In this paper, we consider full-duplex (FD) relay networks with filter-and-forward (FF)-based multiple relays (FD-FF), where relay filters jointly mitigate self-interference (SI), inter-relay interference (IRI), and inter-symbol interference. We consider the filter design problem based on signal-to-noise-plus-interference ratio maximization subject to a total relay transmit power constraint. To make the problem tractable, we propose two methods: one that imposes an additional constraint whereby the filter responses to SI and IRI are nulled, and the other that makes i.i.d. assumptions on the relay transmit signals. Simulation results show that the proposed FD-FF scheme outperforms a conventional FF scheme in half-duplex mode. We also consider the filter design when only second-order statistics of channel path gains are available.

The present paper deals with the blind deconvolution of a Multiple-Input Multiple-Output Finite Impulse Response (MIMO-FIR) system. To deal with the blind deconvolution problem using the second-order statistics (SOS) of the outputs, Hua and Tugnait considered it under the conditions that a) the FIR system is irreducible and b) the input signals are spatially uncorrelated and have distinct power spectra. In the present paper, the problem is considered under a weaker condition than the condition a). Namely, we assume that c) the FIR system is equalizable by means of the SOS of the outputs. Under b) and c), we show that the system can be blindly identified up to a permutation, a scaling, and a delay using the SOS of the outputs. Moreover, based on this identifiability, we show a novel necessary and sufficiently condition for solving the blind deconvolution problem, and then, based on the condition, we propose a new algorithm for finding an equalizer using the SOS of the outputs, while Hua and Tugnait have not proposed any algorithm for solving the blind deconvolution under the conditions a) and b).

This paper proposes a new method for recovering the original signals from their linear mixtures observed by the same number of sensors. It is performed by identifying the linear transform from the sources to the sensors, only using the sensor signals. The only assumption of the source signals is basically the fact that they are statistically mutually independent. In order to perform the 'blind' identification, some time-correlational information in the observed signals are utilized. The most important feature of the method is that the full information of available time-correlation data (second-order statistics) is evaluated, as opposed to the conventional methods. To this end, an information-theoretic cost function is introduced, and the unknown linear transform is found by minimizing it. The propsed method gives a more stable solution than the conventional methods.