Improved PCFICH decoding in LTE systems

Saheed Adegbite; Scott McMeekin; Brian Stewart

doi:10.1109/LANMAN.2015.7114739

Improved PCFICH decoding in LTE systems

Saheed Adegbite, Scott McMeekin, Brian Stewart

SCEBE School Office

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed)

Abstract

This paper studies control format indicator (CFI) estimation methods in 4G-LTE communication systems. CFI is an essential control signal in LTE, and must be correctly detected at the receiver to avoid degraded system performance. The standard method of CFI detection involves the use of the Maximum Likelihood (ML) estimation criterion. Unfortunately, ML decoding performance relies on accurate channel estimation, and as a consequence may result in poor CFI decoding when channel estimates are poor. To improve CFI detection in severe fading channel conditions, a time-domain decision rule that requires no channel estimation, is proposed. Results show that when compared with the ML approach, the proposed method reduces the probability of CFI estimation error in the form of reduced block error rate.

Original language	English
Title of host publication	Proceedings of the 21st IEEE International Workshop on Local and Wide Area Networks (LANMAN 2015)
Publisher	IEEE
Pages	1-6
Number of pages	6
DOIs	https://doi.org/10.1109/LANMAN.2015.7114739
Publication status	Published - Apr 2015

Keywords

control format indicator
4G-LTE communication system
Maximum Liklihood
ML decoding performance

Documents and Links

10.1109/LANMAN.2015.7114739

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Adegbite, S., McMeekin, S., & Stewart, B. (2015). Improved PCFICH decoding in LTE systems. In Proceedings of the 21st IEEE International Workshop on Local and Wide Area Networks (LANMAN 2015) (pp. 1-6). IEEE. https://doi.org/10.1109/LANMAN.2015.7114739

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abstract = "This paper studies control format indicator (CFI) estimation methods in 4G-LTE communication systems. CFI is an essential control signal in LTE, and must be correctly detected at the receiver to avoid degraded system performance. The standard method of CFI detection involves the use of the Maximum Likelihood (ML) estimation criterion. Unfortunately, ML decoding performance relies on accurate channel estimation, and as a consequence may result in poor CFI decoding when channel estimates are poor. To improve CFI detection in severe fading channel conditions, a time-domain decision rule that requires no channel estimation, is proposed. Results show that when compared with the ML approach, the proposed method reduces the probability of CFI estimation error in the form of reduced block error rate.",

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N2 - This paper studies control format indicator (CFI) estimation methods in 4G-LTE communication systems. CFI is an essential control signal in LTE, and must be correctly detected at the receiver to avoid degraded system performance. The standard method of CFI detection involves the use of the Maximum Likelihood (ML) estimation criterion. Unfortunately, ML decoding performance relies on accurate channel estimation, and as a consequence may result in poor CFI decoding when channel estimates are poor. To improve CFI detection in severe fading channel conditions, a time-domain decision rule that requires no channel estimation, is proposed. Results show that when compared with the ML approach, the proposed method reduces the probability of CFI estimation error in the form of reduced block error rate.

AB - This paper studies control format indicator (CFI) estimation methods in 4G-LTE communication systems. CFI is an essential control signal in LTE, and must be correctly detected at the receiver to avoid degraded system performance. The standard method of CFI detection involves the use of the Maximum Likelihood (ML) estimation criterion. Unfortunately, ML decoding performance relies on accurate channel estimation, and as a consequence may result in poor CFI decoding when channel estimates are poor. To improve CFI detection in severe fading channel conditions, a time-domain decision rule that requires no channel estimation, is proposed. Results show that when compared with the ML approach, the proposed method reduces the probability of CFI estimation error in the form of reduced block error rate.

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