Performance evaluation of convolutional encoded partial differential space time OFDM using modified local splines

Convolutional encoding is an effective technique to increase the resistance of the system towards frequency-selective channels, especially fading. In this paper, we investigate a technique in which convolutional encoding and interleaving are combined with the partial differential space time orthogonal frequency division multiplexing (OFDM). The estimation of channel parameters is done using modified local splines method. Space diversity is applied to OFDM for overall improvement of the system performance. The signals transmitted through the time-varying channel experiences multipath fading and results in burst error condition. This burst error degrades the performance of the system and should be overcome by appending a convolutional encoder and an interleaver at the transmitter end and similarly using a deinterleaver and a Viterbi decoder at the receiver end. To combat intercarrier interference (ICI), channel estimation should be done. Another method which does not require channel estimation to overcome ICI is called differential detection. This method is only valid for low Doppler. For high Doppler conditions, differential detection is combined with channel estimation for space time OFDM. This paper compares the performance evaluation of convolutional encoded partial differential ST-OFDM with three other different schemes namely; partial differential ST-OFDM, convolutional encoded coherent ST-OFDM and coherent ST-OFDM. The simulation results show that convolutional encoded partial differential ST-OFDM performs better than other mentioned schemes. Performances have been evaluated in terms of bit error rate (BER), residual ICI power and signal power to ICI power ratio.