Blind channel estimation exploiting transmission filter knowledge

This contribution elaborates on the concept of blind identification of multiple FIR channels with transmission filter knowledge (WTXFK). This prior knowledge could, in fact, include not only the transmitter (TX) (pulse shaping) filter but also the receiver (RX) filter present in digital communication systems. Exploitation of this side information allows the estimation to concentrate on the impulse response of the actual propagation channel itself. Hence this estimation can be done more accurately. Since the prior information is expressed in terms of the channel impulse response, we review a number of blind channel estimation methods that are parameterized directly by the channel and consider their extension to incorporate the prior knowledge. These methods include essentially subchannel response matching (SRM), subspace fitting and maximum likelihood (ML) techniques. All these methods are formulated for burst mode transmission. We also discuss performance limits in the form of Cramer}Rao bounds (CRBs). Both the methods and the CRBs are discussed in a deterministic and a Gaussian context for the unknown transmitted symbols. Simulation results indicate that the exploitation of the prior knowledge can lead to significant improvements, a capability of the extended method to identify ill-conditioned channels, that one particular version SRM WTXFK often outperforms another one, and that ML methods can still further improve performance.