This paper addresses the problem of blind demixing of instantaneous mixtures in a multiple-input multiple-output communication system. The main objective is to present efficient blind source separation (BSS) algorithm...
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This paper addresses the problem of blind demixing of instantaneous mixtures in a multiple-input multiple-output communication system. The main objective is to present efficient blind source separation (BSS) algorithms dedicated to moderate or high-order quadratic-amplitude modulation (QAM) constellations. Four new iterative batch, BSS algorithms are presented dealing with the multimodulus (MM) and alphabetmatched (AM) criteria. For the optimization of these cost functions, iterative methods of Givens and hyperbolic rotations are used. A prewhitening operation is also utilized to reduce the complexity of design problem. It is noticed that the designed algorithms using Givens rotations give satisfactory performance only for a large number of samples. However, for a small number of samples, the algorithms designed by combining both Givens and hyperbolic rotations compensate for the ill-whitening that occurs in this case and thus improves the performance. Two algorithms dealing with the MM criterion are presented for moderate-order QAM signals such as 16-QAM. The other two dealing with the AM criterion are presented for high-order QAM signals. These methods are finally compared with the state-of-the-art batch BSS algorithms in terms of signal-to-interference and noise ratio, symbol error rate, and convergence rate. Simulation results show that the proposed methods outperform the contemporary batch BSS algorithms.
In this paper, we address the problem of identifying the modulation level of the received signal under an unknown frequency selective channel. The modulation level classification is performed using reduced-complexity ...
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ISBN:
(纸本)9781509059904
In this paper, we address the problem of identifying the modulation level of the received signal under an unknown frequency selective channel. The modulation level classification is performed using reduced-complexity Kuiper (rcK) test which utilizes the distribution of signal features such as magnitude of the received samples or phase difference in consecutive received samples. However, in frequency selective channels, these features are severely distorted resulting in a poor classification performance. We propose to use constant modulus algorithm (CMA) to mitigate the impact of the frequency selective channel on the signal feature. Simulation and analytical results show that the proposed CMA-rcK technique outperforms state-of-the-art cumulant-based technique as well as blind equalizer-based technique that uses alphabet matched algorithm.
In this work we investigate the blind equalization of signal blocks of data, which have been transmitted over a multiple-input/multiple-output channel using 16-QAM modulation. We apply the Fast Fourier Transform (FFT)...
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ISBN:
(纸本)9781509047802
In this work we investigate the blind equalization of signal blocks of data, which have been transmitted over a multiple-input/multiple-output channel using 16-QAM modulation. We apply the Fast Fourier Transform (FFT) in order to improve the efficiency of the blind equalizer update. Our equalizer is based on a cost function, which is specified as the sum of the constant modulus algorithm (CMA) and the alphabet-matchedalgorithm (AMA). The equalization is done using a block processing multistage approach as was introduced in [3], but with an FFT implementation. In this work, a 3-input/7-output MIMO communication system is considered, and the performance of the MIMO equalizer is analyzed using Monte Carlo simulations. Performance is measured based on the average symbol error rate (SER) and the total number of equalized cases. In addition, initial comparative results are provided for a fixed number of transmit antennas, but with varying numbers of receive antennas.
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