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

Abstract

ArticleAbstract-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).

Performance evaluation of time-multiplexed and data-dependent superimposed training based transmission with practical power amplifier model

“…With the DDST, we first remove the cyclic mean of the data vector. As shown in [4], this can be expressed as…”

“…We have extended the model provided in [4] to our SC model with FB-based frequencydomain equalizer structure, presented in [14]. The analysis FB converts the time domain signal to the frequency domain (similar to the well known DFT operation) and the synthesis FB converts the frequency domain presentation back to time domain (similar to the IDFT operation).…”

“…To overcome this problem of self-interference (interference from the user data symbols in channel estimation), a data-dependent superimposed training (DDST) scheme was presented in [4,5]. The basic idea is very simple.…”

Recent Developments in Channel Estimation and Detection for MIMO Systems

“…A solution for getting rid of the interference from data symbols in channel estimation is to use data-dependent overlay pilot sequences such that the corresponding pilot and data sequences are orthogonal Ghogho et al (2005). The drawback of this method is that it results in nulls in the equivalent channel impulse response (seen by data symbols), and hence, in a performance degradation.…”

Turbo channel estimation and equalisation for a superposition-based cooperative system

“…In the original protocol [3], the channel is assumed to be flat fading and known, which is, unfortunately, not the case in required for the channel estimation, they can be saved at the relay node by exploring the superposition structure of the transmission data where part of the data is known to the receiving node. This falls into the general area of the superimposed training (see [5] and the references therein). Many related algorithms have been proposed, most of which attempt to minimize the influence of the information data on the training sequence by exploring some periodic properties of the training data.…”