Panagiota Lioliou

Self-interference suppression in full-duplex MIMO relays

Full-duplex relays can provide cost-effective cover-age extension and throughput enhancement. However, the main limiting factor is the resulting self-interference signal which deteriorates the relay performance. In this paper, we propose a novel technique for self-interference suppression in full-duplex Multiple-Input Multiple-Output (MIMO) relays. The relay employs transmit and receive weight filters for suppressing the self-interference signal. Unlike existing techniques that are based on zero forcing of self-interference, we aim at maximizing the ratio between the power of the useful signal to the self-interference power at the relay reception and transmission. Our simulation results show that the proposed algorithm outperforms the existing schemes since it can suppress interference substantially with less impact on the useful signal.

Least-Squares based channel estimation for MIMO relays

A least-squares based channel estimation algorithm is proposed for relay-assisted wireless multiple-input multiple-output (MIMO) channels. The method consists of a sequence of LS-problems with the purpose to arrive to a computationally efficient solution. The performance of the proposed algorithm is evaluated as a function of the input signal-to-noise ratio (SNR) for randomly generated Rayleigh flat fading channels. Finally, we study the effect of the channel estimation error on the performance of a MIMO zero-forcing receiver in order to verify the presented analysis.

Efficient Channel Estimation Techniques for Amplify and Forward Relaying Systems

In this paper, we present a channel estimation scheme for Amplify-and-Forward (AF) relaying systems, using measurements at the destination only. A Least-Squares (LS) based channel estimation algorithm is developed, that provides the destination with full knowledge of all channel responses involved in the transmission. To investigate the algorithm performance, the Cramer-Rao lower bound (CRB) is analytically computed and compared with the asymptotic covariance of the proposed estimator. Since the existing estimator does not reach the CRB, we also propose and analyze an improved algorithm by taking into account the noise characteristics via weighted LS (WLS). The improved algorithm is asymptotically efficient, since it attains the CRB as the SNR tends to infinity.

Performance analysis of relay channel estimation

Amplify-and-Forward (AF) relays can be used to enhance the channel in Multiple-Input-Multiple-Output (MIMO) wireless communication systems. However, optimizing the channel requires Channel State Information (CSI). This paper is concerned with the performance of relay channel estimation. A Least Squares (LS) based algorithm for the estimation of all relevant channel parameters was recently proposed by the authors. It is based on creating different compound channels by varying the gain factors at the relays. From this, the individual links from source to relay and from relay to destination, are revealed using LS. To investigate the algorithm performance, the Cramér-Rao lower Bound (CRB) is computed and compared with the asymptotic covariance of the proposed estimator. Furthermore, we propose and analyze an improved algorithm that is efficient for high Signal-to-Noise Ratio (SNR). Computer simulations demonstrate the performance of the algorithm. The results indicate that the asymptotic expressions are in good agreement with the simulations.

Bayesian Channel Estimation Techniques for AF MIMO Relaying Systems

In this paper, we consider the fundamental problem of channel estimation in multiple-input multiple-output (MIMO) amplify-and-forward (AF) relaying systems operating over random channels. Using the Bayesian framework, linear minimum mean square error (LMMSE) and expectation-maximization (EM) based maximum a posteriori (MAP) channel estimation algorithms are developed, that provide the destination with full knowledge of all channel parameters involved in the transmission. The performance of the proposed algorithms is evaluated in terms of the mean square error (MSE) as a function of the signal-tonoise ratio (SNR) during the training interval. Our simulation results show that the incorporation of prior knowledge into the channel estimation algorithm offers improved performance, especially in the low SNR regime.

Channel Tracking for AF MIMO Relaying Systems

In this paper, we consider the problem of channel estimation in multiple-input multiple-output (MIMO) amplify-and-forward (AF) relaying systems operating over time varying channels. Only data at the receiving end are assumed available for the estimation. By employing a first-order autoregressive (AR) model for characterizing the time-varying nature of the channels to be estimated, we derive an expectation-maximization (EM) Kalman filter (KF) that utilizes the received signal at the destination to track the individual channel links. The extended KF algorithm is also derived and compared to the proposed EM-based KF. Our simulation results show that the proposed EM-based KF offers better estimation performance with less complexity when compared to the EKF algorithm.