Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
158 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Decentralizing Coherent Joint Transmission Precoding via Fast ADMM with Deterministic Equivalents (2403.19127v1)

Published 28 Mar 2024 in eess.SP, cs.IT, and math.IT

Abstract: Inter-cell interference (ICI) suppression is critical for multi-cell multi-user networks. In this paper, we investigate advanced precoding techniques for coordinated multi-point (CoMP) with downlink coherent joint transmission, an effective approach for ICI suppression. Different from the centralized precoding schemes that require frequent information exchange among the cooperating base stations, we propose a decentralized scheme to minimize the total power consumption. In particular, based on the covariance matrices of global channel state information, we estimate the ICI bounds via the deterministic equivalents and decouple the original design problem into sub-problems, each of which can be solved in a decentralized manner. To solve the sub-problems at each base station, we develop a low-complexity solver based on the alternating direction method of multipliers (ADMM) in conjunction with the convex-concave procedure (CCCP). Simulation results demonstrate the effectiveness of our proposed decentralized precoding scheme, which achieves performance similar to the optimal centralized precoding scheme. Besides, our proposed ADMM solver can substantially reduce the computational complexity, while maintaining outstanding performance.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (29)
  1. Y. Liu, X. Bian, Y. Xu, T. Hou, W. Wang, Y. Mao, and J. Zhang, “Decentralizing coherent joint transmission precoding via deterministic equivalents,” in Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), Seoul, Korea, Apr. 2024.
  2. Ericsson, “5G standalone brings new opportunities,” Ericsson Mobility Report, Nov. 2023.
  3. 5G-PPP, “Beyond 5G/6G KPIs and target values,” 5G-PPP White Paper, Jun. 2022.
  4. X. You, D. Wang, and B. Sheng, “Cell edge performance of cellular mobile systems,” IEEE J. Sel. Areas Commun., vol. 29, no. 6, pp. 1139–1150, Jun. 2011.
  5. M. Sawahashi, Y. Kishiyama, A. Morimoto, D. Nishkawa, and M. Tanno, “Coordinated multipoint transmission/reception techniques for LTE-Advanced,” IEEE Trans. Wireless Commun., vol. 17, no. 3, pp. 26–34, Jun. 2010.
  6. 3GPP, “Study on new radio access technology: radio access architecture and interfaces,” 3GPP TR 38.801 version 14.0.0, 2017.
  7. Y. Shi, J. Zhang, K. B. Letaief, B. Bai, and W. Chen, “Large-scale convex optimization for ultra-dense cloud-RAN,” IEEE Wireless Commun., vol. 22, no. 3, pp. 84-91, Jun. 2015.
  8. M. Peng, C. Wang, V. Lau and H. V. Poor, “Fronthaul-constrained cloud radio access networks: insights and challenges,” IEEE Wireless Commun., vol. 22, no. 2, pp. 152-160, Apr. 2015.
  9. D. Pliatsios, P. Sarigiannidis, S. Goudos, and G. K. Karagiannidis, “Realizing 5G vision through cloud RAN: Technologies, challenges, and trends,” EURASIP J. Wireless Commun. Network, no. 136, May 2018.
  10. H. Zhang and H. Dai, “Cochannel interference mitigation and cooperative processing in downlink multicell multiuser MIMO networks,” European J. Wireless Commun. and Networking, no. 2, pp. 222–235, 4th Quarter 2004.
  11. W. Yu and T. Lan, “Transmitter optimization for the multi-antenna donwlink with per-antenna power constraints,” IEEE Trans. Signal Process., vol. 55, no. 6, pp. 2646-2660, Jun. 2007.
  12. S. Shim, J. S. Kwak, R. W. Heath, Jr., and J. G. Andrews, “Block diagonalization for multi-user MIMO with other-cell interference,” IEEE Trans. Wireless Commun., vol. 7, no. 7, pp. 2671-2681, Jul. 2008.
  13. R. Zhang, “Cooperative multi-cell block diagonalization with per-base-station power constraints,” IEEE J. Sel. Areas Commum., vol. 28, no. 9, pp. 1435-1445, Dec. 2010.
  14. J. Zhang, R. Chen, J.G. Andrews, A. Ghosh, and R.W. Heath, “Networked MIMO with clustered linear precoding,” IEEE Trans. Wireless Commun., vol. 8, no. 4, pp. 1910–1921, Apr. 2009.
  15. S. Venkatesan, “Coordinating base stations for greater uplink spectral efficiency in a cellular network,” in Proc. IEEE Int. Symp. Personal Indoor Mob. Radio Commun (PIMRC), Athens, Greece, Sept. 2007.
  16. Q. Shi, M. Razaviyayn, Z.-Q. Luo, and C. He, “An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel,” IEEE Trans. Signal Process., vol. 59, no. 9, pp. 4331–4340, Sep. 2011.
  17. H. Dahrouj and W. Yu, “Coordinated beamforming for the multicell multi-antenna wireless system,” IEEE Trans. Wireless Commun., vol. 9, no. 5, pp. 1748–1759, May 2010.
  18. E. Björnson, M. Bengtsson, and B. Ottersten, “Optimal multiuser transmit beamforming: A difficult problem with a simple solution structure [Lecture Notes],” IEEE Signal Process. Mag., vol. 31, no. 4, pp. 142-148, July. 2014.
  19. Z. Wu, and Z. Fei, “Precoder design in downlink CoMP-JT MIMO network via WMMSE and asynchronous ADMM,” Sci. China Inf. Sci., vol. 61, no. 082306, pp. 1-13, Aug. 2018.
  20. S. H. Park, O. Simeone, O. Sahin, and S. Shamai, “Joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks,” IEEE Trans. Signal Process., vol. 61, no. 22, pp. 5646–5658, Nov. 2013.
  21. J. Kaleva, A.Tölli, M. Juntti, R. Berry, and M. Honig, “Decentralized joint precoding for WSRMax with pilot aided beamformer estimation,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), Singapore, Dec. 2017.
  22. Z.-Q. Luo and W. Yu, “An introduction to convex optimization for communications and signal processing,” IEEE J. Sel. Areas Commun., vol. 24, no. 8, pp. 1426-1438, August. 2006.
  23. J. Silverstein and Z. Bai, “On the empirical distribution of eigenvalues of a class of large dimensional random matrice,” J Multivar Anal., vol.54, no. 2, pp. 175-192, 1995.
  24. Z. Bai and J. Silverstein, “No eigenvalues outside the support of the limiting spectral distribution of large-dimensional sample covariance matrices,” Ann Probab., vol. 26, no. 1, pp. 316-345, 1998.
  25. H. Asgharimoghaddam, A. Tölli, L. Sanguinetti and M. Debbah, “Decentralizing multicell beamforming via deterministic equivalents,” IEEE Trans. Wireless Commun., vol. 67, no. 3, pp. 1894-1909, March 2019.
  26. O. Mehanna, K. Huang, B. Gopalakrishnan, A. Konar, and N. D. Sidiropoulos, “Feasible point pursuit and successive approximation of non-convex QCQPs,” IEEE Signal Process. Lett., vol. 22, no. 7, pp. 804–808, July 2015.
  27. M. Tao, E. Chen, H. Zhou, and W. Yu, “Content-centric sparse multicast beamforming for cache-enabled cloud RAN,” IEEE Trans. Wireless Commun., vol. 15, no. 9, pp. 6118–6131, Sept. 2016.
  28. G. R. Lanckriet and B. K. Sriperumbudur, “On the convergence of the concave-convex procedure,” in Proc. Advances in neural information processing systems, pp. 1759–1767, Canada, Dec. 2009.
  29. F. Burkhardt, E. Eberlein, S. Jaeckel, G. Sommerkorn, and R. Prieto-Cerdeira, “QuaDRiGa: a MIMO channel model for land mobile satellite,” in Proc. 8th European Conference on Antennas and Propagation (EuCAP), The Hague, Netherlands, Sep. 2014.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now