Rank Reduction and Diagonalization of Sensing Matrix for Millimeter Wave Hybrid Precoding Using Particle Swarm Optimization

Abstract

Millimeterwave (mmwave) wireless communication systems is a promising technology which provides a high data rate (up to gigabits per second) due to the large bandwidth available at mmwave frequencies. But it is challenging to estimate the channel for mmwave wireless communication systems with hybrid precoding, since the number of radio frequency chains aremuch smaller as compared to a number of antennas. Due to limited scattering, the Beam space channel model using Dictionary matrices is proposed for mmwave channel model. There were many attempts made to design the precoder and decoder, along with the channel estimation for the mmwave channel model but it remains an unsolved problem. In this paper, we demonstrate the methodology of using Particle Swarm Optimization to design the precoder and decoder of the Beam space channel model with the prior knowledge of Angle of Arrival (AOA) and Angle of Departure (AOD). Particle swarm optimization is used to optimize the precoder and decoder such that the sensing matrix is diagonalized (diagonalization method) and is a reduced rank matrix (rank reduction method) and then the channel matrix is estimated. The results reveal the possible direction to explore the usage of computational intelligence technique in solving the mmwave channel model.