Abstract
This paper presents a novel iterative detection and channel estimation scheme that combines the effort of superimposed training (ST) and pilot-aided training (PAT) for multiple-input multiple-output (MIMO) flat fading channels. The proposed method, hereafter known as joint mean removal ST and PAT (MRST-PAT), implements an iterative detection and channel estimation that achieves the performance of data-dependent ST (DDST) algorithm, with the difference that the data arithmetic cyclic mean is estimated and removed from data at the receiver’s end. It is demonstrated that this iterative and cooperative detection and channel estimator algorithm surpasses the effects of data detection identifiability condition that DDST has shown when higher orders of modulation are used. Theoretical performance of the MRST-PAT scheme is provided and corroborated by numerical simulations. In addition, the performance comparison between the proposed method and different MIMO channel estimation techniques is analyzed. The joint effort between ST and PAT shows that MRST-PAT is a solid candidate in communications systems for multiamplitude constellations in Rayleigh fading channels, while achieving high-throughput data rates with manageable complexity and bit-error rate (BER) as a figure of merit.
Highlights
Estimation theory deals with the basic problem of inferring a set of required statistical parameters of a random experiment based on the observation of its outcome
This approach is normally adopted in practical communications systems where channel estimation is an essential part of standard receiver designs [1] and carried out by transmitting training symbols commonly known as pilot symbols [2]
The mean removal ST (MRST) scheme in [28, 37] was developed from the following two points: (i) the fact that the difference between superimposed training (ST) (27) and dependent ST (DDST) (30) channel estimation techniques is the factor HBand (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
Summary
Estimation theory deals with the basic problem of inferring a set of required statistical parameters of a random experiment based on the observation of its outcome. Two outstanding IT approaches, known as superimposed training (ST) [5, 6] and data-dependent ST (DDST) [7, 8], achieve higher effective data rates with manageable complexity [9] These techniques are based on a training sequence added (superimposed) to the information-bearing symbols. There are, some drawbacks that must be taken into consideration when DDST is used This technique introduces a delay in the transmitted data when it calculates the data-dependent signal; second, it assigns less transmission power to the data signal; the symbol demapping operation is not suitable for higher orders of modulation due to identifiability problems, as was highlighted in [26]. The method is based on a reliable preliminary channel estimate using PAT with a small number of dedicated pilot symbols It uses the time-average estimator with the ST signal added to each transmitted data block, achieving an improvement in system throughput. Denotes trace of a matrix; ⊗ stands for Kronecker product; and CN(a, Σ) denotes a multidimensional complex Gaussian distribution with mean a and covariance matrix Σ
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