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.
Highlights
One of the most widely used approaches to channel estimation is to employ pilot assisted transmission (PAT), where a known training sequence, referred as pilot, is inserted at each block of transmitted data [1]
Because the iterative channel estimation methods depend on and work jointly with the equalization stage, we present the results using two equalizers widely used in communication systems: the minimum mean square error (MMSE) equalizer and the maximum likelihood sequence estimation (MLSE) equalizer
We developed the mean removal ST (MRST) scheme starting from the hypothesis that if we could obtain an estimate of the signal {e(k)} (i.e., a cyclic mean of sequence {b(k)}) at the receiver side, we would achieve the performance of dependent ST (DDST) in terms of channel error estimate MSE but with more power assigned to the data sequence {b(k)}, having an impact on a better performance in terms of bit error rate (BER)
Summary
One of the most widely used approaches to channel estimation is to employ pilot assisted transmission (PAT), where a known training sequence, referred as pilot, is inserted at each block of transmitted data [1]. The last constraints lead to research on iterative implementations of ST as in [6,7,8,9,10], starting from the SISO radio channel These works are based on the use of the decoded data to eliminate the distortion introduced by the received data in the channel estimation process. We introduce a new iterative mean removal ST (MRST) proposal and compare its performance with the previous and most relevant works This MRST yields similar performance to DDST-DDD removal and IST but with less complexity when they are compared with LSDDST and LSST. Because the iterative channel estimation methods depend on and work jointly with the equalization stage, we present the results using two equalizers widely used in communication systems: the minimum mean square error (MMSE) equalizer and the maximum likelihood sequence estimation (MLSE) equalizer.
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