1,809 research outputs found
Reduced Complexity Sphere Decoding
In Multiple-Input Multiple-Output (MIMO) systems, Sphere Decoding (SD) can
achieve performance equivalent to full search Maximum Likelihood (ML) decoding,
with reduced complexity. Several researchers reported techniques that reduce
the complexity of SD further. In this paper, a new technique is introduced
which decreases the computational complexity of SD substantially, without
sacrificing performance. The reduction is accomplished by deconstructing the
decoding metric to decrease the number of computations and exploiting the
structure of a lattice representation. Furthermore, an application of SD,
employing a proposed smart implementation with very low computational
complexity is introduced. This application calculates the soft bit metrics of a
bit-interleaved convolutional-coded MIMO system in an efficient manner. Based
on the reduced complexity SD, the proposed smart implementation employs the
initial radius acquired by Zero-Forcing Decision Feedback Equalization (ZF-DFE)
which ensures no empty spheres. Other than that, a technique of a particular
data structure is also incorporated to efficiently reduce the number of
executions carried out by SD. Simulation results show that these approaches
achieve substantial gains in terms of the computational complexity for both
uncoded and coded MIMO systems.Comment: accepted to Journal. arXiv admin note: substantial text overlap with
arXiv:1009.351
Iterative decoding for MIMO channels via modified sphere decoding
In recent years, soft iterative decoding techniques have been shown to greatly improve the bit error rate performance of various communication systems. For multiantenna systems employing space-time codes, however, it is not clear what is the best way to obtain the soft information required of the iterative scheme with low complexity. In this paper, we propose a modification of the Fincke-Pohst (sphere decoding) algorithm to estimate the maximum a posteriori probability of the received symbol sequence. The new algorithm solves a nonlinear integer least squares problem and, over a wide range of rates and signal-to-noise ratios, has polynomial-time complexity. Performance of the algorithm, combined with convolutional, turbo, and low-density parity check codes, is demonstrated on several multiantenna channels. The results for systems that employ space-time modulation schemes seem to indicate that the best performing schemes are those that support the highest mutual information between the transmitted and received signals, rather than the best diversity gain
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