm. Compared with related studies, the proposed algorithm reduces the computational complexity of the SVD operation and is suitable for cyclic-prefix-free systems. In particular, the necessary condition of the proposed signal matrix to be full rank can be satisfied with fewer OFDM blocks. Simulation results demonstrate that the proposed algorithm outperforms conventional methods in normalized mean-square error.

In this paper, a new approach to channel order selection of single-input multiple-output (SIMO), finite impulse response (FIR) channels is proposed for blind channel estimation. The approach utilizes cross spectral density (CSD) of the channel outputs, and minimizes the distance between two CSD's, one calculated non-parametrically from the observed output data, and the other calculated from the blindly estimated channel parameters. The CSD criterion is numerically tested on randomly generated SIMO-FIR channels, and shown to be very effective compared to existing channel order selection methods especially under low SNR settings. Blind estimates of the channels with the selected channel order also show superiority of the CSD criterion.

This paper proposes a novel MIMO system that introduces a heterogeneous stream (HTS) scheme and a blind signal detection method for mobile radio communications. The HTS scheme utilizes different modulation or coding methods for different MIMO streams, and the blind detection method requires no training sequences for signal separation, detection, and channel estimation. The HTS scheme can remove the ambiguity in identifying separated streams without unique words that are necessary in conventional MIMO blind detection. More specifically, two examples of HTS are considered: modulation type HTS (MHTS) and timing-offset type HTS (THTS). MHTS, which utilizes different modulation constellations with the same bandwidth for different streams, has been previously investigated. This paper proposes THTS which utilizes different transmission timing with the same modulation. THTS can make the blind detection more robust and effective with fractional sampling. The blind joint processing of detection and channel estimation performs adaptive blind MIMO-MLSE and is derived from an adaptive blind MLSE equalizer that employs the recursive channel estimation with the Moore-Penrose generalized inverse. Computer simulations show that the proposed system can achieve superior BER performance with Eb/N0 degradation of 1 dB in THTS and 2.5 dB in MHTS compared with the ideal maximum likelihood detection.

A novel blind channel estimation scheme is proposed for OFDM systems employing PSK modulation. This scheme minimizes the number of possible channels by exploiting the constant modulus property, chooses a best fit over the possible channels by exploiting the finite alphabet property of information signals, and achieves competitive performance with low computational complexity. Results comparing the new scheme with the finite-alphabet based channel estimation are presented.

In this paper, we consider a blind channel estimation and equalization for single input multiple output (SIMO) channels. It is based on the one-step forward multichannel linear prediction error method. The derivation of the existing method is based on the noiseless assumption, however, we analyze the effects of additive noise at the output of the one-step forward multichannel linear prediction error filters. Moreover, we derive analytical results for the error in the blind channel estimation and equalization using linear prediction.

Orthogonal Frequency Division Multiplexing (OFDM) is a promising technique for achieving high bit-rate transmission in radio environments. Various techniques to estimate channel attenuation have been proposed for OFDM transmission. In these techniques, the added pilot semi-blind (APSB) channel estimation has been proposed, which does not require any additional bandwidth. For an OFDM packet transmission that includes preambles, it is difficult to track the channel variation of the latter of packet due to time-varying channels in fast fading environments. We expect that the channel estimation is improved by applying the APSB channel estimation technique to the last symbols of packets without any additional bandwidth or degradation of bit rate. In this paper, we propose an OFDM packet transmission system with APSB channel estimation technique where this technique is applied to the last symbols of packets. We expect that the proposed system can track the fast time-variant channels without decrease of the data-rate, and the packet error rate (PER) is improved. We show that our proposed system is effective for improving the accuracy of the channel estimate in fast fading channels.

Blind adaptive channel identification of communication channels is a problem of important current theoretical and practical concerns. Recently proposed solutions for this problem exploit the diversity induced by antenna array or time oversampling, leading to the so-called, second order statistics techniques. Adaptive blind channel identification techniques based on a off-line least-squares approach have been proposed but this method assumes noise-free case. The method resorts to an adaptive filter with a linear constraint. This paper proposes a new approach based on eigenvalue decomposition. Indeed, the eigenvector corresponding to the minimum eigenvalue of the covariance matrix of the received signals contains the channel impulse response. And we present a adaptive algorithm to solve this problem. The performance of the proposed technique is evaluated over real measured channel and is compared to existing algorithms.

Blind channel estimation algorithm which is applicable to the time-variant channel under frequency-selective fading is proposed. The condition on the blind channel identifiability using temporally and spatially oversampled data is shown. The proposed algorithm consists of two stages. At the first stage, the channel equalization matrix is estimated by taking account of the time-variant characteristics of the channel. At the second stage, the signals and the channel matrix are alternately estimated by using the finite alphabet property of the transmitted symbols. Periodical return from the second stage to the first makes the blind estimation algorithm feasible for the time-variant channel with fast fading. The simulation results confirm the fast convergence property and the effectiveness of the proposed algorithm in coping with the frequency-selective fading.