Low-complexity recursive convolutional precoding for OFDM-based large-scale antenna systems

Yinsheng Liu; Geoffrey Ye Li

ICASSP 2016accepted0 citations

Low-complexity recursive convolutional precoding for OFDM-based large-scale antenna systems

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. In this paper, we propose a low-complexity recursive convolutional pre-coding to address the issues above. The traditional ZF precoding is 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.

BibTeX

@inproceedings{icassp2016_lowcomplexityrec,
  title = {Low-complexity recursive convolutional precoding for OFDM-based large-scale antenna systems},
  author = {Yinsheng Liu and Geoffrey Ye Li},
  booktitle = {ICASSP 2016},
  year = {2016}
}