2012
DOI: 10.1109/wcl.2012.032312.120203
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Designing Good Multi-Dimensional Constellations

Abstract: In this letter we consider the design of multidimensional compact constellations that minimize the average symbol energy for a given minimum Euclidian distance between constellation points. We formulate the constellation design as a non-convex quadratically constrained quadratic programming. We propose a simple and efficient optimization method, which offers good solutions for small to medium sized constellations.

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Cited by 77 publications
(100 citation statements)
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“…This means that f (C)| s (1) ≤ f (C)| s (0) . The algorithm stops when ||s (k) −s (k+1) || < ∆ for some k, where ∆ is an arbitrarily small constant [14], [15].…”
Section: Proposed Methodsmentioning
confidence: 99%
“…This means that f (C)| s (1) ≤ f (C)| s (0) . The algorithm stops when ||s (k) −s (k+1) || < ∆ for some k, where ∆ is an arbitrarily small constant [14], [15].…”
Section: Proposed Methodsmentioning
confidence: 99%
“…Function (13) means that the decision is a joint decision about pair [d, d ′ ]. This type of objective function use strategy [12].…”
Section: Optimization Problemmentioning
confidence: 99%
“…Optimization problem (12) is a standard constellation design problem which is a non-convex quadratically constrained NP hard minimax problem [13]. First of all we reformulate the minimax problem: instead of maximizing minimum, we maximize auxiliary variable and include extra conditions that the auxiliary variable is lower or equal than all the conditions over which minimum operator was assumed.…”
Section: Optimization Toolsmentioning
confidence: 99%
“…The authors in [9] have considered the design of multidimensional compact constellations for minimizing the average symbol energy for a given minimum Euclidian distance between constellation points. In [10], different constellations with different mapping have been compared based on symbol error probability in the presence of strong phase noise.…”
Section: Introductionmentioning
confidence: 99%