Interference alignment based on rank constraint for MIMO cognitive interference channel

Interference alignment (IA) is a breakthrough in interference management and has been applied to the cognitive radio (CR) network. In this paper, we focus on applying IA to the CR network comprised of multiple primary users (PUs) and multiple secondary users (SUs). An IA transceiver design scheme is proposed to mitigate the interference among PUs, called, sum rate maximization algorithm based on interference subspace distances minimization (MISD-MSR). The MISD-MSR scheme first introduces a concept of aligned subspace and designs the precoders via aligning the subspaces spanned by interference with the aligned subspace. Then it moves the designed precoders along the direction given by the gradient of the sum rate to further maximize the information rate. However, MISD-MSR cannot be directly used in the secondary network due to the fact that SUs are required to cause no harmful effect on PUs, whereas MISD-MSR makes no attempt to eliminate the interference from SUs to PUs. To address this, we proposed an improved version of MISD-MSR (IMISD-MSR) for the secondary network, where a rank constraint is put into the precoders calculation to force the interference matrix from SU to PU to be zero. Compared with the commonly used scheme which is equivalent to removing some antennas from the SU's transmitter, IMISD-MSR does not demand the SU to equip with additional antennas to eliminate the interference from SUs to PUs. Simulation results show the effectiveness of the proposed schemes in terms of the average sum rate. We further validate the proposed schemes can be applied to the system no matter whether the transmit antennas of SUs are sufficient or not.