Nested Hybrid Arrays and Bayesian Inference for Robust Communications
Abstract
The central objective of this project is to integrate innovations in new hybrid antenna array architecture and robust Bayesian algorithms to enhance the communication capabilities of next generation mission-critical systems. Nested architecture will be used as the" guiding framework for developing new non-uniform hybrid arrays with unique sub-array geometries. Concurrently, the problem of hybri"d beamforming with such arrays will be addressed by developing aunified optimization framework. Unlike traditional digital beamform"ing, hybrid beamforming problems are inherently nonconvex.This project will develop a systematic theoretical and algorithmic framewo"rk to solve and analyze them using semidefinite relaxation techniques. The proposed research will advance the state-of-the art in beamforming techniquesand offer new perspectives into hybrid beamformers. N ested architectures also overcome a long-standing bottlen"eck of uniform linear (or rectangular arrays) by allowing the localization of more sources than sensors. However, such enhanced capa""bilities typically require larger amount of training data (temporal snapshots). To address this issue, Sparse Bayesian Learning base"d channel estimation techniques will be developed that can improve the performance of nested arrays in the sample-starved regime and" enable robust and reliable inference. Sparse Bayesian Learning techniques unify a large family of statistical priors/signal models," and can naturally exploit the correlation structure of the data. The project will also generalize these techniques from all-digital" to hybrid settings, leading to the development of fast algorithms, and new mathematical tools to analyze such hybrid inference prob""lems. It is understood that any developmental items and specially designed parts, components, accessories and attachments fabricated" under any Department of Defense award resulting from this proposal are being developed for civil and potential military applications.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Dec 20, 2017
- Source ID
- N000141812038
Entities
People
- Piya Pal
Organizations
- Office of Naval Research
- United States Navy
- University of California, San Diego