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Low-Complexity Recursive Convolutional Precoding for OFDM-based Large-Scale Antenna Systems
- Citation Author(s):
- Submitted by:
- Yinsheng Liu
- Last updated:
- 17 March 2016 - 9:56am
- Document Type:
- Presentation Slides
- Document Year:
- 2016
- Event:
- Presenters:
- Wei Han
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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 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.