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In this correspondence, a method is presented for estimating double-selective channels using superimposed training (ST). The estimator is based on a subspace projection of the time-varying channel onto a set of two dimensional orthogonal functions. These functions are formed via the outer product of the discrete prolate spheroidal basis vectors and the universal basis vectors. This approach allows...
Channel estimation is a challenging problem in wireless communication systems because of users mobility and limited bandwidth. A plethora of methods based on pilot assisted transmissions (PAT) have been proposed in most practical systems to overcome this problem, but with the penalty of extra bandwidth consumption for training. Channel estimation based on superimposed training (ST) has emerged as...
Channel estimation based on superimposed training (ST) has been an active research topic around the world in recent years, because it offers similar performance when compared to methods based on pilot assisted transmissions (PAT), with the advantage of a better bandwidth utilization. However, physical implementations of such estimators are still under research, and only few approaches have been reported...
Estimation of single-carrier communication channels based on superimposed training (ST) has been widely studied in the last years, because it offers a way for estimating the channel without the transmission of a pilot sequence multiplexed in time with the data, which implies a saving of valuable bandwidth. In this paper, the use of a variation of ST known as data-dependent superimposed training (DDST)...
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