An Optimized-Hierarchy-Aided Maximum Likelihood Detector for MIMO-OFDM
An Optimized-Hierarchy-Aided Maximum Likelihood Detector for MIMO-OFDM
In this paper we propose a novel low-complexity Maximum Likelihood (ML) space-time detection method, which can be regarded as an advanced extension of the Complex Sphere Decoder (CSD). We demonstrate that as opposed to the previously published variants of the CSD, the proposed technique may be employed in the so-called "over-loaded" scenario, where the number of transmit antennas exceeds that of the receive antennas. The proposed method achieves the optimum performance of the ML detector even in heavily over-loaded scenarios, while the associated computational complexity is only moderately increased.
1526-1530
Akhtman, J.
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Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1
2006
Akhtman, J.
d4fd2b26-c123-463d-847c-80adc83a89fa
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1
Akhtman, J. and Hanzo, L.
(2006)
An Optimized-Hierarchy-Aided Maximum Likelihood Detector for MIMO-OFDM.
IEEE VTC'06 (Spring), Melbourne, Australia.
07 - 10 May 2006.
.
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Conference or Workshop Item
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Abstract
In this paper we propose a novel low-complexity Maximum Likelihood (ML) space-time detection method, which can be regarded as an advanced extension of the Complex Sphere Decoder (CSD). We demonstrate that as opposed to the previously published variants of the CSD, the proposed technique may be employed in the so-called "over-loaded" scenario, where the number of transmit antennas exceeds that of the receive antennas. The proposed method achieves the optimum performance of the ML detector even in heavily over-loaded scenarios, while the associated computational complexity is only moderately increased.
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Published date: 2006
Venue - Dates:
IEEE VTC'06 (Spring), Melbourne, Australia, 2006-05-07 - 2006-05-10
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 263063
URI: http://eprints.soton.ac.uk/id/eprint/263063
PURE UUID: 1f472a79-b87c-4915-9912-3a7ef92345e6
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Date deposited: 04 Oct 2006
Last modified: 18 Mar 2024 02:34
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Contributors
Author:
J. Akhtman
Author:
L. Hanzo
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