137 research outputs found
Trade-off between complexity and BER performance of a polynomial SVD-based broadband MIMO transceiver
In this paper we investigate non-linear precoding solutions for the problem of broadband multiple-input multiple output(MIMO) systems. Based on a polynomial singular value decomposition (PSVD) we can decouple a broadband MIMO channel into independent dispersive spectrally majorised single-input single-output (SISO) subchannels. In this contribution, the focus of our work is to explore the influence of approximations on the PSVD, and the performance degradation that can be expected as a result
Comparison of precoding methods for broadband MIMO systems
In this paper we investigate non-linear precoding solutions for the problem of broadband multiple-input multipleoutput (MIMO) systems. Based on a broadband singular value decomposition (BSVD) we can decouple a broadband MIMO channel into independent dispersive spectrally majorised singleinput single-output (SISO) subchannels. Bit loading is proposed to optimally utilise these SISO subchannels after mitigating their individual inter-symbol-interference (ISI) using Tomlinson- Harashima precoding (THP). This method is benchmarked against recent results of both MMSE linear and THP designed for frequency-selective MIMO channels. Simulation results show that better bit-error-ratio (BER) can be achieved especially for higher throughput targets when compared to the benchmar
Parallel QR decomposition in LTE-A systems
The QR Decomposition (QRD) of communication channel matrices is a fundamental
prerequisite to several detection schemes in Multiple-Input Multiple-Output
(MIMO) communication systems. Herein, the main feature of the QRD is to
transform the non-causal system into a causal system, where consequently
efficient detection algorithms based on the Successive Interference
Cancellation (SIC) or Sphere Decoder (SD) become possible. Also, QRD can be
used as a light but efficient antenna selection scheme. In this paper, we
address the study of the QRD methods and compare their efficiency in terms of
computational complexity and error rate performance. Moreover, a particular
attention is paid to the parallelism of the QRD algorithms since it reduces the
latency of the matrix factorization.Comment: The eleventh IEEE International Workshop on Signal Processing
Advances for Wireless Communications, 5 pages, 4 figures, 4 algorithms, 1
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Multichannel spectral factorization algorithm using polynomial matrix eigenvalue decomposition
In this paper, we present a new multichannel spectral factorization algorithm which can be utilized to calculate the approximate spectral factor of any para-Hermitian polynomial matrix. The proposed algorithm is based on an iterative method for polynomial matrix eigenvalue decomposition (PEVD). By using the PEVD algorithm, the multichannel spectral factorization problem is simply broken down to a set of single channel problems which can be solved by means of existing one-dimensional spectral factorization algorithms. In effect, it transforms the multichannel spectral factorization problem into one which is much easier to solve
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