Iterative Mean Removal Superimposed Training for SISO and MIMO Channel Estimation

Abstract

This contribution describes a novel iterative radio channel estimation algorithm based on superimposed training (ST) estimation technique. The proposed algorithm draws an analogy with the data dependent ST (DDST) algorithm, that is, extracts the cycling mean of the data, but in this case at the receiver's end. We first demonstrate that this mean removal ST (MRST) applied to estimate a single-input single-output (SISO) wideband channel results in similar bit error rate (BER) performance in comparison with other iterative techniques, but with less complexity. Subsequently, we jointly use the MRST and Alamouti coding to obtain an estimate of the multiple-input multiple-output (MIMO) narrowband radio channel. The impact of imperfect channel on the BER performance is evidenced by a comparison between the MRST method and the best iterative techniques found in the literature. The proposed algorithm shows a good tradeoff performance between complexity, channel estimation error, and noise immunity.

Iterative MIMO Detection and Channel Estimation Using Joint Superimposed and Pilot-Aided Training

“…A G2-OSTBC system with = = 2 antennas was used. This simulation scenario was also employed in [28]. The block length was fixed to = 256 symbols, unless another value is indicated, and all simulations were run until 1000 block errors were found.…”

“…The MRST scheme in [28,37] was developed from the following two points: (i) the fact that the difference between ST (27) and DDST (30) channel estimation techniques is the factor HB and (ii) the hypothesis that if we could obtain an estimate of the signal E at the receiver end, we would achieve the performance of DDST in terms of channel estimate MSE. However, as more power is allocated to data signals, BER performance should increase.…”

“…The novel algorithm is based on the use of joint (fusion of both techniques) implicit and explicit training for the channel estimation and symbol detection processes. Previous results of the authors related to implicit training for MIMO systems were presented in [28], which introduced the mean removal ST (MRST) technique devoted to estimate the arithmetic cycling mean of the data block signal at the receiver side (instead of at the transmitter side like DDST does). However, later on it was recognized that MRST is not able to deal with the identifiability problem, just like DDST, when the MIMO system employs high-order digital modulation schemes.…”

Configurable Transmitter and Systolic Channel Estimator Architectures for Data-Dependent Superimposed Training Communications Systems

“…Generator. The operation of this submodule is based on (6a)- (7), which implies to compute two high-demand processing operations: an MVM and the Kronecker product. Moreover, similar to the cyclic prefix insertion case, the DDS can only be generated from data sequence b(k) until it has been completely processed.…”

“…6]. In [4][5][6][7][8] was presented a refinement of ST known as data-dependent superimpose training (DDST), this technique makes it possible to null the interference of data during the estimation process via the addition of a new training sequence, which depends on the transmitted data, together with the data and the ST sequence.…”