Optimal linear pre-coders for opportunistic spectrum sharing with arbitrary input assumption

Cognitive-radio-networks with multiple input multiple output is an effective method to improve spectrum efficiency. Linear pre-coders is a crucial technology to realize this goal. However, most of existing algorithm developed based on the Gaussian input assumption. This assumption limited the application of linear precoding and caused great performance loss. In this paper, for such a problem, a linear pre-coders algorithm for maximizing the secondary users spectrum efficiency is designed. Different from previous spectrum efficient algorithm which is developed under the Gaussian input assumption, the proposed algorithm can work under any input assumption by inheriting the advantage of mercury water filling algorithm (MWF) and singular value decomposition (SVD). Numerical results show that the proposed algorithm exhibits a large gain over existing algorithms when we take a practical modulation type.