Signal Processing for Wireless Basestations

Brian L. Evans, Srikanth Gummadi, and Murat Torlak
Embedded Signal Processing Laboratory
The University of Texas at Austin, Austin, TX

Thursday, Oct. 15th, 11:00 AM
Texas A&M University Telecommunications Seminar

Slides: Part I - Part II - Part III


This talk presents two techniques for channel estimation in wireless basestations using smart antenna systems. Smart antennas apply spatial array signal processing techniques at the antenna array to improve the performance of the physical communications layer. Both techniques use the antenna array to locate mobile users.

The first technique discusses mobile localization using 2D ESPRIT estimation technique at wireless basestations. Many methods for mobile user localization are based on DOA and/or time difference of arrival (TDOA) estimation. A key application of mobile positioning is personal safety, such as emergency localization (wireless E-911 service) and automatic location identification of cell phone users. Mobile positioning can also be used for advanced user hand-off schemes, improving spectral efficiency, link quality, and battery life. A 2D unitary ESPRIT algorithm can be used to jointly estimate the direction-of-arrival (DOA) and time-of-arrival (TOA) of received signals at the antenna array.

The second technique employs blind estimation for asynchronous Code-Division Multiple-Access (CDMA) systems. CDMA finds use in the IS-95 standard and the emerging third-generation wireless communications standards. In a synchronous CDMA model, all mobile radio signals arriving at the basestation are synchronized to within a fraction of a chip time interval, which is typically 200 ns. The use of orthogonal codewords can greatly enhance performance. For large cells with large multipath delays, synchronization may be extremely difficult, and a better channel model is asynchronous.

For Asynchronous CDMA, the receiver should suppress (1) multipath-induced interchip interference (ICI), which causes the intersymbol interference (ISI), and (2) highly structured multiple user interference (MUI). Conventional approaches use training sequences which are sent periodically. Although subspace-based algorithms can eliminate training sequences, they only apply when the basestation is lightly loaded or when a few users are active. We develop a blind subspace-based algorithm that works for overloaded systems, where the number of users exceeds the spreading factor.

Last updated 10/15/98.