Maximum-likelihood (ML) decoding often reduces to finding the closest (skewed) lattice point in N-dimensions to a given point x ϵ C^N. Sphere decoding is an algorithm that does this. We modify the sphere decoder to reduce the computational complexity of decoding while maintaining near-ML performance
The maximum-likelihood (ML) detection problem for channels with memory is investigated. The Viterbi ...
International audienceDespite its reduced complexity, lattice reduction-aided decoding exhibits a wi...
We consider joint maximum-likelihood (ML) detection and decoding in multiple-input multiple-output (...
In many communications problems, maximum-likelihood (ML) decoding reduces to finding the closest (sk...
In many communications problems, maximum-likelihood (ML) decoding reduces to nding the closest (skew...
Maximum-likelihood (ML) decoding often reduces to solving an integer least-squares problem, which is...
It is well known that maximum-likelihood (ML) decoding in many digital communication schemes reduces...
It is well known that maximum-likelihood (ML) decoding in many digital communication schemes reduces...
Most of the calculations in standard sphere decoders are redundant in the sense that they either cal...
The problem of finding the closest lattice point arises in several communications scenarios and is k...
In many communications applications, maximum-likelihood decoding reduces to solving an integer least...
In wireless communications the transmitted signals may be affected by noise. The receiver must decod...
In Part 1, we found a closed-form expression for the expected complexity of the sphere-decoding algo...
Inmany communication applications, maximum-likelihood decoding reduces to solving an integer least-s...
Despite its reduced complexity, lattice reduction-aided decoding exhibits a widening gap to maximum-...
The maximum-likelihood (ML) detection problem for channels with memory is investigated. The Viterbi ...
International audienceDespite its reduced complexity, lattice reduction-aided decoding exhibits a wi...
We consider joint maximum-likelihood (ML) detection and decoding in multiple-input multiple-output (...
In many communications problems, maximum-likelihood (ML) decoding reduces to finding the closest (sk...
In many communications problems, maximum-likelihood (ML) decoding reduces to nding the closest (skew...
Maximum-likelihood (ML) decoding often reduces to solving an integer least-squares problem, which is...
It is well known that maximum-likelihood (ML) decoding in many digital communication schemes reduces...
It is well known that maximum-likelihood (ML) decoding in many digital communication schemes reduces...
Most of the calculations in standard sphere decoders are redundant in the sense that they either cal...
The problem of finding the closest lattice point arises in several communications scenarios and is k...
In many communications applications, maximum-likelihood decoding reduces to solving an integer least...
In wireless communications the transmitted signals may be affected by noise. The receiver must decod...
In Part 1, we found a closed-form expression for the expected complexity of the sphere-decoding algo...
Inmany communication applications, maximum-likelihood decoding reduces to solving an integer least-s...
Despite its reduced complexity, lattice reduction-aided decoding exhibits a widening gap to maximum-...
The maximum-likelihood (ML) detection problem for channels with memory is investigated. The Viterbi ...
International audienceDespite its reduced complexity, lattice reduction-aided decoding exhibits a wi...
We consider joint maximum-likelihood (ML) detection and decoding in multiple-input multiple-output (...
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