Lattice code decoder for space-time codes

By Mohamed Oussama Damen, A. Chkeif, Jean-Claude Belfiore. IEEE Communications Letters, 2000

820 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/e7abe3b9ba0abd2d43497f9936a83f3130384a09

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Citations in the corpus

Switching between diversity and multiplexing in MIMO systems (2005, methodology): “A simple implementation would require searching over all possible transmitted code-words though more efﬁcient near-optimal methods are available [31].”
On the sphere-decoding algorithm I. Expected complexity (2005, background): “In [6], this question is studied in the context of V-BLAST, where it is shown that the exact solution significantly outperforms even the best heuristics.”
Channel Estimation for OFDM (2014, background): “The LSD can be considered as the soft output version of the sphere detector introduced in [159] and [160].”
A unified framework for tree search decoding: rediscovering the sequential decoder (2005, background): “This interest was primarily sparked by the connection between CLPS and maximum likelihood (ML) decoding in multiple-input multiple-output (MIMO) channels [ 12 ].”
Lattice coding and decoding achieve the optimal diversity-multiplexing tradeoff of MIMO channels (2004, background): “tice space-time codes in recent literature do not benefit from the generalization proposed in this paper, and hence, are more appropriately categorized as LD codes (e.g., [17], [ 18 ]).”
Perfect Space–Time Block Codes (2006, methodology): “The linearity property of the LD-STBC enables the use of maximumlikelihood (ML) sphere decoding [32], [20], which exploits the full performance of the code compared to other suboptimal decoders [ 8 ].”
Space-time diversity systems based on linear constellation precoding (2003, background): “It has been shown that for a fixed searching radius and for a given lattice structure, the decoding complexity for transmit antennas is approximately [ 6 ].”
An overview of MIMO communications - a key to gigabit wireless (2004, background): “Assuming equally likely, temporally uncoded vector symbols, the ML receiver forms its estimate of the transmitted signal vector according to (21) where the minimization is performed over all possible transmit vector symbols .”
Linear dispersion codes for MIMO systems based on frame theory (2002, methodology): “Lower complexity decoding with little loss is possible using spherical decoding techniques based on the theory of lattice decoders [20]–[22].”
Closest point search in lattices (2002, background): “The decoding of space–time codes is one example [16], [17], [25].”
Diagonal algebraic space-time block codes (2002, methodology): “Even though the complexity of the lattice decoder is, in general, larger than that of an error correcting code decoder, the sphere decoder yields a very good tradeoff between the complexity and the allowed spectral efficiency [10], [ 11 ].”
An optimal two transmit antenna space-time code and its stacked extensions (2003, methodology): “The linear dispersion codes also enable optimum decoding using the efficient sphere decoding algorithm [10].”
Algorithm and implementation of the K-best sphere decoding for MIMO detection (2006, methodology): “, Fincke–Pohst strategy [4], [7], [8] and Schnorr–Euchner strategy [9]–[11].”
A vector-perturbation technique for near-capacity multiantenna multiuser communication-part II: perturbation (2005, methodology): “The Fincke–Pohst algorithm is used for space–time demodulation in [15], where it is called a sphere decoder.”
On the complexity of sphere decoding in digital communications (2005, background): “In particular, they hold for the systems considered in [6], [7], [10], and [11].”
On maximum-likelihood detection and the search for the closest lattice point (2003, methodology): “We note that this column ordering has not been reported in previous papers on the subject [5], [ 8 ], [36], [22], [6].”
Achieving near-capacity on a multiple-antenna channel (2003, methodology): “Its application as a decoder for fading channels is described in [31], and as an ML decoder for multiple antenna channels in [5], [9], [32],”