Channel estimation and optimal training sequence design for full-duplex one-way relays are investigated. We propose a training scheme to estimate the residual self-interference (RSI) channel and the channels between nodes simultaneously. A maximum likelihood estimator is implemented with the Broyden-Fletcher-Goldfarb-Shanno algorithm. In the presence of RSI, the overall source-to-destination channel becomes an inter-symbol-interference (ISI) channel. With the help of estimates of the RSI channel, the destination is able to cancel the ISI through equalization. We derive and analyze the Cramer-Rao bound (CRB) in closed-form by using the asymptotic properties of Toeplitz matrices. The optimal training sequence is obtained by minimizing the CRB. Extensions for the fundamental one-way relay model to the frequency-selective fading channels and the multiple relays case are also considered. For the former, we propose a training scheme to estimate the overall channel, and for the latter the CRB and the optimal number of relays are derived when the distance between the source and the destination is fixed. Simulations using LTE parameters corroborate our theoretical results.

This paper explores the performance of time-domain synchronous orthogonal frequency-division multiplexing (TDS-OFDM) systems operated under rapidly time-varying mobile channels. Since a rapidly time-varying channel contains more unknown channel coefficients than the number of observations, the mobile channel can conveniently be modeled with the discrete Legendre polynomial basis expansion model to reduce the number of unknowns. The linear minimum mean square error (LMMSE) estimate can be exploited for channel estimation on inter-block-interference-free received signal samples owing to transmitting pseudo-noise (PN) sequences. In conventional TDS-OFDM systems, the channel estimation performance is limited due to estimating channel responses only from the beginning part of the channel. Therefore, a new system model named “partitioned TDS-OFDM system” is proposed to improve the system performance by inserting multiple PN sequences to the middle and end parts of the channel as well. In addition to providing the reconstruction error performance, Bayesian Cramer-Rao lower bound is derived analytically. Also, the LMMSE-based symbol detection is employed. To alleviate the negative effects of inter-carrier-interference (ICI) occuring in mobile channels, ICI cancellation is applied to enhance the detection performance. The simulation results demonstrate that the proposed TDS-OFDM system is superior to the conventional system and its corresponding performance is able to approach the achievable lower performance bound.

Training schemes for full duplex two-way relays are investigated. We propose a novel one-block training scheme with a maximum likelihood estimator to estimate the channels between the nodes as well as the residual self-interference (RSI) channel simultaneously. A quasi-Newton algorithm is used to solve the estimator. As a baseline, a multi-block training scheme is also considered. The Cramer-Rao bounds of the one-block and multi-block training schemes are derived. By using the Szegö's theorem about Toeplitz matrices, we analyze how the channel parameters and transmit powers affect the Fisher information. We show analytically that exploiting the structure arising from the RSI channel increases its Fisher information. Numerical results show the benefits of estimating the RSI channel.

As the analog TV broadcasting channels have become less frequently used in the last decade, there has been a great interest in these frequency bands for the deployment of metropolitan, local and personal area networks. Among them, the local area network standard IEEE 802.11af defines PHY and MAC layer implementation of such networks in these unused frequency bands, also named television white space (TVWS). According to the standard, the systems may use contiguous or non-contiguous channels during their operation, depending on the channel availability. In this paper, we investigate in detail the performance of different operation modes of these systems under realistic channel conditions. While the perfect knowledge of channel would result in similar system performances, as the number of in-between-bands occupying the non-contiguous modes is increased the channel estimation performance degrades drastically, which is quantified in this study. In addition, it is shown that determining the true locations of multipaths heavily relies on the selected channel resolution and has a significant effect on the system performance. Numerical examples are given to demonstrate the effects of both the non-contiguous operation modes and the selected channel resolution.

In this study, the authors investigate the information theoretical limits on the performance of point‐to‐point single‐carrier acoustic systems over frequency‐selective underwater channels with intersymbol interference. Under the assumptions of sparse and frequency‐selective Rician fading channel and non‐white correlated Gaussian ambient noise, the authors derive an expression for channel capacity and demonstrate the dependency on channel parameters such as the number, location and power delay profile of significant taps, as well as environmental parameters such as distance, temperature, salinity, pressure and depth. Then, the authors use this expression to determine the optimal carrier frequency, input signalling and bandwidth for capacity maximisation.

This paper is concerned with a challenging problem of channel estimation for amplify-and-forward cooperative relay based orthogonal frequency division multiplexing (OFDM) systems in the presence of sparse underwater acoustic channels and of the correlative non-Gaussian noise. We exploit the sparse structure of the channel impulse response to improve the performance of the channel estimation algorithm, due to the reduced number of taps to be estimated. The resulting novel algorithm initially estimates the overall sparse channel taps from the source to the destination as well as their locations using the matching pursuit (MP) approach. The correlated non-Gaussian effective noise is modeled as a Gaussian mixture. Based on the Gaussian mixture model, an efficient and low complexity algorithm is developed based on the combinations of the MP and the space-alternating generalized expectation-maximization (SAGE) technique, to improve the estimates of the channel taps and their location as well as the noise distribution parameters in an iterative way. The proposed SAGE algorithm is designed in such a way that, by choosing the admissible hidden data properly on which the SAGE algorithm relies, a subset of parameters is updated for analytical tractability and the remaining parameters for faster convergence Computer simulations show that underwater acoustic (UWA) channel is estimated very effectively and the proposed algorithm has excellent symbol error rate and channel estimation performance.

This paper is concerned with the challenging and timely problem of joint channel estimation and data detection for orthogonal frequency division multiplexing (OFDM) systems in the presence of frequency selective and very rapidly time varying channels. The detection and estimation algorithm is based on the space alternating generalized expectation maximization (SAGE) technique which is particularly well-suited to multicarrier signal formats. In order to reduce the computational complexity of the algorithm, we apply the cosine orthogonal basis functions to describe the time-varying channel. It is shown that, depending on the normalized Doppler frequency, only a small number of expansion coefficients is sufficient to approximate the channel perfectly and there is no need to know the correlation function of the input signal. The proposed SAGE joint detection algorithm updates the data sequences in serial and the channel parameters are updated in parallel, leading to a receiver structure that also incorporates a partial interference cancellation of the interchannel interference. Computer simulations show that the cosine transformation represents the time-varying channel very effectively and the proposed algorithm has excellent symbol error rate and channel estimation performance even with a very small number of channel expansion coefficients employed in the algorithm, resulting in reduction of the computational complexity substantially.

We consider distributed estimation of a source observed by sensors in additive Gaussian noise, where the sensors are connected to a fusion center with unknown orthogonal (parallel) flat Rayleigh fading channels. We adopt a two-phase approach of (i) channel estimation with training, and (ii) source estimation given the channel estimates, where the total power is fixed. We prove that allocating half the total power into training is optimal, and show that compared to the perfect channel case, a performance loss of at least 6 dB is incurred. In addition, we show that unlike the perfect channel case, increasing the number of sensors will lead to an eventual degradation in performance. We characterize the optimum number of sensors as a function of the total power and noise statistics. Simulations corroborate our analytical findings.

The paper proposes a computationally efficient, pilot-aided, minimum mean square error (MMSE), channel estimation algorithm for OFDM systems. The proposed approach employs a convenient representation of the discrete multipath fading channel based on the Karhunen-Loeve (KL) orthogonal expansion and estimates uncorrelated series expansion coefficients. Moreover, optimal rank reduction is achieved in the proposed approach by exploiting the optimal truncation property of the KL expansion, resulting in a smaller computational load on the estimation algorithm. The performance of the proposed approach is studied through analytical and experimental results. We first consider the stochastic Cramer-Rao bound and derive the closed-form expression for the random KL coefficients. We then exploit the performance of the MMSE channel estimator based on the evaluation of minimum Bayesian MSE.