@InProceedings{6952770,
  author = {A. Kammoun and A. Müller and E. Björnson and M. Debbah},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {Low-complexity linear precoding for multi-cell massive MIMO systems},
  year = {2014},
  pages = {2150-2154},
  abstract = {Massive MIMO (multiple-input multiple-output) has been recognized as an efficient solution to improve the spectral efficiency of future communication systems. However, increasing the number of antennas and users goes hand-in-hand with increasing computational complexity. In particular, the precoding design becomes involved since near-optimal precoding, such as regularized-zero forcing (RZF), requires the inversion of a large matrix. In our previous work [1] we proposed to solve this issue in the single-cell case by approximating the matrix inverse by a truncated polynomial expansion (TPE), where the polynomial coefficients are selected for optimal system performance. In this paper, we generalize this technique to multi-cell scenarios. While the optimization of the RZF precoding has, thus far, not been feasible in multi-cell systems, we show that the proposed TPE precoding can be optimized to maximize the weighted max-min fairness. Using simulations, we compare the proposed TPE precoding with RZF and show that our scheme can achieve higher throughput using a TPE order of only 3.},
  keywords = {computational complexity;linear codes;matrix inversion;MIMO communication;minimax techniques;precoding;low-complexity linear precoding;multicell massive MIMO systems;multiple-input multiple-output systems;spectral efficiency improvement;computational complexity;communication systems;near-optimal precoding;regularized-zero forcing;RZF;matrix inversion;single-cell case;truncated polynomial expansion;optimal system performance;weighted max-min fairness;TPE order;Interference;MIMO;Antennas;Polynomials;Covariance matrices;Signal to noise ratio;Optimization;Massive MIMO;linear precoding;low complexity;multi-cell systems;random matrix theory},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569922419.pdf},
}

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However, increasing the number of antennas and users goes hand-in-hand with increasing computational complexity. In particular, the precoding design becomes involved since near-optimal precoding, such as regularized-zero forcing (RZF), requires the inversion of a large matrix. In our previous work [1] we proposed to solve this issue in the single-cell case by approximating the matrix inverse by a truncated polynomial expansion (TPE), where the polynomial coefficients are selected for optimal system performance. In this paper, we generalize this technique to multi-cell scenarios. While the optimization of the RZF precoding has, thus far, not been feasible in multi-cell systems, we show that the proposed TPE precoding can be optimized to maximize the weighted max-min fairness. Using simulations, we compare the proposed TPE precoding with RZF and show that our scheme can achieve higher throughput using a TPE order of only 3.","keywords":"computational complexity;linear codes;matrix inversion;MIMO communication;minimax techniques;precoding;low-complexity linear precoding;multicell massive MIMO systems;multiple-input multiple-output systems;spectral efficiency improvement;computational complexity;communication systems;near-optimal precoding;regularized-zero forcing;RZF;matrix inversion;single-cell case;truncated polynomial expansion;optimal system performance;weighted max-min fairness;TPE order;Interference;MIMO;Antennas;Polynomials;Covariance matrices;Signal to noise ratio;Optimization;Massive MIMO;linear precoding;low complexity;multi-cell systems;random matrix theory","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569922419.pdf","bibtex":"@InProceedings{6952770,\n author = {A. Kammoun and A. Müller and E. Björnson and M. Debbah},\n booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},\n title = {Low-complexity linear precoding for multi-cell massive MIMO systems},\n year = {2014},\n pages = {2150-2154},\n abstract = {Massive MIMO (multiple-input multiple-output) has been recognized as an efficient solution to improve the spectral efficiency of future communication systems. However, increasing the number of antennas and users goes hand-in-hand with increasing computational complexity. In particular, the precoding design becomes involved since near-optimal precoding, such as regularized-zero forcing (RZF), requires the inversion of a large matrix. In our previous work [1] we proposed to solve this issue in the single-cell case by approximating the matrix inverse by a truncated polynomial expansion (TPE), where the polynomial coefficients are selected for optimal system performance. In this paper, we generalize this technique to multi-cell scenarios. While the optimization of the RZF precoding has, thus far, not been feasible in multi-cell systems, we show that the proposed TPE precoding can be optimized to maximize the weighted max-min fairness. 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