Abstract
This paper investigates the joint maximum likelihood (ML) data detection and channel estimation problem for Alamouti space-time block-coded (STBC) orthogonal frequency-division multiplexing (OFDM) wireless systems. The joint ML estimation and data detection is generally considered a hard combinatorial optimization problem. We propose an efficient low-complexity algorithm based on branch-estimate-bound strategy that renders exact joint ML solution. However, the computational complexity of blind algorithm becomes critical at low signal-to-noise ratio (SNR) as the number of OFDM carriers and constellation size are increased especially in multiple-antenna systems. To overcome this problem, a semi-blind algorithm based on a new framework for reducing the complexity is proposed by relying on subcarrier reordering and decoding the carriers with different levels of confidence using a suitable reliability criterion. In addition, it is shown that by utilizing the inherent structure of Alamouti coding, the estimation performance improvement or the complexity reduction can be achieved. The proposed algorithms can reliably track the wireless Rayleigh fading channel without requiring any channel statistics. Simulation results presented against the perfect coherent detection demonstrate the effectiveness of blind and semi-blind algorithms over frequency-selective channels with different fading characteristics.
Highlights
The increasing demand for higher data rates in recent years has called for transmissions over a broadband wireless channel which is frequency selective
8 Conclusions In this paper, we presented a blind maximum likelihood (ML) algorithm for joint channel estimation and data detection in orthogonal frequency-division multiplexing (OFDM) wireless systems using Alamouti space-time block-coded (STBC) coding
Information about channel statistics as it avoids calculating matrix Pi with subcarrier reordering
Summary
The increasing demand for higher data rates in recent years has called for transmissions over a broadband wireless channel which is frequency selective. There are numerous blind estimation and equalization techniques available in the literature, namely, subspacebased methods [10,11], second-order statistics [12], Cholesky factorization [13] , and iterative methods [14] These methods either suffer from slow convergence, higher computational costs or assume channel to be stationary over several OFDM symbols. 2. Using semi-orthogonality of the subcarriers by exploiting the structure of fast Fourier transform (FFT) matrix in order to reduce the computations for calculation of bound during the blind search. 4. Reducing the complexity and or improving the estimation performance of proposed algorithm by exploiting the orthogonal structure of Alamouti coding.
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