Reaping the benefits of dynamic TDD in massive MIMO

Yan Huang, Brian Jalaian, Stephen Russell, Hooman Samani

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Recent advances in massive multiple-input multiple-output (MIMO) communication show that equipping base stations (BSs) with large antenna arrays can significantly improve the performance of cellular networks. Massive MIMO has the potential to mitigate the interference in the network and enhance the average throughput per user. On the other hand, dynamic time-division duplexing (TDD), which allows neighboring cells to operate with different uplink (UL) and downlink (DL) subframe configurations, is a promising enhancement for the conventional static TDD. Compared with static TDD, dynamic TDD can offer more flexibility to accommodate various UL and DL traffic patterns across different cells, but may result in additional interference among cells transmitting in different directions. Based on the unique characteristics and properties of massive MIMO and dynamic TDD, we propose a marriage of these two techniques, i.e., to have massive MIMO address the limitation of dynamic TDD in macrocell (MC) networks. Specifically, we advocate that the benefits of dynamic TDD can be fully extracted in MC networks equipped with massive MIMO, i.e., the BS-to-BS interference can be effectively removed by increasing the number of BS antennas. We provide detailed analysis using random matrix theory to show that the effect of the BS-to-BS interference on UL transmissions vanishes as the number of BS antennas per user grows infinitely large. Last but not least, we validate our analysis by numerical simulations.

Original languageEnglish
Article number8386423
Pages (from-to)117-124
Number of pages8
JournalIEEE Systems Journal
Volume13
Issue number1
DOIs
Publication statusPublished - Mar 2019

Keywords

  • Dynamic time-division duplexing (TDD)
  • Interference suppression
  • Massive multiple-input multiple-output (MIMO)
  • Pilot contamination
  • Random matrix theory

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