For more efficient data transmissions, a new MLP/BP-based channel equalizer is proposed to compensate for multi-path fading in wireless applications. In this work, for better system performance, we apply the soft output and the soft feedback structure as well as the soft decision channel decoding. Moreover, to improve packet error rate (PER) and bit error rate (BER), we search for the optimal scaling factor of the transfer function in the output layer of the MLP/BP neural networks and add small random disturbances to the training data. As compared with the conventional MLP/BP-based DFEs and the soft output MLP/BP-based DFEs, the proposed MLP/BP-based soft DFEs under multi-path fading channels can improve over 3-0.6 dB at PER=10-1 and over 3.3-0.8 dB at BER=10-3.

In direct-conversion orthogonal frequency division multiplexing (OFDM) receivers, the impact of frequency-dependent I/Q mismatch (IQ-M) with carrier frequency offset (CFO) must be considered. A preamble-assisted estimation is developed to circumvent the frequency-dependent IQ-M with CFO. The results of a simulation and an experiment show that the proposed method could provide good estimation efficiency and enhance the system performance. Moreover, the proposed scheme is compatible with current wireless local area network standards.