Rate Adaptive Precoder Design for Massive MIMO Cognitive Radio System

Abstract

Massive Multiple Input Multiple Output (MIMO) is considered a promising technology to get high multiplexing gain in the cognitive network. However, the precoder design at the cognitive base station becomes complex with large number of antennas. In this paper, the design of a two-stage precoder is modified to accommodate Transmit Antenna Selection (TAS) while achieving the required secondary rate. Moreover, fast Antenna Selection (AS) algorithm based on norm selection schemes is proposed for better convergence rate. Simulation results show that the proposed algorithm reduces the computational time by 88%, the number of antennas by 40%, while the capacity reaches 90% relative to the full array without selection scenario.