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Arogyaswami Paulraj

Researcher at Stanford University

Publications -  476
Citations -  41919

Arogyaswami Paulraj is an academic researcher from Stanford University. The author has contributed to research in topics: MIMO & Communication channel. The author has an hindex of 97, co-authored 476 publications receiving 41068 citations. Previous affiliations of Arogyaswami Paulraj include Bharat Electronics & University of Maryland, College Park.

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Proceedings ArticleDOI

On the ergodic sum-rate performance of CDD in multi-user systems

TL;DR: This work derives closed form expressions for the ergodic sum-rate of multi-user CDD and compares it with the sum-capacity and shows that in contrast to what is conventionally known regarding the single-user case, transmit diversity schemes are viable candidates for high rate transmission in multi- user systems.
Proceedings ArticleDOI

Effect of customer premises directional antennas on fixed wireless access systems in the downlink multipath channel

TL;DR: This paper demonstrates and compares the downlink performance of the directional antenna at the CPE under different AOA conditions and calculates theperformance of the adaptive array antenna deployed at theCPE and its beamwidth effect.
Proceedings ArticleDOI

Diversity coding with interference avoidance

TL;DR: It is shown that in the co-operative case, it is possible to find Pareto dominant solutions where the users either maintain or improve their symbol error rate performance.
Proceedings ArticleDOI

CDMA multiuser detection based on state-space estimation techniques

TL;DR: The receiver is based on the fixed-lag smoothed estimator which occurs in the well-researched area of state-space estimation and can work with long codes as opposed to most other multiuser detectors that require cyclic spreading codes.

ResearchArticle Efficient Near Maximum-Likelihood Detection for Underdetermined MIMO Antenna Systems Using a Geometrical Approach

TL;DR: In this article, an efficient detection strategy for underdetermined MIMO systems is proposed based on the geometrical understanding that the ML point happens to be a point that is close to the decoding hyperplane in all directions.