Iterative synchronisation and DC-offset estimation using superimposed training

In this paper, we propose a new iterative approach for superimposed training (ST) that improves synchronisation, DC-offset estimation and channel estimation. While synchronisation algorithms for ST have previously been proposed in [2],[4] and [5], due to interference from the data they performed sub-optimally, resulting in channel estimates with unknown delays. These delay ambiguities (also present in the equaliser) were estimated in previous papers in a non-practical manner. In this paper we avoid the need for estimation of this delay ambiguity by iteratively removing the effect of the data “noise”. The result is a BER performance superior to all other ST algorithms that have not assumed a-priori synchronisation

Block synchronisation for joint channel and DC-offset estimation using data-dependent superimposed training

In this paper, we propose a new (single-step) block synchronisation algorithm for joint channel and DC-offset estimation for data-dependent superimposed training (DDST). While a (two-step) block synchronisation algorithm for DDST has previously been proposed in [5], due to interference from the information-bearing data it performed sub-optimally, resulting in channel estimates with unknown delays. These delay ambiguities (also present in the equaliser) were then estimated in [5] in a non-practical manner. In this paper we avoid the need for estimation of this delay ambiguity by exploiting the special structure of the channel output’s cyclic mean vector. The result is a BER performance superior to the DDST synchronisation algorithm first published in [5]

Channel estimation and symbol detection for block transmission using data-dependent superimposed training

We address the problem of frequency-selective channel estimation and symbol detection using superimposed training. The superimposed training consists of the sum of a known sequence and a data-dependent sequence that is unknown to the receiver. The data-dependent sequence cancels the effects of the unknown data on channel estimation. The performance of the proposed approach is shown to significantly outperform existing methods based on superimposed training (ST)

A low complexity iterative channel estimation and equalisation scheme for (data-dependent) superimposed training

Channel estimation/symbol detection methods based on superimposed training (ST) are known to bemore bandwidth efficient than those based on traditional time-multiplexed training. In this paper we present an iterative version of the ST methodwhere the equalised symbols obtained via ST are used in a second step to improve the channel estimation, approaching the performance of the more recent (and improved) data dependent ST (DDST), but now with less complexity. This iterative ST method (IST) is then compared to a different iterative superimposed training method of Meng and Tugnait (LSST).We show via simulations that the BER of our IST algorithm is very close to that of the LSST but with a reduced computational burden of the order of the channel length. Furthermore, if the LSST iterative approach (originally based on ST) is now implemented using DDST, a faster convergence rate can be achieved for the MSE of the channel estimates

Synchronisation of the superimposed training method for channel estimation in the presence of DC-offset

The superimposed training method estimates the channel from the induced first-order cyclostationary statistics exhibited by the received signal. In this paper, using vector space decomposition, we show that the information needed for training sequence synchronisation, and for DC-offset estimation, can be extracted from the first-order cyclostationary statistics as well. Necessary and sufficient conditions for channel computation and equalisation are derived, when training sequence synchronisation and DC-offset removal are required. The computational burden of the practical implementation of the method presented here is much lighter than for existing algorithms. At the same time, simulation results show that the performance, in terms of the MSE of the channel estimates and BER, is not diminishedwhen compared to these existing algorithms