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


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)



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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