Iterative (turbo) joint channel estimation and signal detection for Quadrature OFDMA systems

Quadrature OFDMA (Q-OFDMA) systems have been recently proposed to reduce the complexity and peak-to-average power ratio (PAPR), and improve carrier frequency offset (CFO) robustness for OFDMA systems. However, Q-OFDMA receiver obtains frequency diversity at the cost of noise enhancement. This paper proposes an iterative (turbo) equalization in conjunction with channel estimation for Q-OFDMA systems to mitigate the noise enhancement effect and improve the BER performance. In the proposed scheme, the channel estimation technique makes use of both training symbols and soft coded data information to suppress the inter-symbol interference (ISI) caused by channel estimation errors in Q-OFDMA systems. Simulation results show that performance improvement can be achieved with the proposed algorithms.