By Topic

On the Overhead of Interference Alignment: Training, Feedback, and Cooperation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
El Ayach, O. ; Dept. of Electr. & Comput. Eng., Univ. of Texas at Austin, Austin, TX, USA ; Lozano, A. ; Heath, R.W.

Interference alignment (IA) is a cooperative transmission strategy that, under some conditions, achieves the interference channel's maximum number of degrees of freedom. Realizing IA gains, however, is contingent upon providing transmitters with sufficiently accurate channel knowledge. In this paper, we study the performance of IA in multiple-input multiple-output systems where channel knowledge is acquired through training and analog feedback. We design the training and feedback system to maximize IA's effective sum-rate: a non-asymptotic performance metric that accounts for estimation error, training and feedback overhead, and channel selectivity. We characterize effective sum-rate with overhead in relation to various parameters such as signal-to-noise ratio, Doppler spread, and feedback channel quality. A main insight from our analysis is that, by properly designing the CSI acquisition process, IA can provide good sum-rate performance in a very wide range of fading scenarios. Another observation from our work is that such overhead-aware analysis can help solve a number of practical network design problems. To demonstrate the concept of overhead-aware network design, we consider the example problem of finding the optimal number of cooperative IA users based on signal power and mobility.

Published in:

Wireless Communications, IEEE Transactions on  (Volume:11 ,  Issue: 11 )