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Limited Feedback Massive MISO Systems with Trellis Coded Quantization for Correlated Channels

Information Theory 2016-11-17 v3 math.IT

Abstract

In this paper, we propose trellis coded quantization (TCQ) based limited feedback techniques for massive multiple-input single-output (MISO) frequency division duplexing (FDD) systems in temporally and spatially correlated channels. We exploit the correlation present in the channel to effectively quantize channel direction information (CDI). For multiuser (MU) systems with matched-filter (MF) precoding, we show that the number of feedback bits required by the random vector quantization (RVQ) codebook to match even a small fraction of the perfect CDI signal-to-interference-plus-noise ratio (SINR) performance is large. With such large numbers of bits, the exhaustive search required by conventional codebook approaches make them infeasible for massive MISO systems. Motivated by this, we propose a differential TCQ scheme for temporally correlated channels that transforms the source constellation at each stage in a trellis using 2D translation and scaling techniques. We derive a scaling parameter for the source constellation as a function of the temporal correlation and the number of BS antennas. We also propose a TCQ based limited feedback scheme for spatially correlated channels where the channel is quantized directly without performing decorrelation at the receiver. Simulation results show that the proposed TCQ schemes outperform the existing noncoherent TCQ (NTCQ) schemes, by improving the spectral efficiency and beamforming gain of the system. The proposed differential TCQ also reduces the feedback overhead of the system compared to the differential NTCQ method.

Keywords

Cite

@article{arxiv.1405.6052,
  title  = {Limited Feedback Massive MISO Systems with Trellis Coded Quantization for Correlated Channels},
  author = {Jawad Mirza and Mansoor Shafi and Peter J. Smith and Pawel A. Dmochowski},
  journal= {arXiv preprint arXiv:1405.6052},
  year   = {2016}
}

Comments

13 pages, 18 figures, IEEE Transactions on Vehicular Technology, accepted for publication

R2 v1 2026-06-22T04:21:55.630Z