logo

SCIENCE CHINA Information Sciences, Volume 59 , Issue 10 : 102315(2016) https://doi.org/10.1007/s11432-016-0060-y

Optimal remote radio head selection for cloud radio access networks

More info
  • ReceivedMay 26, 2016
  • AcceptedJun 30, 2016
  • PublishedSep 12, 2016

Abstract


Funded by

National Basic Research Program of China(973)

(2012CB316004)

(2013CB336600)

National Natural Science Foundation of China(61372101)

National Natural Science Foundation of China(61221002)

National High Technology Research and Development Program of China(863)

(2014AA012104)

Fundamental Research Funds for the Central Universities and the Open Research Fund of Key Lab of Broadband Wireless Communications and Sensor Network Technologies(NJUPT)

Ministry of Education(NYKL201502)


Acknowledgment

Acknowledgments

This work was supported by National Basic Research Program of China (973) (Grant Nos. 2012CB316004, 2013CB336600), National Natural Science Foundation of China (Grant Nos. 61372101, 61221002), National High Technology Research and Development Program of China (863) (Grant Nos. 2014AA012104), Fundamental Research Funds for the Central Universities and the Open Research Fund of Key Lab of Broadband Wireless Communications and Sensor Network Technologies (NJUPT), Ministry of Education (Grant No. NYKL201502), and Funding of Supporting Excellent Young Professors for Teaching and Research in Southeast University.


References

[1] Li N, Fei Z, Xing C, et al. Robust low-complexity MMSE precoding algorithm for cloud radio access networks. IEEE Commun Lett, 2014, 18: 773-776 CrossRef Google Scholar

[2] Peng M, Yang S, Poor H V. Ergodic capacity analysis of remote radio head associations in cloud radio access networks. IEEE Wirel Commun Lett, 2014, 3: 365-368 CrossRef Google Scholar

[3] Shi Y, Zhang J, Letaief K B, et al. Large-scale convex optimization for ultra-dense cloud-RAN. IEEE Wirel Commun, 2015, 22: 84-91 Google Scholar

[4] Zhou S, Zhao T, Niu Z, et al. Software-defined hyper-cellular architecture for green and elastic wireless access. IEEE Commun Mag, 2016, 54: 12-19 Google Scholar

[5] Liu J, Zhao T, Zhou S, et al. CONCERT: a cloud-based arcitecture for next generation cellular systems. IEEE Wirel Commun, 2014, 21: 14-22 Google Scholar

[6] Dai B B, Yu W. Sparse beamforming and user-centric clustering for downlink cloud radio access network. IEEE Access, 2014, 2: 1326-1339 CrossRef Google Scholar

[7] Zhang X, Zhang Y, Yu R, et al. Enhancing spectral-energy efficiency for LTE-advanced heterogeneous networks: a users social pattern perspective. IEEE Wirel Commun, 2012, 21: :-17 Google Scholar

[8] Zhang X, Yu R, Zhang Y, et al. Energy-efficient multimedia transmissions through base station cooperation over heterogeneous cellular networks exploiting user behavior. IEEE Wirel Commun, 2014, 21: 54-61 Google Scholar

[9] Xing C, Ma S, Zhou Y. Matrix-monotonic optimization for MIMO systems. IEEE Trans Signal Process, 2015, 63: 334-348 CrossRef Google Scholar

[10] Dai M, Kwan H Y, Sung C W. Linear network coding strategies for the multiple-access relay channel with packet erasures. IEEE Trans Wirel Commun, 2013, 12: 218-227 CrossRef Google Scholar

[11] Suryaprakash V, Rost P, Fettweis G. Are heterogeneous cloud-based radio access networks cost effictive? IEEE J Sel Areas Commun, 2015, 33: 2239--2240. Google Scholar

[12] Peng M, Xie X, Hu Q, et al. Contract-based interference coordination in heterogeneous cloud radio access networks. IEEE J Sel Areas Commun, 2015, 33: 1140-1153 CrossRef Google Scholar

[13] Vu T X, Nguyen H D, Quek T Q S. Adaptive compression and joint detection for fronthaul uplinks in cloud radio access networks. IEEE Trans Commun, 2015, 63: 4565-4575 CrossRef Google Scholar

[14] Xie X, Peng M, Wang W, et al. Training design and channel estimation in uplink cloud radio access networks. IEEE Signal Process Lett, 2015, 22: 1060-1064 CrossRef Google Scholar

[15] Liu L, Zhang R. Optimized uplink transmission in multi-antenna C-RAN with spatial compression and forward. IEEE Trans Signal Process, 2015, 63: 5083-5095 CrossRef Google Scholar

[16] Li N, Fei Z, Xing C, et al. Robust low-complexity MMSE precoding algorithm for cloud radio access networks. IEEE Commun Lett, 2014, 18: 773-776 CrossRef Google Scholar

[17] Li J, Peng M, Cheng A, et al. Resource allocation optiization for delay-sensitive traffic in fronthaul constrained cloud radio-access networks. IEEE Syst J, 2014, 99: 1-12 Google Scholar

[18] Liu L, Bi S, Zhang R. Joint power control and fronthaul rate allocation for throughput maximization in OFDMA-based cloud radio access network. IEEE Trans Commun, 2015, 63: 4097-4110 CrossRef Google Scholar

[19] Garcia V, Zhou Y, Shi J. Coordinated multipoint transmission in dense cellular networks with user-centric adaptive clustering. IEEE Trans Wirel Commun, 2014, 13: 4297-4308 CrossRef Google Scholar

[20] Kim T M, Sun F, Paulraj A J. Low-complexity MMSE precoding for coordinated multipoint with per-antenna power constraint. IEEE Signal Process Lett, 2013, 20: 395-398 CrossRef Google Scholar

[21] Wang D M, Zhang Y, Wei H, et al. An overview of transmission theory and techniques of large-scaleantenna systems for 5G wireless communications. Sci China Inf Sci, 2016, 46: 081301-398 Google Scholar

[22] Rao X, Lau V K N. Distributed fronthaul compression and joint signal recovery in cloud-RAN. IEEE Trans Signal Process, 2015, 63: 1056-1065 CrossRef Google Scholar

[23] Zhou Y, Yu W. Optimized backhaul compression for uplink cloud radio access network. IEEE J Sel Areas Commun, 2014, 32: 1295-1307 CrossRef Google Scholar

[24] Chen W, Wassell I J. Optimized node selection for compressive sleeping wireless sensor networks. IEEE Trans Veh Tech, 2016, 65: :-836 Google Scholar

[25] Wu J, Bao Y, Miao G, et al. Base-station sleeping control and power matching for energy-delay tradeoffs with bursty traffic. IEEE Transx Veh Tech, 2016, 65: 3657-3675 CrossRef Google Scholar

qqqq

Contact and support