Implicitly-trained channel estimation and equalization with zero mean input data packets

Implicit training (IT) channel estimation adds a periodic training sequence to each input data block/packet, so that no bandwidth is lost (as in a traditionally trained scenario). While the input data is usually assumed to be zero mean, each data packet will have a deterministic mean, which is itself a random variable. In this paper we show that by removing this nonzero mean for each input packet before transmission and then employing the IT method, we improve the channel estimate, when compared to the normal IT approach. In addition, if we then implement a MMSE equalizer (based upon the improved channel estimate), the BER is also improved (even with nonzero mean removal of each packet) when compared to MMSE equalization based on the traditional IT channel estimation.