Linear precoder designs for K-user interference channels

This paper studies linear precoding and decoding schemes for K-user interference channel systems. It was shown by Cadambe and Jafar that the interference alignment (IA) algorithm achieves a theoretical bound on degrees of freedom (DOF) for interference channel systems. Based on this, we first introd...

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Bibliographic Details
Published in:IEEE transactions on wireless communications 2010-01, Vol.9 (1), p.291-301
Main Authors: SUNG, Hakjea, PARK, Seok-Hwan, LEE, Kyoung-Jae, LEE, Inkyu
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Channels
Coding, codes
Decoding
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Filtering
gradient decent
Heuristic
Information theory
Information, signal and communications theory
Interference
interference alignment (IA)
Interference channel
Interference channels
Iterative algorithms
Iterative decoding
linear precoding
Mathematical analysis
Matrices
minimum mean-square error (MMSE) filtering
Nonlinear filters
Optimization methods
Signal and communications theory
Signal to noise ratio
Signal, noise
Studies
System performance
Telecommunications and information theory
Transmitters
Wireless communication
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Summary:This paper studies linear precoding and decoding schemes for K-user interference channel systems. It was shown by Cadambe and Jafar that the interference alignment (IA) algorithm achieves a theoretical bound on degrees of freedom (DOF) for interference channel systems. Based on this, we first introduce a non-iterative solution for the precoding and decoding scheme. To this end, we determine the orthonormal basis vectors of each user's precoding matrix to achieve the maximum DOF, then we optimize precoding matrices in the IA method according to two different decoding schemes with respect to individual rate. Second, an iterative processing algorithm is proposed which maximizes the weighted sum rate. Deriving the gradient of the weighted sum rate and applying the gradient descent method, the proposed scheme identifies a local-optimal solution iteratively. Simulation results show that the proposed iterative algorithm outperforms other existing methods in terms of sum rate. Also, we exhibit that the proposed non-iterative method approaches a local optimal solution at high signal-to-noise ratio with reduced complexity.
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2010.01.090221