This study proposes implementation of two‐way amplify‐and‐forward relaying systems in practical environments where signals from two source nodes arrive at a relay with different propagation delays. The authors develop a self‐interference delay and channel estimation technique applicable for practical environments. Whereas receiver processing in conventional schemes is performed in the baud rate domain, in this work, oversampling (or Nyquist sampling) is introduced at the receiver to avoid any energy loss in the desired signal. They derive the proposed estimator in the oversampled domain on the basis of the least square criterion and also provide a design guide for the oversampling ratio and the application of a self‐interference canceller. They observe from the author's analysis and simulation results that an oversampling of 2 is sufficient, and it is not preferable to use a self‐interference canceller when L /( N· ISNR) is greater than 1, where the interference‐to‐signal‐plus‐noise ratio (ISNR) denotes the power ratio of the self‐interference to the desired signal plus noise, L is the channel length and N is the number of symbols in one time slot.

This paper is concerned with linear relay and destination filter design methods for the multi-user peer-to-peer amplify-and-forward relaying systems. Specifically, the relay and destination filter sets are developed which minimize the sum mean-squared-error (MSE). We first present a joint optimum relay and destination filter calculation method with an iterative algorithm. Motivated by the need to reduce computational complexity of the iterative scheme, we then formulate a simplified sum MSE minimization problem using the relay filter decomposability, which lead to two sub-optimum non-iterative design methods. One is based on zero-forcing channel-inversion and the other on minimum-mean-squared-error channel-inversion. Finally, we propose modified destination filter design methods which require only local channel state information between relay and a specific destination node. The simulation results verify that, compared with the optimum iterative method, the proposed non-iterative schemes suffer a marginal loss in performance while enjoying significantly improved implementation efficiencies.

This letter is concerned with transmit and receive filter optimization for the K-user MIMO interference channel. Specifically, linear transmit and receive filter sets are designed which maximize the weighted sum rate while allowing each transmitter to utilize only the local channel state information. Our approach is based on extending the existing method of minimizing the weighted mean squared error (MSE) for the MIMO broadcast channel to the K-user interference channel at hand. For the case of the individual transmitter power constraint, however, a straightforward generalization of the existing method does not reveal a viable solution. It is in fact shown that there exists no closed-form solution for the transmit filter but simple one-dimensional parameter search yields the desired solution. Compared to the direct filter optimization using gradient-based search, our solution requires considerably less computational complexity and a smaller amount of feedback resources while achieving essentially the same level of weighted sum rate. A modified filter design is also presented which provides desired robustness in the presence of channel uncertainty.

This paper is concerned with transmit and receive filter optimization for the K-user MIMO interference channel. Specifically, linear transmit and receive filter sets are designed which maximize the weighted sum rate while allowing each transmitter to utilize only the local channel state information. Our approach is based on extending the existing method of minimizing the weighted mean squared error (MSE) for the MIMO broadcast channel to the K-user interference channel at hand. For the case of the individual transmitter power constraint, however, a straightforward generalization of the existing method does not reveal a viable solution. It is in fact shown that there exists no closed-form solution for the transmit filter but simple one-dimensional parameter search yields the desired solution. Compared to the direct filter optimization using gradient-based search, our solution requires considerably less computational complexity and a smaller amount of feedback resources while achieving essentially the same level of weighted sum rate.