Record Details

Interference Management For Vector Gaussian Multiple Access Channels

Electronic Theses of Indian Institute of Science

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Field Value
 
Title Interference Management For Vector Gaussian Multiple Access Channels
 
Creator Padakandla, Arun
 
Subject Multiple Access Channel (MAC)
Multiplexing
Data Communication Protocols
Multiple Access Channels - Power Minimization
Convex Optimization
Multi-dimensional Signaling
Colored Noise
Communication Engineering
 
Description In this thesis, we consider a vector Gaussian multiple access channel (MAC) with users demanding reliable communication at specific (Shannon-theoretic) rates. The objective is to assign vectors and powers to these users such that their rate requirements are met and the sum of powers received is minimum.
We identify this power minimization problem as an instance of a separable convex optimization problem with linear ascending constraints. Under an ordering condition on the slopes of the functions at the origin, an algorithm that determines the optimum point in a finite number of steps is described. This provides a complete characterization of the minimum sum power for the vector Gaussian multiple access channel. Furthermore, we prove a strong duality between the above sum power minimization problem and the problem of sum rate maximization under power constraints.
We then propose finite step algorithms to explicitly identify an assignment of vectors and powers that solve the above power minimization and sum rate maximization problems. The distinguishing feature of the proposed algorithms is the size of the output vector sets. In particular, we prove an upper bound on the size of the vector sets that is independent of the number of users.
Finally, we restrict vectors to an orthonormal set. The goal is to identify an assignment of vectors (from an orthonormal set) to users such that the user rate requirements is met with minimum sum power. This is a combinatorial optimization problem. We study the complexity of the decision version of this problem. Our results indicate that when the dimensionality of the vector set is part of the input, the decision version is NP-complete.
 
Contributor Sundaresan, Rajesh
 
Date 2010-06-03T06:14:14Z
2010-06-03T06:14:14Z
2010-06-03
2008-03
 
Type Thesis
 
Identifier http://etd.iisc.ernet.in/handle/2005/702
 
Language en_US
 
Relation G22344