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Element-based lattice reduction aided K-Best detector for large-scale MIMO systems

Element-based lattice reduction aided K-Best detector for large-scale MIMO systems
Element-based lattice reduction aided K-Best detector for large-scale MIMO systems
Recently, large-scale Multiple-Input Multiple-Output (MIMO) systems have caught great attention for increasing the system throughput as well as improving the system performance. The main challenge in the design of these MIMO systems is the detection techniques used at the receiver. Lattice Reduction (LR) techniques have shown good potential in MIMO decoding due to their good performance and low complexity compared to Maximum Likelihood (ML) detector. The Lenstra, Lanstra, and Lovasz (LLL) LR algorithm has been employed for decoding while combined with linear detectors such as ZF as well as with K-Best detection. However, the LLL-aided detectors have shown limited performance, when increasing the number of antennas at the transmitter and receiver. Therefore, in this paper we propose to use the so-called Element-based Lattice Reduction (ELR) combined with K-Best detector for the sake of attaining a better BER performance and lower complexity than the LLL-aided detection. Explicitly, the ELR-aided detectors are capable of attaining a 2 dB performance improvement at BER of 10?5 compared to the LLL-aided detectors when considering a MIMO system with 200 transmit and receive antennas. Furthermore, for the same MIMO configuration, the ELR basis update requires nearly an order of magnitude reduction in the number of arithmetic operations compared to the LLL algorithm.
Toma, Ogeen H.
fb9f5e82-cb9c-413e-942a-628497821b8c
El-Hajjar, Mohammed
3a829028-a427-4123-b885-2bab81a44b6f
Toma, Ogeen H.
fb9f5e82-cb9c-413e-942a-628497821b8c
El-Hajjar, Mohammed
3a829028-a427-4123-b885-2bab81a44b6f

Toma, Ogeen H. and El-Hajjar, Mohammed (2016) Element-based lattice reduction aided K-Best detector for large-scale MIMO systems At 2016 IEEE 17th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), United Kingdom. 03 - 06 Jul 2016. 5 pp. (doi:10.1109/SPAWC.2016.7536870).

Record type: Conference or Workshop Item (Paper)

Abstract

Recently, large-scale Multiple-Input Multiple-Output (MIMO) systems have caught great attention for increasing the system throughput as well as improving the system performance. The main challenge in the design of these MIMO systems is the detection techniques used at the receiver. Lattice Reduction (LR) techniques have shown good potential in MIMO decoding due to their good performance and low complexity compared to Maximum Likelihood (ML) detector. The Lenstra, Lanstra, and Lovasz (LLL) LR algorithm has been employed for decoding while combined with linear detectors such as ZF as well as with K-Best detection. However, the LLL-aided detectors have shown limited performance, when increasing the number of antennas at the transmitter and receiver. Therefore, in this paper we propose to use the so-called Element-based Lattice Reduction (ELR) combined with K-Best detector for the sake of attaining a better BER performance and lower complexity than the LLL-aided detection. Explicitly, the ELR-aided detectors are capable of attaining a 2 dB performance improvement at BER of 10?5 compared to the LLL-aided detectors when considering a MIMO system with 200 transmit and receive antennas. Furthermore, for the same MIMO configuration, the ELR basis update requires nearly an order of magnitude reduction in the number of arithmetic operations compared to the LLL algorithm.

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Published date: July 2016
Venue - Dates: 2016 IEEE 17th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), United Kingdom, 2016-07-03 - 2016-07-06
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 399521
URI: http://eprints.soton.ac.uk/id/eprint/399521
PURE UUID: 7e2df36e-c463-45c8-b959-b579926f9e10
ORCID for Mohammed El-Hajjar: ORCID iD orcid.org/0000-0002-7987-1401

Catalogue record

Date deposited: 19 Aug 2016 08:18
Last modified: 17 Jul 2017 18:22

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Contributors

Author: Ogeen H. Toma
Author: Mohammed El-Hajjar ORCID iD

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