Spread spectrum orthogonalization of superimposed training signals in OFDM systems

Abstract

In this paper, we propose a novel Superimposed Training (ST) technique for Orthogonal Frequency Division Multiplexing (OFDM) systems, where data and training signals are divided in orthogonal code domains in order to mitigate the interference between them. The data signal is partitioned into disjoint bins, which are spread using orthogonal codes and multiplexed in code domain. Then, the new data signal is added to the spread training signal. This novel proposal, named Spread Spectrum Orthogonalization (SSO), exploits these properties to outperform conventional schemes in terms of channel estimation reliability and symbol detection performance, with a lower complexity. Moreover, it turns out to be robust against narrowband interferences.