Abstract
Interference alignment (IA) is a cooperative technique used for multiple-input, multiple-output (MIMO) interference channels to confine the interference to a reduced signal space of the receiver. IA helps all nodes of the network to achieve half of the degree of freedom that can be achieved if there were no interference. In this paper an iterative alternating minimization algorithm is presented, which aims at providing the higher sum rate along with IA for more than three users having complete channel state information. In each iteration of the algorithm precoders and decoders are calculated to align interference in a reduced interference subspace. The precoders are then further improved for sum rate enhancement by scaling with suitable weights to increase the signal power of the desired signal. The simulation results show that the proposed algorithm achieves significant increase in average sum rate and also minimizes the overall interference as compared to conventional IA algorithms at low signal-to-noise ratio.