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SCIENCE CHINA Information Sciences, Volume 60, Issue 4: 040303(2017) https://doi.org/10.1007/s11432-016-9013-0

Towards win-win: weighted-Voronoi-diagram based channel quantization for security enhancement in downlink cloud-RAN with limited CSI feedback

More info
  • ReceivedDec 17, 2016
  • AcceptedJan 22, 2017
  • PublishedMar 17, 2017

Abstract

Physical layer (PHY) security is recently proved to enable improving the security of wireless communication networks. In downlink frequency division duplex (FDD) cloud radio access network (C-RAN), the performance of PHY security highly relies on the channel state information (CSI) which is usually acquired through the codebook-quantization-based technique at the transceiver. However, the conventional quantization method aggravates the leakage of privacy information in C-RAN under the eavesdropping environment. In this paper, a novel channel quantization method is investigated to improve the secrecy-rate performance of C-RAN by exploiting the high-dimension space geometry. Based on this method, it is proved that when the statistical distribution of the channel matrices of {both} the legitimate user {and} the eavesdropper is exploited, a win-win situation can be created where secrecy-rate gains are improved without sacrificing beamforming gains from the point of view of ergodic rate. Particularly, a secrecy-oriented criterion is devised to implement the proposed method for generating codebooks. Then a weighted Voronoi diagram (WVD) is formulated on the complex Grassmann manifold and finally, a vector quantization based algorithm is proposed to build up novel quantization codebooks iteratively. Simulation results further validate the superiority of our proposed codebooks over conventional codebooks in C-RAN systems.


Funded by

National Science and Technology Major Project(2016ZX03001016-005)

"source" : null , "contract" : "2014AA01A707"

National High-Tech R&D Program of China(863)

National Natural Science Foundation of China(61431011)


Acknowledgment

Acknowledgments

This work was supported by National Natural Science Foundation of China (Grant No. 61431011), National High-Tech R&D Program of China (863) (Grant No. 2014AA01A707), National Science and Technology Major Project (Grant No. 2016ZX03001016-005), and Fundamental Research Funds for the Central Universities.


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