HyNet Advanced Network Colloquium: Chandra Murthy, "Reciprocal Multiple Antenna Wireless Links"

Monday, December 17, 2012
3:00 p.m.
1146 A.V. Williams Building
Sennur Ulukus
301 405 4909
ulukus@umd.edu

Chandra R. Murthy
Department of Electrical and Computer Engineering
Indian Institute of Science

How much training is needed in reciprocal multiple antenna wireless links?

Host
Sennur Ulukus

Abstract
In this talk, we discuss the problem of data transmission in a single input, multiple output wireless communication system with channel estimation at both transmitter and receiver, from a Diversity- Multiplexing gain Trade-off (DMT) perspective. We consider a two-way communication system with time-division duplexing, which makes it possible to acquire Channel State Information (CSI) at the transmitter through reverse channel training, i.e., by sending a training signal from the receiver to the transmitter. We propose several schemes for reverse channel training where the training signal is dependent on the receiver's knowledge of CSI (which could be acquired, for example, through an initial forward-link training phase). We explicitly account for the time-overhead in the training schemes, and show that our proposed reverse training and power control schemes offer a significant improvement in terms of DMT compared to conventional channel-agnostic training schemes. Thus, we argue that our proposed reverse channel training schemes are a promising technique to improve the reliability and throughput of current and future wireless communication systems. This is joint work with Bharath Bettagere.

Biography
Chandra R. Murthy is an Assistant Professor in the department of Electrical Communication Engineering at the Indian Institute of Science, Bangalore, India. His research interests are primarily in the areas of digital signal processing, information theory, estimation theory, and their applications in the optimization of MIMO, OFDM and CDMA wireless communication systems.

Audience: Graduate  Undergraduate  Faculty  Post-Docs  Alumni 

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