2009 Loughborough Antennas &Amp; Propagation Conference 2009
DOI: 10.1109/lapc.2009.5352464
|View full text |Cite
|
Sign up to set email alerts
|

An introduction to fibre-to-air for cellular applications

Abstract: This paper presents fibre-to-air, a new system for feeding radio frequency (RF) signals from either the base station controller (BSC) or base transceiver station (BTS) directly to the antenna element at the air interface. The proposed system has lower transmission losses and greater power efficiency than current state-of-the-art implementations. Other advantages include fast antenna main beam steering, reduced antenna weight, and reduced susceptibility to RF interference on the feeder network. Results are pres… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2021
2021
2021
2021

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 1 publication
(1 citation statement)
references
References 10 publications
0
1
0
Order By: Relevance
“…As a benchmark algorithm we consider the adaptive extended Kalman PLL with AR scintillation model presented in [15] and we label this algorithm as EKPLL-sAR-ADAPT in the following. A Kalman DLL would further reduce the noise sensitivity of the code loop [28], [29], but as we are only interested in the evaluation of the proposed algorithms with respect to carrier phase estimation, we use a second order noncoherent DLL configured with the initial time-delay estimate being the timedelay of the simulated input signal as well as with a very small loop bandwidth and extended correlators [28] for the early and late correlators in order to reduce the noise and keep the code replica synchronized with the input signal, so that approximations (18), (19) and (20) The discriminator-based Kalman PLL of KPLL-sKIN is defined by F 1 , Q 1 , H 1 , and R 1 , as described in the previous section. The innovations are directly computed by the phase discriminator given in (19).…”
Section: Evaluation Of the Proposed Algorithms For Scintillation Monitoring And Mitigationmentioning
confidence: 99%
“…As a benchmark algorithm we consider the adaptive extended Kalman PLL with AR scintillation model presented in [15] and we label this algorithm as EKPLL-sAR-ADAPT in the following. A Kalman DLL would further reduce the noise sensitivity of the code loop [28], [29], but as we are only interested in the evaluation of the proposed algorithms with respect to carrier phase estimation, we use a second order noncoherent DLL configured with the initial time-delay estimate being the timedelay of the simulated input signal as well as with a very small loop bandwidth and extended correlators [28] for the early and late correlators in order to reduce the noise and keep the code replica synchronized with the input signal, so that approximations (18), (19) and (20) The discriminator-based Kalman PLL of KPLL-sKIN is defined by F 1 , Q 1 , H 1 , and R 1 , as described in the previous section. The innovations are directly computed by the phase discriminator given in (19).…”
Section: Evaluation Of the Proposed Algorithms For Scintillation Monitoring And Mitigationmentioning
confidence: 99%