On the power leakage problem in millimeter-wave massive MIMO with lens antenna arrays

Tian Xie, Linglong Dai, Derrick Wing Kwan Ng, Chan Byoung Chae

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)


The emerging millimeter-wave massive multipleinput multiple-output (MIMO) with lens antenna arrays, which is also known as "beamspace MIMO,"can effectively reduce the required number of power-hungry radio frequency (RF) chains. Therefore, it has been considered as a promising technique for the upcoming 5G communications and beyond. However, most current studies on beamspace MIMO have not taken into account the important power leakage problem in beamspace channels, which possibly leads to a significant degradation in the signal-to-noise ratio (SNR) and the system sum-rate. To this end, in this paper, we propose a beam-aligning precoding method to handle the power leakage problem. First, a phase shifter network (PSN) structure is proposed, which enables each RF chain in beamspace MIMO to select multiple beams to collect the leakage power. Then, a rotationbased precoding algorithm is designed based on the proposed PSN structure, which aligns the channel gains of the selected beams toward the same direction for maximizing the received SNR at each user. Furthermore, we reveal some system design insights by analyzing the sum-rate and energy efficiency (EE) of the proposed beam-aligning precoding method. In simulations, the proposed approach is found to achieve the near-optimal sum-rate performance compared with that of the ideal case of no power leakage, and obtains a higher EE than those of the existing schemes with either a linear or planar array.

Original languageEnglish
Article number8754803
Pages (from-to)4730-4744
Number of pages15
JournalIEEE Transactions on Signal Processing
Issue number18
Publication statusPublished - 2019 Sept

Bibliographical note

Funding Information:
Manuscript received December 18, 2018; revised April 8, 2019 and May 29, 2019; accepted June 11, 2019. Date of publication July 3, 2019; date of current version August 10, 2019. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Laura Cottatellucci. This work was supported in part by the National Science and Technology Major Project of China under Grant 2018ZX03001004-003, in part by the National Natural Science Foundation of China for Outstanding Young Scholars under Grant 61722109, in part by the National Natural Science Foundation of China under Grant 61571270, and in part by the Royal Academy of Engineering through the UK-China Industry Academia Partnership Programme Scheme Grant UK-CIAPP\49. The work of D. W. K. Ng was supported in part by the UNSW Digital Grid Futures Institute, UNSW, Sydney, under a cross disciplinary fund scheme, and in part by the Australian Research Council’s Discovery Project DP190101363. The work of C.-B. Chae was supported by Institute for Information and Communications Technology Promotion (IITP) under Grants 2018-0-00170 and 2016-0-00208. This paper was presented in part at the 86th IEEE Vehicular Technology Conference, Toronto, ON, Canada, September 2017 [1]. (Corresponding author: Linglong Dai.) T. Xie was with the Beijing National Research Center for Information Science and Technology (BNRist), Department of Electronic Engineering, Tsinghua University, Beijing 100084, China. He is now with the China Mobile Research Institute, Beijing 100053, China (e-mail: xietian@chinamobile.com).

Publisher Copyright:
© 2019 IEEE.

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering


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