FIRE: Enabling reciprocity for FDD MIMO systems

Zikun Liu, Gagandeep Singh, Chenren Xu, Deepak Vasisht

Research output: Contribution to conferencePaperpeer-review


Massive MIMO forms a crucial component for 5G because of its ability to improve quality of service and support multiple streams simultaneously. However, for real-world MIMO deployments, estimating the downlink wireless channel from each antenna on the base station to every client device is a critical bottleneck, especially for the widely used frequency duplexed designs that cannot utilize reciprocity. Typically, this channel estimation requires explicit feedback from client devices and is prohibitive for large antenna deployments. In this paper, we present FIRE, a system that uses an end-to-end machine learning approach to enable accurate channel estimation without requiring any feedback from client devices. FIRE is interpretable, accurate, and has low compute overhead. We show that FIRE can successfully support MIMO transmissions in a real-world testbed and achieves SNR improvement over 10 dB in MIMO transmissions compared to the current state-of-the-art.

Original languageEnglish (US)
Number of pages14
StatePublished - 2021
Event27th ACM Annual International Conference On Mobile Computing And Networking, MobiCom 2021 - New Orleans, United States
Duration: Oct 25 2021Oct 29 2021


Conference27th ACM Annual International Conference On Mobile Computing And Networking, MobiCom 2021
Country/TerritoryUnited States
CityNew Orleans

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Software


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