Semidefinite Relaxation-Based PAPR-Aware Precoding for Massive MIMO-OFDM Systems

A massive multiple-input multiple-output (MIMO) requires a large number of antennas and the same amount of power amplifiers (PAs), one per antenna. As opposed to 4G base stations, which can afford highly linear PAs, next-generation base stations will need to use inexpensive PAs, which have a limited region of linear amplification. One of the research challenges is the effective handling of signals that have high peak-to-average power ratios (PAPRs), such as orthogonal frequency division multiplexing (OFDM). This paper introduces a PAPR-aware precoding scheme that exploits the excessive spatial degrees-of-freedom of large-scale MIMO antenna systems. This typically requires finding a solution to a nonconvex optimization problem. Instead of relaxing the problem to minimize the peak power, we introduce a practical semidefinite relaxation framework that enables an accurate and efficient approximation of the theoretical PAPR-aware precoding performance of OFDM-based massive MIMO systems. The framework allows the incorporation of channel uncertainties and intercell coordination. Numerical results show that several orders of magnitude improvements can be achieved w.r.t. state-of-the-art techniques, such as instantaneous power consumption reduction and multiuser interference cancellation. The proposed PAPR-aware precoding can be effectively handled alongwith the multicell signal processing by the centralized baseband processing platforms of next-generation radio access networks. The performance can be traded for the computing efficiency for other platforms.