Fronthaul Compression and Precoding Design for C-RANs Over Ergodic Fading Channels

IEEE Transactions on Vehicular Technology

Volumn 65, Issue 7, 2016, Pages 5022-5032

  Kang, Jinkyu ^a   Simeone, Osvaldo ^b   Kang, Joonhyuk ^a   Shamai, Shlomo ^c  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

^b NEW JERSEY INSTITUTE OF TECHNOLOGY   (United States)

^c TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

Author keywords

Cloud radio access networks (C RAN); fronthaul compression; multiple input multiple output (MIMO); precoding; stochastic channel state information (CSI)

Indexed keywords

ALGORITHMS; CHANNEL CAPACITY; COMMUNICATION CHANNELS (INFORMATION THEORY); FADING (RADIO); FADING CHANNELS; MIMO SYSTEMS; NETWORK LAYERS; OPTIMIZATION; RADIO LINKS; STOCHASTIC SYSTEMS;

BASE-BAND PROCESSING; INFORMATION OVERHEAD; INSTANTANEOUS CHANNEL STATE INFORMATIONS; INTERFERENCE MANAGEMENT; MOBILE STATION (MSS); OPTIMIZATION ALGORITHMS; PRECODING; RADIO ACCESS NETWORKS;

CHANNEL STATE INFORMATION;

EID: 84979247310     PISSN: 00189545     EISSN: None     Source Type: Journal    
DOI: 10.1109/TVT.2015.2466619     Document Type: Article

References (30)

1. "C-RAN: The road toward green RAN," White Paper, Ver. 2.5, China Mobile Res. Inst., Beijing, China, Oct. 2011.
2. T. Flanagan, "Creating cloud base stations with TI's keystone multicore architecture," White Paper, Texas Inst., Austin, TX, USA, Oct. 2011.
3. P. Marsch et al., "Future mobile communication networks: Challenges in the design and operation," IEEE Veh. Technol. Mag., vol. 7, no. 1, pp. 16-23, Mar. 2012.
4. A. Checko et al., "Cloud RAN for mobile networks-A technology overview," IEEE Commun. Surveys Tuts., vol. 17, no. 1, pp. 405-426, 1st Quart. 2015.
5. D. Samardzija, J. Pastalan, M. MacDonald, S. Walker, and R. Valenzuela, "Compressed transport of baseband signals in radio access networks," IEEE Trans. Wireless Commun., vol. 11, no. 9, pp. 3216-3225, Sep. 2012.
6. K. F. Nieman and B. L. Evans, "Time-domain compression of complexbaseband lte signals for cloud radio access networks," in Proc. IEEE Global Conf. Signal Inf. Process., Dec. 2013, pp. 1198-1201.
7. U. Dotsch et al., "Quantitative analysis of split base station processing and determination of advantageous architectures for LTE," Bell Labs Tech. J., vol. 18, no. 1, pp. 105-128, Jun. 2013.
8. D. Wubben et al., "Benefits and impact of cloud computing on 5G signal processing: Flexible centralization through cloud-RAN," IEEE Signal Process. Mag., vol. 31, no. 6, pp. 35-44, Nov. 2014.
9. O. Simeone, O. Somekh, H. V. Poor, and S. Shamai, "Downlink multicell processing with limited-backhaul capacity," EURASIP J. Adv. Signal Process., vol. 2009, Feb. 2009, Art. ID. 840814.
10. P. Marsch and G. Fettweis, "On downlink network MIMO under a constrained backhaul and imperfect channel knowledge," in Proc. IEEE Global Commmun. Conf., Nov. 2009, pp. 1-6.
11. P. Patil and W. Yu, "Hybrid compression and message-sharing strategy for the downlink cloud radio-access network," in Proc. IEEE Inf. Theory Appl. Workshop, San Diego, CA, USA, Feb. 2014, pp. 1-6.
12. S.-H. Park, O. Simeone, O. Sahin, and S. Shamai, "Fronthaul compression for cloud radio access networks," IEEE Signal Process. Mag., vol. 31, no. 6, pp. 69-79, Nov. 2014.
13. 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.
14. S. Park, C.-B. Chae, and S. Bahk, "Before/after precoded massive MIMO in cloud radio access networks," in Proc. IEEE Int. Conf. Commun., Jun. 2013, pp. 169-173.
15. A. Sanderovich, O. Somekh, H. V. Poor, and S. Shamai, "Uplink macro diversity of limited backhaul cellular network," IEEE Trans. Inf. Theory, vol. 55, no. 8, pp. 3457-3478, Aug. 2009.
16. A. Lozano and N. Jindal, "Are yesterday's information-theoretic fading models and performance metrics adequate for the analysis of today's wireless systems?" IEEE Commun. Mag., vol. 50, no. 11, pp. 210-217, Nov. 2012.
17. A. Ghosh, J. Zhang, J. G. Andrews, and R. Muhamed, Fundamentals of LTE. Upper Saddle River, NJ, USA: Prentice-Hall, 2010.
18. B. Hassibi and B. M. Hochwald, "How much training is needed in multiple-antenna wireless links?" IEEE Trans. Inf. Theory, vol. 49, no. 4, pp. 951-963, Apr. 2003.
19. J. Hoydis, M. Kobayashi, and M. Debbah, "Optimal channel training in uplink network MIMO systems," IEEE Trans. Signal Process., vol. 59, no. 6, pp. 2824-2833, Jun. 2011.
20. Y. Shi, J. Zhang, and K. B. Letaief, "CSI overhead reduction with stochastic beamforming for cloud radio access networks," in Proc. IEEE Int. Conf. Commun., Sydney, NSW, Australia, Jun. 2014, pp. 5154-5159.
21. M. Razaviyayn, M. Sanjabi, and Z.-Q. Luo, "A stochastic successive minimization method for nonsmooth nonconvex optimization with applications to transceiver design in wireless communication networks," arXiv preprint arXiv:1307.4457v2, 2013.
22. J. Kang, O. Simeone, J. Kang, and S. Shamai, "Joint signal and channel state information compression for the backhaul of uplink network MIMO systems," IEEE Trans. Wireless Commun., vol. 13, no. 3, pp. 1555-1567, Mar. 2014.
23. L. Liu, S. Bi, and R. Zhang, "Joint power control and fronthaul rate allocation for throughput maximization in broadband cloud radio access network," arXiv preprint arXiv:1407.3855v4, 2014.
24. A. E. Gamal and Y.-H. Kim, Network Information Theory. Cambridge, U.K.: Cambridge Univ. Press, 2011.
25. A. Adhikary, J. Nam, J.-Y. Ahn, and G. Caire, "Joint spatial division and multiplexing: The large-scale array regime," IEEE Trans. Inf. Theory, vol. 59, no. 10, pp. 6441-6463, Oct. 2014.
26. S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge, U.K.: Cambridge Univ. Press, 2004.
27. Y. Yang, G. Scutari, and D. P. Palomar, "Parallel stochastic decomposition algorithms for multi-agent systems," in Proc. IEEE Workshop Signal Process. Adv. Wireless Commun., Jun. 2013, pp. 180-184.
28. D. R. Hunter and K. Lange, "A tutorial on MM algorithms," Amer. Statistician, vol. 58, no. 1, pp. 30-37, Feb. 2004.
29. A. Beck and M. Teboulle, "Gradient-based algorithms with applications to signal recovery problems," in Convex Optimization in Signal Processing and Communications, Y. Eldar and D. Palomar, Eds. Cambridge, U.K.: Cambridge Univ. Press, 2010, pp. 42-48.
30. L. Vandenberghe and S. Boyd, "Semidefinite relaxation of quadratic optimization problems," SIAM Rev., vol. 38, no. 1, pp. 49-95, 1996.