A New RF Beam Training Method and Asymptotic Performance Analysis for Multi-user Millimeter Wave Systems

Taeseok Oh, Changick Song, Jaehoon Jung, Minki Ahn, Joonsuk Kim, Inkyu Lee

Research output: Contribution to journalArticle


In this paper, we consider a multi-user (MU) radio frequency (RF) beam training scenario with a user selection. We propose a new beam training scheme that alleviates the latency issue in the conventional IEEE 802.11ad by adopting a sequential downlink-downlink transmit sector sweep combination. Then, we analyze the average rate performance of several RF beam training schemes in two different asymptotic scenarios. Also, we characterize the performance gain of the proposed method over other schemes. Our analytic results confirm that the proposed method achieves the average rate performance of the optimal full-search method in the asymptotic region with much reduced training overhead. It is shown from simulation results that the proposed scheme outperforms the conventional beam training schemes and achieves a 35% performance gain over the optimal full search scheme when considering the beam training overhead in practical MU mmWave channel environments.We also confirm that our analytical results match well with the numerical results.

Original languageEnglish
JournalIEEE Access
Publication statusAccepted/In press - 2018 Aug 29


ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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