This study considers the immense problem of sum rate maximisation in K-user multiple-input multiple-output interference channels (IFC). In order to address this problem, the authors propose an iterative Taylor approximation algorithm to find the optimal transmit covariance matrices, where each user maximises the sum rate function through Taylor expansion. Owing to the distributed nature of the ITA algorithm, Taylor terms can be treated as prices, where each user collects current prices from the others to perform update. This process further leads us modelling it as a more general concave n-person game to analyse the existence and uniqueness of the Nash equilibrium (NE). Then an iterative covariance-price updating (ICP) algorithm is proposed for flexible updating and establishing a convergence process to obtain the optimal solution under NE uniqueness conditions. Simulation results show that both algorithms come with similar performance and both significantly outperform the conventional iterative waterfilling algorithm.

We propose two novel power control policies for a two-way amplify-and-forward (AF) relaying system, in which each node (two sources and one relay) is assumed to operate under both minimum and peak power constraints. Through the exploitation of instantaneous channel gains, the first policy can maximize the sum rate of the system. However, the instantaneous channel gains may be unavailable in a rapid time-varying system, where the first policy is inoperable. Consequently, a robust power control policy which requires only mean channel gains is proposed to maximize the upper bound of the average sum rate, and the properties of this policy are investigated. Simulation results show that, by comparison with the policy in which all the nodes use their peak transmit power, the proposed power control policies can provide considerable system performance improvement. Furthermore, the performance difference between the two proposed policies is negligible when the relay is close to one source.