Low-Complexity Recursive Convolutional Precoding for OFDM-based Large-Scale Antenna System

TL;DR

This paper introduces a low-complexity recursive convolutional precoding method for OFDM-based large-scale antenna systems, reducing computational complexity while maintaining performance.

Contribution

It proposes a recursive convolutional precoding technique that replaces traditional zero-forcing precoding, significantly lowering complexity in large-scale antenna OFDM systems.

Findings

Achieves the same performance as traditional ZF precoding

Reduces the number of IFFT operations to one per user

Avoids matrix inversion, lowering computational burden

Abstract

Large-scale antenna (LSA) has gained a lot of attention recently since it can significantly improve the performance of wireless systems. Similar to multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) or MIMO-OFDM, LSA can be also combined with OFDM to deal with frequency selectivity in wireless channels. However, such combination suffers from substantially increased complexity proportional to the number of antennas in LSA systems. For the conventional implementation of LSA-OFDM, the number of inverse fast Fourier transforms (IFFTs) increases with the antenna number since each antenna requires an IFFT for OFDM modulation. Furthermore, zero-forcing (ZF) precoding is required in LSA systems to support more users, and the required matrix inversion leads to a huge computational burden. In this paper, we propose a low-complexity recursive convolutional precoding to address the issues above. The traditional ZF precoding can be implemented through the recursive convolutional precoding in the time domain so that only one IFFT is required for each user and the matrix inversion can be also avoided. Simulation results show that the proposed approach can achieve the same performance as that of ZF but with much lower complexity.