The Journal of China Universities of Posts and Telecommunications ›› 2020, Vol. 27 ›› Issue (2): 10-18.doi: 10.19682/j.cnki.1005-8885.2020.1002

• Wireless • Previous Articles     Next Articles

Interference suppression for ultra dense network based on compressive sensing framework

Hou Huanhuan, Jiang Jing, Lei Ming, Liu Ben   

  1. School of Communications and Information Engineering, Xi'an Universityof Posts and Telecommunications, Xi'an 710061, China
  • Received:2018-08-06 Revised:2020-05-13 Online:2020-04-30 Published:2020-07-07
  • Contact: Hou Huanhuan, E-mail: houhh321@163.com E-mail:houhh321@163.com
  • About author:Hou Huanhuan, E-mail: houhh321@163.com
  • Supported by:
    This work was supported by National Science and Technology Major Project (2016ZX03001016), Innovation Team Project of Shaanxi Province (2017KCT-30-02), National Natural Science Foundation of China (6187012068).

Abstract: Ultra-dense network (UDN) deployment of small cells introduces novel technical challenges, one of which is that the interference levels increase considerably with the network density. This paper proposes interference suppression scheme based on compressive sensing (CS) framework for UDN. Firstly, the measurement matrix is designed by exploiting the sparsity of millimeter wave channels. CS technique is employed to transform the high dimension sparse signal into low dimension signal. Then, the interference is canceled in the compressed domain. Finally, the stagewise weak orthogonal matching pursuit (SWOMP) algorithm is used to reconstruct the useful signal after interference suppression. The analysis and simulation results demonstrate the effectiveness of the algorithm. Simulation results demonstrate that the proposed interference suppression in compressive domain yields performance gains compared to other classical interference suppression schemes. The proposed algorithm can reduce the computational complexity of interference suppression algorithm.

Key words: interference suppression, CS, signal reconstruction, UDN