Partially Linear Bayesian Estimation Using Mixed-Resolution Data

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

Partially Linear Bayesian Estimation Using Mixed-Resolution Data

By: 
Itai E. Berman; Tirza Routtenberg

In this letter, we consider Bayesian parameterestimation using mixed-resolution data consisting of both analog and 1-bit quantized measurements. We investigate the use of the partially linear minimum mean-squared-error (PL-MMSE) estimator for this mixed-resolution scheme. The use of the PL-MMSE estimator, proposed for general models with “straightforward” and “complicated” parts, has not been demonstrated for quantized data. We derive closed-form analytic expressions for the linear minimum mean-squared-error (LMMSE) and for the PL-MMSE estimator for the mixed-resolution scheme with linear Gaussian orthonormal measurements. We discuss the properties of the proposed PL-MMSE estimator and show that in this case, the PL-MMSE is the sum of a linear function of the quantized measurements and a general Borel measurable function of the analog measurements. In the simulations, we show that the PL-MMSE estimator outperforms the LMMSE estimator for the problem of channel estimation in multiple-input-multiple-output (MIMO) communication systems with mixed analog-to-digital converters (ADCs).

SPS on Twitter

  • The DEGAS Webinar Series continues this Thursday, 27 January when Dr. Michael Bronstein presents "Neural diffusion… https://t.co/rljqz7qfis
  • The Brain Space Initiative Talk Series continues on Friday, 28 January when Dr. Russell A. Poldrack presents "Towar… https://t.co/r8ykdh9Vgh
  • Attention students! The 2022 5-Minute Video Clip Contest begins soon! This year's topic, "Graph Signal Processing a… https://t.co/QTMqxDaudy
  • Students, it's time to form your teams! The 2022 Signal Processing Cup competition is underway. This year's topic,… https://t.co/fVw7tA7zTG
  • The DEGAS Webinar Series continues this Thursday, 13 January when Peter Battaglia presents "Modeling Physical Struc… https://t.co/Kndvzl8BpE

SPS Videos


Signal Processing in Home Assistants

 


Multimedia Forensics


Careers in Signal Processing             

 


Under the Radar