Interference Alignment with Complex FDPM-Based Subspace Tracking in MIMO Cognitive Networks

This paper addresses the implementation of interference alignment (IA) in cognitive networks, where the unlicensed secondary transmitter-receiver pairs modeled as a K-user multiple-input and multiple-output (MIMO) interference channel coexist with the licensed multi-antenna primary user. Starting from investigating the constraint conditions of IA scheme in MIMO cognitive networks, a practical IA algorithm is developed based on the minor subspace tracking that utilizes the fast data projection method (FDPM), which requires no channel knowledge of secondary network. In the proposed algorithm, each secondary transmitter first aligns their transmitted signal into the null space of the channel matrix from itself to the primary user without causing any interference to the primary. Then secondary transmitters and receivers alternately design the precoding and post processing matrices through a training period which exploits the complex FDPM-based subspace tracking, thus eliminating interference among secondary users. Simulation results show that the proposed algorithm can achieve a high sum rate performance while requiring low computational complexity.