On the performance of iterative receivers for interfering MIMO-OFDM systems in measured channels

Hammarberg, Peter; Rossi, Pierluigi Salvo; Tufvesson, Fredrik; Edfors, Ove; Kolmonen, Veli-Matti; Almers, Peter; Müller, Ralf R.; Molisch, Andreas F.

doi:10.1109/acssc.2008.5074379

Cited by 1 publication

(2 citation statements)

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“…4a, the suppression is about G(S dB = 10) ≈ 12 dB. This performance difference results from the definition of the signal/noise subspace threshold criteria (14). Higher initial SNR yields higher rank approximation, and the obtainable SNR improvement is upper bounded by the (inverse of the compression) ratio 10 log 10 (R/R A ).…”

Section: Resultsmentioning

confidence: 96%

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Sequential unfolding SVD for low rank orthogonal tensor approximation

Salmi¹,

Richter²,

Koivunen³

2008

2008 42nd Asilomar Conference on Signals, Systems and Computers

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This paper contributes to the field of N-way (N ≥ 3) tensor decompositions, which are increasingly popular in various signal processing applications. A novel PARATREE decomposition structure is introduced, accompanied with Sequential Unfolding SVD (SUSVD) algorithm. SUSVD applies a matrix SVD sequentially on the unfolded tensor, which is reshaped from the right hand basis vectors of the SVD of the previous mode. The consequent PARATREE model is related to the well known family of PARAFAC tensor decompositions, describing a tensor as a sum of rank-1 tensors. PARATREE is an efficient model to be used for orthogonal lower rank approximations, offering significant computational savings in algorithm implementations due to a hierarchical tree structure. The performance of the proposed algorithm is illustrated through an application of measurement noise suppression in wideband MIMO measurements.

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Section: Resultsmentioning

confidence: 96%

“…Improving the SNR is crucial in order to apply noisy measured channel realizations in simulations (see e.g. [14]). An improved estimate of the channel is obtained by estimating the signal subspace from the measured tensor.…”

Section: Introductionmentioning

confidence: 99%