Repository logo
English
  • English
  • Català
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Italiano
  • Latviešu
  • Magyar
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Suomi
  • Svenska
  • Türkçe
  • Tiếng Việt
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Yкраї́нська
  • Log In
    or
    New user? Click here to register.Have you forgotten your password?
Repository logo
  • Communities & Collections
  • All of R-3
English
  • English
  • Català
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Italiano
  • Latviešu
  • Magyar
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Suomi
  • Svenska
  • Türkçe
  • Tiếng Việt
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Yкраї́нська
  • Log In
    or
    New user? Click here to register.Have you forgotten your password?
  1. Home
  2. Browse by Author

Browsing by Author "Zilberstein, Nicolas M"

Now showing 1 - 1 of 1
Results Per Page
Sort Options
  • Loading...
    Thumbnail Image
    Item
    Annealed Langevin Dynamics for MIMO Communications
    (2024-01-29) Zilberstein, Nicolas M; Segarra, Santiago; Sabharwal, Ashutosh
    Solving the optimal data detection problem in multiple-input multiple-output (MIMO) systems is known to be NP-hard. Moreover, the difficulty is exacerbated when the channel state information is unavailable. In this work we propose a MIMO detector for the two scenarios, namely when the CSI is known and when it is unknown. First, for the case of perfect CSI, we proposed a MIMO detector based on an annealed version of Langevin dynamics. More precisely, we define a stochastic dynamical process whose stationary distribution coincides with the posterior distribution of the data given our observations. This allows us to approximate the maximum a posteriori estimator of the transmitted symbols by sampling from the proposed Langevin dynamic. We carefully craft this stochastic dynamic by gradually adding a sequence of noise with decreasing variance to the trajectories, which ensures that the estimated symbols belong to a pre-specified discrete constellation. Second, for the case of unknown CSI, we propose a joint data detection and channel estimation solution, where we define an annealed Langevin diffusion whose stationary distribution is the joint posterior of the channels and data given noisy observations.
  • About R-3
  • Report a Digital Accessibility Issue
  • Request Accessible Formats
  • Fondren Library
  • Contact Us
  • FAQ
  • Privacy Notice
  • R-3 Policies

Physical Address:

6100 Main Street, Houston, Texas 77005

Mailing Address:

MS-44, P.O.BOX 1892, Houston, Texas 77251-1892